Tag Archives: Cancer

Team Quantum Heart Wins NIH Prize for Innovation

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.

Empowering Cancer Care with AI: A Jefferson Medical Student–Led Innovation

I’m excited to share a new collaborative study I had the privilege of co-authoring, which was recently published in Nutrients. Led by Jefferson medical student Julia Logan, this work explores how large language models (LLMs) like ChatGPT and Gemini can deliver accessible, culturally sensitive dietary advice to cancer patients—many of whom lack access to professionalContinue reading “Empowering Cancer Care with AI: A Jefferson Medical Student–Led Innovation”

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”

Deep Learning Segmentation for Accurate GTV and OAR Segmentation in MR-Guided Adaptive Radiotherapy for Pancreatic Cancer Patients

This content does not provide sufficient information to create a concise summary. It mentions AAPM 2023 and ASTRO 2023, which appear to be future events, and provides links to slideshare presentations, but no detailed information or context is given.

Novel Functional Radiomics for Predicting Cardiotoxicity in Lung Cancer Radiotherapy using Cardiac FDG-PET Uptake

The provided content discusses functional Delta-Radiomics and its application in predicting overall survival, showcased through a Slideshare presentation. Additionally, there is mention of a study on the classification of cardiac uptake patterns, also presented on Slideshare, using functional Radiomics.

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”