This document provides an overview of MRI techniques for imaging pediatric brain tumors and summarizes the MRI appearance of common pediatric brain tumor types. It discusses conventional MRI sequences as well as advanced techniques like diffusion tensor imaging, perfusion imaging, and magnetic resonance spectroscopy that provide microstructural, hemodynamic, and metabolic information. The document also notes the importance of spine imaging based on the suspected tumor histology to evaluate for cerebrospinal fluid dissemination of the tumor.
Treatment of Brain Metastases Using the Current Predictive Models: Is the Pro...CrimsonpublishersCancer
Brain metastases from solid tumours are the most common intracranial tumours [1] and it occur in 40% of patients with cancer [2]. The most common primary tumours that metastasize to the brain are lung(40%),breast (25%) and melanoma (20%) [3]. The incidence is expected to be on the increase, due to improved survival, with use of modern cytotoxic drugs, targeted therapy, immunotherapy and modern radiotherapy techniques, in addition to greater use of magnetic resonance imaging of the brain. Brain metastases are common in the elderly, defined as above 60 years [4], and the interval between diagnosis of the primary and the development of brain metastases is variable, however some reported an average of 19 months [5] and adenocarcinoma is the commonest histology that metastasizes to the brain [6].
Enhanced 3D Brain Tumor Segmentation Using Assorted Precision TrainingBIJIAM Journal
A brain tumor is a medical disorder faced by individuals of all demographics. Medically, it is described as the spread of nonessential cells close to or throughout the brain. Symptoms of this ailment include headaches, seizures, and sensory changes. This research explores two main categories of brain tumors: benign and malignant. Benign spreads steadily, and malignant express growth makes it dangerous. Early identification of brain tumors is a crucial factor for the survival of patients. This research provides a state-of-the-art approach to the early identification of tumors within the brain. We implemented the SegResNet architecture, a widely adopted architecture for threedimensional segmentation, and trained it using the automatic multi-precision method. We incorporated the dice loss function and dice metric for evaluating the model. We got a dice score of 0.84. For the tumor core, we got a dice score of 0.84; for the whole tumor, 0.90; and for the enhanced tumor, we got a score of 0.79.
Description of Different Phases of Brain Tumor Classificationasclepiuspdfs
The proposed approach makes contributions in various stages in the development of a computer-aided diagnosis (CAD) system of brain diseases, namely image preprocessing, intermediate processing, detection, segmentation, feature extraction, and classification. Literature study incorporates many important ideas for abnormalities detection and analysis with their advantages and disadvantages. Literature studies have pointed out the needs of dividing task and appropriate ways for accurate abnormality characterization to provide a proper clinical diagnosis.
The editorial discusses how collaborations between academic medical centers and industry have advanced MRI through various examples in the magazine issue. It highlights how such partnerships have leveraged MRI's contrast mechanisms and improved image quality, examination speed, and patient comfort. The growing healthcare needs require continued diagnostic imaging advances. The issue features articles demonstrating innovations in motion-robust imaging, rapid protocols, quantitative mapping, and noise reduction.
External beam radiation therapy plays an important role in the treatment of central nervous system (CNS) tumors like glioblastoma multiforme (GBM). It involves using imaging like MRI and CT to delineate the tumor and plan radiation fields with margins around the enhanced lesion. Techniques like 3D-CRT, IMRT and Rapidarc are used to deliver precise radiation doses while sparing nearby organs-at-risk. Patient immobilization with a mask and daily image-guidance help ensure accurate targeting. Close multidisciplinary care is important due to the complexity and poor prognosis of CNS tumors.
MR Image Segmentation of Patients’ Brain Using Disease Specific a priori Know...CSCJournals
Segmentation of high quality brain MR images using a priori knowledge about brain structures enables a more accurate and comprehensive interpretation. Benefits of applying a priori knowledge about the brain structures may also be employed for image segmentation of specific brain and neural patients. Such procedure may be performed to determine the disease stage or monitor its gradual progression over time. However segmenting brain images of patients using general a priori knowledge which corresponds to healthy subjects would result in inaccurate and unreliable interpretation in the regions which are affected by the disease. In this paper, a technique is proposed for extracting a priori knowledge about structural distribution of different brain tissues affected by a specific disease to be applied for accurate segmentation of the patients’ brain images. For this purpose, extracted a priori knowledge is gradually represented as disease specific probability maps throughout an iterative process, and then is utilized in a statistical approach for segmentation of new patients’ images. Experiments conducted on a large set of images acquired from patients with a similar neurodegenerative disease implied success of the proposed technique for representing meaningful a priori knowledge as disease specific probability maps. Promising results obtained also indicated an accurate segmentation of brain MR images of the new patients using the represented a priori knowledge, into three tissue classes of gray matter, white matter, and cerebrospinal fluid. This enables an accurate estimation of tissues’ thickness and volumes and can be counted as a substantial forward step for more reliable monitoring and interpretation of progression in specific brain and neural diseases.
IIirdem mri brain tumour extraction by multi modality magnetic resonance imag...Iaetsd Iaetsd
This paper proposes a method for automatic brain tumour segmentation using multi-modality magnetic resonance images and support vector machine models. It combines structural MR images with diffusion tensor imaging data to create an integrated tissue profile for brain tumours. Texture features are extracted from normal and tumour regions and used to train an SVM classifier to differentiate between healthy and tumour tissues. The method is tested on real brain MRI data and achieves accurate classification results, demonstrating its potential for assessing tumour growth and computer-guided surgery.
A Review on Multiclass Brain Tumor Detection using Convolutional Neural Netwo...IRJET Journal
This document summarizes a review on using convolutional neural networks and support vector machines for multiclass brain tumor detection. It begins by introducing the importance of accurate brain tumor diagnosis and segmentation. It then describes the proposed 5-stage approach: 1) applying linear contrast stretching for edge detection, 2) developing a CNN architecture for segmentation, 3) using transfer learning from MobileNetV2 for feature extraction, 4) selecting optimal features using entropy control, and 5) classifying tumors into categories using multi-class SVM. Related works applying deep learning and machine learning methods for brain tumor detection, segmentation and classification are also summarized.
Treatment of Brain Metastases Using the Current Predictive Models: Is the Pro...CrimsonpublishersCancer
Brain metastases from solid tumours are the most common intracranial tumours [1] and it occur in 40% of patients with cancer [2]. The most common primary tumours that metastasize to the brain are lung(40%),breast (25%) and melanoma (20%) [3]. The incidence is expected to be on the increase, due to improved survival, with use of modern cytotoxic drugs, targeted therapy, immunotherapy and modern radiotherapy techniques, in addition to greater use of magnetic resonance imaging of the brain. Brain metastases are common in the elderly, defined as above 60 years [4], and the interval between diagnosis of the primary and the development of brain metastases is variable, however some reported an average of 19 months [5] and adenocarcinoma is the commonest histology that metastasizes to the brain [6].
Enhanced 3D Brain Tumor Segmentation Using Assorted Precision TrainingBIJIAM Journal
A brain tumor is a medical disorder faced by individuals of all demographics. Medically, it is described as the spread of nonessential cells close to or throughout the brain. Symptoms of this ailment include headaches, seizures, and sensory changes. This research explores two main categories of brain tumors: benign and malignant. Benign spreads steadily, and malignant express growth makes it dangerous. Early identification of brain tumors is a crucial factor for the survival of patients. This research provides a state-of-the-art approach to the early identification of tumors within the brain. We implemented the SegResNet architecture, a widely adopted architecture for threedimensional segmentation, and trained it using the automatic multi-precision method. We incorporated the dice loss function and dice metric for evaluating the model. We got a dice score of 0.84. For the tumor core, we got a dice score of 0.84; for the whole tumor, 0.90; and for the enhanced tumor, we got a score of 0.79.
Description of Different Phases of Brain Tumor Classificationasclepiuspdfs
The proposed approach makes contributions in various stages in the development of a computer-aided diagnosis (CAD) system of brain diseases, namely image preprocessing, intermediate processing, detection, segmentation, feature extraction, and classification. Literature study incorporates many important ideas for abnormalities detection and analysis with their advantages and disadvantages. Literature studies have pointed out the needs of dividing task and appropriate ways for accurate abnormality characterization to provide a proper clinical diagnosis.
The editorial discusses how collaborations between academic medical centers and industry have advanced MRI through various examples in the magazine issue. It highlights how such partnerships have leveraged MRI's contrast mechanisms and improved image quality, examination speed, and patient comfort. The growing healthcare needs require continued diagnostic imaging advances. The issue features articles demonstrating innovations in motion-robust imaging, rapid protocols, quantitative mapping, and noise reduction.
External beam radiation therapy plays an important role in the treatment of central nervous system (CNS) tumors like glioblastoma multiforme (GBM). It involves using imaging like MRI and CT to delineate the tumor and plan radiation fields with margins around the enhanced lesion. Techniques like 3D-CRT, IMRT and Rapidarc are used to deliver precise radiation doses while sparing nearby organs-at-risk. Patient immobilization with a mask and daily image-guidance help ensure accurate targeting. Close multidisciplinary care is important due to the complexity and poor prognosis of CNS tumors.
MR Image Segmentation of Patients’ Brain Using Disease Specific a priori Know...CSCJournals
Segmentation of high quality brain MR images using a priori knowledge about brain structures enables a more accurate and comprehensive interpretation. Benefits of applying a priori knowledge about the brain structures may also be employed for image segmentation of specific brain and neural patients. Such procedure may be performed to determine the disease stage or monitor its gradual progression over time. However segmenting brain images of patients using general a priori knowledge which corresponds to healthy subjects would result in inaccurate and unreliable interpretation in the regions which are affected by the disease. In this paper, a technique is proposed for extracting a priori knowledge about structural distribution of different brain tissues affected by a specific disease to be applied for accurate segmentation of the patients’ brain images. For this purpose, extracted a priori knowledge is gradually represented as disease specific probability maps throughout an iterative process, and then is utilized in a statistical approach for segmentation of new patients’ images. Experiments conducted on a large set of images acquired from patients with a similar neurodegenerative disease implied success of the proposed technique for representing meaningful a priori knowledge as disease specific probability maps. Promising results obtained also indicated an accurate segmentation of brain MR images of the new patients using the represented a priori knowledge, into three tissue classes of gray matter, white matter, and cerebrospinal fluid. This enables an accurate estimation of tissues’ thickness and volumes and can be counted as a substantial forward step for more reliable monitoring and interpretation of progression in specific brain and neural diseases.
IIirdem mri brain tumour extraction by multi modality magnetic resonance imag...Iaetsd Iaetsd
This paper proposes a method for automatic brain tumour segmentation using multi-modality magnetic resonance images and support vector machine models. It combines structural MR images with diffusion tensor imaging data to create an integrated tissue profile for brain tumours. Texture features are extracted from normal and tumour regions and used to train an SVM classifier to differentiate between healthy and tumour tissues. The method is tested on real brain MRI data and achieves accurate classification results, demonstrating its potential for assessing tumour growth and computer-guided surgery.
A Review on Multiclass Brain Tumor Detection using Convolutional Neural Netwo...IRJET Journal
This document summarizes a review on using convolutional neural networks and support vector machines for multiclass brain tumor detection. It begins by introducing the importance of accurate brain tumor diagnosis and segmentation. It then describes the proposed 5-stage approach: 1) applying linear contrast stretching for edge detection, 2) developing a CNN architecture for segmentation, 3) using transfer learning from MobileNetV2 for feature extraction, 4) selecting optimal features using entropy control, and 5) classifying tumors into categories using multi-class SVM. Related works applying deep learning and machine learning methods for brain tumor detection, segmentation and classification are also summarized.
IRJET- Brain Tumor Detection using Convolutional Neural NetworkIRJET Journal
This document summarizes research on using convolutional neural networks (CNNs) to detect brain tumors from MRI images. It begins with an abstract describing how earlier tumor detection was done manually by doctors, which took more time and was sometimes inaccurate. CNN models provide quicker and more precise results. The document then reviews several existing techniques for brain tumor segmentation and classification, noting their advantages and limitations. It proposes using a CNN-based classifier to overcome these limitations by comparing trained and test data to get the best results. Key steps in tumor detection using image processing techniques are described as image pre-processing, segmentation, feature extraction, and classification.
Primary challenges are the identification, segmentation, and extraction of the afflicted area from the scanning of magnetic resonance. However, it is a time-consuming and tiresome for clinical specialists. In this paper,
an automated brain tumor system is proposed. The proposed system employs hybrid image processing techniques such as contrast correction, histogram normalization, thresholding techniques, arithmetic, and morphological operations to quarantine nearby organs and other tissue from the brain for improving the localization of the affected region. At first, the skull stripping process is proposed to segregate the non-designated regions to extract the designated brain regions. Those resultant brain region images are further subjected to discover the brain tumor. The planned scheme is studied on the magnetic resonance (MR) images with the use of T1, T2, T1c, and fluid-attenuated inversion recovery (FLAIR). The proposed hybrid method employed. The results reveal that the proposed method is quite efficient to extract the tumor region. The accuracy rate for segmentation and separation of area of interest in brain tumor reached to 95%. Finally, the significance of the proposed procedure is confirmed using the real image clinical dataset got from ten patients were diagnosed as begin, malignant, and metastatic brain tumors in Al-Yarmouk and Baghdad teaching hospital in Baghdad, Iraq.
A malignant tumor, also called brain cancer, grows rapidly and often invades or crowds healthy areas of the brain. Brain tumors can affect white matter fibers by either infiltrating or displacing the tissue. When the myelin sheath is damaged or disappears, the conduction of impulses along nerve fibers slows down or fails completely. Diffusion Tensor Imaging (DTI) is a relatively new imaging technique that can be used to evaluate white matter in the brain. DTI has diagnostic implications by being able to pinpoint areas where normal water flow is disrupted, providing valuable information about the location of specific lesions. Edema, infiltration and destruction of white matter reduces the anisotropic nature of the white matter. The paper aims to segment tumor from the healthy brain tissues in Diffusion Tensor brain tumor images using Fuzzy C-Means clustering.
Tumor Detection and Classification of MRI Brain Images using SVM and DNNijtsrd
The brain is one of the most complex organ in the human body that works with billions of cells. A cerebral tumor occurs when there is an uncontrolled division of cells that form an abnormal group of cells around or within the brain. This cell group can affect the normal functioning of brain activity and can destroy healthy cells. Brain tumors are classified as benign or low grade Grade 1 and 2 and malignant tumors or high grade Grade 3 and 4 . The proposed methodology aims to differentiate between normal brain and tumor brain Benign or Melign . The proposed method in this paper is automated framework for differentiate between normal brain and tumor brain. Then our method is used to predict the diseases accurately. Then these methods are used to predict the disease is affected or not by using a comparison method. These methodology are validated by a comprehensive set of comparison against competing and well established image registration methods, by using real medical data sets and classic measures typically employed as a benchmark by the medical imaging community our proposed method is mostly used in medical field. It is used to easily detect the diseases. We demonstrate the accuracy and effectiveness of the preset framework throughout a comprehensive set of qualitative comparisons against several influential state of the art methods on various brain image databases. Sanmathi. R | Sujitha. K | Susmitha. G | Gnanasekaran. S ""Tumor Detection and Classification of MRI Brain Images using SVM and DNN"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020,
URL: https://www.ijtsrd.com/papers/ijtsrd30192.pdf
Paper Url : https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/30192/tumor-detection-and-classification-of-mri-brain-images-using-svm-and-dnn/sanmathi-r
Automatic brain tumor detection using adaptive region growing with thresholdi...IAESIJAI
Brain cancer affects many people around the world. It's not just limited to the elderly; it is also recognized in children. With the development of image processing, early detection of mental development is possible. Some designers suggest deformable models, histogram averaging, or manual division. Due to constant manual intervention, these cycles can be uncomfortable and tiring. This research introduces a high-level system for the removal of malignant tumors from attractive reverberation images, based on a programmed and rapid distribution strategy for surface extraction and recreation for clinicians. To test the proposed system, acquired tomography images from the Cancer Imaging Archive were used. The results of the study strongly demonstrate that the intended structure is viable in brain tumor detection.
Neuroradiology is the subspecialty of radiology focused on imaging the central and peripheral nervous systems. The document discusses several key imaging modalities used in neuroradiology including CT, MRI, ultrasound, angiography, and myelography. It provides details on the techniques, advantages, and limitations of each modality. CT and MRI are currently the main modalities used for evaluating neurological pathology, though each has specific scenarios where it is particularly useful over the other. Recent technological advances have improved imaging capabilities and increased accessibility of various modalities.
Automated Intracranial Neoplasm Detection Using Convolutional Neural NetworksIRJET Journal
This document presents a study that uses convolutional neural networks to automatically detect intracranial neoplasms (brain tumors) from MRI scans. The researchers developed a CNN model that achieved 97.87% accuracy in identifying tumors. They used preprocessed MRI images to train and test the model for tumor detection. Convolutional neural networks are a type of deep learning that can provide efficient results for medical image classification tasks like tumor detection compared to traditional methods. The study demonstrates that CNNs are a promising approach for automated brain tumor identification from MRI scans.
This document describes the step-by-step process for stereotactic radiotherapy planning of a surgically resected brain metastasis. It begins with a case history of a 40-year-old male patient who underwent surgery to remove a metastatic papillary adenocarcinoma from his brain. Imaging including MRI and PET-CT was performed before and after surgery. The tumor board then decided on stereotactic radiotherapy followed by chemotherapy. The document outlines the detailed planning process for stereotactic radiotherapy treatment of the postoperative cavity including simulation, contouring, treatment planning, and plan evaluation. It discusses target volumes and organs at risk as well as techniques for fractionated stereotactic radiosurgery.
This document presents an automatic brain tumor detection and segmentation scheme using MRI images. The proposed method involves 4 main stages: 1) preprocessing to reduce noise and improve image quality, 2) feature extraction of shape, intensity, and texture features, 3) tumor segmentation, and 4) post-processing including regularization and constraints. The method aims to identify and segment tumor portions of brain images successfully and with less time than manual methods. Evaluation results suggest it outperforms other peer methods in accuracy metrics.
Recognition of brain cancer and cerebrospinal fluid due to the usage of diffe...journalBEEI
Medicinal images assume an important part in the diagnosis of tumors as well as Cerebrospinal fluid (CSF) leak. Similarly, MRI could be the cutting-edge regenerative imaging technology that allows for a sectional angle perspective of the body that gives specialists convenience and will inspect the person-concerned. In this paper, the author has attempted the strategy to classify MRI images at the beginning of production to have a tumor or recognition. The study aims to address the aforementioned problems associated with brain cancer with a CSF leak. This research, the author focuses on brain tumor and applies the statistical model for the testing and also discusses the images of a brain tumor. They can judge the tumor region by conducting a comparative image analysis and applying Histogram function afterwards to construct a classifier that could be prepared to predict tumor and non-tumor MRI examinees based on the support vector machine. Our system is capable of detecting the right region that a pathologist also highlights. In the future, this should be more driven with the objective that tumors can be arranged and describe the solution in the medical terms & implementation with gives some predictions about the future generated by modified technology.
Intensity-modulated radiotherapy with simultaneous modulated accelerated boos...Enrique Moreno Gonzalez
To present our experience of intensity-modulated radiotherapy (IMRT) with simultaneous modulated accelerated radiotherapy (SMART) boost technique in patients with nasopharyngeal carcinoma (NPC).
Tumor Detection Based On Symmetry InformationIJERA Editor
Various subjects that are paired usually are not identically the same, asymmetry is perfectly normal but sometimes asymmetry can benoticeable too much. Structural and functional asymmetry in the human brain and nervous system is reviewed in a historical perspective. Brainasymmetry is one of such examples, which is a difference in size or shape, or both. Asymmetry analysis of brain has great importance because itis not only indicator for brain cancer but also predict future potential risk for the same. In our work, we have concentrated to segment theanatomical regions of brain, isolate the two halves of brain and to investigate each half for the presence of asymmetry of anatomical regions inMRI.
A Survey On Identification Of Multiple Sclerosis Lesions From Brain MRIJanelle Martinez
This document summarizes several papers on using MRI and image analysis techniques to identify multiple sclerosis (MS) lesions in the brain. It discusses using metrics like fractional anisotropy and apparent diffusion coefficient from diffusion tensor imaging to analyze changes in normal-appearing white matter. It also reviews methods like texture analysis using gray-level co-occurrence matrices, convolutional neural networks, and phase and orientation analysis to segment and identify MS lesions in MRI scans. The goal is to characterize subtle microscopic changes in nerve fibers caused by MS through analysis of MRI textures and alignments to improve diagnosis and understanding of the disease.
Is the increasing availability of automated image analysis a possibility to strengthen the application of diffusion-MRI as a biometric parameter, and to enhance the future of image biobanks? Or is this evolution threatening the position of radiologists as medical doctors. Is a redefinition of radiologist as computer technicians inevitable?
This document summarizes a study that evaluated the efficacy of ultrasonography and computed tomography in diagnosing palpable neck masses. 40 patients with neck masses were examined clinically and underwent ultrasound and CT scans. The results found that ultrasound was useful for characterizing masses as solid or cystic and identifying features like margins, calcifications and necrosis. CT provided additional information on tissue attenuation, extent of lesions, and involvement of surrounding structures or distant spread. The study concluded that ultrasound combined with CT provides valuable information to accurately diagnose neck masses and guide their management.
In recent years, the application of deep learning has demonstrated significant progress in various scientific subfields. Compared to other cutting-edge methods of processing and analysing images, deep learning algorithms performed significantly better. When applied in areas such as self-driving cars, where deep learning has been utilized, and the results are the best and most up-to-date currently available. In some situations, such as recognizing objects and playing games, deep learning performed significantly better than people did. One more industry that appears to have a lot to gain from deep learning is the medical field. There are a lot of patient records and data, and providing individualized care to each patient is becoming an increasingly important priority as a result. This indicates an immediate need for methods that are both efficient and reliable for processing and analyzing health informatics.
Identifying brain tumour from mri image using modified fcm and supportIAEME Publication
This document summarizes a research paper that proposes a technique for identifying brain tumors in MRI images. The technique involves 4 steps: 1) preprocessing the MRI image, 2) segmenting the image using a modified fuzzy C-means algorithm, 3) extracting features from the segmented regions like mean, standard deviation, and pixel orientation, and 4) classifying the image as tumorous or normal using support vector machine classification on the extracted features. The technique is evaluated on MRI brain images and achieves a testing accuracy of 93%, demonstrating its effectiveness at detecting brain tumors compared to other segmentation and classification methods.
This document summarizes recent advances in using deep learning to analyze magnetic resonance imaging (MRI) data from glioma patients. It discusses:
1) How MRI is used to diagnose gliomas and evaluate key biomarkers like tumor grade, molecular characteristics, and treatment response.
2) Publicly available datasets like the Multimodal Brain Tumor Segmentation Challenge that have helped develop new computational tools.
3) The current state-of-the-art in glioma image segmentation using deep learning models developed through challenges like BraTS.
Radiation Oncology in 21st Century - Changing the ParadigmsApollo Hospitals
Since its inception radiation therapy has been used as one of
the essential treatment options in the management of malignant and some benign tumors. With better understanding of tumor biology many new molecules have been added to the armamentarium of an oncologist. There is continuous improvement in surgical techniques with more emphasis on minimally invasive, organ- and function-preserving techniques. Neoadjuvant chemotherapy with or without addition of radiation therapy has helped surgeon downsizing the tumor and obtaining clearer margins.
Glioblastoma Multiforme Identification from Medical Imaging Using Computer Vi...ijscmcj
A tumor also known as neoplasm is a growth in the abnormal tissue which can be differentiated from the surrounding tissue by its structure. A tumor may lead to cancer, which is a major leading cause of death and responsible for around 13% of all deaths world-wide. Cancer incidence rate is growing at an alarming rate in the world. Great knowledge and experience on radiology are required for accurate tumor detection in medical imaging. Automation of tumor detection is required because there might be a shortage of skilled radiologists at a time of great need. We propose an automatic brain tumor detection and localization framework that can detect and localize brain tumor in magnetic resonance imaging. The proposed brain tumor detection and localization framework comprises five steps: image acquisition, pre-processing, edge detection, modified histogram clustering and morphological operations. After morphological operations, tumors appear as pure white color on pure black backgrounds. We used 50 neuroimages to optimize our system and 100 out-of-sample neuroimages to test our system. The proposed tumor detection and localization system was found to be able to accurately detect and localize brain tumor in magnetic resonance imaging. The preliminary results demonstrate how a simple machine learning classifier with a set of simple image-based features can result in high classification accuracy. The preliminary results also demonstrate the efficacy and efficiency of our five-step brain tumor detection and localization approach and motivate us to extend this framework to detect and localize a variety of other types of tumors in other types of medical imagery.
IRJET- Brain Tumor Detection using Convolutional Neural NetworkIRJET Journal
This document summarizes research on using convolutional neural networks (CNNs) to detect brain tumors from MRI images. It begins with an abstract describing how earlier tumor detection was done manually by doctors, which took more time and was sometimes inaccurate. CNN models provide quicker and more precise results. The document then reviews several existing techniques for brain tumor segmentation and classification, noting their advantages and limitations. It proposes using a CNN-based classifier to overcome these limitations by comparing trained and test data to get the best results. Key steps in tumor detection using image processing techniques are described as image pre-processing, segmentation, feature extraction, and classification.
Primary challenges are the identification, segmentation, and extraction of the afflicted area from the scanning of magnetic resonance. However, it is a time-consuming and tiresome for clinical specialists. In this paper,
an automated brain tumor system is proposed. The proposed system employs hybrid image processing techniques such as contrast correction, histogram normalization, thresholding techniques, arithmetic, and morphological operations to quarantine nearby organs and other tissue from the brain for improving the localization of the affected region. At first, the skull stripping process is proposed to segregate the non-designated regions to extract the designated brain regions. Those resultant brain region images are further subjected to discover the brain tumor. The planned scheme is studied on the magnetic resonance (MR) images with the use of T1, T2, T1c, and fluid-attenuated inversion recovery (FLAIR). The proposed hybrid method employed. The results reveal that the proposed method is quite efficient to extract the tumor region. The accuracy rate for segmentation and separation of area of interest in brain tumor reached to 95%. Finally, the significance of the proposed procedure is confirmed using the real image clinical dataset got from ten patients were diagnosed as begin, malignant, and metastatic brain tumors in Al-Yarmouk and Baghdad teaching hospital in Baghdad, Iraq.
A malignant tumor, also called brain cancer, grows rapidly and often invades or crowds healthy areas of the brain. Brain tumors can affect white matter fibers by either infiltrating or displacing the tissue. When the myelin sheath is damaged or disappears, the conduction of impulses along nerve fibers slows down or fails completely. Diffusion Tensor Imaging (DTI) is a relatively new imaging technique that can be used to evaluate white matter in the brain. DTI has diagnostic implications by being able to pinpoint areas where normal water flow is disrupted, providing valuable information about the location of specific lesions. Edema, infiltration and destruction of white matter reduces the anisotropic nature of the white matter. The paper aims to segment tumor from the healthy brain tissues in Diffusion Tensor brain tumor images using Fuzzy C-Means clustering.
Tumor Detection and Classification of MRI Brain Images using SVM and DNNijtsrd
The brain is one of the most complex organ in the human body that works with billions of cells. A cerebral tumor occurs when there is an uncontrolled division of cells that form an abnormal group of cells around or within the brain. This cell group can affect the normal functioning of brain activity and can destroy healthy cells. Brain tumors are classified as benign or low grade Grade 1 and 2 and malignant tumors or high grade Grade 3 and 4 . The proposed methodology aims to differentiate between normal brain and tumor brain Benign or Melign . The proposed method in this paper is automated framework for differentiate between normal brain and tumor brain. Then our method is used to predict the diseases accurately. Then these methods are used to predict the disease is affected or not by using a comparison method. These methodology are validated by a comprehensive set of comparison against competing and well established image registration methods, by using real medical data sets and classic measures typically employed as a benchmark by the medical imaging community our proposed method is mostly used in medical field. It is used to easily detect the diseases. We demonstrate the accuracy and effectiveness of the preset framework throughout a comprehensive set of qualitative comparisons against several influential state of the art methods on various brain image databases. Sanmathi. R | Sujitha. K | Susmitha. G | Gnanasekaran. S ""Tumor Detection and Classification of MRI Brain Images using SVM and DNN"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020,
URL: https://www.ijtsrd.com/papers/ijtsrd30192.pdf
Paper Url : https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/30192/tumor-detection-and-classification-of-mri-brain-images-using-svm-and-dnn/sanmathi-r
Automatic brain tumor detection using adaptive region growing with thresholdi...IAESIJAI
Brain cancer affects many people around the world. It's not just limited to the elderly; it is also recognized in children. With the development of image processing, early detection of mental development is possible. Some designers suggest deformable models, histogram averaging, or manual division. Due to constant manual intervention, these cycles can be uncomfortable and tiring. This research introduces a high-level system for the removal of malignant tumors from attractive reverberation images, based on a programmed and rapid distribution strategy for surface extraction and recreation for clinicians. To test the proposed system, acquired tomography images from the Cancer Imaging Archive were used. The results of the study strongly demonstrate that the intended structure is viable in brain tumor detection.
Neuroradiology is the subspecialty of radiology focused on imaging the central and peripheral nervous systems. The document discusses several key imaging modalities used in neuroradiology including CT, MRI, ultrasound, angiography, and myelography. It provides details on the techniques, advantages, and limitations of each modality. CT and MRI are currently the main modalities used for evaluating neurological pathology, though each has specific scenarios where it is particularly useful over the other. Recent technological advances have improved imaging capabilities and increased accessibility of various modalities.
Automated Intracranial Neoplasm Detection Using Convolutional Neural NetworksIRJET Journal
This document presents a study that uses convolutional neural networks to automatically detect intracranial neoplasms (brain tumors) from MRI scans. The researchers developed a CNN model that achieved 97.87% accuracy in identifying tumors. They used preprocessed MRI images to train and test the model for tumor detection. Convolutional neural networks are a type of deep learning that can provide efficient results for medical image classification tasks like tumor detection compared to traditional methods. The study demonstrates that CNNs are a promising approach for automated brain tumor identification from MRI scans.
This document describes the step-by-step process for stereotactic radiotherapy planning of a surgically resected brain metastasis. It begins with a case history of a 40-year-old male patient who underwent surgery to remove a metastatic papillary adenocarcinoma from his brain. Imaging including MRI and PET-CT was performed before and after surgery. The tumor board then decided on stereotactic radiotherapy followed by chemotherapy. The document outlines the detailed planning process for stereotactic radiotherapy treatment of the postoperative cavity including simulation, contouring, treatment planning, and plan evaluation. It discusses target volumes and organs at risk as well as techniques for fractionated stereotactic radiosurgery.
This document presents an automatic brain tumor detection and segmentation scheme using MRI images. The proposed method involves 4 main stages: 1) preprocessing to reduce noise and improve image quality, 2) feature extraction of shape, intensity, and texture features, 3) tumor segmentation, and 4) post-processing including regularization and constraints. The method aims to identify and segment tumor portions of brain images successfully and with less time than manual methods. Evaluation results suggest it outperforms other peer methods in accuracy metrics.
Recognition of brain cancer and cerebrospinal fluid due to the usage of diffe...journalBEEI
Medicinal images assume an important part in the diagnosis of tumors as well as Cerebrospinal fluid (CSF) leak. Similarly, MRI could be the cutting-edge regenerative imaging technology that allows for a sectional angle perspective of the body that gives specialists convenience and will inspect the person-concerned. In this paper, the author has attempted the strategy to classify MRI images at the beginning of production to have a tumor or recognition. The study aims to address the aforementioned problems associated with brain cancer with a CSF leak. This research, the author focuses on brain tumor and applies the statistical model for the testing and also discusses the images of a brain tumor. They can judge the tumor region by conducting a comparative image analysis and applying Histogram function afterwards to construct a classifier that could be prepared to predict tumor and non-tumor MRI examinees based on the support vector machine. Our system is capable of detecting the right region that a pathologist also highlights. In the future, this should be more driven with the objective that tumors can be arranged and describe the solution in the medical terms & implementation with gives some predictions about the future generated by modified technology.
Intensity-modulated radiotherapy with simultaneous modulated accelerated boos...Enrique Moreno Gonzalez
To present our experience of intensity-modulated radiotherapy (IMRT) with simultaneous modulated accelerated radiotherapy (SMART) boost technique in patients with nasopharyngeal carcinoma (NPC).
Tumor Detection Based On Symmetry InformationIJERA Editor
Various subjects that are paired usually are not identically the same, asymmetry is perfectly normal but sometimes asymmetry can benoticeable too much. Structural and functional asymmetry in the human brain and nervous system is reviewed in a historical perspective. Brainasymmetry is one of such examples, which is a difference in size or shape, or both. Asymmetry analysis of brain has great importance because itis not only indicator for brain cancer but also predict future potential risk for the same. In our work, we have concentrated to segment theanatomical regions of brain, isolate the two halves of brain and to investigate each half for the presence of asymmetry of anatomical regions inMRI.
A Survey On Identification Of Multiple Sclerosis Lesions From Brain MRIJanelle Martinez
This document summarizes several papers on using MRI and image analysis techniques to identify multiple sclerosis (MS) lesions in the brain. It discusses using metrics like fractional anisotropy and apparent diffusion coefficient from diffusion tensor imaging to analyze changes in normal-appearing white matter. It also reviews methods like texture analysis using gray-level co-occurrence matrices, convolutional neural networks, and phase and orientation analysis to segment and identify MS lesions in MRI scans. The goal is to characterize subtle microscopic changes in nerve fibers caused by MS through analysis of MRI textures and alignments to improve diagnosis and understanding of the disease.
Is the increasing availability of automated image analysis a possibility to strengthen the application of diffusion-MRI as a biometric parameter, and to enhance the future of image biobanks? Or is this evolution threatening the position of radiologists as medical doctors. Is a redefinition of radiologist as computer technicians inevitable?
This document summarizes a study that evaluated the efficacy of ultrasonography and computed tomography in diagnosing palpable neck masses. 40 patients with neck masses were examined clinically and underwent ultrasound and CT scans. The results found that ultrasound was useful for characterizing masses as solid or cystic and identifying features like margins, calcifications and necrosis. CT provided additional information on tissue attenuation, extent of lesions, and involvement of surrounding structures or distant spread. The study concluded that ultrasound combined with CT provides valuable information to accurately diagnose neck masses and guide their management.
In recent years, the application of deep learning has demonstrated significant progress in various scientific subfields. Compared to other cutting-edge methods of processing and analysing images, deep learning algorithms performed significantly better. When applied in areas such as self-driving cars, where deep learning has been utilized, and the results are the best and most up-to-date currently available. In some situations, such as recognizing objects and playing games, deep learning performed significantly better than people did. One more industry that appears to have a lot to gain from deep learning is the medical field. There are a lot of patient records and data, and providing individualized care to each patient is becoming an increasingly important priority as a result. This indicates an immediate need for methods that are both efficient and reliable for processing and analyzing health informatics.
Identifying brain tumour from mri image using modified fcm and supportIAEME Publication
This document summarizes a research paper that proposes a technique for identifying brain tumors in MRI images. The technique involves 4 steps: 1) preprocessing the MRI image, 2) segmenting the image using a modified fuzzy C-means algorithm, 3) extracting features from the segmented regions like mean, standard deviation, and pixel orientation, and 4) classifying the image as tumorous or normal using support vector machine classification on the extracted features. The technique is evaluated on MRI brain images and achieves a testing accuracy of 93%, demonstrating its effectiveness at detecting brain tumors compared to other segmentation and classification methods.
This document summarizes recent advances in using deep learning to analyze magnetic resonance imaging (MRI) data from glioma patients. It discusses:
1) How MRI is used to diagnose gliomas and evaluate key biomarkers like tumor grade, molecular characteristics, and treatment response.
2) Publicly available datasets like the Multimodal Brain Tumor Segmentation Challenge that have helped develop new computational tools.
3) The current state-of-the-art in glioma image segmentation using deep learning models developed through challenges like BraTS.
Radiation Oncology in 21st Century - Changing the ParadigmsApollo Hospitals
Since its inception radiation therapy has been used as one of
the essential treatment options in the management of malignant and some benign tumors. With better understanding of tumor biology many new molecules have been added to the armamentarium of an oncologist. There is continuous improvement in surgical techniques with more emphasis on minimally invasive, organ- and function-preserving techniques. Neoadjuvant chemotherapy with or without addition of radiation therapy has helped surgeon downsizing the tumor and obtaining clearer margins.
Glioblastoma Multiforme Identification from Medical Imaging Using Computer Vi...ijscmcj
A tumor also known as neoplasm is a growth in the abnormal tissue which can be differentiated from the surrounding tissue by its structure. A tumor may lead to cancer, which is a major leading cause of death and responsible for around 13% of all deaths world-wide. Cancer incidence rate is growing at an alarming rate in the world. Great knowledge and experience on radiology are required for accurate tumor detection in medical imaging. Automation of tumor detection is required because there might be a shortage of skilled radiologists at a time of great need. We propose an automatic brain tumor detection and localization framework that can detect and localize brain tumor in magnetic resonance imaging. The proposed brain tumor detection and localization framework comprises five steps: image acquisition, pre-processing, edge detection, modified histogram clustering and morphological operations. After morphological operations, tumors appear as pure white color on pure black backgrounds. We used 50 neuroimages to optimize our system and 100 out-of-sample neuroimages to test our system. The proposed tumor detection and localization system was found to be able to accurately detect and localize brain tumor in magnetic resonance imaging. The preliminary results demonstrate how a simple machine learning classifier with a set of simple image-based features can result in high classification accuracy. The preliminary results also demonstrate the efficacy and efficiency of our five-step brain tumor detection and localization approach and motivate us to extend this framework to detect and localize a variety of other types of tumors in other types of medical imagery.
Integrating Ayurveda into Parkinson’s Management: A Holistic ApproachAyurveda ForAll
Explore the benefits of combining Ayurveda with conventional Parkinson's treatments. Learn how a holistic approach can manage symptoms, enhance well-being, and balance body energies. Discover the steps to safely integrate Ayurvedic practices into your Parkinson’s care plan, including expert guidance on diet, herbal remedies, and lifestyle modifications.
These lecture slides, by Dr Sidra Arshad, offer a quick overview of the physiological basis of a normal electrocardiogram.
Learning objectives:
1. Define an electrocardiogram (ECG) and electrocardiography
2. Describe how dipoles generated by the heart produce the waveforms of the ECG
3. Describe the components of a normal electrocardiogram of a typical bipolar lead (limb II)
4. Differentiate between intervals and segments
5. Enlist some common indications for obtaining an ECG
6. Describe the flow of current around the heart during the cardiac cycle
7. Discuss the placement and polarity of the leads of electrocardiograph
8. Describe the normal electrocardiograms recorded from the limb leads and explain the physiological basis of the different records that are obtained
9. Define mean electrical vector (axis) of the heart and give the normal range
10. Define the mean QRS vector
11. Describe the axes of leads (hexagonal reference system)
12. Comprehend the vectorial analysis of the normal ECG
13. Determine the mean electrical axis of the ventricular QRS and appreciate the mean axis deviation
14. Explain the concepts of current of injury, J point, and their significance
Study Resources:
1. Chapter 11, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 9, Human Physiology - From Cells to Systems, Lauralee Sherwood, 9th edition
3. Chapter 29, Ganong’s Review of Medical Physiology, 26th edition
4. Electrocardiogram, StatPearls - https://www.ncbi.nlm.nih.gov/books/NBK549803/
5. ECG in Medical Practice by ABM Abdullah, 4th edition
6. Chapter 3, Cardiology Explained, https://www.ncbi.nlm.nih.gov/books/NBK2214/
7. ECG Basics, http://www.nataliescasebook.com/tag/e-c-g-basics
Here is the updated list of Top Best Ayurvedic medicine for Gas and Indigestion and those are Gas-O-Go Syp for Dyspepsia | Lavizyme Syrup for Acidity | Yumzyme Hepatoprotective Capsules etc
Rasamanikya is a excellent preparation in the field of Rasashastra, it is used in various Kushtha Roga, Shwasa, Vicharchika, Bhagandara, Vatarakta, and Phiranga Roga. In this article Preparation& Comparative analytical profile for both Formulationon i.e Rasamanikya prepared by Kushmanda swarasa & Churnodhaka Shodita Haratala. The study aims to provide insights into the comparative efficacy and analytical aspects of these formulations for enhanced therapeutic outcomes.
Histololgy of Female Reproductive System.pptxAyeshaZaid1
Dive into an in-depth exploration of the histological structure of female reproductive system with this comprehensive lecture. Presented by Dr. Ayesha Irfan, Assistant Professor of Anatomy, this presentation covers the Gross anatomy and functional histology of the female reproductive organs. Ideal for students, educators, and anyone interested in medical science, this lecture provides clear explanations, detailed diagrams, and valuable insights into female reproductive system. Enhance your knowledge and understanding of this essential aspect of human biology.
Osteoporosis - Definition , Evaluation and Management .pdfJim Jacob Roy
Osteoporosis is an increasing cause of morbidity among the elderly.
In this document , a brief outline of osteoporosis is given , including the risk factors of osteoporosis fractures , the indications for testing bone mineral density and the management of osteoporosis
Adhd Medication Shortage Uk - trinexpharmacy.comreignlana06
The UK is currently facing a Adhd Medication Shortage Uk, which has left many patients and their families grappling with uncertainty and frustration. ADHD, or Attention Deficit Hyperactivity Disorder, is a chronic condition that requires consistent medication to manage effectively. This shortage has highlighted the critical role these medications play in the daily lives of those affected by ADHD. Contact : +1 (747) 209 – 3649 E-mail : sales@trinexpharmacy.com
2. Diagnostics 2022, 12, 961 2 of 24
but often require 30–60 min. Therefore, sedation or general anesthesia is typically needed
in children younger than six years of age, although this can be assessed on a case-by-case
basis. Child life specialists can be very helpful in improving patient compliance. The use
of gadolinium contrast material is standard for tumor imaging, although there may be
some exceptions.
A conventional brain MRI includes pre- and post-contrast T1-weighted, T2-weighted,
fluid-attenuated inversion recovery (FLAIR), and diffusion-weighted sequences. The use of
high-resolution, thin section, 3-dimensional imaging, which can be reformatted in multiple
planes, is now standard for T1-weighted imaging and is gaining wider acceptance for T2
and FLAIR sequences. In addition, technical advances in imaging methodology such as
compressed sensing and parallel imaging allow for the acquisition of high-resolution and
thinner slices with a reasonable scan time. The Response Assessment in Pediatric Neuro-
Oncology (RAPNO) working groups have published guidelines for minimum essential
MRI sequences for different types of pediatric brain tumors, and the reader is referred to
those detailed recommendations [4–7].
Diffusion-weighted images measure the mobility of water molecules, which in turn
depends on the complexity of cytoarchitecture. High-grade tumors, because of their
higher cellularity and high nuclear-to-cytoplasmic ratios, demonstrate restricted diffusion,
which can be measured quantitatively as decreased apparent diffusion coefficient (ADC)
values [8]. Diffusion-weighted imaging is very valuable for the differential diagnosis
of brain tumors as well as for the detection of leptomeningeal metastasis, particularly
for non-enhancing tumors [9]. Susceptibility-weighted imaging (SWI), which is highly
sensitive for paramagnetic or diamagnetic compounds and can detect areas of hemorrhage
or calcifications, is also commonly included in standard brain imaging [10].
2.1. Advanced Techniques
Advanced imaging techniques may provide microstructural, hemodynamic, and
metabolic information, and can be performed at most centers, although they do require
additional imaging time and post-processing capabilities. Diffusion tensor imaging (DTI)
assesses the magnitude as well as the direction of water diffusion and can be postprocessed
to generate white matter tracts. DTI and tractography can be overlaid on anatomical images
to provide valuable information about the relationship of the tumor to the major white
matter tracts and help guide the surgical approach [11].
MR perfusion techniques provide an assessment of tumor vascularity and hemody-
namics. Perfusion can be performed using contrast-enhanced techniques, such as dynamic
susceptibility (DSC), dynamic contrast-enhanced (DCE) perfusion, or non-contrast arterial
spin labeling (ASL) technique. Depending on the method use, MR perfusion can generate
hemodynamic parameters, such as relative cerebral blood flow (rCBF), relative cerebral
blood volume (rCBV), time to peak (TTP), mean transit time (MTT), and vascular perme-
ability or transfer coefficient (K-trans). These can provide valuable insights into tumor
grade, treatment response, and help in differentiating tumor from radiation injury [11].
Magnetic Resonance Spectroscopy (MRS) provides an analysis of the biochemical
composition of the tissue and can be useful for the differential diagnosis and grading of
tumors. Proton MRS is the most widely used technique and can be performed as single-
voxel or chemical shift imaging (multi-voxel). High-grade tumors typically have elevated
choline peaks, decreased n-acetyl aspartate (NAA) peak, and variable lipid/lactate peak.
However, there are exceptions, and low-grade tumors such as pilocytic astrocytoma can
demonstrate a similar spectroscopic appearance. Short TE MRS (35 milliseconds) allows
the detection of additional metabolite peaks, such as myo-inositol (mI, a glial cell marker),
glycine, glutamine/glutamate (Glx), taurine (Tau), alanine (Ala), and citrate (Cit), which
can be further helpful in tumor characterization. MRS can also be helpful in differentiating
tumors from post-treatment change [12,13].
Functional MRI (fMRI) uses regional changes in brain perfusion induced by brain
activation to generate an MRI signal. This can non-invasively map various eloquent areas,
3. Diagnostics 2022, 12, 961 3 of 24
such as motor, sensory, language, and visual, during the performance of specific tasks.
This information can be used by neurosurgeons to plan the approach and extent of tumor
resection [14].
2.2. Spine Imaging
Based on the suspected tumor histology, imaging of the spine may be added for
the evaluation of cerebrospinal fluid (CSF) dissemination. As with intracranial disease,
contrast is typically required for spine evaluation, although high-resolution 3D steady-
state T2 imaging, such as constructive interference in steady state, Siemens (CISS), or fast
imaging employing steady-state acquisition, GE (FIESTA-C), can be particularly helpful in
identifying small lesions and non-enhancing disease along the surface of the spinal cord or
cauda equina nerve roots [15]. Limited spine imaging with post-contrast T1 and 3D steady-
state T2 sequences is usually adequate for routine metastatic surveillance, without the need
for pre-contrast imaging, thus decreasing the total imaging and, if applicable, anesthesia
time in these patients. As with intracranial disease, DWI can be a valuable technique
for metastasis detection in the spine, especially for non-enhancing lesions. However,
due to the relatively poor resolution of DWI in the spine, it has limited added value for
routine surveillance spine MRI and is often best used as a problem-solving tool [16]. It is
strongly recommended that spine MRI for initial staging in the setting of a newly diagnosed
posterior fossa tumor be performed pre-operatively, as blood products and dural reactions
can significantly limit interpretation in the immediate post-operative period. If the spine
MRI is not performed pre-operatively, waiting for at least 2 weeks after posterior fossa
surgery is recommended to avoid false positive results [17,18].
Recent advances in artificial intelligence and computer vision in medicine have opened
a new frontier in neuro-oncology research. Techniques such as radiomics and deep learn-
ing have shown great promise in prospective radiological diagnosis, molecular subtype
prediction, and outcome predictions for both pediatric and adult brain tumors [19–21].
2.3. Post-Operative and Surveillance Imaging
Post-operative imaging is essential to evaluate the extent of surgical resection, to
identify any immediate post-operative complications, and as a baseline for assessing the
response to radiation and chemotherapy. In specialized centers, intraoperative MRI may
also be used to guide surgical management, with recent advances allowing the use of
high-field-strength systems, diagnostic-quality images, and the ability to integrate with the
surgical navigation systems, making it an important tool for neurosurgeons [22–24].
Post-operative MRI should be performed within 72 h of surgery and preferably within
the first 24 h, as distinguishing residual tumors from reactive enhancement on delayed
imaging can be challenging [25]. In the post-operative period, hemorrhage along the
resection margins may demonstrate T1 shortening; therefore, a direct comparison of pre-
and post-contrast images obtained in the same imaging plane is important to distinguish
hemorrhage from true enhancement. DWI is a very helpful technique for both post-
operative and long-term surveillance imaging. In the immediate post-operative period,
restricted diffusion along the surgical margins may be related to post-surgical change, and
a careful correlation of these areas with contrast enhancement on follow-up imaging should
be performed to distinguish post-surgical enhancement from tumors. DWI can also be
helpful for assessing residual or recurrent disease in high-grade tumors [22].
As a potential complication of treatment, radiation necrosis can occur months to
decades after treatment and is characterized by areas of enhancement, T2 prolongation,
and edema that can mimic tumor recurrence on conventional MR images. Advanced
techniques including MR spectroscopy and MR perfusion imaging are useful in increasing
the diagnostic confidence [26,27]. Another long-term complication of cranial radiation is
the development of intracranial cavernomas, which can sometimes cause larger bleeds
and require surgical intervention. An assessment with susceptibility-weighted images is
important for monitoring this finding [28].
4. Diagnostics 2022, 12, 961 4 of 24
Olivary degeneration is a specific complication related to posterior fossa surgery and
is believed to be secondary to damage to the dentato-rubro-olivary pathway. On imaging,
it can manifest as T2 signal changes in the inferior olivary nuclei of the ventral medulla
(with or without hypertrophy) and cerebellar dentate nuclei. These findings have been
suggested as an imaging biomarker for posterior fossa syndrome, a severe clinical condition
presenting as mutism, dysmetria, and ataxia [29].
3. Conventional and Advanced Imaging Features of Major Tumor Types
3.1. Embryonal Tumors
Embryonal tumors of the central nervous system are highly malignant, undifferenti-
ated, or poorly differentiated tumors of neuroepithelial origin, designated as WHO grade 4.
The classification of embryonal tumors has evolved over multiple iterations of WHO classi-
fication [3,30]. Medulloblastoma, which are exclusive to the posterior fossa, are the most
common embryonal tumors, accounting for two-thirds of cases. Atypical teratoid/rhabdoid
tumor (AT/RT) can occur in both supra- and infratentorial locations, although they are
more common in the posterior fossa. The remaining embryonal tumors are predominantly
supratentorial and replace the previously used category of primitive neuroectodermal
tumors (PNET); they are subgrouped based on molecular features [31]. Key MRI features
of embryonal tumors are summarized in Table 1.
Table 1. Summary of key imaging features of embryonal tumors.
Tumor Type Common Location Key MRI Features
Medulloblastoma
Exclusively posterior fossa
Most commonly in fourth
ventricle/cerebellar vermis (non-WNT,
non-SHH, or WNT),
can involve cerebellopontine angle
(WNT) or cerebellar hemispheres with
extra-axial extension (SHH)
Diffusion restricting
Variable enhancement
Cystic/necrotic change may be present
Calcifications uncommon
Taurine peak characteristic
Atypical teratoid/rhabdoid tumor
Posterior fossa (slightly more common)
or cerebral hemispheres
May be extra-axial
Diffusion restricting
Enhancement usually present
More heterogenous than
medulloblastomas, with cysts/necrosis,
calcification, and hemorrhage
Supratentorial embryonal
Cerebral hemispheres or deep nuclei,
rarely intraventricular
Large tumor
Diffusion restricting solid components
Variable cysts/necrosis and hemorrhage
3.2. Medulloblastoma
Medulloblastoma accounts for 25% of all intracranial pediatric tumors and is the
second most common pediatric brain tumor, after pilocytic astrocytoma. It is the most
common malignant pediatric CNS tumor and the most common posterior fossa tumor,
comprising approximately 38% of posterior fossa tumors in children. The median age
is 9 years at presentation, although nearly 25% of all medulloblastomas arise in adults.
Amongst those older than 3 years, males are more commonly affected (1.7:1), while there is
no gender predilection for those younger than 3 years [31].
Medulloblastomas are WHO grade 4 embryonal tumors and their MR imaging charac-
teristics reflect a high cellularity. A key imaging feature of medulloblastoma is diffusion
restriction, with DWI signal greater than that of adjacent cerebellum and corresponding low
ADC values. The ADC values in medulloblastoma are usually less than 1 × 10−3 mm2/s,
with mean ADC values reported from 0.5–0.7 [32,33]. These lesions typically have a
rounded morphology, relatively well-defined margins, and variable T2 signal ranging from
T2 hyperintense to T2 hypointense compared to the cortex. Areas of lower T2 signal is
typically an indicator of greater cellularity [34]. Enhancement is also variable, ranging
5. Diagnostics 2022, 12, 961 5 of 24
from robust to mild or no identifiable enhancement. Cysts or necrosis are not uncommon,
estimated to be present in 40–50% of medulloblastomas, but are typically small [35]. Calci-
fication can occur but is uncommon and not a predominant feature. Surrounding edema
may be present [36].
Perfusion imaging of medulloblastoma is variable but typically shows increased
relative cerebral blood volume, as with many malignant tumors. MR spectroscopy demon-
strates significantly elevated choline and reduced NAA, although this is not a specific
finding and can be seen in many high-grade tumors. Taurine metabolites have been iden-
tified in some medulloblastomas and the identification of a taurine peak to the left of
choline on MRS can be a more specific finding [36]. The characteristic imaging findings of
medulloblastoma are illustrated in Figure 1, and leptomeningeal metastasis in the spine is
illustrated in Figure 2.
Diagnostics 2022, 12, x FOR PEER REVIEW 5 of 26
with mean ADC values reported from 0.5–0.7 [32,33]. These lesions typically have a
rounded morphology, relatively well-defined margins, and variable T2 signal ranging
from T2 hyperintense to T2 hypointense compared to the cortex. Areas of lower T2 signal
is typically an indicator of greater cellularity [34]. Enhancement is also variable, ranging
from robust to mild or no identifiable enhancement. Cysts or necrosis are not uncommon,
estimated to be present in 40–50% of medulloblastomas, but are typically small [35]. Cal-
cification can occur but is uncommon and not a predominant feature. Surrounding edema
may be present [36].
Perfusion imaging of medulloblastoma is variable but typically shows increased rel-
ative cerebral blood volume, as with many malignant tumors. MR spectroscopy demon-
strates significantly elevated choline and reduced NAA, although this is not a specific
finding and can be seen in many high-grade tumors. Taurine metabolites have been iden-
tified in some medulloblastomas and the identification of a taurine peak to the left of cho-
line on MRS can be a more specific finding [36]. The characteristic imaging findings of
medulloblastoma are illustrated in Figure 1, and leptomeningeal metastasis in the spine
is illustrated in Figure 2.
Diagnostics 2022, 12, x FOR PEER REVIEW 6 of 26
Figure 1. A 7-year-old boy with medulloblastoma. Axial T2 (A), sagittal post-contrast T1 (B), images
demonstrate a mildly heterogeneous midline posterior fossa mass. DWI (C) and ADC (D) imaging
demonstrates diffusion restriction of the lesion relative to the adjacent cerebellar parenchyma, typ-
ical of these highly cellular neoplasms. Perfusion imaging (E) demonstrates increased relative cere-
bral blood volume within the lesion and MRS (F) demonstrates a high choline peak (long arrow) as
well as a small taurine peak (short arrow).
Figure 1. A 7-year-old boy with medulloblastoma. Axial T2 (A), sagittal post-contrast T1 (B), images
demonstrate a mildly heterogeneous midline posterior fossa mass. DWI (C) and ADC (D) imaging
demonstrates diffusion restriction of the lesion relative to the adjacent cerebellar parenchyma, typical
of these highly cellular neoplasms. Perfusion imaging (E) demonstrates increased relative cerebral
blood volume within the lesion and MRS (F) demonstrates a high choline peak (long arrow) as well
as a small taurine peak (short arrow).
6. Diagnostics 2022, 12, 961 6 of 24
Figure 1. A 7-year-old boy with medulloblastoma. Axial T2 (A), sagittal post-contrast T1 (B), images
demonstrate a mildly heterogeneous midline posterior fossa mass. DWI (C) and ADC (D) imaging
demonstrates diffusion restriction of the lesion relative to the adjacent cerebellar parenchyma, typ-
ical of these highly cellular neoplasms. Perfusion imaging (E) demonstrates increased relative cere-
bral blood volume within the lesion and MRS (F) demonstrates a high choline peak (long arrow) as
well as a small taurine peak (short arrow).
Figure 2. A 6-year-old girl with leptomeningeal metastasis from medulloblastoma. Sagittal CISS (A)
and post-contrast T1 (B) imaging demonstrate nodular and enhancing metastatic foci along the dor-
sal cervical spinal cord (arrows). DWI (C) imaging shows associated diffusion restriction.
Classically, medulloblastomas have been considered as midline posterior fossa le-
sions arising from the medullary vellum and compressing the fourth ventricle. However,
these tumors are known to occur eccentrically as well. The variability in appearance and
location reflects the heterogeneous molecular pathology of these tumors. Traditionally,
these tumors have been classified into four histological types: classic medulloblastoma,
Figure 2. A 6-year-old girl with leptomeningeal metastasis from medulloblastoma. Sagittal CISS
(A) and post-contrast T1 (B) imaging demonstrate nodular and enhancing metastatic foci along the
dorsal cervical spinal cord (arrows). DWI (C) imaging shows associated diffusion restriction.
Classically, medulloblastomas have been considered as midline posterior fossa le-
sions arising from the medullary vellum and compressing the fourth ventricle. However,
these tumors are known to occur eccentrically as well. The variability in appearance and
location reflects the heterogeneous molecular pathology of these tumors. Traditionally,
these tumors have been classified into four histological types: classic medulloblastoma,
desmoplastic/nodular medulloblastoma, medulloblastoma with extensive nodularity, and
large cell/anaplastic medulloblastoma [30]. However, recently updated classifications
incorporate important molecular subtypes, which more accurately predict tumor behavior,
prognosis, and may guide treatment. The 2016 WHO classification introduced four dis-
tinct molecular subtypes of medulloblastoma, which have been further expanded in the
most recent 2021 WHO classification. The current classification includes: WNT-activated,
sonic hedgehog (SHH)-activated and TP53 wildtype, SHH-activated and TP53 mutant,
and non-WNT/non-SHH. The SHH-activated are further divided into four subgroups and
non-WNT/non-SHH into eight subgroups [3,30].
As the understanding of the importance of molecular and genetic profiles of these
tumors increases, there has been growing interest in identifying imaging characteristics
specific to the different molecular subtypes and some distinguishing features have emerged
(Figure 3). Non-WNT/non-SHH tumors are the most common and are classically located
in the midline, involving the fourth ventricle. Of the subgroups of non-WNT/non-SHH,
group 3 tumors tend to show robust enhancement while group 4 tumors have been reported
to be hypoenhancing [37]. In contrast to most medulloblastomas, group 3 tumors are often
reported to have ill-defined margins [35]. WNT tumors are the least common, but have
the best prognosis, and can arise near the cerebellopontine angle or in the midline. SHH
tumors have intermediate prognosis, are associated with desmoplastic pathology, and
are often located laterally in the cerebellar hemispheres [38]. It is important to note that
while suggestive, location and imaging characteristics are not yet absolute predictors of
tumor type. Machine learning approaches such as radiomics and multi-parameter imaging
including MR spectroscopy have shown promise in teasing out molecular subgroups by
imaging [39–42].
7. Diagnostics 2022, 12, 961 7 of 24
ogy, and are often located laterally in the cerebellar hemispheres [38]. It is important to
note that while suggestive, location and imaging characteristics are not yet absolute pre-
dictors of tumor type. Machine learning approaches such as radiomics and multi-param-
eter imaging including MR spectroscopy have shown promise in teasing out molecular
subgroups by imaging [39–42].
Diagnostics 2022, 12, x FOR PEER REVIEW 8 of 26
Figure 3. Imaging appearances of molecular subtypes of medulloblastoma. Each row includes axial
T2-weighted, axial post-contrast T1-weighted and axial diffusion-weighted images from left to
right. (Row (A)): SHH. Imaging demonstrates an off-midline lesion with the classic cerebellar hem-
ispheric location of these lesions. (Row (B)): WNT. Imaging demonstrates the classic CP angle loca-
tion of this subtype, although many of these lesions may arise in the midline as well. (Row (C)):
Group 3 and (Row (D)): Group 4. These tumors are classically located in the midline. Note the rela-
tive hypoenhancement of the group 4 tumor, which has been a described feature. All lesions demon-
strate characteristic restricted diffusion.
3.3. Atypical Teratoid/Rhabdoid Tumors
AT/RT is a rare, highly malignant embryonal CNS tumor occurring in young chil-
dren, with the majority of cases presenting before 3 years of age [43]. These tumors can
occur in both supra- and infratentorial locations, have a slight preponderance in the pos-
terior fossa, and may be intra- or extra-axial [44]. AT/RT is an aggressive small round blue
cell tumor and demonstrates imaging characteristics similar to other highly cellular tu-
mors. As with medulloblastoma, these lesions demonstrate diffusion restriction with low
signal on ADC, and the T2 signal is variable but often relatively low [35,45]. Cysts and
susceptibility related to calcification or hemorrhage, are common in AT/RTs [44]. AT/RT
demonstrates increased perfusion, and high choline on MRS and a high lipid peak are
Figure 3. Imaging appearances of molecular subtypes of medulloblastoma. Each row includes axial
T2-weighted, axial post-contrast T1-weighted and axial diffusion-weighted images from left to right.
(Row (A)): SHH. Imaging demonstrates an off-midline lesion with the classic cerebellar hemispheric
location of these lesions. (Row (B)): WNT. Imaging demonstrates the classic CP angle location of
this subtype, although many of these lesions may arise in the midline as well. (Row (C)): Group 3
and (Row (D)): Group 4. These tumors are classically located in the midline. Note the relative
hypoenhancement of the group 4 tumor, which has been a described feature. All lesions demonstrate
characteristic restricted diffusion.
3.3. Atypical Teratoid/Rhabdoid Tumors
AT/RT is a rare, highly malignant embryonal CNS tumor occurring in young children,
with the majority of cases presenting before 3 years of age [43]. These tumors can occur in
both supra- and infratentorial locations, have a slight preponderance in the posterior fossa,
and may be intra- or extra-axial [44]. AT/RT is an aggressive small round blue cell tumor
and demonstrates imaging characteristics similar to other highly cellular tumors. As with
8. Diagnostics 2022, 12, 961 8 of 24
medulloblastoma, these lesions demonstrate diffusion restriction with low signal on ADC,
and the T2 signal is variable but often relatively low [35,45]. Cysts and susceptibility related
to calcification or hemorrhage, are common in AT/RTs [44]. AT/RT demonstrates increased
perfusion, and high choline on MRS and a high lipid peak are often encountered [45]. Myo-
inositol and taurine metabolites are typically not observed. Younger age at presentation and
relatively more heterogenous appearance can point towards AT/RT, although conventional
MRI findings are not reliable in distinguishing AT/RT from other high-grade supra- and
infratentorial tumors. Machine learning-based radiomic technique has shown promise in
non-invasively distinguishing AT/RT from medulloblastoma [46]. An imaging example of
posterior fossa AT/RT is presented in Figure 4.
3.3. Atypical Teratoid/Rhabdoid Tumors
AT/RT is a rare, highly malignant embryonal CNS tumor occurring in young chil-
dren, with the majority of cases presenting before 3 years of age [43]. These tumors can
occur in both supra- and infratentorial locations, have a slight preponderance in the pos-
terior fossa, and may be intra- or extra-axial [44]. AT/RT is an aggressive small round blue
cell tumor and demonstrates imaging characteristics similar to other highly cellular tu-
mors. As with medulloblastoma, these lesions demonstrate diffusion restriction with low
signal on ADC, and the T2 signal is variable but often relatively low [35,45]. Cysts and
susceptibility related to calcification or hemorrhage, are common in AT/RTs [44]. AT/RT
demonstrates increased perfusion, and high choline on MRS and a high lipid peak are
often encountered [45]. Myo-inositol and taurine metabolites are typically not observed.
Younger age at presentation and relatively more heterogenous appearance can point to-
wards AT/RT, although conventional MRI findings are not reliable in distinguishing
AT/RT from other high-grade supra- and infratentorial tumors. Machine learning-based
radiomic technique has shown promise in non-invasively distinguishing AT/RT from me-
dulloblastoma [46]. An imaging example of posterior fossa AT/RT is presented in Figure
4.
Diagnostics 2022, 12, x FOR PEER REVIEW 9 of 26
Figure 4. A 2.5-year-old girl with AT/RT. Axial T2 (A), post-contrast T1 (B), images demonstrate a
heterogeneous lesion in the right cerebellopontine angle extending into the internal auditory canal.
There is also a metastatic lesion in the left IAC (arrow). DWI (C) and SWI (D) imaging demonstrate
restricted diffusion and susceptibility common in these lesions.
3.4. Supratentorial Embryonal Tumors
Supratentorial embryonal tumors represent approximately 15% of CNS neoplasms
in children and are biologically distinct from medulloblastomas, their infratentorial coun-
terparts. The classification of supratentorial embryonal tumors has rapidly evolved over
the last decade, reflecting the improved understanding of the molecular and genetic
make-up of these tumors [47,48]. This category includes specific subtypes, such as embry-
onal tumor with multilayered rosettes (ETMR); CNS neuroblastoma, FOXR2-activated;
and atypical teratoid/rhabdoid tumor (AT/RT), in addition to a broader designation of
CNS embryonal tumor (not otherwise specified; NOS) [3]. On MRI, these tumors are typ-
ically large with a median size of 5.7 cm and variable amounts of surrounding vasogenic
edema. The solid components demonstrate diffusion restriction and post-contrast en-
hancement. Cystic and necrotic change of varying extent is present in the majority, and
hemorrhage/calcification is seen in up to 40% of cases (Figure 5) [49]. Embryonal tumors
Figure 4. A 2.5-year-old girl with AT/RT. Axial T2 (A), post-contrast T1 (B), images demonstrate a
heterogeneous lesion in the right cerebellopontine angle extending into the internal auditory canal.
There is also a metastatic lesion in the left IAC (arrow). DWI (C) and SWI (D) imaging demonstrate
restricted diffusion and susceptibility common in these lesions.
3.4. Supratentorial Embryonal Tumors
Supratentorial embryonal tumors represent approximately 15% of CNS neoplasms in
children and are biologically distinct from medulloblastomas, their infratentorial counter-
parts. The classification of supratentorial embryonal tumors has rapidly evolved over the
last decade, reflecting the improved understanding of the molecular and genetic make-up
of these tumors [47,48]. This category includes specific subtypes, such as embryonal tumor
with multilayered rosettes (ETMR); CNS neuroblastoma, FOXR2-activated; and atypical
teratoid/rhabdoid tumor (AT/RT), in addition to a broader designation of CNS embryonal
tumor (not otherwise specified; NOS) [3]. On MRI, these tumors are typically large with a
9. Diagnostics 2022, 12, 961 9 of 24
median size of 5.7 cm and variable amounts of surrounding vasogenic edema. The solid
components demonstrate diffusion restriction and post-contrast enhancement. Cystic and
necrotic change of varying extent is present in the majority, and hemorrhage/calcification
is seen in up to 40% of cases (Figure 5) [49]. Embryonal tumors may be indistinguishable
from supratentorial high-grade gliomas and ependymomas by imaging, a distinction that
is also challenging by routine histopathology and often requiring immunohistochemistry
and/or molecular profiling for accurate diagnosis [49].
Diagnostics 2022, 12, x FOR PEER REVIEW 10 of 26
Figure 5. A 6-month-old male with CNS embryonal tumor, NOS, WHO Grade 4. Axial DWI (A),
axial SWI (B), axial T2 FLAIR (C) and sagittal post-contrast T1-weighted (D) images demonstrate a
large mass in the left frontal lobe, with diffusion restriction of the peripheral solid components,
scattered small amounts of blood products and/or mineralization, isointense T2 signal of solid por-
tion, large area of central necrosis/cyst, and heterogenous post-contrast enhancement of the solid
portions. Note the similarities in appearance to the HGG illustrated in Figure 1; however, this tumor
presented at a much younger age.
3.5. Pediatric Gliomas
Gliomas are overall the most common pediatric brain tumors and can have varying
tumor grades. The WHO CNS5 classifies gliomas into diffuse and circumscribed types,
and separates diffuse gliomas into adult and pediatric types, with further subclassification
Diagnostics 2022, 12, x FOR PEER REVIEW 10 of 26
Figure 5. A 6-month-old male with CNS embryonal tumor, NOS, WHO Grade 4. Axial DWI (A),
axial SWI (B), axial T2 FLAIR (C) and sagittal post-contrast T1-weighted (D) images demonstrate a
large mass in the left frontal lobe, with diffusion restriction of the peripheral solid components,
scattered small amounts of blood products and/or mineralization, isointense T2 signal of solid por-
tion, large area of central necrosis/cyst, and heterogenous post-contrast enhancement of the solid
portions. Note the similarities in appearance to the HGG illustrated in Figure 1; however, this tumor
presented at a much younger age.
3.5. Pediatric Gliomas
Gliomas are overall the most common pediatric brain tumors and can have varying
tumor grades. The WHO CNS5 classifies gliomas into diffuse and circumscribed types,
and separates diffuse gliomas into adult and pediatric types, with further subclassification
based on combined histological and molecular characteristics [3]. Key MRI features of glial
Figure 5. A 6-month-old male with CNS embryonal tumor, NOS, WHO Grade 4. Axial DWI (A), axial
SWI (B), axial T2 FLAIR (C) and sagittal post-contrast T1-weighted (D) images demonstrate a large
mass in the left frontal lobe, with diffusion restriction of the peripheral solid components, scattered
small amounts of blood products and/or mineralization, isointense T2 signal of solid portion, large
area of central necrosis/cyst, and heterogenous post-contrast enhancement of the solid portions. Note
the similarities in appearance to the HGG illustrated in Figure 1; however, this tumor presented at a
much younger age.
10. Diagnostics 2022, 12, 961 10 of 24
3.5. Pediatric Gliomas
Gliomas are overall the most common pediatric brain tumors and can have varying
tumor grades. The WHO CNS5 classifies gliomas into diffuse and circumscribed types,
and separates diffuse gliomas into adult and pediatric types, with further subclassification
based on combined histological and molecular characteristics [3]. Key MRI features of glial
tumors are summarized in Table 2.
Table 2. Summary of key imaging features of glial and glioneuronal tumors.
Tumor Type Common Location Key MRI Features
Diffuse high-grade gliomas
Diffuse midline glioma (H3K27
altered)
Ventral pons and thalami
Expansile, ill-defined mass
No diffusion restriction
Usually, non-enhancing at presentation
Encasement of basilar artery without narrowing
Supratentorial high-grade glioma
(H3 G34 mutant or H3 wild-type)
Hemispheric or deep nuclei; most
common in frontal and parietal lobes
Large, circumscribed tumor
Diffusion restricting solid components
Variable cysts/necrosis and hemorrhage
Circumscribed gliomas
Pilocytic astrocytoma
Cerebellum (most common), brainstem,
optic chiasm/hypothalamus
Most commonly, cyst with enhancing mural
nodule although cystic component can be
variable, and may be completely solid
Ependymoma
Posterior fossa ependymoma
Fourth ventricle (PFB) or
cerebellopontine angle (PFA)
Heterogeneous mass
Calcifications common
Intermediate diffusion (between
medulloblastoma and pilocytic astrocytoma)
Usually enhancing
High myo-inositol on MRS
Supratentorial ependymoma Frontal or parietal parenchyma
Large mass with necrosis
Central chunky calcifications
Diffusion restriction in two-thirds
Neuronal and glioneuronal tumors
Ganglioglioma Temporal lobe
Variable, although commonly cystic with
enhancing mural nodule
No diffusion restriction
Dysembryoplastic neuroepithelial
tumor (DNET)
Frontal or temporal lobes, cortically
based
Well-circumscribed, triangular configuration,
‘bubbly’ appearance
Absent or minimal enhancement
May be associated with cortical dysplasia
Scalloping of the overlying calvarium
(Abbreviations: rCBV, relative cerebral blood volume; MRS, Magnetic Resonance Spectroscopy; NAA,
N-acetylaspartate; PFA, posterior fossa A; PFB, posterior fossa B).
3.6. Pediatric-Type Diffuse High-Grade Gliomas
High-grade gliomas constitute 8–12% of all pediatric CNS neoplasms, and one-third
of these are supratentorial [50]. The current classification of pediatric-type diffuse high-
grade gliomas includes: Diffuse midline glioma, H3 K27-altered; Diffuse hemispheric
glioma, H3 G34-mutant; Diffuse pediatric-type high-grade glioma, H3-wildtype and IDH-
wildtype; and infant-type hemispheric glioma. The previously used terms ‘anaplastic
astrocytoma’ (grade 3) and ‘glioblastoma’ (grade 4) are no longer used in the context of
pediatric tumors [3].
11. Diagnostics 2022, 12, 961 11 of 24
3.6.1. Diffuse Midline Glioma
Diffuse midline glioma H3 K27-altered (DMG) is a specific tumor entity that involves
midline locations such as pons, thalami, and spinal cord, and has a dismal prognosis. This
group encompasses nearly 80% of the pontine high-grade gliomas traditionally referred
to as diffuse intrinsic pontine gliomas (DIPG). The pontine gliomas are more common
in younger children with a median age of 6.2 years [51]. Historically, because of the
characteristic location and imaging appearance, the diagnosis of DIPG was made with
imaging alone. However, given the deepening biological understanding of this disease,
potential for new therapies, and increased safety of stereotactic biopsy, tissue acquisition is
developing a central role in the management of these tumors [52].
Pontine DMG have a predilection for the ventral or basal portion of pons, and demon-
strate expansile and infiltrative T2 hyperintensity, ill-defined margins, and often engulf the
basilar artery without vascular narrowing. The majority of these lesions are non-enhancing
at diagnosis, with patchy or ring-like enhancement seen in one-third of cases, although
enhancement and necrosis are much more common after radiation treatment [53]. These
lesions do not demonstrate the same degree of diffusion restriction as medulloblastoma or
AT/RT; however, localized areas of relative diffusion restriction can be visually identified
in up to 63% of cases [51,54]. Significant hemorrhage is uncommon at diagnosis, although
small foci of internal hemorrhage may be seen in half the cases on more sensitive SWI or
GRE sequences. Various conventional and quantitative imaging features have been studied
as predictors of overall survival. The presence of necrosis, enhancement, and lower ADC
(diffusion restriction) at diagnosis have all been correlated with worse survival chances [51].
Extrapontine extension of the tumor can be seen in up to 90% of cases and includes mid-
brain, thalami, internal capsules, medulla, middle cerebellar peduncles, and cerebellum.
Lateral extension into the cerebellar peduncles has been associated with a worse prognosis,
but not craniocaudal extension into the midbrain or medulla [55]. MRS may be variable but
it typically demonstrates elevated choline with respect to NAA metabolites [13]. Metastases
are rare, but they are described in about 3% of cases at diagnosis in one large series [51]. The
typical imaging appearance of a pontine diffuse midline glioma is presented in Figure 6.
In the supratentorial compartment, the diffuse midline glioma, H3 K27-altered tumors
are typically centered in the thalami, often with bilateral but asymmetric involvement.
The imaging features are similar to pontine midline gliomas, with hyperintensity on
T2-weighted images, ill-defined, infiltrative margins, and little to no surrounding edema.
Diffusion restriction, hemorrhage, and enhancement are less common at diagnosis, al-
though patchy or ring-like enhancement associated with areas of necrosis may be present.
Diagnostics 2022, 12, x FOR PEER REVIEW 12 of 26
Pontine DMG have a predilection for the ventral or basal portion of pons, and
demonstrate expansile and infiltrative T2 hyperintensity, ill-defined margins, and often
engulf the basilar artery without vascular narrowing. The majority of these lesions are
non-enhancing at diagnosis, with patchy or ring-like enhancement seen in one-third of
cases, although enhancement and necrosis are much more common after radiation treat-
ment [53]. These lesions do not demonstrate the same degree of diffusion restriction as
medulloblastoma or AT/RT; however, localized areas of relative diffusion restriction can
be visually identified in up to 63% of cases [51,54]. Significant hemorrhage is uncommon
at diagnosis, although small foci of internal hemorrhage may be seen in half the cases on
more sensitive SWI or GRE sequences. Various conventional and quantitative imaging
features have been studied as predictors of overall survival. The presence of necrosis, en-
hancement, and lower ADC (diffusion restriction) at diagnosis have all been correlated
with worse survival chances [51]. Extrapontine extension of the tumor can be seen in up
to 90% of cases and includes midbrain, thalami, internal capsules, medulla, middle cere-
bellar peduncles, and cerebellum. Lateral extension into the cerebellar peduncles has been
associated with a worse prognosis, but not craniocaudal extension into the midbrain or
medulla [55]. MRS may be variable but it typically demonstrates elevated choline with
respect to NAA metabolites [13]. Metastases are rare, but they are described in about 3%
of cases at diagnosis in one large series [51]. The typical imaging appearance of a pontine
diffuse midline glioma is presented in Figure 6.
Figure 6. Cont.
12. Diagnostics 2022, 12, 961 12 of 24
Figure 6. A 5-year-old boy with diffuse midline glioma H3K27-altered. Axial T2 (A), post-contrast T1
(B) images demonstrate a poorly marginated infiltrative, expansile T2 hyperintense lesion centered in
the pons with little to no enhancement. DWI (C) and ADC (D) images demonstrate heterogenous
signal without significant diffusion restriction.
3.6.2. Non-Midline Diffuse High-Grade Gliomas
Supratentorial, non-midline, high-grade gliomas include H3 G34 mutant and H3
wild-type, and they are most commonly seen in adolescents [50]. On imaging, these
usually present as large hemispheric and/or deep gray masses with a variable degree of
heterogeneity, cyst formation, hemorrhage, and calcifications. On T2-weighted images,
the solid components are iso- to mildly hyperintense, and there is surrounding vasogenic
edema, mass effect, and often hydrocephalus. In contrast to pontine lesions, diffusion
restriction is common in the solid components, reflecting the high cellularity and high
nuclear-to-cytoplasmic ratio. Post-contrast enhancement is also common, although to
a variable extent within the tumor. (Figure 7) Hemispheric high-grade gliomas share
these imaging features with supratentorial embryonal tumors and ependymomas, and
radiological distinctions between these entities may not be possible, although HGG are
usually more often seen in older children than embryonal tumors [49,50].
Diagnostics 2022, 12, x FOR PEER REVIEW 13 of 26
Figure 6. A 5-year-old boy with diffuse midline glioma H3K27-altered. Axial T2 (A), post-contrast
T1 (B) images demonstrate a poorly marginated infiltrative, expansile T2 hyperintense lesion cen-
tered in the pons with little to no enhancement. DWI (C) and ADC (D) images demonstrate hetero-
genous signal without significant diffusion restriction.
In the supratentorial compartment, the diffuse midline glioma, H3 K27-altered tu-
mors are typically centered in the thalami, often with bilateral but asymmetric involve-
ment. The imaging features are similar to pontine midline gliomas, with hyperintensity
on T2-weighted images, ill-defined, infiltrative margins, and little to no surrounding
edema. Diffusion restriction, hemorrhage, and enhancement are less common at diagno-
sis, although patchy or ring-like enhancement associated with areas of necrosis may be
present.
3.6.2. Non-Midline Diffuse High-Grade Gliomas
Supratentorial, non-midline, high-grade gliomas include H3 G34 mutant and H3
wild-type, and they are most commonly seen in adolescents [50]. On imaging, these usu-
ally present as large hemispheric and/or deep gray masses with a variable degree of het-
erogeneity, cyst formation, hemorrhage, and calcifications. On T2-weighted images, the
solid components are iso- to mildly hyperintense, and there is surrounding vasogenic
edema, mass effect, and often hydrocephalus. In contrast to pontine lesions, diffusion re-
striction is common in the solid components, reflecting the high cellularity and high nu-
clear-to-cytoplasmic ratio. Post-contrast enhancement is also common, although to a var-
iable extent within the tumor. (Figure 7) Hemispheric high-grade gliomas share these im-
aging features with supratentorial embryonal tumors and ependymomas, and radiologi-
cal distinctions between these entities may not be possible, although HGG are usually
more often seen in older children than embryonal tumors [49,50].
Figure 7. Cont.
13. Diagnostics 2022, 12, 961 13 of 24
Diagnostics 2022, 12, x FOR PEER REVIEW 14 of 26
Figure 7. A 14-year-old male with H3 G34R mutant high-grade glioma. Axial DWI (A), axial SWI
(B), axial T2 (C) and coronal post-contrast T1-weighted (D) images demonstrate a large mass in the
right parieto-temporal lobes, with diffusion restriction of the solid portions, central areas of blood
products and/or mineralization (arrow), isointense T2 signal with central necrosis (curved arrow),
and heterogenous post-contrast enhancement.
3.7. Pediatric-Type Diffuse Low-Grade Gliomas
Diffuse low-grade gliomas are much less common compared to circumscribed low-
grade gliomas in children. Diffuse astrocytoma is the prototypical subtype, most com-
monly seen in the frontal or parietal lobes, and on MRI appears as a homogenously T2-
hyperintense, T1-hypointense lesion with ill-defined margins [56]. These tumors generally
lack diffusion restriction and post-contrast enhancement. Although in adults, diffuse as-
trocytomas often degenerate into higher grade, such a transformation is considered rare
in children [50]. Other diffuse low-grade gliomas include angiocentric glioma; polymor-
phous low-grade neuroepithelial tumor of the young (PLNTY); and diffuse low-grade gli-
oma, MAPK pathway-altered, with the last one encompassing tumors with an astrocytic
or oligodendroglial morphology [3].
3.8. Circumscribed Astrocytic Gliomas
The most common circumscribed glioma is pilocytic astrocytoma, which accounts for
one-third of all gliomas and is the most common intracranial neoplasm in children 0–14
years of age [57]. It is a benign, grade 1 tumor that has excellent prognosis with resection
only. The tumorigenesis is related to alteration in MAPK pathway, commonly as BRAF
fusion or BRAF V600E point mutation, and can also be seen in association with neurofi-
bromatosis-type 1 [58].
Pilocytic astrocytomas are most often encountered in the posterior fossa [35] as intra-
axial lesions, classically arising from the cerebellar hemispheres. The typical imaging ap-
pearance is a well-circumscribed cystic lesion with a solid, mural nodule. However, other
presentations including heterogeneously solid, multicystic, and occasionally hemorrhagic
masses may be encountered [59]. The cystic component demonstrates hyperintense, near-
fluid signal on T2 and increased diffusivity, but it may have a variable signal on T2 FLAIR
imaging due to proteinaceous contents. The solid component often demonstrates robust
enhancement and there is often enhancement of the cyst wall. The solid component does
Figure 7. A 14-year-old male with H3 G34R mutant high-grade glioma. Axial DWI (A), axial SWI
(B), axial T2 (C) and coronal post-contrast T1-weighted (D) images demonstrate a large mass in the
right parieto-temporal lobes, with diffusion restriction of the solid portions, central areas of blood
products and/or mineralization (arrow), isointense T2 signal with central necrosis (curved arrow),
and heterogenous post-contrast enhancement.
3.7. Pediatric-Type Diffuse Low-Grade Gliomas
Diffuse low-grade gliomas are much less common compared to circumscribed low-
grade gliomas in children. Diffuse astrocytoma is the prototypical subtype, most com-
monly seen in the frontal or parietal lobes, and on MRI appears as a homogenously
T2-hyperintense, T1-hypointense lesion with ill-defined margins [56]. These tumors gener-
ally lack diffusion restriction and post-contrast enhancement. Although in adults, diffuse
astrocytomas often degenerate into higher grade, such a transformation is considered rare
in children [50]. Other diffuse low-grade gliomas include angiocentric glioma; polymor-
phous low-grade neuroepithelial tumor of the young (PLNTY); and diffuse low-grade
glioma, MAPK pathway-altered, with the last one encompassing tumors with an astrocytic
or oligodendroglial morphology [3].
3.8. Circumscribed Astrocytic Gliomas
The most common circumscribed glioma is pilocytic astrocytoma, which accounts
for one-third of all gliomas and is the most common intracranial neoplasm in children
0–14 years of age [57]. It is a benign, grade 1 tumor that has excellent prognosis with
resection only. The tumorigenesis is related to alteration in MAPK pathway, commonly
as BRAF fusion or BRAF V600E point mutation, and can also be seen in association with
neurofibromatosis-type 1 [58].
Pilocytic astrocytomas are most often encountered in the posterior fossa [35] as intra-
axial lesions, classically arising from the cerebellar hemispheres. The typical imaging
appearance is a well-circumscribed cystic lesion with a solid, mural nodule. However, other
presentations including heterogeneously solid, multicystic, and occasionally hemorrhagic
masses may be encountered [59]. The cystic component demonstrates hyperintense, near-
fluid signal on T2 and increased diffusivity, but it may have a variable signal on T2 FLAIR
imaging due to proteinaceous contents. The solid component often demonstrates robust
enhancement and there is often enhancement of the cyst wall. The solid component does
not restrict diffusion, with reported ADC values of 1.7–1.8 × 10−3 mm/s [2], which is a
distinguishing feature from high-grade tumors such as medulloblastoma [33,60]. On MR
perfusion, there is often increased relative cerebral blood volume in the solid component,
14. Diagnostics 2022, 12, 961 14 of 24
and on MRS there may be significantly elevated choline, despite the low-grade nature of
the tumor [61]. High lipid/lactate peaks are common in JPAs [13]. The classic imaging
appearance of a posterior fossa JPA is shown in Figure 8. Metastasis at presentation or
recurrence is rare in in posterior fossa JPAs although spine imaging at diagnosis should be
considered, as the histology may be uncertain pre-operatively [62].
Diagnostics 2022, 12, x FOR PEER REVIEW 15 of 26
not restrict diffusion, with reported ADC values of 1.7–1.8 × 10−3 mm/s [2], which is a dis-
tinguishing feature from high-grade tumors such as medulloblastoma [33,60]. On MR per-
fusion, there is often increased relative cerebral blood volume in the solid component, and
on MRS there may be significantly elevated choline, despite the low-grade nature of the
tumor [61]. High lipid/lactate peaks are common in JPAs [13]. The classic imaging appear-
ance of a posterior fossa JPA is shown in Figure 8. Metastasis at presentation or recurrence
is rare in in posterior fossa JPAs although spine imaging at diagnosis should be consid-
ered, as the histology may be uncertain pre-operatively [62].
Figure 8. A 6-year-old boy with pilocytic astrocytoma. Axial T2 (A), post-contrast T2-FLAIR (B)
sagittal post-contrast T1 (C) and axial DWI (D) images demonstrate a characteristic large cystic le-
sion in the left cerebellar hemisphere with a solid nodule. The nodule enhances intensely without
Figure 8. A 6-year-old boy with pilocytic astrocytoma. Axial T2 (A), post-contrast T2-FLAIR (B)
sagittal post-contrast T1 (C) and axial DWI (D) images demonstrate a characteristic large cystic lesion
in the left cerebellar hemisphere with a solid nodule. The nodule enhances intensely without signifi-
cant diffusion restriction compared with adjacent parenchyma. Perfusion imaging (E) demonstrates
significantly increased perfusion of the solid nodule. MRS (F) shows elevated choline (arrow) and
high lipid lactate peaks (curved arrow).
Supratentorial pilocytic astrocytomas are overall less common than their posterior
fossa counterparts, and most frequently involve the optic pathway–hypothalamic axis. On
MRI, these tumors present as expansile T2 hyperintense masses, typically centered in the
optic chiasm, with variable involvement of the optic nerves, optic tracts, lateral geniculate
15. Diagnostics 2022, 12, 961 15 of 24
bodies, hypothalamus and third ventricle. Post-contrast enhancement is often present
but variable [63]. Therapeutic decisions are generally based on changes in tumor size
(which can be assessed on T2-weighted images) and ophthalmic examination, with isolated
changes in tumor enhancement considered less important. The suggestion has been made
to forego post-contrast imaging in routine surveillance of optic pathway gliomas to decrease
the risk of gadolinium deposition and to also decrease imaging time and costs [64,65].
Also belonging to the category of circumscribed astrocytomas are pleomorphic xan-
thoastrocytomas, which are rare astrocytic neoplasms of childhood and young adulthood.
Most of these are WHO grade 2 and have a favorable prognosis, although 10–23% may have
a more aggressive behavior [66]. These tumors are most commonly seen in the temporal
or frontal lobes, are peripheral in location, abutting the meninges, and often scallop the
inner table of the calvarium because of slow growth. The dominant cystic component is
seen in two-thirds of cases and surrounding vasogenic edema is common. The solid com-
ponents display avid but heterogenous enhancement, and diffusion restriction is usually
absent [67,68].
3.9. Ependymoma
Ependymomas constitute 10% of all primary CNS neoplasms in children, and although
slightly more common in the posterior fossa, approximately 40% are supratentorial [50].
The supratentorial, infratentorial, and spinal ependymomas are considered genetically
distinct and are now classified separately by WHO CNS5 based on specific molecular
alterations [3].
3.9.1. Infratentorial Ependymomas
In the posterior fossa, ependymomas are most commonly encountered in the region
of the fourth ventricle [35]. In comparison to medulloblastoma, ependymomas have the
appearance of pliable tumors that characteristically extend through the foramina of Luschka
and/or Magendie, encasing nerve roots and vascular structures. These lesions are typically
T2 hyperintense and demonstrate variable enhancement, although the signal characteristics
can be heterogenous. Cysts are common and often larger than seen in medulloblastomas.
Calcification is also common, occurring in approximately 50% of lesions, and are better
characterized in CT than MRI [69]. These lesions are less cellular than medulloblastomas,
typically grade 2 or 3, and classically do not have the same degree of diffusion restriction,
with a reported mean ADC of approximately 1.1 [33]. However, greater degrees of diffusion
restriction may occasionally be encountered, likely reflecting higher grade or anaplastic
lesions, and DWI values overlapping with medulloblastomas have been reported [70]. The
typical imaging appearance of a posterior fossa ependymoma is demonstrated in Figure 9.
Two molecular subgroups of posterior fossa ependymoma have been identified: pos-
terior fossa A (PFA) and posterior fossa B (PFB) [3]. PFB lesions typically originate from
the floor of the fourth ventricle more inferiorly toward the obex, seen in older children
and adolescents, and have an excellent prognosis. PFA lesions more commonly arise along
the lateral recess and may extend into the prepontine recess, making complete resection
difficult. PFA lesions are predominantly seen in very young children, and portend poor
prognosis [58].
Similar to other CNS neoplasms, ependymomas typically demonstrate increased
relative cerebral blood volume in perfusion imaging, and high choline levels and reduced
NAA in spectroscopy. Ependymomas often demonstrate significantly elevated myo-inositol
metabolites (to the left of the choline peak) and lipid/lactate peaks may be present [71].
Although not as common as in medulloblastomas, leptomeningeal metastasis is seen
in an estimated 3–10% of cases at presentation [72]. More frequently, metastases occur
during disease recurrence [69] and, as with medulloblastoma, these patients require brain
and whole spine imaging for disease workup and surveillance.
16. Diagnostics 2022, 12, 961 16 of 24
Diagnostics 2022, 12, x FOR PEER REVIEW 17 of 26
Figure 9. A 6-year-old male with ependymoma. Sagittal and axial T2 images (A,B) demonstrate a
heterogeneous lesion involving the fourth ventricle. Note the characteristic extension through the
foramen of Luska (arrow). Post-contrast T1 imaging (C) demonstrates typical heterogeneous en-
hancement. SWI imaging (D) demonstrates a focus of low signal correlating with calcification
(curved arrow). DWI (E) and ADC (F) imaging show increased ADC values of the tumor compared
with adjacent parenchyma.
Two molecular subgroups of posterior fossa ependymoma have been identified: pos-
terior fossa A (PFA) and posterior fossa B (PFB) [3]. PFB lesions typically originate from
the floor of the fourth ventricle more inferiorly toward the obex, seen in older children
and adolescents, and have an excellent prognosis. PFA lesions more commonly arise along
the lateral recess and may extend into the prepontine recess, making complete resection
difficult. PFA lesions are predominantly seen in very young children, and portend poor
prognosis [58].
Figure 9. A 6-year-old male with ependymoma. Sagittal and axial T2 images (A,B) demonstrate
a heterogeneous lesion involving the fourth ventricle. Note the characteristic extension through
the foramen of Luska (arrow). Post-contrast T1 imaging (C) demonstrates typical heterogeneous
enhancement. SWI imaging (D) demonstrates a focus of low signal correlating with calcification
(curved arrow). DWI (E) and ADC (F) imaging show increased ADC values of the tumor compared
with adjacent parenchyma.
3.9.2. Supratentorial Ependymomas
The majority of supratentorial ependymomas are parenchymal in location, with a
predilection for the frontal and parietal lobes, although a small fraction can be intraventric-
ular [50,73]. These are typically large masses, with both solid and cystic components, and
surrounding vasogenic edema. Central or centripetal, chunky calcifications are common,
and there may be areas of necrosis within the solid component. Enhancement is usually
present but heterogenous, and diffusion restriction may be seen in two-thirds of cases [73].
The differential considerations for parenchymal lesions include high-grade glioma and
embryonal tumor, while intraventricular locations include choroid plexus tumors and
giant cell astrocytoma. Of the two molecular subtypes of supratentorial EP, ZFTA fusion-
positive usually occur in older children and have a poor prognosis compared with the
YAP1 fusion-positive, which are more typical in infants and associated with a very good
prognosis [58].
17. Diagnostics 2022, 12, 961 17 of 24
3.10. Glioneuronal and Neuronal Tumors
These are rare tumors, accounting for 1% of pediatric brain tumors and usually
seen in older children and adolescents. The most common tumors in this category are
ganglioglioma and dysembryoplastic neuroepithelial tumor (DNET). Key MRI features of
glial and glioneuronal tumors are summarized in Table 2.
Gangliogliomas are composed of neoplastic neuronal elements and astrocytes, have
an indolent course and excellent prognosis. Over three-fourths are seen in the temporal
lobes, particularly mesial temporal lobes, and the patients typically present with seizures.
On MRI, the appearance can be variable, although 40% tumors present as mixed solid and
cystic masses, with an avidly enhancing mural nodule. Calcifications are common and
diffusion restriction is absent [50,56] (Figure 10).
Diagnostics 2022, 12, x FOR PEER REVIEW 19 of 26
Figure 10. A 16-year-old male with ganglioglioma, presenting with seizures. Axial and coronal T2-
weighted (A,B), sagittal post-contrast T1-weighted (C) and axial DWI (D) images demonstrate a
large predominantly cystic mass with eccentric solid component in the right temporal lobe. There is
patchy but avid enhancement within the solid component. Lack of diffusion restriction within the
solid component helps distinguish this from higher grade tumors.
DNETs are cortically based benign tumors, presenting with seizures, and 30% have
an adjacent cortical dysplasia. On MRI, they are well-circumscribed, may have a triangu-
lar configuration, broader at the cortex and tapering towards the ventricles. The tumors
may display small cysts and bubbly appearance, and often there is scalloping of the over-
lying calvarium. DNETs typically do not enhance, although small nodular or ring-like foci
of enhancement may be seen in one-third of cases [56,74].
3.11. Choroid Plexus Tumors
Choroid plexus tumors are rare, accounting for 2–4% of brain tumors in children.
These are most commonly located in the lateral ventricles, followed by the fourth and the
third ventricles. The mean age at presentation is 4.6 years. Both choroid plexus papillomas
Figure 10. A 16-year-old male with ganglioglioma, presenting with seizures. Axial and coronal
T2-weighted (A,B), sagittal post-contrast T1-weighted (C) and axial DWI (D) images demonstrate a
large predominantly cystic mass with eccentric solid component in the right temporal lobe. There is
patchy but avid enhancement within the solid component. Lack of diffusion restriction within the
solid component helps distinguish this from higher grade tumors.
DNETs are cortically based benign tumors, presenting with seizures, and 30% have an
adjacent cortical dysplasia. On MRI, they are well-circumscribed, may have a triangular
configuration, broader at the cortex and tapering towards the ventricles. The tumors may
display small cysts and bubbly appearance, and often there is scalloping of the overlying
calvarium. DNETs typically do not enhance, although small nodular or ring-like foci of
enhancement may be seen in one-third of cases [56,74].
18. Diagnostics 2022, 12, 961 18 of 24
3.11. Choroid Plexus Tumors
Choroid plexus tumors are rare, accounting for 2–4% of brain tumors in children. These
are most commonly located in the lateral ventricles, followed by the fourth and the third
ventricles. The mean age at presentation is 4.6 years. Both choroid plexus papillomas and
carcinomas appear as papillary or lobulated, enhancing, intraventricular masses, although
carcinomas are more likely to demonstrate internal heterogeneity, necrosis, parenchymal
invasion, and perilesional edema (Figure 11). Hydrocephalus is often present and may
be secondary to CSF overproduction and/or obstruction, depending on the location and
tumor differentiation [75,76]. On MRS, choroid plexus papillomas demonstrate higher
myo-inositol and lower choline peaks compared to carcinomas [12]. Key MRI features of
choroid plexus tumors are summarized in Table 3.
Diagnostics 2022, 12, x FOR PEER REVIEW 20 of 26
and carcinomas appear as papillary or lobulated, enhancing, intraventricular masses, alt-
hough carcinomas are more likely to demonstrate internal heterogeneity, necrosis, paren-
chymal invasion, and perilesional edema (Figure 11). Hydrocephalus is often present and
may be secondary to CSF overproduction and/or obstruction, depending on the location
and tumor differentiation [75,76]. On MRS, choroid plexus papillomas demonstrate
higher myo-inositol and lower choline peaks compared to carcinomas [12]. Key MRI fea-
tures of choroid plexus tumors are summarized in Table 3.
Figure 11. A 2-year-old male with choroid plexus carcinoma. Axial and coronal T2-weighted (A,B),
axial post-contrast T1-weighted (C) and axial perfusion rCBF (D) images demonstrate a large, pre-
dominantly solid mass with scattered tiny cystic foci, within the atrium and occipital horn of the left
lateral ventricle. There is diffuse enhancement of the tumor and increased blood flow on the perfu-
sion maps. The presence of parenchymal invasion (arrows on (A–C)) distinguishes choroid plexus
carcinoma from papilloma.
Figure 11. A 2-year-old male with choroid plexus carcinoma. Axial and coronal T2-weighted
(A,B), axial post-contrast T1-weighted (C) and axial perfusion rCBF (D) images demonstrate a large,
predominantly solid mass with scattered tiny cystic foci, within the atrium and occipital horn of the
left lateral ventricle. There is diffuse enhancement of the tumor and increased blood flow on the
perfusion maps. The presence of parenchymal invasion (arrows on (A–C)) distinguishes choroid
plexus carcinoma from papilloma.
19. Diagnostics 2022, 12, 961 19 of 24
Table 3. Summary of key imaging features of pineal region and choroid plexus tumors.
Tumor Type Common Location Key MRI Features
Pineal region tumors
Pineoblastoma Pineal region
Heterogenous, ‘explode’ pineal calcification
Diffusion restricting
Enhancement variable
Germ cell tumor Pineal and/or suprasellar region
Heterogenous, usually calcified, ‘engulf’ pineal calcification
Diffusion restricting
Enhancement usually present
Choroid plexus tumors
Choroid plexus papilloma
Lateral ventricle followed by fourth
ventricle
Lobular or papillary lesions
Diffuse enhancement
No diffusion restriction
High myo-inositol, low creatine, moderate choline on MRS
Choroid plexus carcinoma Lateral ventricle
More heterogenous than papillomas
Parenchymal invasion/edema
May have diffusion restriction
Moderate myo-inositol and high choline on MRS
3.12. Pineal Region Tumors
Pineal region tumors account for approximately 4% of childhood intracranial tumors
and include germ cell tumors and pineal parenchymal tumors, which are often difficult
to distinguish by imaging. Key MRI features of pineal region tumors are summarized in
Table 3.
Germ cell tumors are relatively more common, typically seen in adolescent males,
and include germinomas and non-germinomatous germ cell tumors. Serum and CSF
oncoprotein markers, such as alpha-fetoprotein, beta-human chorionic gonadotropin, and
placental alkaline phosphatase, are helpful in diagnosis and follow-up. Pineal parenchymal
tumors include pineocytomas, pineal parenchymal tumors of intermediate differentiation,
and pineoblastomas. Of these, pineoblastomas are the most common, accounting for 40%
of pineal parenchymal tumors. Pineoblastomas are WHO grade 4, undifferentiated tumors,
that are prone to adjacent parenchymal invasion and CSF dissemination [77]. Both germ cell
tumors and pineoblastomas can have heterogenous signal and enhancement with diffusion
restriction. Apparent diffusion coefficient (ADC) values have been used to distinguish
between the two, with mixed results. The relationship between the tumor and the pattern
of pineal gland calcification on CT can sometimes help in diagnosis, with germinomas
typically ‘engulfing’ the calcification, and pineoblastomas ‘exploding’ the calcification to
the periphery of the mass. Germinomas can also involve the pituitary infundibulum, either
in isolation or concurrent with pineal involvement (Figure 12). Pineoblastomas can be
associated with retinoblastomas as a part of ‘trilateral’ disease in a small percentage of
patients [78].
20. Diagnostics 2022, 12, 961 20 of 24
Diagnostics 2022, 12, x FOR PEER REVIEW 22 of 26
Figure 12. A 19-month-old male with with germ cell tumor and synchronous involvement of the
pineal and suprasellar regions. Axial and coronal T2-weighted (A,B) images of the pineal region
demonstrate heterogeneously T2-isointense mass with obstructive hydrocephalus. Sagittal post-
contrast T1-weighted image (C) demonstrates heterogenous enhancement of the pineal region mass
with obstruction of the cerebral aqueduct, and also a smaller, similar-appearing mass, involving the
pituitary infundibulum (arrowhead). Sagittal non-contrast CT image (D) of the same patient
demonstrates a rounded pineal calcification ‘engulfed’ by the mass (arrow), which can help to
distinguish germ cell tumors from pineoblastomas.
4. Conclusions
MRI plays a key role in the diagnosis and treatment of pediatric brain tumors. This
review provides an overview of conventional and advanced MRI imaging techniques and
the key imaging features of major intracranial tumors, a basic understanding of which can
be very helpful in the management of these children.
Author Contributions: Conceptualization, All authors; writing—original draft preparation, A.J.
and M.E.R.; writing—review and editing, All authors; All authors have read and agreed to the
published version of the manuscript.
Funding: This research received no external funding.
Conflicts of Interest: The authors declare no conflict of interest.
Figure 12. A 19-month-old male with with germ cell tumor and synchronous involvement of the
pineal and suprasellar regions. Axial and coronal T2-weighted (A,B) images of the pineal region
demonstrate heterogeneously T2-isointense mass with obstructive hydrocephalus. Sagittal post-
contrast T1-weighted image (C) demonstrates heterogenous enhancement of the pineal region mass
with obstruction of the cerebral aqueduct, and also a smaller, similar-appearing mass, involving
the pituitary infundibulum (arrowhead). Sagittal non-contrast CT image (D) of the same patient
demonstrates a rounded pineal calcification ‘engulfed’ by the mass (arrow), which can help to
distinguish germ cell tumors from pineoblastomas.
4. Conclusions
MRI plays a key role in the diagnosis and treatment of pediatric brain tumors. This
review provides an overview of conventional and advanced MRI imaging techniques and
the key imaging features of major intracranial tumors, a basic understanding of which can
be very helpful in the management of these children.
Author Contributions: Conceptualization, all authors; writing—original draft preparation, A.J. and
M.E.R.; writing—review and editing, all authors. All authors have read and agreed to the published
version of the manuscript.
Funding: This research received no external funding.
Conflicts of Interest: The authors declare no conflict of interest.
21. Diagnostics 2022, 12, 961 21 of 24
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