Radiomics on Qualia Radiomics

Selected for ASTRO 2026 BEST of Physics — Oral Presentation in Boston

Mon, 18 May 2026 09:00:00 -0400

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

qradiomics — Radiomics Research CLI

Sun, 17 May 2026 20:31:21 -0400

License: MIT · Python: 3.11+ · Repo: choilab-jefferson/qradiomics

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:

The Nexus featured our cardiac PET radiomics study

Thu, 27 Jun 2024 13:38:07 -0400

Jefferson Investigates: Artificial Intelligence and Heart Disease — The Nexus

https://medicalxpress.com/news/2024-06-machine-lung-cancer-scans-heart.html

Shining a Light: Unveiling Cardiac Risks Using PET Imaging in Lung Cancer Radiotherapy

Sun, 14 Apr 2024 16:38:31 -0400

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

Exploring published and novel pre-treatment CT and PET radiomics to stratify risk of progression among early-stage non-small cell lung cancer patients treated with stereotactic radiation

Tue, 07 Nov 2023 17:36:22 -0500

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

Novel Functional Radiomics for Predicting Cardiotoxicity in Lung Cancer Radiotherapy using Cardiac FDG-PET Uptake

Mon, 11 Sep 2023 12:03:17 -0400

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.

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.

2023 Accepted/Invited Annual Meeting abstracts

Mon, 08 May 2023 17:22:21 -0400

AAPM Annual Meeting (Houston, TX • July 23 ‒ 27, 2023)
1. 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
2. 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

Longitudinal CBCT radiomics in Lung Cancer supported by Varian Medical Systems Inc.

Fri, 10 Feb 2023 15:23:27 -0500

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.

Clinically-Interpretable Radiomics

Wed, 29 Jun 2022 21:01:32 -0400

MICCAI'22 Paper | CMPB'21 Paper | CIRDataset

This library serves as a one-stop solution for analyzing datasets using clinically-interpretable radiomics (CIR) in cancer imaging (https://github.com/choilab-jefferson/CIR). The primary motivation for this comes from our collaborators in radiology and radiation oncology inquiring about the importance of clinically-reported features in state-of-the-art deep learning malignancy/recurrence/treatment response prediction algorithms. Previous methods have performed such prediction tasks but without robust attribution to any clinically reported/actionable features (see extensive literature on the sensitivity of attribution methods to hyperparameters). This motivated us to curate datasets by annotating clinically-reported features at the voxel/vertex level on public datasets (using our published advanced mathematical algorithms) and relating these to prediction tasks (bypassing the “flaky” attribution schemes). With the release of these comprehensively-annotated datasets, we hope that previous malignancy prediction methods can also validate their explanations and provide clinically-actionable insights. We also provide strong end-to-end baselines for extracting these hard-to-compute clinically-reported features and using these in different prediction tasks.

Lung Cancer Screening Radiomics

Wed, 08 Jun 2022 11:32:36 -0400

A comprehensive framework for lung cancer screening radiomics using LIDC-IDRI and LUNGx dataset.

Data preprocessing - download data, conversion, etc.
Radiomics feature extraction including spiculation features
AutoML model building and validation

Source code https://github.com/choilab-jefferson/LungCancerScreeningRadiomics

Publications

Wookjin Choi, Jung Hun Oh, Sadegh Riyahi, Chia-Ju Liu, Feng Jiang, Wengen Chen, Charles White, Andreas Rimner, James G. Mechalakos, Joseph O. Deasy, and Wei Lu, “Radiomics analysis of pulmonary nodules in low-dose CT for early detection of lung cancer”, Medical Physics, Vol. 45, No. 4, pp. 1537-1549, April 2018. https://doi.org/10.1002/mp.12820
Wookjin Choi, Saad Nadeem, Sadegh Riyahi, Joseph O. Deasy, Allen Tannenbaum, Wei Lu, “Reproducible and Interpretable Spiculation Quantification for Lung Cancer Screening.” Computer methods and programs in biomedicine. 200 2021. https://doi.org/10.1016/j.cmpb.2020.105839

Hiring a Postdoctoral Fellow

Wed, 22 Dec 2021 17:52:41 -0500

Postdoctoral Fellow - Developing Clinically Interpretable Medical Imaging AI in Radiation Therapy

https://recruit.jefferson.edu/psp/hcmp/EMPLOYEE/HRMS/c/HRS_HRAM_FL.HRS_CG_SEARCH_FL.GBL?Page=HRS_APP_JBPST_FL&Action=U&FOCUS=Applicant&SiteId=1&JobOpeningId=9272548&PostingSeq=1

PI: Wookjin Choi, Ph.D. <Wookjin.Choi@jefferson.edu>
Assistant Professor of Radiation Oncology, Thomas Jefferson University
2 Years

Responsibilities

POST-DOCTORAL POSITION, DEPARTMENT OF RADIATION ONCOLOGY: Thomas Jefferson University is now accepting applications for a post-doctoral fellow in the Department of Radiation Oncology with the Choi lab. The post-doctoral position is for developing AI techniques for image-guided radiation therapy and clinical outcome prediction and decision-making using radiomics, deep learning, and other computationally intensive techniques. Trainees must have the opportunity to carry out supervised biomedical research with the primary objective of developing or extending their research skills and knowledge in preparation for an independent research career.

Artificial Intelligence in Radiation Oncology

Fri, 15 Oct 2021 00:49:35 -0400

Reproducible and Interpretable Spiculation Quantification for Lung Cancer Screening

Tue, 17 Nov 2020 20:24:09 -0500

Choi, W., Nadeem, S., Alam, S. R., Deasy, J. O., Tannenbaum, A., & Lu, W. (2020). Reproducible and Interpretable Spiculation Quantification for Lung Cancer Screening. Computer Methods and Programs in Biomedicine, 105839. https://doi.org/10.1016/j.cmpb.2020.105839

Source codes: https://github.com/choilab-jefferson/LungCancerScreeningRadiomics

Highlights

A novel interpretable spiculation feature is presented, computed using the area distortion metric from spherical conformal (angle-preserving) parameterization.
A simple one-step feature and prediction model is introduced which only uses our interpretable features (size, spiculation, lobulation, vessel/wall attachment) and has the added advantage of using weak-labeled training data.

Quantitative Cancer Image Analysis

Sun, 03 Nov 2019 01:17:37 -0400

Radiomics in Lung Cancer

Mon, 01 Oct 2018 14:04:27 -0400

Quantitative Image Analysis for Cancer Diagnosis and Radiation Therapy

Thu, 21 Jun 2018 01:59:26 -0400

Sep 17, 2018

May 21, 2018

Radiomics and Deep Learning for Lung Cancer Screening

Sun, 12 Nov 2017 08:12:54 -0500

KOCSEA Technical Symposium 2017, Invited Talk, KSEA Travel Grant

Robust Normal Lung CT Texture Features for the Prediction of Radiation-Induced Lung Disease

Mon, 02 Oct 2017 01:30:51 -0400

2017 ASTRO annual meeting

http://www.redjournal.org/article/S0360-3016(17)31540-7/fulltext

Aggressive Lung Adenocarcinoma Subtype Prediction Using FDG-PET/CT Radiomics

Tue, 01 Aug 2017 12:07:40 -0400

This paper has been published in the Computational and Structural Biotechnology Journal.

Preoperative 18F-FDG PET/CT and CT radiomics for identifying aggressive histopathological subtypes in early stage lung adenocarcinoma

Wookjin Choi a d1, Chia-Ju Liu b 1, Sadegh Riyahi Alam a, Jung Hun Oh a, Raj Vaghjiani c, John Humm a, Wolfgang Weber b, Prasad S. Adusumilli c, Joseph O. Deasy a, Wei Lu a

a Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA b Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA c Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA d Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, PA 19107, USA

Current Projects - Sep 13, 2016

Wed, 14 Sep 2016 10:30:00 -0400

Identification of Robust Normal Lung CT Texture Features for the Prediction of Radiation-Induced Lung Disease

Fri, 05 Aug 2016 01:29:53 -0400

2016 AAPM annual meeting

http://onlinelibrary.wiley.com/doi/10.1118/1.4955803/abstract

Image processing in lung cancer screening and treatment

Thu, 10 Mar 2016 21:11:35 -0500

Invited talk in GIST, Nov 2014

Radiomics: Novel Paradigm of Deep Learning for Clinical Decision Support toward Plan B using Liquid Biopsy (Korean)

Tue, 15 Sep 2015 20:13:29 -0400

Radiomics: Novel Paradigm of Deep Learning for Clinical Decision Support toward Plan B using Liquid Biopsy

Tue, 15 Sep 2015 20:12:38 -0400

Quantitative Image Feature Analysis of Multiphase Liver CT for Hepatocellular Carcinoma (HCC) in Radiation Therapy

Tue, 15 Sep 2015 19:58:52 -0400

2015 AAPM annual meeting