Deep-Learning on Qualia Radiomics

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.

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

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.

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.

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

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

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