Tag Archives: CT

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

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,Continue reading “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”

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

Jefferson Whole Lung CBCT radiomics

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 Abstract Spiculations are important predictors of lung cancer malignancy, which are spikes on the surface of the pulmonary nodules.Continue 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/