Clinically-Interpretable Radiomics

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. PreviousContinue reading “Clinically-Interpretable Radiomics”

Lung Cancer Screening Radiomics

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,Continue reading “Lung Cancer Screening Radiomics”

Reproducible and Interpretable Spiculation Quantification for Lung Cancer Screening

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. AContinue reading “Reproducible and Interpretable Spiculation Quantification for Lung Cancer Screening”

Interpretable Spiculation Quantification for Lung Cancer Screening

UKC2018 Aug 4, 2018 MSKCC Postdoctoral Research Symposium Sep 28, 2018 Interpretable Spiculation Quantification for Lung Cancer Screening. https://t.co/QucUlu2QVE pic.twitter.com/FaOukTeIPJ — arxiv (@arxiv_org) August 29, 2018 Presented at MICCAI ShapeMI Workshop https://shapemi.github.io/program/  

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

2017 AAPM annual meeting http://www.aapm.org/meetings/2017AM/PRAbs.asp?mid=127&aid=37917  

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

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