Image on Qualia Radiomics

Team Quantum Heart Wins NIH Prize for Innovation

Wed, 20 Aug 2025 17:41:41 -0400

https://datascience.nih.gov/tools-and-analytics/quantum-computing-new-frontiers-biomedical-research-innovation-lab

Last December, I had the incredible opportunity to be part of something truly special. The NIH Office of Data Science Strategy (ODSS) and the National Cancer Institute (NCI) gathered 27 of us from wildly different fields for a five-day Innovation Lab. The goal? To answer a question that sounds like science fiction: How can quantum computing solve today’s most complex biomedical challenges?

The room buzzed with a vibrant mix of quantum physicists, computer scientists (both quantum and traditional computing), computational physicists, computational biologists, data scientists, and biomedical researchers. For five intense days, we were immersed in a whirlwind of collaboration, brainstorming, and problem-solving. The energy was electric as we united to bridge the gap between our disciplines and forge new paths for the future of medicine.

AI-Powered Auto-Segmentation in Liver Cancer Therapy

Tue, 08 Apr 2025 11:48:42 -0400

We’re excited to share our latest work published in Technology in Cancer Research & Treatment: “Deep Learning-Based Auto-Segmentation for Liver Yttrium-90 Selective Internal Radiation Therapy” — a collaboration between Jun Li, Rani Anne, and myself.

This study introduces a deep learning (DL) model built on the 3D U-Net architecture, developed to automatically segment the liver in CT scans for patients undergoing Y-90 Selective Internal Radiation Therapy (SIRT). Accurate liver segmentation is a critical step for calculating Y-90 dosage, traditionally done manually — a time-consuming and subjective process.

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

Deep Learning Segmentation for Accurate GTV and OAR Segmentation in MR-Guided Adaptive Radiotherapy for Pancreatic Cancer Patients

Sat, 07 Oct 2023 17:35:36 -0400

AAPM 2023, ASTRO 2023

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

PathCNN: interpretable convolutional neural networks for survival prediction and pathway analysis applied to glioblastoma

Thu, 22 Jul 2021 12:57:18 -0400

Jung Hun Oh, Wookjin Choi, Euiseong Ko, Mingon Kang, Allen Tannenbaum, Joseph O Deasy

The authors wish it to be known that, in their opinion, Jung Hun Oh and Wookjin Choi should be regarded as Joint First Authors.

https://academic.oup.com/bioinformatics/article/37/Supplement_1/i443/6319702

https://github.com/mskspi/PathCNN/raw/main/img/pathcnn.png

An illustration of biological interpretation. (A) Grad-CAM procedure to generate class activation maps. The two images on the left bottom represent an example of the class activation maps for a sample in the cohort, which were generated from Grad-CAM procedure; (B) statistical analysis to identify significantly different pathways between the LTS and non-LTS groups. LTS, long-term survival; CNN, convolutional neural network; ReLU, rectified linear unit

Radiomics in Lung Cancer

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

Interpretable Spiculation Quantification for Lung Cancer Screening

Tue, 11 Sep 2018 14:02:03 -0400

UKC2018 Aug 4, 2018

MSKCC Postdoctoral Research Symposium Sep 28, 2018

https://twitter.com/arxiv_org/status/1034746650089021445

Presented at MICCAI ShapeMI Workshop https://shapemi.github.io/program/

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

Individually Optimized Contrast-Enhanced 4D-CT for Radiotherapy Simulation in Pancreatic Adenocarcinoma

Wed, 21 Sep 2016 09:30:00 -0400

2016 ASTRO annual meeting This poster has been selected for the ARRO poster walk (6 out of 250 physics posters).

http://onlinelibrary.wiley.com/doi/10.1118/1.4963213/full

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

Individually Optimized Contrast-Enhanced 4D-CT for Radiotherapy Simulation in Pancreatic Ductal Adenocarcinoma

Fri, 05 Aug 2016 01:26:35 -0400

2016 AAPM annual meeting

http://scitation.aip.org/content/aapm/journal/medphys/43/6/10.1118/1.4958261

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

Image quality assessment of contrast-enhanced 4D-CT for pancreatic adenocarcinoma in radiotherapy simulation

Tue, 15 Sep 2015 19:57:49 -0400

2015 AAPM annual meeting

Computer Aided Detection of Pulmonary Nodules in CT Scans

Fri, 03 Oct 2014 00:43:00 -0400

Lung structure segmentation and nodule detection based on 3D block analysis in CT image (Korean)

Thu, 02 Oct 2014 01:38:33 -0400

Lung Volume Segmentation using Graph-Cut (Korean)

Thu, 02 Oct 2014 01:36:49 -0400

Computer-aided Detection of Pulmonary Nodules using Genetic Programming (Korean)

Thu, 02 Oct 2014 01:22:27 -0400

Computer aided detection of pulmonary nodules using genetic programming

Thu, 02 Oct 2014 01:16:49 -0400

Computer-aided Detection of Pulmonary Nodules using Genetic Programming

Thu, 02 Oct 2014 01:15:12 -0400

2010 IEEE ICIP

Automatic detection of pulmonary nodules in lung CT images

Thu, 02 Oct 2014 00:53:56 -0400

Previous Works

Thu, 02 Oct 2014 00:21:19 -0400

Automatic Pulmonary Nodule Detection

https://www.youtube.com/watch?v=1rbdBf_-USo

Three dimensional shape reconstruction from auto-focused microscopic image