This document provides guidelines for reporting breast ultrasound examinations, including:
- Reports should be organized and include indication, findings, comparison to prior exams, assessment, and management.
- Findings should be described using standardized terminology and include lesion description, location, and measurements.
- The assessment should use the BI-RADS categories and clearly state the likelihood of malignancy and recommended management.
- For combined exams, the overall assessment and management should reflect the highest level of abnormality found.
This document provides guidelines for reporting the results of mammography examinations using the BI-RADS assessment and reporting system. It describes the standardized structure for mammography reports, including sections for the indication, breast composition, findings, comparison to prior exams, assessment, and management. The breast composition section provides definitions and illustrations for the four breast composition categories based on fibroglandular density. The assessment category section matches each BI-RADS assessment category with its corresponding management recommendation and likelihood of cancer.
This summarizes a study that investigated using bilateral asymmetry analysis of breast MR images to detect breast diseases. The study used directional statistics of breast parenchymal edges and texture analysis to characterize differences between left and right breast images. On a dataset of 40 MR scans (20 normal, 20 malignant), the method achieved an average classification accuracy of 70% at detecting cancer, with sensitivity of 75% and specificity of 65%. The results support that bilateral asymmetry analysis of MR images can provide additional information for breast disease detection.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Signs of Benign Breast Disease in 3D TomosynthesisApollo Hospitals
This document discusses signs of benign breast disease that can be identified on 3D tomosynthesis imaging. It notes that 3D tomosynthesis is better able to characterize lesions by reducing tissue overlap and allowing analysis of individual 1mm slices. Specific signs of benign disease highlighted include the halo sign around masses, internal contents of masses, superficial plane lesions, and morphology of microcalcifications. The study found 3D tomosynthesis identified these benign signs with a higher rate than 2D mammography alone. This can help radiologists more accurately categorize lesions, avoiding unnecessary additional testing or biopsy.
Role of Tomosynthesis in Assessing the Size of the Breast LesionApollo Hospitals
To assess the role of 3D tomosynthesis in the evaluation of the size of malignant breast lesions and to compare it with the size in 2D, Ultrasound and final Histopatholgy.
Breast Cancer Prediction using Machine LearningIRJET Journal
This document discusses using machine learning algorithms to predict breast cancer from patient data and imaging results. It first provides background on breast cancer, noting it is the most commonly diagnosed cancer worldwide. The document then reviews prior works applying machine learning to breast cancer prediction, finding support vector machines achieved the highest accuracy. It describes the dataset used, from the University of Wisconsin, containing patient data and tumor characteristics. Finally, it explores the data and discusses implementing classification algorithms like logistic regression, support vector machines, random forests and neural networks to predict cancer type, finding logistic regression achieved the highest accuracy of 98.24%.
This document provides an overview of mammography, including definitions, indications, equipment, technique, findings, and assessment categories. It defines mammography as an x-ray examination of the breast to detect changes. Key indications include focal signs in women aged 40 or older and screening for high-risk women. Equipment has advanced from film-screen to digital mammography and tomosynthesis. Standard views are mediolateral oblique and craniocaudal. Findings can include masses, asymmetries, distortions, and calcifications, which are categorized based on characteristics like shape, margin, density, and distribution.
A Review of Segmentation of Mammographic Images Based on Breast DensityIJERA Editor
1) The document reviews approaches for segmenting breast regions in mammograms according to breast density. Breast density is an important risk factor for breast cancer.
2) Segmentation of mammographic images can identify glandular and fibroglandular tissues, which appear bright on mammograms. This segmentation is the first step in computer-aided detection and diagnosis of breast cancer.
3) Several approaches have been used for segmentation, including statistical methods based on Gaussian mixture modeling, thresholding techniques, and classification of breast density into categories like the BI-RADS system. Segmentation of specific breast regions like the pectoral muscle have also been studied.
This document provides guidelines for reporting the results of mammography examinations using the BI-RADS assessment and reporting system. It describes the standardized structure for mammography reports, including sections for the indication, breast composition, findings, comparison to prior exams, assessment, and management. The breast composition section provides definitions and illustrations for the four breast composition categories based on fibroglandular density. The assessment category section matches each BI-RADS assessment category with its corresponding management recommendation and likelihood of cancer.
This summarizes a study that investigated using bilateral asymmetry analysis of breast MR images to detect breast diseases. The study used directional statistics of breast parenchymal edges and texture analysis to characterize differences between left and right breast images. On a dataset of 40 MR scans (20 normal, 20 malignant), the method achieved an average classification accuracy of 70% at detecting cancer, with sensitivity of 75% and specificity of 65%. The results support that bilateral asymmetry analysis of MR images can provide additional information for breast disease detection.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Signs of Benign Breast Disease in 3D TomosynthesisApollo Hospitals
This document discusses signs of benign breast disease that can be identified on 3D tomosynthesis imaging. It notes that 3D tomosynthesis is better able to characterize lesions by reducing tissue overlap and allowing analysis of individual 1mm slices. Specific signs of benign disease highlighted include the halo sign around masses, internal contents of masses, superficial plane lesions, and morphology of microcalcifications. The study found 3D tomosynthesis identified these benign signs with a higher rate than 2D mammography alone. This can help radiologists more accurately categorize lesions, avoiding unnecessary additional testing or biopsy.
Role of Tomosynthesis in Assessing the Size of the Breast LesionApollo Hospitals
To assess the role of 3D tomosynthesis in the evaluation of the size of malignant breast lesions and to compare it with the size in 2D, Ultrasound and final Histopatholgy.
Breast Cancer Prediction using Machine LearningIRJET Journal
This document discusses using machine learning algorithms to predict breast cancer from patient data and imaging results. It first provides background on breast cancer, noting it is the most commonly diagnosed cancer worldwide. The document then reviews prior works applying machine learning to breast cancer prediction, finding support vector machines achieved the highest accuracy. It describes the dataset used, from the University of Wisconsin, containing patient data and tumor characteristics. Finally, it explores the data and discusses implementing classification algorithms like logistic regression, support vector machines, random forests and neural networks to predict cancer type, finding logistic regression achieved the highest accuracy of 98.24%.
This document provides an overview of mammography, including definitions, indications, equipment, technique, findings, and assessment categories. It defines mammography as an x-ray examination of the breast to detect changes. Key indications include focal signs in women aged 40 or older and screening for high-risk women. Equipment has advanced from film-screen to digital mammography and tomosynthesis. Standard views are mediolateral oblique and craniocaudal. Findings can include masses, asymmetries, distortions, and calcifications, which are categorized based on characteristics like shape, margin, density, and distribution.
A Review of Segmentation of Mammographic Images Based on Breast DensityIJERA Editor
1) The document reviews approaches for segmenting breast regions in mammograms according to breast density. Breast density is an important risk factor for breast cancer.
2) Segmentation of mammographic images can identify glandular and fibroglandular tissues, which appear bright on mammograms. This segmentation is the first step in computer-aided detection and diagnosis of breast cancer.
3) Several approaches have been used for segmentation, including statistical methods based on Gaussian mixture modeling, thresholding techniques, and classification of breast density into categories like the BI-RADS system. Segmentation of specific breast regions like the pectoral muscle have also been studied.
Image processing and machine learning techniques used in computer-aided dete...IJECEIAES
This paper aims to review the previously developed Computer-aided detection (CAD) systems for mammogram screening because increasing death rate in women due to breast cancer is a global medical issue and it can be controlled only by early detection with regular screening. Till now mammography is the widely used breast imaging modality. CAD systems have been adopted by the radiologists to increase the accuracy of the breast cancer diagnosis by avoiding human errors and experience related issues. This study reveals that in spite of the higher accuracy obtained by the earlier proposed CAD systems for breast cancer diagnosis, they are not fully automated. Moreover, the false-positive mammogram screening cases are high in number and over-diagnosis of breast cancer exposes a patient towards harmful overtreatment for which a huge amount of money is being wasted. In addition, it is also reported that the mammogram screening result with and without CAD systems does not have noticeable difference, whereas the undetected cancer cases by CAD system are increasing. Thus, future research is required to improve the performance of CAD system for mammogram screening and make it completely automated.
1) The study developed a computational system called C-Path to automatically quantify over 6,600 morphological features from breast cancer epithelium and stroma in histology slides.
2) When applied to two independent patient cohorts (n=248 and n=328), a prognostic model based on the quantified features was strongly associated with patient survival, independent of other factors.
3) Three stromal features were significantly associated with survival, even more so than epithelial features, implicating tumor stroma morphology as a previously unrecognized prognostic factor for breast cancer.
This document discusses techniques for early detection of breast cancer through image processing of mammograms. It begins by introducing breast cancer and the importance of early detection. It then discusses current mammography screening approaches, including the two standard views and BI-RADS assessment system. The key abnormalities that may indicate breast cancer are described: masses with characteristics like shape, margin, density; calcifications described by size, shape and clustering; architectural distortion; and asymmetries. Current challenges are noted around detecting calcifications and masses in dense breast tissue. The paper aims to review techniques used in image processing for early breast cancer detection, including preprocessing, segmentation, and decomposition steps typically used.
IRJET - Classifying Breast Cancer Tumour Type using Convolution Neural Netwo...IRJET Journal
This document presents a study that uses a convolutional neural network (CNN) deep learning model to classify breast cancer tumors as benign or malignant based on ultrasonic images. The researchers trained a CNN model using a dataset of ultrasonic breast images labeled as benign or malignant. The trained model can then analyze new ultrasonic images and determine the tumor type, which could help doctors diagnose and treat breast cancer more accurately. The document provides background on breast cancer and existing diagnosis methods, describes the proposed CNN classification system, and reviews related work applying machine learning to breast cancer analysis.
Development and Validation of a Nomogram for Predicting Response to Neoadjuva...semualkaira
Retrospective analysis of clinical data on female patients with breast cancer was performed. Model 1 was developed by entering variables from the univariate analysis (P < 0.1) into a multivariate logistic regression analysis. Model 2 was developed via the stepwise forward-backward variable selection technique in partial least squares regression. For model 3, the least absolute shrinkage and selection operator (LASSO) method was used to choose suitable variables, followed by the multivariate logistic regression analysis.
Development and Validation of a Nomogram for Predicting Response to Neoadjuva...semualkaira
Retrospective analysis of clinical data on female
patients with breast cancer was performed. Model 1 was developed by entering variables from the univariate analysis (P < 0.1)
into a multivariate logistic regression analysis. Model 2 was developed via the stepwise forward-backward variable selection technique in partial least squares regression. For model 3, the least
absolute shrinkage and selection operator (LASSO) method was
used to choose suitable variables, followed by the multivariate
logistic regression analysis. Harrell’s C-index, calibration curves,
and decision curve analyses (DCA) were used to compare the
performance of the models. In the validation cohort, these results
were validated
Role of Breast Tomosynthesis in the Morphological Analysis of Breast LesionsApollo Hospitals
1. To assess the role of Breast Tomosynthesis (by 3D Combined View) versus 2D Full
Field Digital Mammogram alone in the morphological analysis of breast lesions.
2. To evaluate the potential role of Tomosynthesis in BIRADS Categorisation and Final
Histopathology.
In May 2011 we migrated from an Analogue Mammogram with a dedicated Mammogram
CR system to a Full Field Digital System with 3D Tomosynthesis.
In India there is no official screening programme. All screening is opportunistic, self-
initiated and self-funded. Most Mammograms done at our hospital, a Corporate Tertiary
care Oncology facility, are performed as Diagnostic Mammograms followed by mandatory Breast Ultrasound and additional views, if necessary, on the same day obviating the need for recall.
Reducing the number of cases for additional views and breast ultrasound will help
in decreasing the patient's waiting time, making reporting more efficient, without
compromising on the accuracy. We used BIRADS categorisation as an evaluating tool
and compared the BIRADS categorisation with the final HPE.
Performance and Evaluation of Data Mining Techniques in Cancer DiagnosisIOSR Journals
Abstract: We analyze the breast Cancer data available from the WBC, WDBC from UCI machine learning with
the aim of developing accurate prediction models for breast cancer using data mining techniques. Data mining
has, for good reason, recently attracted a lot of attention, it is a new Technology, tackling new problem, with
great potential for valuable commercial and scientific discoveries. The experiments are conducted in WEKA.
Several data mining classification techniques were used on the proposed data. There are many classification
techniques in data mining such as Decision Tree, Rules NNge, Tree random forest, Random Tree, lazy IBK. The
aim of this paper is to investigate the performance of different classification techniques. The data breast cancer
data with a total 286 rows and 10 columns will be used to test and justify the different between the classification
methods and algorithm.
Keywords - Machine learning, data mining Weka, classification, breast cancer
A Comprehensive Evaluation of Machine Learning Approaches for Breast Cancer C...IRJET Journal
This document compares the performance of three machine learning models - Convolutional Neural Networks (CNN), Artificial Neural Networks (ANN), and Support Vector Machines (SVM) - for classifying breast cancer from histopathology images. CNN achieved the highest classification accuracy of 87%, followed by SVM at 76% and ANN at 71%. CNN also exhibited superior sensitivity in detecting malignant cases. The document proposes using CNN, ANN, and SVM models on a breast cancer histopathology image dataset to determine which model provides the most accurate cancer classification.
A Virtual Instrument to Detect Masses In Breast Cancer using CAD toolstheijes
Breast cancer is the second-most driving and normal explanation behind death in view of tumor among one in every ten women. It has become a major health problem in the world over the past 50 years, and it has increased in recent years. Early detection is an effective way to diagnose and manage breast cancer. Mammography is the best and most suitable imaging technique for treatment of cancer at the early stage. The problems in mammography images such as high brightness value, dense tissues, noise and inefficient contrast level make analysis of these images a hard task for physicians for mass identification. This paper presents a CAD tool which are combination of image processing techniques to remove noise and enhancement of mammography images for identification & classification of masses. Efficient methods includes wavelet transformation and adaptive histogram equalization techniques, in addition with fusion techniques are used. Algorithms for identification of signs are tested on five patients, the associated abnormalities are clearly identified. The images for experimentation are taken from radiopedia. Experimental results show that a detection rate of 94.44% or higher can be achieved using this method, hence improved accuracy in breast cancer lesion detection. The proposed system achieves 100% sensitivity and 2.56 false positive for every image
intraoperative margin assesment for breast conserving surgery.pdfAndhikaRezaAkbar1
This systematic review analyzed 19 studies with a total of 6,769 breast cancer patients who underwent breast-conserving surgery with intraoperative frozen section analysis of margins. The review found that frozen section has high accuracy, sensitivity, and specificity compared to final pathology. The reoperation rate averaged 5.9% across studies. Local recurrence rates were also low, with the highest rate being 7.5% after 3 years. The quality of included studies was assessed as having low risk of bias. The review concluded that frozen section analysis is a reliable method that can help reduce reoperation rates for positive margins in breast-conserving surgery.
MEDICAL IMAGING MUTIFRACTAL ANALYSIS IN PREDICTION OF EFFICIENCY OF CANCER TH...cscpconf
Based on pressing need for predictive performance improvement, we explored the value of pretherapy
tumour histology image analysis to predict chemotherapy response. It was shown that
multifractal analysis of breast tumour tissue prior to chemotherapy indeed has the capacity to
distinguish between histological images of the different chemotherapy responder groups with
accuracies of 91.4% for pPR, 82.9% for pCR and 82.1% for PD/SD.
MEDICAL IMAGING MUTIFRACTAL ANALYSIS IN PREDICTION OF EFFICIENCY OF CANCER TH...csandit
Multifractal analysis of breast tumor tissue prior to chemotherapy can predict chemotherapy response with high accuracy. It was shown to distinguish histological images of different response groups with over 82% accuracy for pathological complete response and progressive/stable disease. The maximum of the multifractal spectrum parameter f(α)max provided the most important predictive value, suggesting it may detect unknown structural clues related to drug resistance. Further investigation of f(α)max could help characterize its predictive potential.
This document describes a study that developed a neuro-fuzzy model to predict ovarian cancer malignancy preoperatively. The model uses demographic, serum tumor marker, and ultrasound criteria. It was trained using a database of 525 patients and evaluated for classification performance. The neuro-fuzzy model achieved an accuracy of 84% and an area under the ROC curve of 0.85 for discriminating between benign and malignant ovarian tumors, outperforming existing methods. The neuro-fuzzy approach combines the benefits of neural networks and fuzzy logic for modeling imprecise medical data while maintaining interpretability.
This document presents a new method for analyzing asymmetry in breast MR images to detect early signs of breast cancer. The method extracts six features from bilateral breast MR images, including directional statistics and texture measurements. Linear discriminant analysis with leave-one-out cross validation was used to classify images as cancer or non-cancer. The best classification results used combinations of two or three features, achieving average accuracies from 65.6% to 78.1%, sensitivities from 53.3% to 73.3%, and specificities from 60% to 94.1%. Preliminary results suggest this asymmetry analysis method shows potential for detecting breast cancer on MR images despite using a small number of features and a simple classifier.
BRAIN TUMOUR DETECTION AND CLASSIFICATIONIRJET Journal
This document summarizes a method for detecting and classifying brain tumors using MRI images. A deep learning model based on ResNet152 is trained on labeled MRI images to identify different types of tumors. The model extracts features from MRI images and classifies tumors with 97% accuracy on one dataset and 96% accuracy on another. ResNet152 performed better than other models tested. The method provides automated tumor detection and classification to help with diagnosis and treatment planning in neurosurgery and radiation oncology.
Iaetsd classification of lung tumour usingIaetsd Iaetsd
This document describes a study that aims to classify lung tumors using geometric and texture features extracted from chest x-ray images. The study uses 75 chest x-ray images (25 from small-cell lung cancer, 25 from non-small cell lung cancer, and 25 from tuberculosis) to extract geometric features like area, shape, and distance from texture features calculated using gray level co-occurrence matrices. Active shape models are used to segment the lung fields for feature extraction. The extracted features are then analyzed to determine the optimal features for classifying different types of lung abnormalities.
IRJET - Cervical Cancer Prognosis using MARS and ClassificationIRJET Journal
This document describes a study that used data mining techniques like MARS and C5.0 classification to analyze cervical cancer prognosis and recurrence. The study used a dataset of 168 cervical cancer patients with 12 variables from a hospital tumor registry. MARS and C5.0 models were developed using a training set of 118 patients and tested on 50 patients. The C5.0 model had a higher average correct classification rate of 96% compared to 86% for MARS. The C5.0 model also identified the most important prognostic factors as pStage, pT, cell type and RT target Summary. The study demonstrated that data mining can help identify specific risk factors for recurrent cervical cancer.
Machine Learning - Breast Cancer DiagnosisPramod Sharma
Machine learning is helping in making smart decisions faster. In this presentation measurements carried out on FNAC was analysed. The results were validated using 20 percent of the data. The data used for POC is from UCI Repository/
Enhancing Segmentation Approaches from Super Pixel Division Algorithm to Hidd...Christo Ananth
Christo Ananth, S. Amutha, K. Niha, Djabbarov Botirjon Begimovich, “Enhancing Segmentation Approaches from Super Pixel Division Algorithm to Hidden Markov Random Fields with Expectation Maximization (HMRF-EM)”, International Journal of Early Childhood Special Education, Volume 14, Issue 05, 2022,pp. 2400-2410.
Christo Ananth et al. discussed that In surgical planning and cancer treatment, it is crucial to segment and measure a liver tumor's volume accurately. Because it would involve automation, standardisation, and the incorporation of complete volumetric information, accurate automatic liver tumor segmentation would substantially affect the processes for therapy planning and follow-up reporting. Based on the Hidden Markov random field, Automatic liver tumor detection in CT scans is possible using hidden Markov random fields (HMRF-EM). A CT scan of the liver may be too low-resolution for this software. CT liver tissue segmentation is based on the HMRF model. When building an accurate HMRF model, an accurate initial image estimate is crucial. Adaptive K-means clustering is often used for initial estimation. HMRF's performance can be greatly improved by clustering. This project aims to segment liver tissue quickly. This paper proposes an adaptive K-means clustering approach for estimating liver images in the HMRF-EM model. The previous strategy had flaws, so this one fixed them. We compare the current and proposed methods. The proposed method outperforms the currently used method.
hematic appreciation test is a psychological assessment tool used to measure an individual's appreciation and understanding of specific themes or topics. This test helps to evaluate an individual's ability to connect different ideas and concepts within a given theme, as well as their overall comprehension and interpretation skills. The results of the test can provide valuable insights into an individual's cognitive abilities, creativity, and critical thinking skills
The technology uses reclaimed CO₂ as the dyeing medium in a closed loop process. When pressurized, CO₂ becomes supercritical (SC-CO₂). In this state CO₂ has a very high solvent power, allowing the dye to dissolve easily.
Image processing and machine learning techniques used in computer-aided dete...IJECEIAES
This paper aims to review the previously developed Computer-aided detection (CAD) systems for mammogram screening because increasing death rate in women due to breast cancer is a global medical issue and it can be controlled only by early detection with regular screening. Till now mammography is the widely used breast imaging modality. CAD systems have been adopted by the radiologists to increase the accuracy of the breast cancer diagnosis by avoiding human errors and experience related issues. This study reveals that in spite of the higher accuracy obtained by the earlier proposed CAD systems for breast cancer diagnosis, they are not fully automated. Moreover, the false-positive mammogram screening cases are high in number and over-diagnosis of breast cancer exposes a patient towards harmful overtreatment for which a huge amount of money is being wasted. In addition, it is also reported that the mammogram screening result with and without CAD systems does not have noticeable difference, whereas the undetected cancer cases by CAD system are increasing. Thus, future research is required to improve the performance of CAD system for mammogram screening and make it completely automated.
1) The study developed a computational system called C-Path to automatically quantify over 6,600 morphological features from breast cancer epithelium and stroma in histology slides.
2) When applied to two independent patient cohorts (n=248 and n=328), a prognostic model based on the quantified features was strongly associated with patient survival, independent of other factors.
3) Three stromal features were significantly associated with survival, even more so than epithelial features, implicating tumor stroma morphology as a previously unrecognized prognostic factor for breast cancer.
This document discusses techniques for early detection of breast cancer through image processing of mammograms. It begins by introducing breast cancer and the importance of early detection. It then discusses current mammography screening approaches, including the two standard views and BI-RADS assessment system. The key abnormalities that may indicate breast cancer are described: masses with characteristics like shape, margin, density; calcifications described by size, shape and clustering; architectural distortion; and asymmetries. Current challenges are noted around detecting calcifications and masses in dense breast tissue. The paper aims to review techniques used in image processing for early breast cancer detection, including preprocessing, segmentation, and decomposition steps typically used.
IRJET - Classifying Breast Cancer Tumour Type using Convolution Neural Netwo...IRJET Journal
This document presents a study that uses a convolutional neural network (CNN) deep learning model to classify breast cancer tumors as benign or malignant based on ultrasonic images. The researchers trained a CNN model using a dataset of ultrasonic breast images labeled as benign or malignant. The trained model can then analyze new ultrasonic images and determine the tumor type, which could help doctors diagnose and treat breast cancer more accurately. The document provides background on breast cancer and existing diagnosis methods, describes the proposed CNN classification system, and reviews related work applying machine learning to breast cancer analysis.
Development and Validation of a Nomogram for Predicting Response to Neoadjuva...semualkaira
Retrospective analysis of clinical data on female patients with breast cancer was performed. Model 1 was developed by entering variables from the univariate analysis (P < 0.1) into a multivariate logistic regression analysis. Model 2 was developed via the stepwise forward-backward variable selection technique in partial least squares regression. For model 3, the least absolute shrinkage and selection operator (LASSO) method was used to choose suitable variables, followed by the multivariate logistic regression analysis.
Development and Validation of a Nomogram for Predicting Response to Neoadjuva...semualkaira
Retrospective analysis of clinical data on female
patients with breast cancer was performed. Model 1 was developed by entering variables from the univariate analysis (P < 0.1)
into a multivariate logistic regression analysis. Model 2 was developed via the stepwise forward-backward variable selection technique in partial least squares regression. For model 3, the least
absolute shrinkage and selection operator (LASSO) method was
used to choose suitable variables, followed by the multivariate
logistic regression analysis. Harrell’s C-index, calibration curves,
and decision curve analyses (DCA) were used to compare the
performance of the models. In the validation cohort, these results
were validated
Role of Breast Tomosynthesis in the Morphological Analysis of Breast LesionsApollo Hospitals
1. To assess the role of Breast Tomosynthesis (by 3D Combined View) versus 2D Full
Field Digital Mammogram alone in the morphological analysis of breast lesions.
2. To evaluate the potential role of Tomosynthesis in BIRADS Categorisation and Final
Histopathology.
In May 2011 we migrated from an Analogue Mammogram with a dedicated Mammogram
CR system to a Full Field Digital System with 3D Tomosynthesis.
In India there is no official screening programme. All screening is opportunistic, self-
initiated and self-funded. Most Mammograms done at our hospital, a Corporate Tertiary
care Oncology facility, are performed as Diagnostic Mammograms followed by mandatory Breast Ultrasound and additional views, if necessary, on the same day obviating the need for recall.
Reducing the number of cases for additional views and breast ultrasound will help
in decreasing the patient's waiting time, making reporting more efficient, without
compromising on the accuracy. We used BIRADS categorisation as an evaluating tool
and compared the BIRADS categorisation with the final HPE.
Performance and Evaluation of Data Mining Techniques in Cancer DiagnosisIOSR Journals
Abstract: We analyze the breast Cancer data available from the WBC, WDBC from UCI machine learning with
the aim of developing accurate prediction models for breast cancer using data mining techniques. Data mining
has, for good reason, recently attracted a lot of attention, it is a new Technology, tackling new problem, with
great potential for valuable commercial and scientific discoveries. The experiments are conducted in WEKA.
Several data mining classification techniques were used on the proposed data. There are many classification
techniques in data mining such as Decision Tree, Rules NNge, Tree random forest, Random Tree, lazy IBK. The
aim of this paper is to investigate the performance of different classification techniques. The data breast cancer
data with a total 286 rows and 10 columns will be used to test and justify the different between the classification
methods and algorithm.
Keywords - Machine learning, data mining Weka, classification, breast cancer
A Comprehensive Evaluation of Machine Learning Approaches for Breast Cancer C...IRJET Journal
This document compares the performance of three machine learning models - Convolutional Neural Networks (CNN), Artificial Neural Networks (ANN), and Support Vector Machines (SVM) - for classifying breast cancer from histopathology images. CNN achieved the highest classification accuracy of 87%, followed by SVM at 76% and ANN at 71%. CNN also exhibited superior sensitivity in detecting malignant cases. The document proposes using CNN, ANN, and SVM models on a breast cancer histopathology image dataset to determine which model provides the most accurate cancer classification.
A Virtual Instrument to Detect Masses In Breast Cancer using CAD toolstheijes
Breast cancer is the second-most driving and normal explanation behind death in view of tumor among one in every ten women. It has become a major health problem in the world over the past 50 years, and it has increased in recent years. Early detection is an effective way to diagnose and manage breast cancer. Mammography is the best and most suitable imaging technique for treatment of cancer at the early stage. The problems in mammography images such as high brightness value, dense tissues, noise and inefficient contrast level make analysis of these images a hard task for physicians for mass identification. This paper presents a CAD tool which are combination of image processing techniques to remove noise and enhancement of mammography images for identification & classification of masses. Efficient methods includes wavelet transformation and adaptive histogram equalization techniques, in addition with fusion techniques are used. Algorithms for identification of signs are tested on five patients, the associated abnormalities are clearly identified. The images for experimentation are taken from radiopedia. Experimental results show that a detection rate of 94.44% or higher can be achieved using this method, hence improved accuracy in breast cancer lesion detection. The proposed system achieves 100% sensitivity and 2.56 false positive for every image
intraoperative margin assesment for breast conserving surgery.pdfAndhikaRezaAkbar1
This systematic review analyzed 19 studies with a total of 6,769 breast cancer patients who underwent breast-conserving surgery with intraoperative frozen section analysis of margins. The review found that frozen section has high accuracy, sensitivity, and specificity compared to final pathology. The reoperation rate averaged 5.9% across studies. Local recurrence rates were also low, with the highest rate being 7.5% after 3 years. The quality of included studies was assessed as having low risk of bias. The review concluded that frozen section analysis is a reliable method that can help reduce reoperation rates for positive margins in breast-conserving surgery.
MEDICAL IMAGING MUTIFRACTAL ANALYSIS IN PREDICTION OF EFFICIENCY OF CANCER TH...cscpconf
Based on pressing need for predictive performance improvement, we explored the value of pretherapy
tumour histology image analysis to predict chemotherapy response. It was shown that
multifractal analysis of breast tumour tissue prior to chemotherapy indeed has the capacity to
distinguish between histological images of the different chemotherapy responder groups with
accuracies of 91.4% for pPR, 82.9% for pCR and 82.1% for PD/SD.
MEDICAL IMAGING MUTIFRACTAL ANALYSIS IN PREDICTION OF EFFICIENCY OF CANCER TH...csandit
Multifractal analysis of breast tumor tissue prior to chemotherapy can predict chemotherapy response with high accuracy. It was shown to distinguish histological images of different response groups with over 82% accuracy for pathological complete response and progressive/stable disease. The maximum of the multifractal spectrum parameter f(α)max provided the most important predictive value, suggesting it may detect unknown structural clues related to drug resistance. Further investigation of f(α)max could help characterize its predictive potential.
This document describes a study that developed a neuro-fuzzy model to predict ovarian cancer malignancy preoperatively. The model uses demographic, serum tumor marker, and ultrasound criteria. It was trained using a database of 525 patients and evaluated for classification performance. The neuro-fuzzy model achieved an accuracy of 84% and an area under the ROC curve of 0.85 for discriminating between benign and malignant ovarian tumors, outperforming existing methods. The neuro-fuzzy approach combines the benefits of neural networks and fuzzy logic for modeling imprecise medical data while maintaining interpretability.
This document presents a new method for analyzing asymmetry in breast MR images to detect early signs of breast cancer. The method extracts six features from bilateral breast MR images, including directional statistics and texture measurements. Linear discriminant analysis with leave-one-out cross validation was used to classify images as cancer or non-cancer. The best classification results used combinations of two or three features, achieving average accuracies from 65.6% to 78.1%, sensitivities from 53.3% to 73.3%, and specificities from 60% to 94.1%. Preliminary results suggest this asymmetry analysis method shows potential for detecting breast cancer on MR images despite using a small number of features and a simple classifier.
BRAIN TUMOUR DETECTION AND CLASSIFICATIONIRJET Journal
This document summarizes a method for detecting and classifying brain tumors using MRI images. A deep learning model based on ResNet152 is trained on labeled MRI images to identify different types of tumors. The model extracts features from MRI images and classifies tumors with 97% accuracy on one dataset and 96% accuracy on another. ResNet152 performed better than other models tested. The method provides automated tumor detection and classification to help with diagnosis and treatment planning in neurosurgery and radiation oncology.
Iaetsd classification of lung tumour usingIaetsd Iaetsd
This document describes a study that aims to classify lung tumors using geometric and texture features extracted from chest x-ray images. The study uses 75 chest x-ray images (25 from small-cell lung cancer, 25 from non-small cell lung cancer, and 25 from tuberculosis) to extract geometric features like area, shape, and distance from texture features calculated using gray level co-occurrence matrices. Active shape models are used to segment the lung fields for feature extraction. The extracted features are then analyzed to determine the optimal features for classifying different types of lung abnormalities.
IRJET - Cervical Cancer Prognosis using MARS and ClassificationIRJET Journal
This document describes a study that used data mining techniques like MARS and C5.0 classification to analyze cervical cancer prognosis and recurrence. The study used a dataset of 168 cervical cancer patients with 12 variables from a hospital tumor registry. MARS and C5.0 models were developed using a training set of 118 patients and tested on 50 patients. The C5.0 model had a higher average correct classification rate of 96% compared to 86% for MARS. The C5.0 model also identified the most important prognostic factors as pStage, pT, cell type and RT target Summary. The study demonstrated that data mining can help identify specific risk factors for recurrent cervical cancer.
Machine Learning - Breast Cancer DiagnosisPramod Sharma
Machine learning is helping in making smart decisions faster. In this presentation measurements carried out on FNAC was analysed. The results were validated using 20 percent of the data. The data used for POC is from UCI Repository/
Enhancing Segmentation Approaches from Super Pixel Division Algorithm to Hidd...Christo Ananth
Christo Ananth, S. Amutha, K. Niha, Djabbarov Botirjon Begimovich, “Enhancing Segmentation Approaches from Super Pixel Division Algorithm to Hidden Markov Random Fields with Expectation Maximization (HMRF-EM)”, International Journal of Early Childhood Special Education, Volume 14, Issue 05, 2022,pp. 2400-2410.
Christo Ananth et al. discussed that In surgical planning and cancer treatment, it is crucial to segment and measure a liver tumor's volume accurately. Because it would involve automation, standardisation, and the incorporation of complete volumetric information, accurate automatic liver tumor segmentation would substantially affect the processes for therapy planning and follow-up reporting. Based on the Hidden Markov random field, Automatic liver tumor detection in CT scans is possible using hidden Markov random fields (HMRF-EM). A CT scan of the liver may be too low-resolution for this software. CT liver tissue segmentation is based on the HMRF model. When building an accurate HMRF model, an accurate initial image estimate is crucial. Adaptive K-means clustering is often used for initial estimation. HMRF's performance can be greatly improved by clustering. This project aims to segment liver tissue quickly. This paper proposes an adaptive K-means clustering approach for estimating liver images in the HMRF-EM model. The previous strategy had flaws, so this one fixed them. We compare the current and proposed methods. The proposed method outperforms the currently used method.
hematic appreciation test is a psychological assessment tool used to measure an individual's appreciation and understanding of specific themes or topics. This test helps to evaluate an individual's ability to connect different ideas and concepts within a given theme, as well as their overall comprehension and interpretation skills. The results of the test can provide valuable insights into an individual's cognitive abilities, creativity, and critical thinking skills
The technology uses reclaimed CO₂ as the dyeing medium in a closed loop process. When pressurized, CO₂ becomes supercritical (SC-CO₂). In this state CO₂ has a very high solvent power, allowing the dye to dissolve easily.
Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...University of Maribor
Slides from talk:
Aleš Zamuda: Remote Sensing and Computational, Evolutionary, Supercomputing, and Intelligent Systems.
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Inter-Society Networking Panel GRSS/MTT-S/CIS Panel Session: Promoting Connection and Cooperation
https://www.etran.rs/2024/en/home-english/
Phenomics assisted breeding in crop improvementIshaGoswami9
As the population is increasing and will reach about 9 billion upto 2050. Also due to climate change, it is difficult to meet the food requirement of such a large population. Facing the challenges presented by resource shortages, climate
change, and increasing global population, crop yield and quality need to be improved in a sustainable way over the coming decades. Genetic improvement by breeding is the best way to increase crop productivity. With the rapid progression of functional
genomics, an increasing number of crop genomes have been sequenced and dozens of genes influencing key agronomic traits have been identified. However, current genome sequence information has not been adequately exploited for understanding
the complex characteristics of multiple gene, owing to a lack of crop phenotypic data. Efficient, automatic, and accurate technologies and platforms that can capture phenotypic data that can
be linked to genomics information for crop improvement at all growth stages have become as important as genotyping. Thus,
high-throughput phenotyping has become the major bottleneck restricting crop breeding. Plant phenomics has been defined as the high-throughput, accurate acquisition and analysis of multi-dimensional phenotypes
during crop growing stages at the organism level, including the cell, tissue, organ, individual plant, plot, and field levels. With the rapid development of novel sensors, imaging technology,
and analysis methods, numerous infrastructure platforms have been developed for phenotyping.
Describing and Interpreting an Immersive Learning Case with the Immersion Cub...Leonel Morgado
Current descriptions of immersive learning cases are often difficult or impossible to compare. This is due to a myriad of different options on what details to include, which aspects are relevant, and on the descriptive approaches employed. Also, these aspects often combine very specific details with more general guidelines or indicate intents and rationales without clarifying their implementation. In this paper we provide a method to describe immersive learning cases that is structured to enable comparisons, yet flexible enough to allow researchers and practitioners to decide which aspects to include. This method leverages a taxonomy that classifies educational aspects at three levels (uses, practices, and strategies) and then utilizes two frameworks, the Immersive Learning Brain and the Immersion Cube, to enable a structured description and interpretation of immersive learning cases. The method is then demonstrated on a published immersive learning case on training for wind turbine maintenance using virtual reality. Applying the method results in a structured artifact, the Immersive Learning Case Sheet, that tags the case with its proximal uses, practices, and strategies, and refines the free text case description to ensure that matching details are included. This contribution is thus a case description method in support of future comparative research of immersive learning cases. We then discuss how the resulting description and interpretation can be leveraged to change immersion learning cases, by enriching them (considering low-effort changes or additions) or innovating (exploring more challenging avenues of transformation). The method holds significant promise to support better-grounded research in immersive learning.
The debris of the ‘last major merger’ is dynamically youngSérgio Sacani
The Milky Way’s (MW) inner stellar halo contains an [Fe/H]-rich component with highly eccentric orbits, often referred to as the
‘last major merger.’ Hypotheses for the origin of this component include Gaia-Sausage/Enceladus (GSE), where the progenitor
collided with the MW proto-disc 8–11 Gyr ago, and the Virgo Radial Merger (VRM), where the progenitor collided with the
MW disc within the last 3 Gyr. These two scenarios make different predictions about observable structure in local phase space,
because the morphology of debris depends on how long it has had to phase mix. The recently identified phase-space folds in Gaia
DR3 have positive caustic velocities, making them fundamentally different than the phase-mixed chevrons found in simulations
at late times. Roughly 20 per cent of the stars in the prograde local stellar halo are associated with the observed caustics. Based
on a simple phase-mixing model, the observed number of caustics are consistent with a merger that occurred 1–2 Gyr ago.
We also compare the observed phase-space distribution to FIRE-2 Latte simulations of GSE-like mergers, using a quantitative
measurement of phase mixing (2D causticality). The observed local phase-space distribution best matches the simulated data
1–2 Gyr after collision, and certainly not later than 3 Gyr. This is further evidence that the progenitor of the ‘last major merger’
did not collide with the MW proto-disc at early times, as is thought for the GSE, but instead collided with the MW disc within
the last few Gyr, consistent with the body of work surrounding the VRM.
The binding of cosmological structures by massless topological defectsSérgio Sacani
Assuming spherical symmetry and weak field, it is shown that if one solves the Poisson equation or the Einstein field
equations sourced by a topological defect, i.e. a singularity of a very specific form, the result is a localized gravitational
field capable of driving flat rotation (i.e. Keplerian circular orbits at a constant speed for all radii) of test masses on a thin
spherical shell without any underlying mass. Moreover, a large-scale structure which exploits this solution by assembling
concentrically a number of such topological defects can establish a flat stellar or galactic rotation curve, and can also deflect
light in the same manner as an equipotential (isothermal) sphere. Thus, the need for dark matter or modified gravity theory is
mitigated, at least in part.
ESR spectroscopy in liquid food and beverages.pptxPRIYANKA PATEL
With increasing population, people need to rely on packaged food stuffs. Packaging of food materials requires the preservation of food. There are various methods for the treatment of food to preserve them and irradiation treatment of food is one of them. It is the most common and the most harmless method for the food preservation as it does not alter the necessary micronutrients of food materials. Although irradiated food doesn’t cause any harm to the human health but still the quality assessment of food is required to provide consumers with necessary information about the food. ESR spectroscopy is the most sophisticated way to investigate the quality of the food and the free radicals induced during the processing of the food. ESR spin trapping technique is useful for the detection of highly unstable radicals in the food. The antioxidant capability of liquid food and beverages in mainly performed by spin trapping technique.
Immersive Learning That Works: Research Grounding and Paths ForwardLeonel Morgado
We will metaverse into the essence of immersive learning, into its three dimensions and conceptual models. This approach encompasses elements from teaching methodologies to social involvement, through organizational concerns and technologies. Challenging the perception of learning as knowledge transfer, we introduce a 'Uses, Practices & Strategies' model operationalized by the 'Immersive Learning Brain' and ‘Immersion Cube’ frameworks. This approach offers a comprehensive guide through the intricacies of immersive educational experiences and spotlighting research frontiers, along the immersion dimensions of system, narrative, and agency. Our discourse extends to stakeholders beyond the academic sphere, addressing the interests of technologists, instructional designers, and policymakers. We span various contexts, from formal education to organizational transformation to the new horizon of an AI-pervasive society. This keynote aims to unite the iLRN community in a collaborative journey towards a future where immersive learning research and practice coalesce, paving the way for innovative educational research and practice landscapes.
EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...Sérgio Sacani
Context. With a mass exceeding several 104 M⊙ and a rich and dense population of massive stars, supermassive young star clusters
represent the most massive star-forming environment that is dominated by the feedback from massive stars and gravitational interactions
among stars.
Aims. In this paper we present the Extended Westerlund 1 and 2 Open Clusters Survey (EWOCS) project, which aims to investigate
the influence of the starburst environment on the formation of stars and planets, and on the evolution of both low and high mass stars.
The primary targets of this project are Westerlund 1 and 2, the closest supermassive star clusters to the Sun.
Methods. The project is based primarily on recent observations conducted with the Chandra and JWST observatories. Specifically,
the Chandra survey of Westerlund 1 consists of 36 new ACIS-I observations, nearly co-pointed, for a total exposure time of 1 Msec.
Additionally, we included 8 archival Chandra/ACIS-S observations. This paper presents the resulting catalog of X-ray sources within
and around Westerlund 1. Sources were detected by combining various existing methods, and photon extraction and source validation
were carried out using the ACIS-Extract software.
Results. The EWOCS X-ray catalog comprises 5963 validated sources out of the 9420 initially provided to ACIS-Extract, reaching a
photon flux threshold of approximately 2 × 10−8 photons cm−2
s
−1
. The X-ray sources exhibit a highly concentrated spatial distribution,
with 1075 sources located within the central 1 arcmin. We have successfully detected X-ray emissions from 126 out of the 166 known
massive stars of the cluster, and we have collected over 71 000 photons from the magnetar CXO J164710.20-455217.
Authoring a personal GPT for your research and practice: How we created the Q...Leonel Morgado
Thematic analysis in qualitative research is a time-consuming and systematic task, typically done using teams. Team members must ground their activities on common understandings of the major concepts underlying the thematic analysis, and define criteria for its development. However, conceptual misunderstandings, equivocations, and lack of adherence to criteria are challenges to the quality and speed of this process. Given the distributed and uncertain nature of this process, we wondered if the tasks in thematic analysis could be supported by readily available artificial intelligence chatbots. Our early efforts point to potential benefits: not just saving time in the coding process but better adherence to criteria and grounding, by increasing triangulation between humans and artificial intelligence. This tutorial will provide a description and demonstration of the process we followed, as two academic researchers, to develop a custom ChatGPT to assist with qualitative coding in the thematic data analysis process of immersive learning accounts in a survey of the academic literature: QUAL-E Immersive Learning Thematic Analysis Helper. In the hands-on time, participants will try out QUAL-E and develop their ideas for their own qualitative coding ChatGPT. Participants that have the paid ChatGPT Plus subscription can create a draft of their assistants. The organizers will provide course materials and slide deck that participants will be able to utilize to continue development of their custom GPT. The paid subscription to ChatGPT Plus is not required to participate in this workshop, just for trying out personal GPTs during it.
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A. REPORT ORGANIZATION
ThereportshouldbeconciseandorganizedusingastructuresuchasthatprovidedinTable2(below).
Assessments and management recommendations are discussed in item B of this chapter on the re-
porting system, as well as in the Guidance chapter and in answer to some specific questions among
the Frequently Asked Questions.
The indication for examination, relevant clinical history, and pertinent risk factor information should
be clearly stated. If the study is performed for follow-up of a specific mass or area of concern, this
should be described. The dates of any comparison examinations should be specified. As detailed in
the General Considerations section on Labeling and Measurement (see page 30), when a specific
sonographic finding is documented by recording a complete set of images, the longest horizontal
dimension should be reported first, followed by the vertical measurement, and the orthogonal hori-
zontal dimension last. Multiple simple cysts or a combination of multiple simple and complicated
cysts need not be reported individually. If any lesions have been biopsied previously, this should be
noted together with the prior biopsy results, if known. Correlation of any clinical, mammographic,
and MRI findings with the sonographic findings should be specifically stated in the report. For diag-
nostic evaluations involving US characterization of mammographic abnormalities or confirmation of
amasssuspectedbutnotdelineatedmammographically, asinglereportintegratingthetwo modali-
ties will clearly communicate a final assessment based on the highest likelihood of malignancy and
appropriate management recommendations.
Consistent use of BI-RADS® descriptors for US, as for mammography and MRI, helps in lesion as-
sessment and clarifies communication with physicians and patients. Also, structured, software-
based reporting should be based on BI-RADS® terminology.
For coding and reimbursement, consider the advisability of splitting the report combining the
findings of two or more concurrently performed imaging modalities or procedures into specific
sections or paragraphs, one for each type of examination. However, a single assessment and rec-
ommendation for patient management should reflect integration of the findings from all of the
imaging studies. Note that an assessment based on specific findings needing most urgent atten-
tion will have the greatest clinical utility.
1. INDICATION FOR EXAMINATION
The reason for performing the examination should be stated briefly at the beginning
of the report. The most common indications for breast US are confirmation and charac-
Table 2. Report Organization
Report Structure
1. Indication for examination
2. Statement of scope and technique of breast US examination
3. Succinct description of the overall breast composition (screening only)
4. Clear description of any important findings
5. Comparison to previous examination(s), including correlation with physical, mammography, or MRI findings
6. Composite reports
7. Assessment
8. Management
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terization of a palpable mass or mammographic or MRI abnormality, guidance of inter-
ventional procedures, and as the initial imaging technique for young, pregnant, or lac-
tating patients. Additional applications are listed in the ACR Practice Guideline for the
Performance of the Breast Ultrasound Examination and include the extent of disease
evaluation supplementing mammography in high-risk women who are not candidates
for breast MRI or who have no easy access to MRI, and in breast imaging practices that
provide the service, supplementary whole-breast screening in order to increase cancer
detection in asymptomatic women with mammographically dense breasts.
2. STATEMENT OF SCOPE ANDTECHNIQUE OF BREAST US EXAMINATION
The scope of examination and technique used should be stated, for example, whether the
examination was directed or targeted to a specific location, or whether it was performed
for supplementary screening. It is important, since US is a real-time examination, to indi-
cate who performed the examination (sonographer, sonographer and physician, physician
alone) or whether an automated whole-breast scanning system was used. If a lesion was
evaluated with color or power Doppler or with strain or shear-wave elastography, observa-
tions relevant to the interpretation should be reported.
In certain situations, it may be beneficial to describe the position of the patient during the
examination (e.g., “The breasts were imaged in both supine and lateral decubitus position.”
or“The patient was imaged in seated position, the position in which she feels the left breast
thickening best.”).
Automated whole breast scanners that acquire in 3-D are available for clinical use and can
be formatted in three planes.These scanners depict the entire breast in coronal, transverse,
and sagittal planes, with the coronal view similar to the coronal MRI view. Reporting of
these studies continue to evolve, but where possible the interpretation structure outlined
in Table 2 (see page 123) and the reporting procedures described earlier in this section
should be followed.
3. SUCCINCT DESCRIPTION OFTHE OVERALL BREAST COMPOSITION (screening only)
Tissue composition patterns can be estimated more easily in the large FOVs of automat-
ed US scans but can also be discerned in the small FOV of a handheld US scan. The three
US descriptors for tissue composition described earlier in the US lexicon,“homogeneous
background echotexture-fat,” “homogeneous background echotexture-fibroglandular,”
and “heterogeneous background echotexture” (Table 3) (below) correspond loosely to
the four density descriptors of mammography and the four fibroglandular tissue descrip-
tors of MRI. At US, breast tissue composition is determined by echogenicity. Subcutane-
ous fat, the tissue relative to which echogenicity is compared, is medium gray and darker
than fibroglandular tissue, which is light gray. Heterogeneous breasts show an admixture
of hypoechoic and more echogenic areas. Careful real-time scanning will help differenti-
ate a small hypoechoic area of normal tissue from a mass.
Table 3. BreastTissue
Tissue Composition
a. Homogeneous background echotexture-fat
b. Homogeneous background echotexture-fibroglandular
c. Heterogeneous background echotexture
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4. CLEAR DESCRIPTION OF ANY IMPORTANT FINDINGS
The description of important findings should be made, in order of clinical relevance, using
lexicon terminology, and should include:
a. Characterization of a mass using the morphological descriptors of shape, margin, and ori-
entation. Note should be made of the lesion’s effect on the surrounding tissue, such as
architectural distortion. Feature categories, such as posterior features and echogenicity,
and techniques, such as color or power Doppler and elastography, may contribute infor-
mation to the analysis, but only pertinent positives need to be described. Recognition of
special case findings, such as simple and complicated cysts, clustered microcysts, intra-
mammary lymph nodes, and foreign bodies, should simplify interpretation. In reporting
screening examinations in asymptomatic women, as in mammography, characteristically
benign findings may be reported (assessment category 2), but it is not obligatory, and the
appropriate assessment would then be negative (assessment category 1).
b. For important findings, lesion size should be given in at least two dimensions; three di-
mensions are preferable, especially if the volume of a mass is compared with one or
more previous examinations. It is not necessary to report the measurements of every
small simple cyst, and if numerous cysts are present, especially in both breasts; location
and measurements of the largest cyst in each breast will suffice.
If a mass is measured, images should be recorded with and without calipers. Marginal
characteristics are one of the most important criteria to be applied in assessing the likeli-
hood of malignancy of a mass, and, particularly with small masses, caliper markings may
obscure the margin, hindering analysis.
c. Location of the lesion(s) should be indicated using a consistent and reproducible system,
such as clock-face location and distance from the nipple. When more than one mass or
abnormality is located in the same scan frame or in the same locale, measurement of the
distance from the skin to the center of the mass or its anterior aspect may help to differ-
entiate one lesion from another.This measurement may be particularly useful when one
mass is singled out for biopsy and others are depicted in the field.
Theremaybevariabilitywithinbreastimagingpractices,andmembersofagrouppractice
should agree upon a consistent policy for documenting lesion location on subsequent
examinations. In some practices, for all examinations that follow the initial US study, the
lesion location annotation will be repeated without change. Other breast imagers may
report a different location to signify the same lesion but indicate in their reports that the
lesion is now seen at another clock-face position and distance from the nipple (these dif-
ferences are often related to positioning and technique). A more complete discussion of
this common scenario is provided in the Frequently Asked Questions, see page 142).
d. As at mammography, multiple bilateral circumscribed masses usually are assessed as
benign (category 2) unless one mass has different imaging features than all the oth-
ers. In the unusual circumstance in which the interpreting physician chooses to describe
multiple benign-appearing masses individually within the US report, the masses should
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be listed by breast, by location within in the breast, and by size.The reader of the report
will be less confused, and, if surveillance is suggested as management, the performer of
the subsequent examination will appreciate a list rather than verbose text. For bilateral
findings, describe all the findings in each breast in a separate paragraph.
5. COMPARISON TO PREVIOUS EXAMINATION(S), INCLUDING CORRELATION WITH PHYSICAL,
MAMMOGRAPHY, OR MRI FINDINGS
Breast US should be correlated with physical findings, mammography, MRI, or other imag-
ing studies, if performed. If no statement of comparison is included in the US report, it will
be assumed that no comparison was made. Note that some report templates include a
“comparison”heading, in which the word“none”(if appropriate) may be entered.
When correlating US findings with those seen at mammography and/or MRI, the opera-
tor performing handheld scanning should correlate the size and location of lesions and
match the type and arrangement of tissues surrounding the lesion in order to reduce the
likelihood of misregistration (identifying a different lesion or lesions at different imaging
modalities). In doing this, allowance for positional changes should be made going from
upright with mammography and prone with MRI to supine or supine-oblique with US. If
it is determined that a sonographic finding corresponds to a palpable abnormality, or to a
mammographic or MRI finding, this should be stated explicitly in the US report. If the US
finding is new or has no correlate, this should also be stated in the report.
If the US examination was performed as part of a surveillance protocol to assess a previously
identifiedfinding,orifthefindingwasreportedonapreviousexamination,thecurrentreport
should describe any changes. An increase of 20% or more in the longest dimension of a prob-
ably benign solid mass within 6 months may prompt biopsy.1
An increase of only 1–2 mm in
lesion size may be related to differences in scanning technique or patient positioning.
6. COMPOSITE REPORTS
When more than one type of examination is performed concurrently (on the same day), it is
preferable that the examinations be reported together. The findings for each examination
should be described in separate paragraphs with an overall assessment and management
recommendations for the combined examinations. In general, when the assessments for two
examinationsdiffer,theoverallassessment(andconcordantmanagementrecommendations)
should reflect the more abnormal of the individual assessments (whatever management is
expected to come first, supplemented by likelihood of maligancy), according to the following
hierarchy of increasing abnormality: category 1, 2, 3, 6, 0, 4, 5 (Table 4, see page 127).
Exceptions to this rule occur when the characteristically benign features of a given imag-
ing finding on one examination supersede the less specifically benign features of the same
finding on the other examination. An example is that of a partially circumscribed, noncalci-
fied mass at mammography, superseded by simple cyst at US.
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BI-RADS Assessment
Category
Degree of Abnormality
1 Lowest
2
3
6
0
4
5 Highest
Table 4. Abnormality Hierarchy
7. ASSESSMENT
The report should conclude with a concise summary of pertinent US findings with a final
assessment using BI-RADS® US categories 1–6 and the phrases associated with them. If re-
port of a US examination is integrated with that of a concurrently performed mammogra-
phy examination, the combined final assessment should reflect the highest likelihood of
malignancy assessed at the two examinations. Clear and consistent communication is a
goal that can be achieved for breast US by using the same assessment categories and simi-
lar wording described in the BI-RADS® Mammography section.
In some cases, the interpreting physician may render an incomplete assessment (category
0) in order to request additional examination(s), such as mammography, comparison with
previous but currently unavailable examinations, or additional physican-performed real-time
scanning after either a sonographer-produced, real-time or automated whole-breast screen-
ing US examination.
8. MANAGEMENT
Management recommendations should be included in every report. Clear recommendations
should be made as to the next course of action. Recommendations may include routine age-
appropriatescreening,surveillanceimagingforaprobablybenignmass,annualfollow-upafter
percutaneousorsurgicalbiopsy,andclinicalmanagement.Ifanimaging-guidedinterventional
procedureisrecommended,thetypeofimagingfortheproceduremightalsobesuggested,for
example, stereotactic, US, or MRI guidance.
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B. ASSESSMENT CATEGORIES
Table 5. Concordance Between BI-RADS® Assessment Categories and Management Recommendations.
Assessment Management Likelihood of Cancer
Category 0: Incomplete — Need
Additional Imaging Evaluation
Recall for additional
imaging
N/A
Category 1: Negative Routine screening Essentially 0% likelihood of malignancy
Category 2: Benign Routine screening Essentially 0% likelihood of malignancy
Category 3: Probably Benign Short-interval (6-month)
follow-up or continued
surveillance
> 0% but ≤ 2% likelihood of malignancy
Category 4: Suspicious
Category 4A: Low suspicion for
malignancy
Category 4B: Moderate suspicion for
malignancy
Category 4C: High suspicion for
malignancy
Tissue diagnosis > 2% but < 95% likelihood of
malignancy
> 2% to ≤ 10% likelihood of malignancy
> 10% to ≤ 50% likelihood of
malignancy
> 50% to < 95% likelihood of
malignancy
Category 5: Highly Suggestive of
Malignancy
Tissue diagnosis ≥ 95% likelihood of malignancy
Category 6: Known Biopsy-Proven
Malignancy
Surgical excision when
clinically appropriate
N/A
a. Assessment Is Incomplete
Category 0: Incomplete — Need Additional Imaging Evaluation and/or Prior Images for
Comparison
There is a finding for which additional imaging evaluation is needed. This is almost always used
in a screening situation. In this context, additional imaging evaluation includes the recording of
(nonstandard) US images to supplement the standard images recorded for a screening examina-
tion. Note that this does not include repeat real-time scanning by the interpreting physician and/
or colleague as long as additional images are not recorded. This respects the unique real-time
nature of US and does not penalize its use. (For further information please refer to the Follow-Up
and Outcome Monitoring section, see FOM on page 128.)
Under certain circumstances, assessment category 0 may be used in a diagnostic US report, such
as when equipment or personnel are not immediately available to perform a needed concurrent
diagnostic mammography examination, or when the patient is unable or unwilling to wait for
completion of a full diagnostic examination. Category 0 should not be used for diagnostic breast
imaging findings that warrant further evaluation with MRI. Rather, the interpreting physician
should issue a final assessment in a report that is made before the MRI examination is performed.
In most circumstances and when feasible, if a screening US examination is not assessed as nega-
tive or benign, the current examination should be compared to prior examination(s), if any exist.
The interpreting physician should use judgment on how vigorously to attempt obtaining prior ex-
aminations, given the likelihood of success of such an endeavor and the likelihood that comparison
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will affect the final assessment. In this context, it is important to note that comparison to previous
examination(s) may be irrelevant when a finding is inherently suspicious for malignancy.
Category 0 should be used for prior image comparison only when such comparison is required
to make a final assessment. When category 0 is used in the context of awaiting prior examina-
tions for comparison, there should be in place a tracking system guaranteeing with 100% reli-
ability that a final assessment will be made within 30 days (preferably sooner), even if prior ex-
aminations do not become available. Some breast imaging practices may reasonably choose
never to use category 0 in the context of awaiting prior examinations simply because they do
not have a 100% reliable tracking system. If an US examination is assessed as category 0 in the
context of awaiting prior examinations and then the prior examinations do become available, an
addendum to the initial US report should be issued, including a revised assessment. For auditing
purposes, the revised assessment should replace the initial assessment.
A need for previous studies to determine appropriate management might also temporarily defer
a final assessment.
b. Assessment Is Complete — Final Categories
Category 1: Negative
There is nothing to comment on.This is a normal examination.
Category 2: Benign
As with category 1, this is a “normal” assessment, but here the interpreter chooses to describe
a benign finding in the US report. For example, the interpreter may choose to describe one or
more simple cysts, intramammary lymph nodes, postsurgical fluid collections, breast implants,
or complicated cysts/probable fibroadenomas that are unchanged for at least 2 or 3 years, while
still concluding that there is no sonographic evidence of malignancy. On the other hand, the
interpreter may choose not to describe such findings, in which case the examination should be
assessed as negative (category 1).
Note that both category 1 and category 2 assessments indicate that there is no sonographic
evidenceofmalignancy.Bothshouldbefollowedbythemanagementrecommendationforrou-
tine age-appropriate screening. The difference is that category 2 should be used when describ-
ing one or more specific benign sonographic findings in the report, whereas category 1 should
be used when no such findings are described (even if such findings are present).
Category 3: Probably Benign (Guidance chapter, see page 139.)
Assessment category 3, probably benign, is not an indeterminate category for use simply when
theradiologistisunsurewhethertorenderabenign(BI-RADS®category2)orsuspicious(BI-RADS®
category 4) assessment, but one that is reserved for specific imaging findings known to have > 0%
but ≤ 2% likelihood of malignancy. For US, there is robust evidence that a solid mass with a cir-
cumscribedmargin,ovalshape,andparallelorientation(mostcommonlyfibroadenoma),and
an isolated complicated cyst have a likelihood of malignancy in the defined (≤ 2%) probably
benign range, for which short-interval (6-month) follow-up sonography and then periodic so-
nographicsurveillancemayrepresentappropriatemanagement.2–4
Similardatahavebeenre-
portedforclusteredmicrocysts,butthesedataarelessstrongbecausetheyinvolvemanyfewer
cases.2
Theuseofassessmentcategory3forsonographicfindingsotherthanthesethreeshouldbe
considered only if the radiologist has personal experience to justify a watchful-waiting approach,
preferably involving observation of a sufficient number of cases of an additional sonographic find-
ing to suggest a likelihood of malignancy within the defined (≤ 2%) probably benign range.
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This edition of the BI-RADS® Atlas also emphasizes the recommendation that a category 3 assess-
ment should not be made at screening; rather, this should be done only after completion of a full
diagnostic breast imaging examination.This recommendation is appropriate for screening mam-
mography, for which batch interpretation usually is utilized, because in this setting there is no
opportunity to complete the diagnostic workup before interpreting the screening examination.
However, screening US almost always is interpreted online, so a full diagnostic examination also
is completed while the patient remains in the breast imaging facility, and a single breast imaging
report may be issued that combines the findings of both screening and diagnostic components
of the examination. Hence, there is no purpose in recommending against category 3 assessment
at screening US because the diagnostic workup would be completed simultaneously. This issue
is discussed in more detail in Frequently Asked Question #2 for US in the Follow-up and Outcome
Monitoring section, see FOM on page 62). Note that for auditing purposes, the screening compo-
nent of a category 3-assessed screening US examination will be audit-positive, not only because
additional nonstandard (diagnostic) images will be recorded but also because a category 3 as-
sessment at screening is defined as being audit-positive.
For category 3 assessments, the initial short-term follow-up interval is usually 6 months, involv-
ing the breast(s) containing the probably benign finding(s). Assuming stability at this 6-month
examination, a category 3 assessment again is rendered with a management recommendation
for a second short-interval follow-up examination in 6 months. Again assuming stability at this
second short-interval follow-up, the examination is once more assessed as category 3, but now
the recommended follow-up interval usually is lengthened to 1 year due the already-observed
12-month stability. Note that although the 1-year follow-up coincides with the routine screening
interval in the United States, a category 3 assessment is rendered, to indicate that the period of
imaging surveillance is still underway. As with surveillance using mammography, after 2–3 years
of stability, the final assessment category should be changed to benign (BI-RADS® category 2). A
benign evaluation may also be rendered before completion of category 3 analysis if, in the opin-
ion of the interpreter , the finding has no chance of malignancy and is thus a category 2.
Category 4: Suspicious
This category is reserved for findings that do not have the classic appearance of malignancy but
are sufficiently suspicious to justify a recommendation for biopsy. The ceiling for category 3 as-
sessment is a 2% likelihood of malignancy, and the floor for category 5 assessment is 95%, so cat-
egory 4 assessments cover the wide range of likelihood of malignancy in between. Thus, almost
all recommendations for breast interventional procedures will come from assessments made us-
ing this category. By subdividing category 4 into 4A, 4B, and 4C, as recommended in and using
the cut points indicated in the Guidance chapter, it is hoped that patients and referring clinicians
will more readily make informed decisions on the ultimate course of action. An example of sepa-
rating the BI-RADS® assessment category from the management recommendation (new to fifth
edition — see Follow-up and Outcome Monitoring section) occurs when a simple cyst, correctly
assessed as BI-RADS® 2, undergoes cyst aspiration for pain control.
Category 5: Highly Suggestive of Malignancy
These assessments carry a very high probability (≥ 95%) of malignancy. This category initially was
established to involve lesions for which 1-stage surgical treatment could be considered without
preliminary biopsy in an era when preoperative wire localization was the primary breast interven-
tional procedure. Nowadays, given the widespread acceptance of imaging-guided percutaneous
biopsy, 1-stage surgery rarely if ever is performed. Rather, current oncologic management almost
11. ACR BI-RADS® ATLAS — BREAST ULTRASOUND
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always involves tissue diagnosis of malignancy via percutaneous tissue sampling to facilitate
treatment options, such as when sentinel node imaging is included in surgical management or
when neoadjuvant chemotherapy is administered prior to surgery. Therefore, the current ratio-
nale for using a category 5 assessment is to identify lesions for which any nonmalignant percu-
taneous tissue diagnosis is considered discordant, resulting in the recommendation for repeat
(usually vacuum-assisted or surgical) biopsy. Also note that whereas the fourth edition simply
indicated that“appropriate action should be taken”as management for category 5 assessments,
the fifth edition provides the more directed management recommendation that“biopsy should
be performed in the absence of clinical contraindication.”This new text unequivocally specifies
tissue diagnosis as the interpreting physician’s management recommendation for category 5
assessments, appropriately and effectively transferring the burden of establishing a contraindi-
cation to this recommendation to the referring clinician.
Category 6: Known Biopsy-Proven Malignancy
This category is reserved for examinations performed after biopsy proof of malignancy (imaging
performed after percutaneous biopsy but prior to surgical excision), in which there are no abnor-
malities other than the known cancer that might need additional evaluation.
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C. WORDINGTHE REPORT
When performed concurrently, breast US examinations are sometimes reported separately from
mammography examinations and sometimes reported as part of a combined examination. In both
situations, the current examination should be compared to prior examination(s) when appropriate.
The indication for examination, such as screening or diagnostic (targeted), should be stated. The re-
port should be organized with a brief description of the composition of the breast (screening only)
and any pertinent findings, followed by the assessment and management recommendations. Any
verbaldiscussionsbetweentheinterpretingphysicianandthereferringclinicianorpatientshould
bedocumentedintheoriginalreportorinanaddendumtothereport.
The report should be succinct, using terminology from the latest approved lexicon without em-
bellishment. Definitions of lexicon terms for mammographic findings should not appear in the
report narrative. Following the impression section and the (concordant) management recom-
mendation section of the report, both the assessment category number and text for the assess-
ment category should be stated. Other aspects of the report should comply with the ACR Practice
Guideline for Communication of Diagnostic Imaging Findings.5
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