Radiomics

We are excited to share the release of a specialized Gemini Gem designed specifically for Computational Medical Physicists. This AI agent acts as a knowledgeable co-pilot for research, computational modeling, dosimetry, and medical image computing. What is the Computational Medical Physicist Gem? The Computational Medical Physicist Gem is a custom-tuned assistant tailored to the complex, multidisciplinary domain of medical physics. It bridges the gap between physics modeling, coding, and clinical guidelines. ...

We are releasing qradiomics — an open-source Python CLI that unifies more than a decade of Choi Lab radiomics work into a single, reproducible, pip-installable toolkit. What is qradiomics? qradiomics (command: qr) is a radiomics research CLI built for the full data flow from raw DICOM to published-grade results: DICOM download → conversion → feature extraction → clinical merge → modeling Each step is a single Unix-style command. Pipelines are assembled from those atomic commands using plain JSON plans, executed by Nextflow (per-patient parallel), Prefect, or inline. One command gets you started: ...

Thrilled to share that our work has been selected for an Oral Scientific Presentation in the BEST of Physics session at the American Society for Radiation Oncology (ASTRO) 2026 Annual Meeting, September 26–30 in Boston, MA. Out of ~2,700 abstracts submitted to ASTRO this year, only 300 were chosen for oral presentation, and BEST of Physics gathers the highest-rated physics work of the meeting. Presentation details Abstract # 75557 Title Early Adaptive Interventions in Lung Cancer: Leveraging Fusion of Longitudinal CBCT Trajectories and Clinical Variables for Robust Survival Prediction Session SS 19 — BEST of Physics Date / Time September 28, 2026 · 10:45 AM – 12:00 PM ET Venue Thomas M. Menino Convention & Exhibition Center, Boston, MA Format 7-min oral + 3-min Q&A Publication Red Journal supplement Authors Wookjin Choi, Pradeep Bhetwal, Michael Dichmann, Yingcui Jia, Wenchao Cao, Danfu Liang, Yingxuan Chen, Adam Dicker, Yevgeniy Vinogradskiy ...

License: MIT · Python: 3.11+ · Version: 0.9.0 · Repo: choilab-jefferson/qradiomics Active successor for three earlier Choi Lab radiomics codebases. The C++/MATLAB pipelines in taznux/radiomics-tools, taznux/lung-image-analysis, and choilab-jefferson/LungCancerScreeningRadiomics are superseded by this repo. The feature extractors are now in qradiomics.feature.rtools (Python ITK port, numerically exact to the C++ binary). New work should land here. Radiomics research CLI. qr does two things equally well: Atomic tasks — convert DICOM, extract features, merge clinical, fit a model. Each is a single command, files in / files out. Workflow assembly — generate, mutate, scaffold, and run multi-step pipelines from those atomic tasks. Default executor is Nextflow (per-patient parallel + cache + HPC); Prefect is the secondary executor; inline is the small-cohort fallback. The canonical radiomics data flow has four stages — data → image → features → modeling — and one qr workflow plan call instantiates the whole chain: ...

Jefferson Investigates: Artificial Intelligence and Heart Disease — The Nexus https://medicalxpress.com/news/2024-06-machine-lung-cancer-scans-heart.html

Our study on cardiac toxicity in lung cancer treatment is now featured in a JCO CCI editorial. Discoveries that could change patient care are on the horizon. Stay tuned! #CardiacToxicity#LungCancer#Innovation Shining a Light: Unveiling Cardiac Risks Using Positron Emission Tomography Imaging in Lung Cancer Radiotherapy

Maria Thor 1,4, Kelly Fitzgerald 2,4, Aditya Apte 1, Jung Hun Oh 1, Aditi Iyer 1, Otasowie Odiase 2, Saad Nadeem 1, Ellen D. Yorke 1, Jamie Chaft 3, Abraham J. Wu 2, Michael Offin 3, Charles B Simone II 2, Isabel Preeshagul 3, Daphna Y. Gelblum 2, Daniel Gomez 2, Joseph O. Deasy 1, Andreas Rimner 2 1Department of Medical Physics, Memorial Sloan Kettering Cancer Center 2Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center 3Department of Medicine, Memorial Sloan Kettering Cancer Center ...

Our paper “Novel Functional Radiomics for Prediction of Cardiac Positron Emission Tomography Avidity in Lung Cancer Radiotherapy” has been published in JCO CCI. This research work delves into an innovative approach to predict clinical cardiac assessment using functional imaging. Note: This post summarizes broader functional cardiac PET radiomics work. Only one related presentation below is specifically a delta-radiomics analysis. Abstract: Traditional methods for evaluating cardiotoxicity primarily focus on radiation doses to the heart. However, functional imaging offers the potential to enhance early prediction of cardiotoxicity in lung cancer patients undergoing radiotherapy. In this context, Fluorine-18 (18F) fluorodeoxyglucose (FDG)-positron emission tomography (PET)/computed tomography (CT) imaging plays a crucial role. This study aims to develop a radiomics model that predicts clinical cardiac assessment using 18F-FDG PET/CT scans before thoracic radiation therapy. ...

AAPM Annual Meeting (Houston, TX • July 23 ‒ 27, 2023) Novel Functional Delta-Radiomics for Predicting Overall Survival in Lung Cancer Radiotherapy Using Cardiac FDG-PET Uptake Wookjin Choi, Yevgeniy Vinogradskiy Interactive ePoster Discussions: Sunday, July 23, 2023: 3:00 PM - 3:30 PM, GRBCC, Exhibit Hall | Forum 6 SU-300-IePD-F6-4 Novel Functional Delta-Radiomics for Predicting Overall Survival in Lung Cancer Radiotherapy Using Cardiac FDG-PET Uptake Deep Learning Segmentation for Accurate GTV and OAR Segmentation in MR-Guided Adaptive Radiotherapy for Pancreatic Cancer Patients Wookjin Choi, Hamidreza Nourzadeh, Yingxuan Chen, Christopher G. Ainsley, Vimal K. Desai, Alexander A. Kubli, Yevgeniy Vinogradskiy, Maria Werner-Wasik, Adam Mueller, and Karen E. Mooney PO-GePV-D-50 Deep Learning Segmentation for Accurate GTV and OAR Segmentation in MR-Guided Adaptive Radiotherapy for Pancreatic Cancer Patients ...

Varian will support my research project entitled “Longitudinal CBCT radiomics analysis for lung cancer radiotherapy response and prognosis prediction” with $230,000 over 2 years. This is the first research grant from Varian to the Department of Radiation Oncology, Sidney Kimmel Medical College at Thomas Jefferson University. This project can potentially impact the clinical practice of lung cancer patients by using standard imaging modalities (CBCT and 4D-CBCT) to provide early prediction of prognosis and toxicity. ...