4 papers
The Cost of Binarizing Survival Outcomes in Clinical Prognostic Modeling
Shashank Yadav, David M. Routman, Andrew Y. K. Foong
Survival analysis is an established framework for analyzing time-to-event data, yet many clinical machine learning studies still binarize the outcome before model training. This pr…
A Two-Stage Framework for Fast Proton Spot Map Generation in Pencil Beam Scanning Prostate SBRT Planning
Xueyan Tang, Hok Wan Chan Tseung, Mark Pepin +6
Background: In pencil beam scanning (PBS) proton therapy, plans are delivered as proton spot maps (PSMs). Although deep learning can rapidly predict 3D dose, direct conversion of d…
The Daily Dose: Workflow-Integrated Large Language Model Automation for Clinical Summarization and Trial Identification in Radiation Oncology
Jason Holmes, Federico Mastroleo, Mariana Borras-Osorio +17
Objective: To describe the design and early clinical evaluation of The Daily Dose (TDD), an LLM-driven, automated clinical summarization and clinical-trial identification system in…
RadOnc-GPT: An Autonomous LLM Agent for Real-Time Patient Outcomes Labeling at Scale
Jason Holmes, Yuexing Hao, Mariana Borras-Osorio +22
Manual labeling limits the scale, accuracy, and timeliness of patient outcomes research in radiation oncology. We present RadOnc-GPT, an autonomous large language model (LLM)-based…