Traditionally, radiation-induced cardiotoxicity has been studied using cardiac radiation doses rather than functional imaging. We developed artificial intelligence (AI) models based on novel cardiac delta radiomics using pre- and post-treatment FDG-PET/CT scans to predict overall survival in lung cancer patients undergoing radiotherapy. We identified four clinically relevant delta radiomics features with the AI prediction models. The best model achieved an AUC of 0.91 on the training set and 0.87 on the test set. We are a pioneering group in AI for functional cardiac imaging. If validated, this approach will enable to use standard PET/CT scans as functional cardiac imaging with good predictive AUC for OS, as well as provide automated methods to provide functional cardiac information for clinical outcome prediction AI in lung cancer patients.
Novel Functional Delta-Radiomics for Predicting Overall Survival in Lung Canc...Wookjin Choi
AAPM2023_SU-300-IePD-F6-4
Purpose: Traditional methods of evaluating cardiotoxicity rely on cardiac radiation doses and do not incorporate functional imaging. Cardiac functional imaging can improve the ability to provide early prediction for clinical outcomes for lung cancer patients undergoing radiotherapy. FDG-based PET/CT imaging is routinely obtained for staging and disease assessment after treatment. Although FDG PET/CT scans are typically used to evaluate the tumor, studies have shown that the PET cardiac signal is predictive of clinical outcomes. Our study aimed to develop novel functional cardiac delta radiomics using pre and post-treatment FDG PET/CT scans to predict for overall survival (OS).
Methods: We conducted a study of 109 lung cancer patients who underwent standard FDG-PET/CT scans pre- and post-radiotherapy. Data from ACRIN 6668 (N=70) and an investigator-initiated lung cancer trial (N=39) for functional avoidance radiotherapy were used. The heart was delineated, and 200 cardiac CT and PET functional radiomics features were selected. Delta radiomics was calculated as the change between pre- and post-PET/CT. The data were divided into 80%/20% training/test set, and feature reduction was performed using Wilcoxon test, hierarchical clustering, and recursive feature elimination. A Gradient Boosting Classifier machine learning model evaluated the ability of the delta PET/CT cardiac radiomics to predict for OS using 10-fold cross-validation for training and area-under-the-curve (AUC) for model assessment.
Results: Median survival was 431 days (range 144 to 1640 days). 4 clinically relevant delta features were identified: pre-CT_Maximum, post-CT_Minimum, delta-CT_GLRM_Run_Variance, delta-PET_GLRM_Run_Entropy. The model showed an AUC of 0.91 on the training set and an AUC of 0.87 on the test set.
Conclusion: This is the first study to evaluate functional cardiac delta radiomic features from standard PET/CT scans with data showing good predictive AUC for OS. If validated, this work provides automated methods to provide functional cardiac information for clinical outcome prediction in lung cancer patients.
Novel Functional Radiomics for Prediction of Cardiac PET Avidity in Lung Canc...Wookjin Choi
Purpose/Objective(s)
Traditional methods of evaluating cardiotoxicity focus solely on radiation doses to the heart and do not incorporate functional imaging information. Functional imaging has great potential to improve the ability to provide early prediction for cardiotoxicity for lung cancer patients undergoing radiotherapy. FDG-based PET/CT imaging is routinely obtained as part of standard staging work up for lung cancer patients. Although FDG PET/CT scans are typically used to evaluate the tumor, imaging guidelines note that FDG PET/CT scans are an FDA-approved method to image for cardiac inflammation, and studies have noted that the PET cardiac signal can be predictive of clinical outcomes. The purpose of this work was to develop a radiomics model to predict clinical cardiac assessment of standard of care FDG PET/CT scans.
Materials/Methods
The study included 100 consecutive lung cancer patients treated with radiotherapy who underwent standard pre-treatment FDG-PET/CT staging scans. A clinician reviewed the PET/CT scans per clinical cardiac assessment guidelines and classified the cardiac uptake as: 0 = uniform diffuse, 1 = absent, 2 = heterogeneous, with event rates of 20%, 44%, and 35%, respectively. The heart was delineated and 200 novel functional radiomics features were selected to classify cardiac FDG uptake patterns. We divided the data into an 80% training set and a 20% test set to train and evaluate the classification models. Feature reduction was carried out using the Wilcoxon test (with Bonferroni adjusted p<0.05), hierarchical clustering, and Recursive Feature Elimination. Two automatic machine learning (AutoML) frameworks were used to determine classification models: a Random Forest Classifier (Tree-based Pipeline Optimization Tool, TPOT) and Linear Discriminant Analysis (AutoSklearn). 10-fold cross validation was carried out for training and the accuracy of the ability of the models to predict for clinical cardiac assessment is reported.
Results
Fifty-one independent radiomics features were reduced to 3 clinically pertinent features (PET 2D Skewness, PET Grey Level Co-occurrence Matrix Correlation, and PET Median) using feature reduction techniques. The model selected by TPOT showed 89.8% predictive accuracy in the cross validation of the training set and 85% predictive accuracy on the test set. The model selected by AutoSklearn showed 89.7% predictive accuracy in the cross validation of the training set and 80% predictive accuracy on the test set.
Conclusion
The novelty of this work is that it is the first study to develop and evaluate functional cardiac radiomic features from standard of care FDG PET/CT scans with the data showing good predictive accuracy with clinical imaging evaluation. If validated, the current work provides automated methods to provide functional cardiac information using standard of care imaging that can be used as an imaging biomarker for early clinical toxicity prediction for lung cancer patients.
PET GUIDED TARGET CONTOURING GUIDELINES.pptxGaurav Jaswal
PET/CT can improve the accuracy of target volume delineation for radiation therapy planning in three key ways:
1) PET/CT may help avoid geographic misses and better guide target volume definition compared to CT alone.
2) Functional imaging with PET tracers like 18FDG can facilitate identification of biologically relevant tumor sub-volumes that may benefit from dose escalation.
3) PET provides tumor characterization that can enable adaptive radiotherapy and other personalized treatment strategies when used before and during treatment.
However, while promising, issues remain unresolved and widespread clinical adoption is not yet recommended until prospective studies validate the benefits.
This document provides definitions and examples of random and systematic errors that can occur during the radiotherapy treatment process. It discusses various sources of errors including patient setup, organ motion, and target deformation. Methods for managing errors such as offline and online correction techniques, immobilization devices, and image-guidance are presented. The importance of distinguishing between random and systematic errors when establishing appropriate planning target volume margins is also emphasized.
Kshivets O. Local Advanced Lung Cancer Surgery Oleg Kshivets
The document summarizes research on optimal surgery strategies for patients with local advanced lung cancer. The study analyzed 155 patients who underwent radical combined procedures. Key findings include:
1) Adjuvant chemoimmunoradiotherapy after surgery significantly improved 5-year survival compared to surgery alone or postoperative radiotherapy alone.
2) Factors associated with improved 5-year survival in multivariate analysis included nodal stage, adjuvant therapy, gender, blood markers, and cell ratios.
3) Neural network modeling accurately predicted 5-year survival based on clinicopathological factors, with nodal stage and adjuvant therapy being the top predictors.
Nuclear medicine uses radioactive tracers and imaging techniques like PET and SPECT to produce functional images of the body. It has many clinical applications in areas like oncology, cardiology, and neurology. PET radiotracers like FDG are used to study glucose metabolism that can help identify cancer and other diseases. Nuclear medicine also has an important role in drug development by evaluating whether experimental drugs reach their targets and have the intended biological effect. It helps make drug development more efficient and cost-effective. However, expanding nuclear medicine in India faces challenges in training sufficient technical expertise across various disciplines needed to advance personalized medicine.
Health economic modelling in the diagnostics development processcheweb1
This document discusses the use of health economic modelling in the diagnostics development process. It highlights the need for early decision modelling to efficiently design clinical research studies for new diagnostics. Decision modelling can also be used to assess the potential clinical impact and cost-effectiveness of diagnostics across different stages of the validation process. The document describes an example of decision modelling used to help design the OPTIMA trial, which evaluated multiple biomarker tests for stratifying breast cancer treatment. Close collaboration between different stakeholders is important for effective diagnostics evaluation.
Novel Functional Delta-Radiomics for Predicting Overall Survival in Lung Canc...Wookjin Choi
AAPM2023_SU-300-IePD-F6-4
Purpose: Traditional methods of evaluating cardiotoxicity rely on cardiac radiation doses and do not incorporate functional imaging. Cardiac functional imaging can improve the ability to provide early prediction for clinical outcomes for lung cancer patients undergoing radiotherapy. FDG-based PET/CT imaging is routinely obtained for staging and disease assessment after treatment. Although FDG PET/CT scans are typically used to evaluate the tumor, studies have shown that the PET cardiac signal is predictive of clinical outcomes. Our study aimed to develop novel functional cardiac delta radiomics using pre and post-treatment FDG PET/CT scans to predict for overall survival (OS).
Methods: We conducted a study of 109 lung cancer patients who underwent standard FDG-PET/CT scans pre- and post-radiotherapy. Data from ACRIN 6668 (N=70) and an investigator-initiated lung cancer trial (N=39) for functional avoidance radiotherapy were used. The heart was delineated, and 200 cardiac CT and PET functional radiomics features were selected. Delta radiomics was calculated as the change between pre- and post-PET/CT. The data were divided into 80%/20% training/test set, and feature reduction was performed using Wilcoxon test, hierarchical clustering, and recursive feature elimination. A Gradient Boosting Classifier machine learning model evaluated the ability of the delta PET/CT cardiac radiomics to predict for OS using 10-fold cross-validation for training and area-under-the-curve (AUC) for model assessment.
Results: Median survival was 431 days (range 144 to 1640 days). 4 clinically relevant delta features were identified: pre-CT_Maximum, post-CT_Minimum, delta-CT_GLRM_Run_Variance, delta-PET_GLRM_Run_Entropy. The model showed an AUC of 0.91 on the training set and an AUC of 0.87 on the test set.
Conclusion: This is the first study to evaluate functional cardiac delta radiomic features from standard PET/CT scans with data showing good predictive AUC for OS. If validated, this work provides automated methods to provide functional cardiac information for clinical outcome prediction in lung cancer patients.
Novel Functional Radiomics for Prediction of Cardiac PET Avidity in Lung Canc...Wookjin Choi
Purpose/Objective(s)
Traditional methods of evaluating cardiotoxicity focus solely on radiation doses to the heart and do not incorporate functional imaging information. Functional imaging has great potential to improve the ability to provide early prediction for cardiotoxicity for lung cancer patients undergoing radiotherapy. FDG-based PET/CT imaging is routinely obtained as part of standard staging work up for lung cancer patients. Although FDG PET/CT scans are typically used to evaluate the tumor, imaging guidelines note that FDG PET/CT scans are an FDA-approved method to image for cardiac inflammation, and studies have noted that the PET cardiac signal can be predictive of clinical outcomes. The purpose of this work was to develop a radiomics model to predict clinical cardiac assessment of standard of care FDG PET/CT scans.
Materials/Methods
The study included 100 consecutive lung cancer patients treated with radiotherapy who underwent standard pre-treatment FDG-PET/CT staging scans. A clinician reviewed the PET/CT scans per clinical cardiac assessment guidelines and classified the cardiac uptake as: 0 = uniform diffuse, 1 = absent, 2 = heterogeneous, with event rates of 20%, 44%, and 35%, respectively. The heart was delineated and 200 novel functional radiomics features were selected to classify cardiac FDG uptake patterns. We divided the data into an 80% training set and a 20% test set to train and evaluate the classification models. Feature reduction was carried out using the Wilcoxon test (with Bonferroni adjusted p<0.05), hierarchical clustering, and Recursive Feature Elimination. Two automatic machine learning (AutoML) frameworks were used to determine classification models: a Random Forest Classifier (Tree-based Pipeline Optimization Tool, TPOT) and Linear Discriminant Analysis (AutoSklearn). 10-fold cross validation was carried out for training and the accuracy of the ability of the models to predict for clinical cardiac assessment is reported.
Results
Fifty-one independent radiomics features were reduced to 3 clinically pertinent features (PET 2D Skewness, PET Grey Level Co-occurrence Matrix Correlation, and PET Median) using feature reduction techniques. The model selected by TPOT showed 89.8% predictive accuracy in the cross validation of the training set and 85% predictive accuracy on the test set. The model selected by AutoSklearn showed 89.7% predictive accuracy in the cross validation of the training set and 80% predictive accuracy on the test set.
Conclusion
The novelty of this work is that it is the first study to develop and evaluate functional cardiac radiomic features from standard of care FDG PET/CT scans with the data showing good predictive accuracy with clinical imaging evaluation. If validated, the current work provides automated methods to provide functional cardiac information using standard of care imaging that can be used as an imaging biomarker for early clinical toxicity prediction for lung cancer patients.
PET GUIDED TARGET CONTOURING GUIDELINES.pptxGaurav Jaswal
PET/CT can improve the accuracy of target volume delineation for radiation therapy planning in three key ways:
1) PET/CT may help avoid geographic misses and better guide target volume definition compared to CT alone.
2) Functional imaging with PET tracers like 18FDG can facilitate identification of biologically relevant tumor sub-volumes that may benefit from dose escalation.
3) PET provides tumor characterization that can enable adaptive radiotherapy and other personalized treatment strategies when used before and during treatment.
However, while promising, issues remain unresolved and widespread clinical adoption is not yet recommended until prospective studies validate the benefits.
This document provides definitions and examples of random and systematic errors that can occur during the radiotherapy treatment process. It discusses various sources of errors including patient setup, organ motion, and target deformation. Methods for managing errors such as offline and online correction techniques, immobilization devices, and image-guidance are presented. The importance of distinguishing between random and systematic errors when establishing appropriate planning target volume margins is also emphasized.
Kshivets O. Local Advanced Lung Cancer Surgery Oleg Kshivets
The document summarizes research on optimal surgery strategies for patients with local advanced lung cancer. The study analyzed 155 patients who underwent radical combined procedures. Key findings include:
1) Adjuvant chemoimmunoradiotherapy after surgery significantly improved 5-year survival compared to surgery alone or postoperative radiotherapy alone.
2) Factors associated with improved 5-year survival in multivariate analysis included nodal stage, adjuvant therapy, gender, blood markers, and cell ratios.
3) Neural network modeling accurately predicted 5-year survival based on clinicopathological factors, with nodal stage and adjuvant therapy being the top predictors.
Nuclear medicine uses radioactive tracers and imaging techniques like PET and SPECT to produce functional images of the body. It has many clinical applications in areas like oncology, cardiology, and neurology. PET radiotracers like FDG are used to study glucose metabolism that can help identify cancer and other diseases. Nuclear medicine also has an important role in drug development by evaluating whether experimental drugs reach their targets and have the intended biological effect. It helps make drug development more efficient and cost-effective. However, expanding nuclear medicine in India faces challenges in training sufficient technical expertise across various disciplines needed to advance personalized medicine.
Health economic modelling in the diagnostics development processcheweb1
This document discusses the use of health economic modelling in the diagnostics development process. It highlights the need for early decision modelling to efficiently design clinical research studies for new diagnostics. Decision modelling can also be used to assess the potential clinical impact and cost-effectiveness of diagnostics across different stages of the validation process. The document describes an example of decision modelling used to help design the OPTIMA trial, which evaluated multiple biomarker tests for stratifying breast cancer treatment. Close collaboration between different stakeholders is important for effective diagnostics evaluation.
Aggressive Lung Adenocarcinoma Subtype Prediction Using FDG-PET/CT RadiomicsWookjin Choi
Purpose: To predict the histopathologic subtypes with poor surgery prognosis in early stage lung adenocarcinomas using CT and PET radiomics.
Methods: We retrospectively enrolled 53 patients with stage I lung adenocarcinoma who underwent both diagnostic CT and 18F-fluorodeoxyglucose (FDG) PET/CT before complete surgical resection of the tumors. Tumor segmentation was manually contoured by a physician on both the diagnostic CT and the attenuation CT of PET/CT.A total of 170 radiomics features were extracted on both PET and CT images to design predictive models for two histopathologic endpoints: (1) tumors with solid or micropapillary predominant subtype (aggressiveness), and (2) tumors with micropapillary component more than 5% (MIP5). We used least absolute shrinkage and selection operator (LASSO) as a model building method coupled with a class separability feature selection (CSFS) method. For an unbiased model estimate, a 10-fold cross validation approach was used. The area under the curve (AUC) and prediction accuracy were employed to evaluate the performance of the model. P-values were computed using Wilcoxon rank-sum test.
Results: Of the 53 patients, 9 and 15 had tumors with aggressiveness and MIP5, respectively. For both endpoints, LASSO models with two PET radiomics features achieved the best performance. For aggressiveness, the LASSO model with PET Cluster Shade and PET 2D Variance resulted in 77.6±2.3% accuracy and 0.71±0.02 AUC (P = 0.011). For MIP5, the LASSO model with PET Eccentricity and PET Cluster Shade resulted in 69.6±3.1% accuracy and 0.68±0.04 AUC (P=0.014). The PET Cluster Shade was commonly selected in both models. Cluster shade is a texture feature that measures the skewness of the co-occurrence matrix. Higher PET cluster shade predicted that the tumor was more aggressive and more likely MIP5.
Conclusion: We showed that PET/CT radiomics features can predict tumor aggressiveness.
Funding Support, Disclosures, and Conflict of Interest: This work was supported in part by the National Cancer Institute Grants R01CA172638.
This document discusses image-guided radiation therapy (IGRT) and its evolution and applications. It begins by defining IGRT as external beam radiation therapy using imaging prior to each treatment fraction to verify patient positioning. IGRT allows for reduction of safety margins by compensating for set-up errors and organ motion. The document then reviews the history of IGRT from early portal imaging to modern cone-beam CT and other volumetric imaging techniques. It provides examples of IGRT protocols and clinical outcomes for sites such as prostate, lung, liver, and central nervous system tumors.
This document discusses radiotherapy techniques for early breast cancer, including:
1) Modern techniques like IMRT and 4D radiotherapy allow for better treatment planning and delivery while avoiding nearby organs.
2) Several randomized clinical trials found that a shorter, hypofractionated course of radiotherapy was not inferior to standard radiotherapy in terms of local recurrence or toxicity.
3) Partial breast irradiation techniques are being studied as a way to further reduce treatment volumes and time for selected low-risk patients.
Sophie Taieb : Breast MRI in neoadjuvant chemotherapy : A predictive respons...breastcancerupdatecongress
This document discusses the use of breast MRI in evaluating response to neoadjuvant chemotherapy. MRI can provide both morphological and functional information about tumors. Studies show DCE-MRI and DWI-MRI may help assess response after 1-2 cycles of chemotherapy, with changes in tumor size, kinetic parameters and ADC values predicting pathological complete or near-complete response. Larger prospective trials are still needed to standardize MRI methods and thresholds to determine if changes on MRI could guide modifications to chemotherapy regimens for non-responders. Overall, MRI shows potential as a predictive marker and non-invasive method for monitoring early response to neoadjuvant breast cancer treatment.
Single-photon emission computed tomography is a nuclear medicine tomographic imaging technique using gamma rays. It is very similar to conventional nuclear medicine planar imaging using a gamma camera. but is able to provide true 3D information
This document summarizes guidelines for radiotherapy planning for lung cancer. It discusses:
- Defining the gross tumor volume (GTV) based on imaging like PET which can help reduce margins.
- Adding margins to the GTV to create the clinical target volume (CTV) accounting for microscopic spread. There is debate around elective nodal irradiation.
- Further expanding the CTV to create the planning target volume (PTV) accounting for set-up uncertainty and tumor motion. Techniques like gating can help reduce this.
- Contouring the lungs as organs at risk and calculating dosimetric parameters like V20 and V5 to quantify lung dose and risk of toxicity. Dose needs to
TexRAD is software that analyzes textures in existing medical scans to provide prognostic information and risk stratification to clinicians. It does this by measuring fine, medium, and coarse textures in scans of tumors like those in the liver, lungs, and other organs. This additional texture information can help predict factors like cancer stage, metastasis risk, and prognosis. TexRAD requires no new scanning procedures and can analyze routine clinical images, providing more information to clinicians to guide patient care decisions.
Radiosurgery in urological malignancies can be effectively used to treat prostate cancer, renal cell cancer, and urinary bladder cancer. For prostate cancer, Cyberknife allows for hypofractionated radiotherapy with its ability to track tumor motion and correct for it during treatment. Studies have shown dose escalation and hypofractionated regimens improve local control rates for prostate cancer while maintaining low toxicity rates when delivered with precision techniques like Cyberknife. Cyberknife is particularly useful for treating prostate cancer given its ability to track and correct for intra-fraction motion of the prostate tumor.
The document discusses treatment options for locally advanced cervical cancer. It summarizes several meta-analyses and clinical trials that show concurrent chemoradiation (CCT-RT) is the standard of care, rather than neoadjuvant chemotherapy followed by surgery (NACT+Surgery). While some older trials showed a benefit of NACT+Surgery, most recent evidence suggests it does not provide benefits and adds unnecessary morbidity compared to CCT-RT. The takeaway message is that in clinical practice, only standard guidelines accepted by major organizations like NCCN and NCI should be followed, and experimental treatments belong only in clinical trials.
Quantitative image analysis for cancer diagnosis and radiation therapyWookjin Choi
1.Lung Cancer Screening
1.1.Deep learning (feasible but not interpretable)
1.2.Radiomics (concise model)
1.3.Spiculation quantification (interpretable feature)
2.PET/CT Tumor Response
2.1.Aggressive Lung ADC subtype prediction (helpful for surgeons)
2.2.Pathologic response prediction (accurate but not concise)
2.3.Local tumor morphological changes (accurate and interpretable)
The document discusses the International Commission on Radiation Units and Measurements (ICRU). It summarizes that ICRU defines physical quantities and units related to ionizing radiation. It has published over 85 reports on topics like dose specifications, clinical terms, and recommendations for intensity modulated radiation therapy. ICRU works to establish international standards for radiation quantities, units, and nomenclature used in radiation oncology and other applications of ionizing radiation.
This document discusses various techniques for optimizing radiation dose in thoracic computed tomography (CT) scans. It begins with an introduction to the growth of CT technology and increasing use of CT exams. It then covers conventional techniques like using indication-specific protocols, limiting scan passes and length, optimizing patient positioning, and adjusting tube current, potential, and rotation time. Contemporary techniques discussed include iterative reconstruction, high pitch scanning, automatic tube potential selection, and organ-based dose modulation. The document emphasizes that chest CT is important but doses should be optimized to get necessary information while keeping radiation exposure as low as reasonably possible.
This document summarizes the findings of a large study conducted by the National Cancer Center Network (NCCN) and National Oncologic PET Registry (NOPR) on the impact of positron emission tomography (PET) on cancer management. The study analyzed over 130,000 patient scans from 1,891 PET facilities. It found that in 38% of cases, PET led to a change in the referring physician's intended patient management approach, such as changing or adjusting therapy. The study demonstrates the utility of PET in cancer evaluation and treatment decision making.
First of its kind in South India GE IQ PET/CT at MIOT HospitalsMIOT Hospitals
MIOT Hospitals provides nuclear medicine services including PET/CT scanning and targeted radionuclide therapy for cancers such as thyroid cancer, neuroendocrine tumors, and liver cancers. The department is equipped with a radionuclide therapy ward and offers therapies including radioiodine for thyroid cancer, radioiodinated MIBG for rare tumors, peptide receptor radionuclide therapy for neuroendocrine tumors, and radioembolization for liver cancers. MIOT aims to offer complete cancer care from diagnosis to rehabilitation all in one facility.
Quality Assurance Programme in Computed TomographyRamzee Small
Introduction to Computed Tomography
Basic description of the components of a CT System
Introduction to Quality Assurance
Quality Assurance and Quality Control Tests in Computed Tomography base on frequency
Objective of QA/QC Test
This study assessed the feasibility of reducing radiation exposure during coronary CT angiography (CCTA) using only modified acquisition parameters on a 64-slice CT scanner. Over 85% of patients were able to undergo prospective CCTA, which significantly reduced radiation dose compared to historical levels and conventional angiography. Image quality remained high, with over 97% of coronary segments evaluated as having either excellent, good, or fair quality. The study demonstrated that very low dose CCTA is possible using standard equipment by optimizing acquisition settings.
NTCP MODELLING OF ACUTE TOXICITY IN CARCINOMA CERVIX TREATED WITH CONCURRENT ...Dr. Rituparna Biswas
1. The study aimed to develop a predictive nomogram and dose constraints for hematological toxicity in cervical cancer patients treated with chemoradiation including IMRT.
2. Thirty-seven patients were treated with IMRT and cisplatin, and bone marrow was re-delineated to include the entire marrow volume.
3. Dose-volume histograms were combined with toxicity data to create a nomogram from which hematological toxicity probabilities can be estimated based on bone marrow dosimetry.
Novel RT techniques for treating lung cancer 1403Yong Chan Ahn
- Novel RT techniques such as SBRT, IMRT, IGRT and particle beam therapy can provide high local control rates for lung cancer with reduced toxicity compared to conventional RT.
- SBRT achieved 90% local control and favorable 5-year survival for primary and metastatic lung cancers at SMC with very low complication risks.
- IMRT may be beneficial for large or centrally-located tumors but further study is needed due to the study's retrospective nature and heterogeneous patient population.
- Particle beam therapy, such as proton therapy, can further reduce dose to organs-at-risk compared to photon therapies and may allow dose escalation for improved outcomes, particularly for locally advanced lung cancers.
Deep Learning-based Histological SegmentationDifferentiates Cavitation Patte...Wookjin Choi
Unsupervised segmentation (unlabeled regions of interest, ROIs) and autoencoder (AE)-based classification were used to classify differences in cavitation patterns in knees and digits using the stained images (n=20-30 images/group).
Each image was divided into 256 x 256 pixel patches, and a convolutional neural network (CNN)-based unsupervised segmentation was used to identify ROIs. These patches were subsequently fed into a CNN-based AE whose latent space layer was connected to a classifier for input patch classification.
The AE was trained using the ROIs identified by the unsupervised segmentation, and the image classes were used to train the classifier. Whole image classifications were determined by maximum voting of the patch results and evaluated by accuracy.
Novel Deep Learning Segmentation Models for Accurate GTV and OAR Segmentation...Wookjin Choi
Purpose/Objective(s)
MR-guided adaptive radiotherapy (MRgART) improves target coverage and organ-at-risk (OAR) sparing in pancreatic cancer radiation therapy (RT). Inter-fractional changes in patients undergoing RT require time intensive re-delineation of gross tumor volume (GTV) and OARs prior to adaptive optimization. Accurate automatic segmentation has the potential to significantly improve efficiency of the adaptive workflow. We hypothesized that state-of-the-art deep learning (DL) segmentation models could adequately segment GTV and OARs in both planning and daily fractional MR scans.
Materials/Methods
The study included 21 patients with pancreatic cancer treated with MRgART (10 Gy x 5 fractions). The planning MR as well as all daily MR images and registrations were collected (6 image sets per patient and a total of 126 image sets). The planning MR and fraction 1-4 image sets were used as the training set (N = 105), while the test set (N = 21) comprised images for fraction 5, to simulate the last step of incremental learning from planning to final fraction. Evaluated contours included the GTV, Small Bowel, Large Bowel, Duodenum, Left and Right Kidney, Liver, Spinal Cord, and Stomach. To mimic clinical conditions, contour accuracy was evaluated within the ring structure surrounding the PTV, inside of which daily adaptive re-contouring is applied (2 cm expansion in the cradio-caudal direction, 3 cm expansion otherwise). We evaluated three DL model architectures: SegResNet, SegResNet 2D, and SwinUNETR to autosegment GTV and OARs. The segmentation models were trained on the training set using 5-fold cross-validation (CV) and quantitatively analyzed by comparing against clinically used contours with DICE scores. Qualitative analysis was performed by a radiation oncologist using a scoring scale: 1 = perfect, 2 = minor discrepancy, 3 = moderate discrepancy, and 4 = rejected.
Results
Overall, the DL segmentations were in acceptable agreement with clinical contours. The best performing model was the SwinUNETR model with overall training DICE = 0.88±0.06, test DICE = 0.78±0.11, and qualitative score of 1.6±0.8. The agreement between the DL model and clinical segmentation for the GTV was 0.79±0.08, with a qualitative score of 2.2±0.9
Conclusion
We report here the most comprehensive work on DL segmentation for pancreatic cancer MRgART, including quantitative and clinically-pertinent qualitative evaluations of 126 image sets and 3 DL architectures. Our data show good quantitative agreement between DL and clinical contours, and acceptable clinician evaluations for the majority of GTVs and OARs. The current work has great potential to significantly reduce a major bottleneck in the MRgART workflow for pancreatic cancer patients.
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Similar to Artificial Intelligence To Reduce Radiation-induced Cardiotoxicity In Lung Cancer Radiotherapy: A Novel Functional Radiomics Using Cardiac FDG-PET/CT
Aggressive Lung Adenocarcinoma Subtype Prediction Using FDG-PET/CT RadiomicsWookjin Choi
Purpose: To predict the histopathologic subtypes with poor surgery prognosis in early stage lung adenocarcinomas using CT and PET radiomics.
Methods: We retrospectively enrolled 53 patients with stage I lung adenocarcinoma who underwent both diagnostic CT and 18F-fluorodeoxyglucose (FDG) PET/CT before complete surgical resection of the tumors. Tumor segmentation was manually contoured by a physician on both the diagnostic CT and the attenuation CT of PET/CT.A total of 170 radiomics features were extracted on both PET and CT images to design predictive models for two histopathologic endpoints: (1) tumors with solid or micropapillary predominant subtype (aggressiveness), and (2) tumors with micropapillary component more than 5% (MIP5). We used least absolute shrinkage and selection operator (LASSO) as a model building method coupled with a class separability feature selection (CSFS) method. For an unbiased model estimate, a 10-fold cross validation approach was used. The area under the curve (AUC) and prediction accuracy were employed to evaluate the performance of the model. P-values were computed using Wilcoxon rank-sum test.
Results: Of the 53 patients, 9 and 15 had tumors with aggressiveness and MIP5, respectively. For both endpoints, LASSO models with two PET radiomics features achieved the best performance. For aggressiveness, the LASSO model with PET Cluster Shade and PET 2D Variance resulted in 77.6±2.3% accuracy and 0.71±0.02 AUC (P = 0.011). For MIP5, the LASSO model with PET Eccentricity and PET Cluster Shade resulted in 69.6±3.1% accuracy and 0.68±0.04 AUC (P=0.014). The PET Cluster Shade was commonly selected in both models. Cluster shade is a texture feature that measures the skewness of the co-occurrence matrix. Higher PET cluster shade predicted that the tumor was more aggressive and more likely MIP5.
Conclusion: We showed that PET/CT radiomics features can predict tumor aggressiveness.
Funding Support, Disclosures, and Conflict of Interest: This work was supported in part by the National Cancer Institute Grants R01CA172638.
This document discusses image-guided radiation therapy (IGRT) and its evolution and applications. It begins by defining IGRT as external beam radiation therapy using imaging prior to each treatment fraction to verify patient positioning. IGRT allows for reduction of safety margins by compensating for set-up errors and organ motion. The document then reviews the history of IGRT from early portal imaging to modern cone-beam CT and other volumetric imaging techniques. It provides examples of IGRT protocols and clinical outcomes for sites such as prostate, lung, liver, and central nervous system tumors.
This document discusses radiotherapy techniques for early breast cancer, including:
1) Modern techniques like IMRT and 4D radiotherapy allow for better treatment planning and delivery while avoiding nearby organs.
2) Several randomized clinical trials found that a shorter, hypofractionated course of radiotherapy was not inferior to standard radiotherapy in terms of local recurrence or toxicity.
3) Partial breast irradiation techniques are being studied as a way to further reduce treatment volumes and time for selected low-risk patients.
Sophie Taieb : Breast MRI in neoadjuvant chemotherapy : A predictive respons...breastcancerupdatecongress
This document discusses the use of breast MRI in evaluating response to neoadjuvant chemotherapy. MRI can provide both morphological and functional information about tumors. Studies show DCE-MRI and DWI-MRI may help assess response after 1-2 cycles of chemotherapy, with changes in tumor size, kinetic parameters and ADC values predicting pathological complete or near-complete response. Larger prospective trials are still needed to standardize MRI methods and thresholds to determine if changes on MRI could guide modifications to chemotherapy regimens for non-responders. Overall, MRI shows potential as a predictive marker and non-invasive method for monitoring early response to neoadjuvant breast cancer treatment.
Single-photon emission computed tomography is a nuclear medicine tomographic imaging technique using gamma rays. It is very similar to conventional nuclear medicine planar imaging using a gamma camera. but is able to provide true 3D information
This document summarizes guidelines for radiotherapy planning for lung cancer. It discusses:
- Defining the gross tumor volume (GTV) based on imaging like PET which can help reduce margins.
- Adding margins to the GTV to create the clinical target volume (CTV) accounting for microscopic spread. There is debate around elective nodal irradiation.
- Further expanding the CTV to create the planning target volume (PTV) accounting for set-up uncertainty and tumor motion. Techniques like gating can help reduce this.
- Contouring the lungs as organs at risk and calculating dosimetric parameters like V20 and V5 to quantify lung dose and risk of toxicity. Dose needs to
TexRAD is software that analyzes textures in existing medical scans to provide prognostic information and risk stratification to clinicians. It does this by measuring fine, medium, and coarse textures in scans of tumors like those in the liver, lungs, and other organs. This additional texture information can help predict factors like cancer stage, metastasis risk, and prognosis. TexRAD requires no new scanning procedures and can analyze routine clinical images, providing more information to clinicians to guide patient care decisions.
Radiosurgery in urological malignancies can be effectively used to treat prostate cancer, renal cell cancer, and urinary bladder cancer. For prostate cancer, Cyberknife allows for hypofractionated radiotherapy with its ability to track tumor motion and correct for it during treatment. Studies have shown dose escalation and hypofractionated regimens improve local control rates for prostate cancer while maintaining low toxicity rates when delivered with precision techniques like Cyberknife. Cyberknife is particularly useful for treating prostate cancer given its ability to track and correct for intra-fraction motion of the prostate tumor.
The document discusses treatment options for locally advanced cervical cancer. It summarizes several meta-analyses and clinical trials that show concurrent chemoradiation (CCT-RT) is the standard of care, rather than neoadjuvant chemotherapy followed by surgery (NACT+Surgery). While some older trials showed a benefit of NACT+Surgery, most recent evidence suggests it does not provide benefits and adds unnecessary morbidity compared to CCT-RT. The takeaway message is that in clinical practice, only standard guidelines accepted by major organizations like NCCN and NCI should be followed, and experimental treatments belong only in clinical trials.
Quantitative image analysis for cancer diagnosis and radiation therapyWookjin Choi
1.Lung Cancer Screening
1.1.Deep learning (feasible but not interpretable)
1.2.Radiomics (concise model)
1.3.Spiculation quantification (interpretable feature)
2.PET/CT Tumor Response
2.1.Aggressive Lung ADC subtype prediction (helpful for surgeons)
2.2.Pathologic response prediction (accurate but not concise)
2.3.Local tumor morphological changes (accurate and interpretable)
The document discusses the International Commission on Radiation Units and Measurements (ICRU). It summarizes that ICRU defines physical quantities and units related to ionizing radiation. It has published over 85 reports on topics like dose specifications, clinical terms, and recommendations for intensity modulated radiation therapy. ICRU works to establish international standards for radiation quantities, units, and nomenclature used in radiation oncology and other applications of ionizing radiation.
This document discusses various techniques for optimizing radiation dose in thoracic computed tomography (CT) scans. It begins with an introduction to the growth of CT technology and increasing use of CT exams. It then covers conventional techniques like using indication-specific protocols, limiting scan passes and length, optimizing patient positioning, and adjusting tube current, potential, and rotation time. Contemporary techniques discussed include iterative reconstruction, high pitch scanning, automatic tube potential selection, and organ-based dose modulation. The document emphasizes that chest CT is important but doses should be optimized to get necessary information while keeping radiation exposure as low as reasonably possible.
This document summarizes the findings of a large study conducted by the National Cancer Center Network (NCCN) and National Oncologic PET Registry (NOPR) on the impact of positron emission tomography (PET) on cancer management. The study analyzed over 130,000 patient scans from 1,891 PET facilities. It found that in 38% of cases, PET led to a change in the referring physician's intended patient management approach, such as changing or adjusting therapy. The study demonstrates the utility of PET in cancer evaluation and treatment decision making.
First of its kind in South India GE IQ PET/CT at MIOT HospitalsMIOT Hospitals
MIOT Hospitals provides nuclear medicine services including PET/CT scanning and targeted radionuclide therapy for cancers such as thyroid cancer, neuroendocrine tumors, and liver cancers. The department is equipped with a radionuclide therapy ward and offers therapies including radioiodine for thyroid cancer, radioiodinated MIBG for rare tumors, peptide receptor radionuclide therapy for neuroendocrine tumors, and radioembolization for liver cancers. MIOT aims to offer complete cancer care from diagnosis to rehabilitation all in one facility.
Quality Assurance Programme in Computed TomographyRamzee Small
Introduction to Computed Tomography
Basic description of the components of a CT System
Introduction to Quality Assurance
Quality Assurance and Quality Control Tests in Computed Tomography base on frequency
Objective of QA/QC Test
This study assessed the feasibility of reducing radiation exposure during coronary CT angiography (CCTA) using only modified acquisition parameters on a 64-slice CT scanner. Over 85% of patients were able to undergo prospective CCTA, which significantly reduced radiation dose compared to historical levels and conventional angiography. Image quality remained high, with over 97% of coronary segments evaluated as having either excellent, good, or fair quality. The study demonstrated that very low dose CCTA is possible using standard equipment by optimizing acquisition settings.
NTCP MODELLING OF ACUTE TOXICITY IN CARCINOMA CERVIX TREATED WITH CONCURRENT ...Dr. Rituparna Biswas
1. The study aimed to develop a predictive nomogram and dose constraints for hematological toxicity in cervical cancer patients treated with chemoradiation including IMRT.
2. Thirty-seven patients were treated with IMRT and cisplatin, and bone marrow was re-delineated to include the entire marrow volume.
3. Dose-volume histograms were combined with toxicity data to create a nomogram from which hematological toxicity probabilities can be estimated based on bone marrow dosimetry.
Novel RT techniques for treating lung cancer 1403Yong Chan Ahn
- Novel RT techniques such as SBRT, IMRT, IGRT and particle beam therapy can provide high local control rates for lung cancer with reduced toxicity compared to conventional RT.
- SBRT achieved 90% local control and favorable 5-year survival for primary and metastatic lung cancers at SMC with very low complication risks.
- IMRT may be beneficial for large or centrally-located tumors but further study is needed due to the study's retrospective nature and heterogeneous patient population.
- Particle beam therapy, such as proton therapy, can further reduce dose to organs-at-risk compared to photon therapies and may allow dose escalation for improved outcomes, particularly for locally advanced lung cancers.
Similar to Artificial Intelligence To Reduce Radiation-induced Cardiotoxicity In Lung Cancer Radiotherapy: A Novel Functional Radiomics Using Cardiac FDG-PET/CT (20)
Deep Learning-based Histological SegmentationDifferentiates Cavitation Patte...Wookjin Choi
Unsupervised segmentation (unlabeled regions of interest, ROIs) and autoencoder (AE)-based classification were used to classify differences in cavitation patterns in knees and digits using the stained images (n=20-30 images/group).
Each image was divided into 256 x 256 pixel patches, and a convolutional neural network (CNN)-based unsupervised segmentation was used to identify ROIs. These patches were subsequently fed into a CNN-based AE whose latent space layer was connected to a classifier for input patch classification.
The AE was trained using the ROIs identified by the unsupervised segmentation, and the image classes were used to train the classifier. Whole image classifications were determined by maximum voting of the patch results and evaluated by accuracy.
Novel Deep Learning Segmentation Models for Accurate GTV and OAR Segmentation...Wookjin Choi
Purpose/Objective(s)
MR-guided adaptive radiotherapy (MRgART) improves target coverage and organ-at-risk (OAR) sparing in pancreatic cancer radiation therapy (RT). Inter-fractional changes in patients undergoing RT require time intensive re-delineation of gross tumor volume (GTV) and OARs prior to adaptive optimization. Accurate automatic segmentation has the potential to significantly improve efficiency of the adaptive workflow. We hypothesized that state-of-the-art deep learning (DL) segmentation models could adequately segment GTV and OARs in both planning and daily fractional MR scans.
Materials/Methods
The study included 21 patients with pancreatic cancer treated with MRgART (10 Gy x 5 fractions). The planning MR as well as all daily MR images and registrations were collected (6 image sets per patient and a total of 126 image sets). The planning MR and fraction 1-4 image sets were used as the training set (N = 105), while the test set (N = 21) comprised images for fraction 5, to simulate the last step of incremental learning from planning to final fraction. Evaluated contours included the GTV, Small Bowel, Large Bowel, Duodenum, Left and Right Kidney, Liver, Spinal Cord, and Stomach. To mimic clinical conditions, contour accuracy was evaluated within the ring structure surrounding the PTV, inside of which daily adaptive re-contouring is applied (2 cm expansion in the cradio-caudal direction, 3 cm expansion otherwise). We evaluated three DL model architectures: SegResNet, SegResNet 2D, and SwinUNETR to autosegment GTV and OARs. The segmentation models were trained on the training set using 5-fold cross-validation (CV) and quantitatively analyzed by comparing against clinically used contours with DICE scores. Qualitative analysis was performed by a radiation oncologist using a scoring scale: 1 = perfect, 2 = minor discrepancy, 3 = moderate discrepancy, and 4 = rejected.
Results
Overall, the DL segmentations were in acceptable agreement with clinical contours. The best performing model was the SwinUNETR model with overall training DICE = 0.88±0.06, test DICE = 0.78±0.11, and qualitative score of 1.6±0.8. The agreement between the DL model and clinical segmentation for the GTV was 0.79±0.08, with a qualitative score of 2.2±0.9
Conclusion
We report here the most comprehensive work on DL segmentation for pancreatic cancer MRgART, including quantitative and clinically-pertinent qualitative evaluations of 126 image sets and 3 DL architectures. Our data show good quantitative agreement between DL and clinical contours, and acceptable clinician evaluations for the majority of GTVs and OARs. The current work has great potential to significantly reduce a major bottleneck in the MRgART workflow for pancreatic cancer patients.
CIRDataset: A large-scale Dataset for Clinically-Interpretable lung nodule Ra...Wookjin Choi
The CIRDataset provides a large-scale dataset of 956 annotated lung nodules with segmentations and classifications of spiculations and lobulations, which are important radiomic features for assessing malignancy. It aims to address the lack of publicly available datasets capturing these subtle radiological features typically assessed by radiologists but often smoothed over by deep learning segmentation models. The dataset is accompanied by code, models, and a pipeline to enable the development of AI systems for joint nodule segmentation, classification of spiculations/lobulations, and malignancy prediction using an end-to-end deep learning approach.
Artificial Intelligence in Radiation Oncology.pptxWookjin Choi
The document discusses artificial intelligence applications in radiation oncology, including automatic delineation of organs-at-risk using deep learning models like OARNet. It also discusses radiomics approaches for clinical decision support and outcomes prediction using features extracted from medical images with techniques like spiculation quantification for lung cancer screening.
Artificial Intelligence in Radiation OncologyWookjin Choi
This document discusses artificial intelligence applications in radiation oncology. It begins with acknowledgements and then outlines several AI applications including radiomics tools for lung cancer screening, tumor response prediction, and predicting aggressive lung adenocarcinoma subtypes. It also discusses using AI for automatic tumor delineation and quantification of delineation variability as well as local tumor morphological changes prediction and metabolic tumor volume changes. The document provides details on methods and results for several of these AI applications in radiation oncology.
Artificial Intelligence in Radiation OncologyWookjin Choi
The document discusses artificial intelligence applications in radiation oncology. It begins with acknowledgements and then outlines topics including radiomics decision support tools, automatic delineation and variability analysis, and applications like lung cancer screening, tumor response prediction, and aggressive lung adenocarcinoma subtype prediction. Radiomics frameworks and deep learning models are presented. Results show potential for AI to provide quantitative imaging biomarkers and improve outcomes in areas like screening, treatment planning, and response assessment.
Artificial Intelligence in Radiation OncologyWookjin Choi
- The document discusses the use of artificial intelligence and radiomics in radiation oncology. It presents frameworks for radiomics analysis involving image registration, tumor segmentation, feature extraction, and predictive modeling.
- Specific applications discussed include using radiomics for lung cancer screening and prediction of tumor response. Radiomics features combined with machine learning models show improved performance over clinical guidelines for assessing lung nodule malignancy.
- Methods are also presented for quantifying tumor characteristics like spiculation through image analysis and extracting interpretable radiomics features. This can provide semantic information to radiologists for assessment.
Automatic motion tracking system for analysis of insect behaviorWookjin Choi
Undergraduate research.
We present a multi-object tracking system to track small insects such as ants and bees. Motion-based object tracking recognizes the movements of objects in videos using information extracted from the given video frames. We applied several computer vision techniques, such as blob detection and appearance matching, to track ants. Moreover, we discussed different object detection methodologies and investigated the various challenges of object detection, such as illumination variations and blob merge/split. The proposed system effectively tracked multiple objects in various environments.
Assessing the Dosimetric Links between Organ-At-Risk Delineation Variability ...Wookjin Choi
Purpose: To determine the relative dosimetric impact of delineation variability (DV) when inter-observer and inter-technique planning variability (PV), and setup variability (SV) with are considered.
Methods: 409 plans for a single head-and-neck patient from the 2017 Radiation Knowledge plan competition were used. Plans were created with Eclipse (N=227), Pinnacle (N=49), RayStation (N=25), Monaco (N=75), and TomoTherapy (N=33) with delivery techniques conventional linac IMRT (N=142), volumetric modulated arc therapy (VMAT, N=234), and helical TomoTherapy (N=33). All plans were optimized using a consistent set of target volumes and a single OAR structure set. Four additional OAR structure sets were contoured by radiation oncologists (N=2) and medical physics residents (N=2) who had completed head-and-neck contouring training. Probabilistic DVHs, dose-volume coverage maps (DVCM), which shows the probability of achieving a dose metric, were computed for each OAR on the following scenarios: SV alone (N=1000), SV+PV (N=1000*409), SV+DV (N=1000*5), SV+PV+DV (total variability [TV], N=1000*409*5). Analysis focused on the probability of exceeding the maximum dose constraint exceeded 5% for each OAR.
Results: The primary source of variability was PV, which was expected due to inter-observer planning abilities and preferences during the optimization planning process, even when all participants utilized the same constraints. The parotid had the most significant interquartile range (IQR) on the PV scenario. Conversely, adding SV, DV, and TV each reduced the IQR, showing a washing out effect on the DVCM.
Conclusion: Assessment of OAR sensitivity to DV will be highly sensitive to the specific planning technique and planner, likely requiring plan-specific assessment of in-tolerance delineation variations. Incorporation SV and DV variabilities in plan assessments washes out their relative impacts on maximum dose.
Simulation of Realistic Organ-At-Risk Delineation Variability in Head and Nec...Wookjin Choi
(Sunday, 7/14/2019) 4:00 PM - 5:00 PM
Room: 225BCD
Purpose: To simulate realistic manual delineation (MD) organ-at-risk (OAR) delineation variability (DV) the purpose of quantifying DV’s dosimetric impact.
Methods: Fourteen independent MD head-and-neck OAR structure sets (SS) were obtained from the ESTRO Falcon group. Seven OARs were available (BrainStem, Esophagus, OralCavity, Parotid_L, Parotid_R, SpinalCord, and Thyroid). A consensus MD SS was generated by the simultaneous truth and performance level estimation (STAPLE) method. MD DV was evaluated with respect to the STAPLE SS using the Dice coefficient and Hausdorff distance (HD) geometric similarity metrics. DVs were simulated using auto-delineation (AD)
methods: an average surface of standard deviation (ASSD) method, GrowCut segmentation, and a random walker (RW) segmentation. Each OAR AD was repeated five times with a different seed or variability level. Dice and HD were computed for each OAR AD with respect to the STAPLE SS. Dosimetric analysis was achieved by intercomparing dose-volume histograms (DVH) from a plan developed with a reference MD SS with DVHs for each MD and AD. DVH confidence bands are reported for MD and each AD method.
Results: The MD Dice was 0.7±0.2 (μ±σ). AD Dice values (ASSD, GrowCut, and RW) were 0.5±0.2, 0.7±0.2, and 0.8±0.1, respectively. HDs were 35.4±45.2, 27.3±19.1, 29.3±19.9, and 14.6±10.3. The simulated DV increased with increasing the seed standard deviations or variability level. The dosimetric effect was largest for MD DVs (larger OAR DVH confidence intervals and larger HD), even though the MD Dice was greater than the ASSD and GrowCut Dice values. GrowCut DV resulted in less dosimetric variation than RW, unlike the geometric indices.
Conclusion: We developed a framework to simulate DVs and demonstrated its feasibility. ADs were able to simulate different magnitudes of DVs, but did not replicate the dosimetric consequences of human delineation variability. The correlation between geometric similarity metrics and dosimetric consequences of DV is poor.
Interpretable Spiculation Quantification for Lung Cancer ScreeningWookjin Choi
Spiculations are spikes on the surface of pulmonary nodule and are important predictors of malignancy in lung cancer. In this work, we introduced an interpretable, parameter-free technique for quantifying this critical feature using the area distortion metric from the spherical conformal (angle-preserving) parameterization. The conformal factor in the spherical mapping formulation provides a direct measure of spiculation which can be used to detect spikes and compute spike heights for geometrically-complex spiculations. The use of the area distortion metric from conformal mapping has never been exploited before in this context. Based on the area distortion metric and the spiculation height, we introduced a novel spiculation score. A combination of our spiculation measures was found to be highly correlated (Spearman's rank correlation coefficient ρ = 0.48) with the radiologist's spiculation score. These measures were also used in the radiomics framework to achieve state-of-the-art malignancy prediction accuracy of 88.9% on a publicly available dataset.
Interpretable Spiculation Quantification for Lung Cancer ScreeningWookjin Choi
Spiculations are spikes on the surface of pulmonary nodule and are important predictors of malignancy in lung cancer. In this work, we introduced an interpretable, parameter-free technique for quantifying this critical feature using the area distortion metric from the spherical conformal (angle-preserving) parameterization. The conformal factor in the spherical mapping formulation provides a direct measure of spiculation which can be used to detect spikes and compute spike heights for geometrically-complex spiculations. The use of the area distortion metric from conformal mapping has never been exploited before in this context. Based on the area distortion metric and the spiculation height, we introduced a novel spiculation score. A combination of our spiculation measures was found to be highly correlated (Spearman's rank correlation coefficient ρ = 0.48) with the radiologist's spiculation score. These measures were also used in the radiomics framework to achieve state-of-the-art malignancy prediction accuracy of 88.9% on a publicly available dataset.
Quantitative Image Analysis for Cancer Diagnosis and Radiation TherapyWookjin Choi
1.Lung Cancer Screening
1.1.Deep learning (feasible but not interpretable)
1.2.Radiomics (concise model)
1.3.Spiculation quantification (interpretable feature)
2.PET/CT Tumor Response
2.1.Aggressive Lung ADC subtype prediction (helpful for surgeons)
2.2.Pathologic response prediction (accurate but not concise)
2.3.Local tumor morphological changes (accurate and interpretable)
Radiomics and Deep Learning for Lung Cancer ScreeningWookjin Choi
The document summarizes research on using radiomics and deep learning approaches for lung cancer screening. It describes:
1) Using radiomic features like shape, texture, and intensity from lung nodules on CT scans and an SVM-LASSO model to classify nodules with 87.9% sensitivity and 78.2% specificity, outperforming the Lung-RADS system.
2) A deep learning model developed for a Kaggle competition that achieved 67.4% accuracy on nodule classification but only ranked 99th due to overfitting issues without enough data.
3) Future work could integrate quantification of nodule characteristics like spiculation with plasma biomarkers to improve diagnostic accuracy.
Robust Normal Lung CT Texture Features for the Prediction of Radiation-Induce...Wookjin Choi
Abstract
Purpose: Normal lung CT texture features have been used for the prediction of radiation-induced lung disease (RILD) - pneumonitis and fibrosis. For these features to be clinically useful, they should be robust (relatively invariant or unbiased) to tumor size variations and not correlated (non-redundant) with the normal lung volume of interest, i.e., volume of the peri-tumoral region.
Methods: CT images of 14 lung cancer patients were studied. Different sizes of gross tumor volumes (GTVs) were simulated with spheres (diameters 10 to 60 mm) and placed in the lung contralateral to the tumor. 27 texture features [nine from intensity histogram, eight from the gray-level co-occurrence matrix (GLCM) and ten from the gray-level run-length matrix (GLRM)] were extracted from the peri-tumoral region (uniform 30 mm expansion around the GTV in the lung). The Bland-Altman analysis was applied to measure the normalized range of agreement (nRoA) for each feature when GTV size varied. A feature was considered as robust when its nRoA was less than the threshold (100%), which was chosen at the nRoA of the volume of the peri-tumoral region with modification based on the cumulative graph of features vs. nRoA. A feature was regarded as not correlated with the volume of the peri-tumoral region when their correlation was lower than 0.70.
Results: 16 of the 27 texture features were identified as robust. All intensity histogram features were robust except sum and kurtosis. All GLCM features were robust except cluster shade, cluster prominence, and Haralick's Correlation. Five GLRM features (two run emphasis and three high gray-level emphasis) were robust while the other five (two nonuniformity and three low gray-level emphasis) were unrobost. None of the robust features was correlated with the volume of the peri-tumoral region. No feature showed statistically significant differences (P<0.05) on GTV location (upper vs. lower lobe).
Conclusion: We identified 16 robust normal lung CT texture features that can be further examined for the prediction of RILD. Particularly, GLRM high gray-level emphasis features were robust and characterized the radiologic manifestations of pulmonary abnormalities.
Robust Normal Lung CT Texture Features for the Prediction of Radiation-Induce...Wookjin Choi
Abstract
Purpose/Objective(s)
Normal lung CT texture features have been used for the prediction of radiation-induced lung disease (RILD) - pneumonitis and fibrosis. For these features to be clinically useful, they need to be relatively invariant (robust) to tumor size and not correlated with normal lung volume.
Materials/Methods
The free-breathing CTs of 14 lung SBRT patients were studied. Different sizes of GTVs were simulated with spheres (diameters 10 to 60 mm) placed in the lung contralateral to the tumor. Twenty-seven texture features (9 from intensity histogram, 8 from the gray-level co-occurrence matrix [GLCM], and 10 from the gray-level run-length matrix [GLRM]) were extracted from [lung – GTV]. The Bland-Altman method was applied to measure the normalized range of agreement (nRoA) of each texture feature when GTV size varied. A feature was considered as robust when its nRoA was less than that of [lung – GTV] volume (8.8%) and regarded as not correlated when their absolute correlation coefficient was lower than 0.70.
Results
Eighteen texture features were identified as robust. All intensity histogram features were robust except sum and kurtosis. All GLCM features were robust except energy and Haralick's Correlation. Five GLRM features (two run emphasis and three high gray-level emphasis) were robust while the other five (two nonuniformity and three low gray-level emphasis) were nonrobust. Particularly, all three low gray-level emphasis features had extremely large nRoAs (∼30%), indicating huge variations when GTV size changed. None of the robust features was correlated with the normal lung [lung – GTV] volume, suggesting that they can provide additional information. Three nonrobust features (sum and two nonuniformity features) were highly correlated with the normal lung volume. None feature showed statistically significant differences (P < 0.05) with respect to GTV location (upper vs. lower lobe).
Conclusion
We identified 18 robust lung CT texture features which were invariant to varying tumor volumes. Particularly the three GLRM high gray-level emphasis features can characterize the radiologic manifestations of pulmonary abnormalities. Hence these features can be further examined for the prediction of the RILD.
Radiomics Analysis of Pulmonary Nodules in Low Dose CT for Early Detection of...Wookjin Choi
Purpose: To predict the histopathologic subtypes with poor surgery prognosis in early stage lung adenocarcinomas using CT and PET radiomics.
Methods: We retrospectively enrolled 53 patients with stage I lung adenocarcinoma who underwent both diagnostic CT and 18F-fluorodeoxyglucose (FDG) PET/CT before complete surgical resection of the tumors. Tumor segmentation was manually contoured by a physician on both the diagnostic CT and the attenuation CT of PET/CT.A total of 170 radiomics features were extracted on both PET and CT images to design predictive models for two histopathologic endpoints: (1) tumors with solid or micropapillary predominant subtype (aggressiveness), and (2) tumors with micropapillary component more than 5% (MIP5). We used least absolute shrinkage and selection operator (LASSO) as a model building method coupled with a class separability feature selection (CSFS) method. For an unbiased model estimate, a 10-fold cross validation approach was used. The area under the curve (AUC) and prediction accuracy were employed to evaluate the performance of the model. P-values were computed using Wilcoxon rank-sum test.
Results: Of the 53 patients, 9 and 15 had tumors with aggressiveness and MIP5, respectively. For both endpoints, LASSO models with two PET radiomics features achieved the best performance. For aggressiveness, the LASSO model with PET Cluster Shade and PET 2D Variance resulted in 77.6±2.3% accuracy and 0.71±0.02 AUC (P = 0.011). For MIP5, the LASSO model with PET Eccentricity and PET Cluster Shade resulted in 69.6±3.1% accuracy and 0.68±0.04 AUC (P=0.014). The PET Cluster Shade was commonly selected in both models. Cluster shade is a texture feature that measures the skewness of the co-occurrence matrix. Higher PET cluster shade predicted that the tumor was more aggressive and more likely MIP5.
Conclusion: We showed that PET/CT radiomics features can predict tumor aggressiveness.
Funding Support, Disclosures, and Conflict of Interest: This work was supported in part by the National Cancer Institute Grants R01CA172638.
Individually Optimized Contrast-Enhanced 4D-CT for Radiotherapy Simulation in...Wookjin Choi
Purpose/Objectives: To develop an individually optimized contrast-enhanced (CE) 4D-CT for radiotherapy simulation in pancreatic adenocarcinoma (PDA).
Materials/Methods: Ten PDA patients were enrolled and underwent three CT scans: a 4D-CT immediately following a CE 3D-CT, and an individually optimized CE 4D-CT using a test injection to estimate the peak contrast enhancement time and to optimize the delay time. Three physicians contoured the tumor and pancreatic tissues. We compared image quality scores, tumor volume, motion, image noise, tumor-to-pancreas contrast, and contrast-to- noise ratio (CNR) in the three CTs. We also evaluated inter-observer variations in contouring the tumor using simultaneous truth and performance level estimation (STAPLE).
Results: The average image quality scores for CE 3D-CT and CE 4D-CT were comparable (4.0 and 3.8, p=0.47), and both were significantly better than that for 4D-CT (2.6, p<0.001). The tumor-to- pancreas contrast in CE 3D-CT and CE 4D-CT were comparable (15.5 and 16.7 HU, p=0.71), and the later was significantly higher than that in 4D-CT (9.2 HU, p=0.03). Image noise in CE 3D-CT (12.5 HU) was significantly lower than that in CE 4D-CT (22.1 HU, p<0.001) and 4D-CT (19.4 HU, p=0.005). The CNR in CE 3D-CT and CE 4D-CT were comparable (1.4 and 0.8, p=0.23), and the former was significantly better than that in 4D-CT (0.6, p=0.04). The average tumor volume was smaller in CE 3D-CT (29.8 cm 3 ) and CE 4D-CT (22.8 cm 3 ) than in 4D-CT (42.0 cm 3 ), though the differences were not statistically significant. The tumor motion was comparable in 4D-CT and CE 4D-CT (7.2 and 6.2 mm, p=0.23). The inter-observer variations were comparable in CE 3D-CT and CE 4D-CT (Jaccard index 66.0% and 61.9%), and the former was significantly smaller than that of 4D-CT (55.6%, p=0.047).
Conclusions: The CE 4D-CT demonstrated largely comparable characteristics to the CE 3D-CT. It has high potential for simultaneously delineating the tumor and quantifying the tumor motion with a single scan.
Muktapishti is a traditional Ayurvedic preparation made from Shoditha Mukta (Purified Pearl), is believed to help regulate thyroid function and reduce symptoms of hyperthyroidism due to its cooling and balancing properties. Clinical evidence on its efficacy remains limited, necessitating further research to validate its therapeutic benefits.
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
Basavarajeeyam is a Sreshta Sangraha grantha (Compiled book ), written by Neelkanta kotturu Basavaraja Virachita. It contains 25 Prakaranas, First 24 Chapters related to Rogas& 25th to Rasadravyas.
Promoting Wellbeing - Applied Social Psychology - Psychology SuperNotesPsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
Local Advanced Lung Cancer: Artificial Intelligence, Synergetics, Complex Sys...Oleg Kshivets
Overall life span (LS) was 1671.7±1721.6 days and cumulative 5YS reached 62.4%, 10 years – 50.4%, 20 years – 44.6%. 94 LCP lived more than 5 years without cancer (LS=2958.6±1723.6 days), 22 – more than 10 years (LS=5571±1841.8 days). 67 LCP died because of LC (LS=471.9±344 days). AT significantly improved 5YS (68% vs. 53.7%) (P=0.028 by log-rank test). Cox modeling displayed that 5YS of LCP significantly depended on: N0-N12, T3-4, blood cell circuit, cell ratio factors (ratio between cancer cells-CC and blood cells subpopulations), LC cell dynamics, recalcification time, heparin tolerance, prothrombin index, protein, AT, procedure type (P=0.000-0.031). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and N0-12 (rank=1), thrombocytes/CC (rank=2), segmented neutrophils/CC (3), eosinophils/CC (4), erythrocytes/CC (5), healthy cells/CC (6), lymphocytes/CC (7), stick neutrophils/CC (8), leucocytes/CC (9), monocytes/CC (10). Correct prediction of 5YS was 100% by neural networks computing (error=0.000; area under ROC curve=1.0).
Does Over-Masturbation Contribute to Chronic Prostatitis.pptxwalterHu5
In some case, your chronic prostatitis may be related to over-masturbation. Generally, natural medicine Diuretic and Anti-inflammatory Pill can help mee get a cure.
Vestibulocochlear Nerve by Dr. Rabia Inam Gandapore.pptx
Artificial Intelligence To Reduce Radiation-induced Cardiotoxicity In Lung Cancer Radiotherapy: A Novel Functional Radiomics Using Cardiac FDG-PET/CT
1. AI to Reduce Radiation-induced Cardiotoxicity in Lung Cancer Radiotherapy:
A Novel Functional Radiomics
using Cardiac FDG-PET/CT
Wookjin Choi, Adam P. Dicker, and Yevgeniy Vinogradskiy
Department of Radiation Oncology,
Sidney Kimmel Medical College / Cancer Center
at Thomas Jefferson University, Philadelphia, PA
2. • Assistant Professor / Computational Medical Physicist
Sidney Kimmel Medical College / Cancer Center at Thomas Jefferson University
• A research grant from ViewRay, Inc.
• A research grant from Varian Medical Systems, Inc.
• A research grant from the PROPEL Center
• Research software license for INT Contour from Carina Medical
• This project was partially supported by the Sidney Kimmel Cancer Center Support Grant
(NIH/NCI P30 CA056036)
2
Disclosure
5. • Radiation has been an effective tool for
treating cancer for more than 100 years
• More than 60 percent of patients diagnosed
with cancer will receive radiation therapy as
part of their treatment
• Radiation oncologists are cancer specialists
who manage the care of cancer patients with
radiation for either cure or palliation
5
Radiation Oncology
Patient being treated with modern
radiation therapy equipment.
6. 6
Advances in Radiation Oncology
Figure 1(b) of Lee et al. "Physical and radiobiological evaluation of radiotherapy treatment plan." Evolution of ionizing radiation research. InTech (2015): 109-50.
7. • Radiation Oncologist prescribes and oversees the radiation therapy treatments
• Medical Physicist ensures that treatment plans are properly tailored for each
patient, and is responsible for the calibration and accuracy of treatment equipment
• Dosimetrist works with the radiation oncologist and medical physicist to calculate
the proper dose of radiation given to the tumor
• Radiation Therapist administers the daily radiation under the doctor’s prescription
and supervision
• Radiation Oncology Nurse interacts with the patient and family at the time of
consultation, throughout the treatment process and during follow-up care
7
The Radiation Oncology Team
8. • Medical physics is easy to define
Application of physics to medicine
• There is a long tradition of using applied physics to improve medicine (both
treatments and diagnostics)
8
Medical Physics
From the early radiographs to the modern medical linear accelerator
9. 9
AI in Radiation Oncology
Huynh et al. Nat Rev Clin Oncol 2020
A general overview of the radiation therapy workflow with brief descriptions of expected applications of AI at each step.
10. Diagnosis Simulation Contouring
Treatment
Planning
Treatment
QA
Treatment
Delivery
MIM Protégé AI &
Auto export to
Eclipse
Auto-contouring
of daily
CBCT/MR
Eclipse
DVH Metric
script
MIM Archive &
Auto export to
RadCalc
AI Dose
Prediction using
Diagnostic
PET/CT
Weekly
Dashboard
Dosimetry
Whiteboard
Longitudinal
Radiomics
Customized AI
Contouring
Current Solutions in Jefferson SKCC Radiation Oncology
10
Cardiac PET
Heart Toxicity
Prediction
Cardiac PET
Heart Toxicity
Assessment
11. Radiomics
11
Aerts et al. Nature Communications, 2014
Figure 1: Extracting radiomics data from images. Figure 2: Analysis workflow.
Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
12. • Quantitative approach to medical imaging
• Extracts many features from medical images
• These features can be used to predict prognosis and therapeutic response
• Radiomics has the potential to revolutionize the way we diagnose and treat cancer
12
Radiomics
Fully automated pipeline, Woznicki et al. Frontiers in Radiology 2022 https://github.com/pwoznicki/AutoRadiomics
14. • Traditional methods of evaluating cardiotoxicity:
Focus solely on radiation doses to the heart.
Do not incorporate functional imaging information.
• Functional imaging has potential to improve cardiotoxicity prediction for
lung cancer patients undergoing radiotherapy:
FDG PET/CT imaging is routinely obtained for lung cancer staging.
PET scans can be used to evaluate cardiac inflammation.
Studies indicate that PET cardiac signal can predict clinical outcomes.
• The purpose of this work:
Develop a radiomics model to predict clinical cardiac assessment using FDG PET/CT
scans.
Heart Toxicity Prediction using PET/CT Radiomics for Lung Radiotherapy
14
15. • Pre-Treatment only - before treatment
Dose prediction
Cardiac PET/CT Radiomics to predict existing heart disease
CT coronary artery calcium scoring
Collected TJU 100 pts data and continue to collect more data
70 pts from ACRIN 6668/RTOG 0235, 39 pts from CU/BU
Manual and auto heart contouring
• Pre & Post Treatment – during/after treatment
PET/CT Radiomics
Delta-radiomics between pre and post PET/CT
70 pts from ACRIN 6668/RTOG 0235, 39 pts from CU/BU
Manual and auto heart contouring
15
Heart Toxicity Prediction using PET/CT Radiomics for Lung Radiotherapy
16. 16
AI in Radiation Oncology – Heart Toxicity Prediction
Huynh et al. Nat Rev Clin Oncol 2020
A general overview of the radiation therapy workflow with brief descriptions of expected applications of AI at each step.
Dose Map
Prediction
Cardiac Uptake
Classification
Overall Survival
Prediction
17. Diagnostic PET/CT Radiation Target delineation Normal tissue delineation Radiation dose distribution
Diagnostic PET/CT Radiation dose distribution
AI/ML
Dose Prediction before Treatment Planning
17
18. • The study included 209 pre-treatment 18F-FDG PET-CT images of lung cancer patients from 3
different study populations (TJU: 100, ACRIN: 70, CU: 39).
Low quality images were excluded 7 (TJU: 1, ACRIN: 4, CU:2).
Cardiac PET/CT Radiomics - Dataset
CLASS
LV MYOCARDIAL
UPTAKE PATTERN
TJU ACRIN CU TOTAL
0 Uniform 19 (19.2%) 21 (31.8%) 11 (29.8%) 51 (25.2%)
1 Absent 44 (44.4%) 23 (34.8%) 13 (35.1%) 80 (39.6%)
2 Heterogeneous 36 (36.4%) 22 (33.3%) 13 (35.1%) 71 (35.1%)
ALL 99 (100.0%) 66 (100.0%) 37 (100.0%) 202 (100.0%)
Table 1. Statistics of LV FDG Uptake Pattern Classification
Figure 2. Cardiac uptake classifications, Uniform, Absent, and Heterogeneous (Non-uniform and Focal )
18
19. 19
Method - Study Flow Diagram
Figure 1. Study and cardiac model diagram, training dataset: TJU (N=99), and external validation data sets: ACRIN (N=66) and CU (N=37).
RFE: Recursive Feature Elimination, SFS: Sequential Feature Selection
1. Pre-processing
LV FDG uptake patter
review
By Radiation Oncologist
Manual
Heart Contouring
By Medical Physicist
Automatic
Heart Contouring
By 1Commercial Software
Training
Data set
(N=99)
2. Feature Analysis
Feature Extraction
- PyRadiomics
- In-house SW
Feature Reduction
- Wilcoxon test
- Clustering
Feature Selection
- RFE
- SFS
3. Model Building
Model Exploration
- TPOT
Optimization
- Hyper-parameters
- Feature Selection
Evaluation
- Validation
- External validation
Predicted
LV Uptake
Pattern
External
Validation
Data sets
(N=103)
1INTContour, Carina Medical https://www.carinaai.com/intcontour.html
20. • Training and Validation Data:
Training dataset: TJU (N=100), 80% training set (TPOT optimization), 20% validation set.
External validation datasets: ACRIN (N=70), CU (N=39)
• Radiomics Feature Extraction
Heart delineation: Manual and Auto
Selected 200 novel functional radiomics features – In-house: 103, PyRadiomics: 97
Feature categories include Shape (2D and 3D), First-order (2D and 3D), GLCM, GLSZM, GLRLM, NGTDM,
GLDM.
• Feature Reduction
Feature robustness test using ICC between features from Auto and Manual delineations
Wilcoxon test (Bonferroni adjusted p<0.05)
Hierarchical clustering
Method – Radiomics Feature Extraction and Reduction
20
21. 21
Results: Pipeline Optimization using TPOT
Feature Selector
Variance Threshold
Threshold: 0.0001
56 features
Threshold: 0.005
52 features
RFE ranking and
SFS with TPOT optimization
Top 9 features
Feature Transformer
One Hot Encoder
Minimum_fraction: 0.15,
Sparse: False,
Threshold: 10
Minimum_fraction: 0.25
Minimum_fraction: 0.15
Classifier
Extra Trees Classifier
Bootstrap: False,
criterion: gini,
max_features: 0.95,
min_samples_leaf: 3,
min_samples_split: 20
Criterion: entropy,
max_features: 0.75,
min_samples_leaf: 2,
min_samples_split: 17
Bootstrap: True,
criterion: gini,
max_features: 0.8,
min_samples_split: 10
Model
Discovery
Hyper-
parameter
Optimization
Feature
Optimization
Validation Accuracy (%)
TJU ACRIN CU
90.0 78.8 90.2
90.0 78.8 91.9
95.0 80.3 91.9
22. • TPOT (Tree-Based Pipeline Optimization Tool)
100 populations, 10 generations, 10-fold CV
Evaluated 10,000 pipeline configurations to identify the best model.
• Pipeline Construction
Three component template: Feature Selector—Feature Transformer—Classifier
o Feature Selector: P-value (ANOVA F-statistic), Family Wise Error Rate, Percentile, Low-Variance Feature Removal, RFE
with Extra Trees Classifier
o Feature Transformer: Binarization, ICA, Feature Agglomeration (Various Linkage & Affinity Methods), Scaling (Max
Absolute Value, Min-Max Values), Normalization (l1, l2, max norm) ,Kernel Approximation (RBF, Cosine, χ2, etc.), PCA,
Polynomial Features, RBF Sampler, Robust Scaler, Standard Scaler, Zero Count, One Hot Encoder
o Classifier: Naïve Bayesian (Bernoulli, Multinomial), Decision Tree Classifier, Extra Trees Classifier, Random Forest
Classifier, Gradient Boosting Classifier, XGBoost, KNN Classifier, Linear SVM Classifier, Logistic Regression, SGD, MLP
• Hyper-parameter Optimization: optimize parameters of the discovered pipeline
• Feature Optimization: Utilized RFE for feature ranking followed by SFS with TPOT optimization.
Method - Machine Learning Model Pipeline Selection and Optimization
22
23. Results – Feature reduction and Prediction results
Model iteration and explanation
Number of
features
10-fold cross
validation
accuracy (%)
Training
accuracy (%)
Validation
accuracy (%)
External
Validation
accuracy -
ACRIN (%)
External
Validation
accuracy – CU
(%)
Iteration 1:
Model discovery 56 88.9 94.9 90.0 78.8 89.2
Iteration 2:
Hyperparameter optimization
52 89.9 96.2 90.0 78.8 91.9
Iteration 3:
Feature optimization 9 89.8 92.4 95.0 80.3 91.9
9 Selected Features: 2D Maximum, 2D Skewness, Mean, GLCM Cluster Prominence, GLCM Sum
Average, GLCM Cluster Shade, GLDM Dependence Variance, Maximum, 2D Kurtosis
Feature reduction The number of remaining Features
ICC 153
Wilcoxon Test 141
Hierarchical Clustering 62
Table 3. Model pipeline optimization results and validation results.
Table 2. Feature reduction results
23
24. • Delta-Radiomics calculation:
The change between pre- and post-PET/CT.
Difference Δ𝐹𝑖
= 𝐹𝑝𝑜𝑠𝑡
𝑖
− 𝐹𝑝𝑟𝑒
𝑖
𝐹: the feature vector, 𝑖: the index of the feature vector
• The study identified four clinically relevant delta-
radiomics features
Pre-CT Maximum
Post-CT Minimum
Delta-CT GLRM Run Variance
Delta-PET GLRM Run Entropy
• Model performance
CV on the training set: 0.91 AUC
The test set: 0.87 AUC
24
Results - Overall survival prediction using delta radiomics Pre & Post
Figure 1: Example of patients with variable pre and post-treatment
cardiac PET/CT signal.
Vinogradskiy Y, Diot Q, Jones B, et al. Evaluating Positron Emission Tomography-
Based Functional Imaging Changes in the Heart After Chemo-Radiation for Patients
With Lung Cancer. International Journal of Radiation Oncology* Biology* Physics.
2020;106(5):1063-1070.
25. • Utilized dataset of 209 patients for functional cardiac radiomics model
development.
• Achieved high accuracy:
9 optimal features were selected
Training set validation: TJU: 95%
External validation : ACRIN: 80.3%, CU: 91.1%
• Clinical Application:
Automated prediction of existing cardiac conditions.
Early biomarker for post-radiotherapy cardiac complications.
Conclusion
25
Most recent advances aim to:
Better see the tumor target
Better focus radiation doses on small targets
Improve sparing of surrounding tissues to reduce side effects
The radiation therapy treatment team works closely to ensure that patients are receiving safe, quality treatment.
Best described as the application of physics concepts, theories, and principles to medicine or healthcare
Responsible for the technical foundations of radiology, radiation oncology, and nuclear medicine
Built on foundation of physics, but with distinct body of knowledge and scholarship
Distinct from biophysics
Incorporates both theoretical and experimental methods, but inherently an applied discipline
The workflow begins with the decision to treat the patient with radiation therapy,
followed by a simulation appointment during which medical images are acquired for treatment planning.
Subsequently, the patient-specific treatment plan is created,
and then the plan is subjected to approval, review and quality assurance (QA) measures prior to delivery of radiation to the patient.
The patient then receives follow-up care.
AI has the potential to improve radiation therapy for patients with cancer by increasing efficiency for the staff involved, improving the quality of treatments, and providing additional clinical information and predictions of treatment response to assist and improve clinical decision-making.
Human cancers exhibit strong phenotypic differences that can be visualized noninvasively by medical imaging. Radiomics refers to the comprehensive quantification of tumour phenotypes by applying a large number of quantitative image features. Here we present a radiomic analysis of 440 features quantifying tumour image intensity, shape and texture, which are extracted from computed tomography data of 1,019 patients with lung or head-and-neck cancer. We find that a large number of radiomic features have prognostic power in independent data sets of lung and head-and-neck cancer patients, many of which were not identified as significant before. Radiogenomics analysis reveals that a prognostic radiomic signature, capturing intratumour heterogeneity, is associated with underlying gene-expression patterns. These data suggest that radiomics identifies a general prognostic phenotype existing in both lung and head-and-neck cancer. This may have a clinical impact as imaging is routinely used in clinical practice, providing an unprecedented opportunity to improve decision-support in cancer treatment at low cost.
Figure 1: Extracting radiomics data from images.
(a) Tumours are different. Example computed tomography (CT) images of lung cancer patients. CT images with tumour contours left, three-dimensional visualizations right. Please note strong phenotypic differences that can be captured with routine CT imaging, such as intratumour heterogeneity and tumour shape. (b) Strategy for extracting radiomics data from images. (I) Experienced physicians contour the tumour areas on all CT slices. (II) Features are extracted from within the defined tumour contours on the CT images, quantifying tumour intensity, shape, texture and wavelet texture. (III) For the analysis the radiomics features are compared with clinical data and gene-expression data.
Figure 2: Analysis workflow.
The defined radiomic features algorithms were applied to seven different data sets. Two data sets were used to calculate the feature stability ranks, RIDER test/retest and multiple delineation respectively (both orange). The Lung1 data set, containing data of 422 non-small cell lung cancer (NSCLC) patients, was used as training data set. Lung2 (n=225), H&N1 (n=136) and H&N2 (n=95) were used as validation data sets. The Lung3 data set (n=89) was used for association of the radiomic signature with gene expression profiles. For the multivariate analysis, only one fixed four-feature radiomic signature was tested in the validation data sets.
Standard Staging: FDG PET-CT imaging is routinely used as part of the standard staging method for cancer patients.
Cardiac Complications: Cancer therapy can result in cardiac complications, making early detection and management crucial.
RTOG 0617 was the first study that highlighted the issue of cardiac dose in lung cancer radiotherapy.
The original article revealed that the volume of heart receiving greater than or equal to 5 Gy (V5) or greater than or equal to 30 Gy (V30) was associated with worse overall survival.
The workflow begins with the decision to treat the patient with radiation therapy,
followed by a simulation appointment during which medical images are acquired for treatment planning.
Subsequently, the patient-specific treatment plan is created,
and then the plan is subjected to approval, review and quality assurance (QA) measures prior to delivery of radiation to the patient.
The patient then receives follow-up care.
AI has the potential to improve radiation therapy for patients with cancer by increasing efficiency for the staff involved, improving the quality of treatments, and providing additional clinical information and predictions of treatment response to assist and improve clinical decision-making.
gray level co-occurrence matrix (GLCM), gray level size zone matrix (GLSZM), gray level run length matrix (GLRLM), neighboring gray tone difference matrix (NGTDM), gray level dependence matrix (GLDM), and 2D and 3D shape features.
gray level co-occurrence matrix (GLCM), gray level size zone matrix (GLSZM), gray level run length matrix (GLRLM), neighboring gray tone difference matrix (NGTDM), gray level dependence matrix (GLDM), and 2D and 3D shape features.