Pulmonary on Qualia Radiomics

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

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

Artificial Intelligence in Radiation Oncology

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

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/

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

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

Computer Aided Detection of Pulmonary Nodules in CT Scans

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

Pulmonary Nodule Detection using Voxel Classification in Lung CT images (Korean)

Thu, 02 Oct 2014 01:41:10 -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

Image Analysis and Nodule Detection System in 3D Lung CT Images using Insight toolkit (Korean)

Thu, 02 Oct 2014 01:00:15 -0400

Invited talk in CNUH, Apr 2014

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