In December, I participated in an inspiring Innovation Lab hosted by the NIH and NCI, uniting diverse experts to explore how quantum computing can tackle complex biomedical challenges. My team, Quantum Heart, won a $25,000 prize for our innovative project. This experience fostered optimism and strong connections for future breakthroughs in medicine.
Tag Archives: CT
AI-Powered Auto-Segmentation in Liver Cancer Therapy
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 liverContinue reading “AI-Powered Auto-Segmentation in Liver Cancer Therapy”
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”
2023 Accepted/Invited Annual Meeting abstracts
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”
Artificial Intelligence in Radiation Oncology
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/
Qualia Radiomics 