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20232026
most citedDeep-Learning-based Fast and Accurate 3D CT Deformable Image Registration in Lung Cancer

24 citations · 111 across the 24 of their papers we have counts for

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Showing 2025Show all

6 papers · 1 filter

cs.AI2025

An Automated Retrieval-Augmented Generation LLaMA-4 109B-based System for Evaluating Radiotherapy Treatment Plans

Junjie Cui, Peilong Wang, Jason Holmes +15

Purpose: To develop a retrieval-augmented generation (RAG) system powered by LLaMA-4 109B for automated, protocol-aware, and interpretable evaluation of radiotherapy treatment plan…

cs.AI2025★ 1 cited

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…

physics.med-ph2025

Diffusion Transformer-based Universal Dose Denoising for Pencil Beam Scanning Proton Therapy

Yuzhen Ding, Jason Holmes, Hongying Feng +13

Purpose: Intensity-modulated proton therapy (IMPT) offers precise tumor coverage while sparing organs at risk (OARs) in head and neck (H&N) cancer. However, its sensitivity to anat…

physics.med-ph2025

Critical review of patient outcome study in head and neck cancer radiotherapy

Jingyuan Chen, Yunze Yang, Chenbin Liu +8

Rapid technological advances in radiation therapy have significantly improved dose delivery and tumor control for head and neck cancers. However, treatment-related toxicities cause…

physics.med-ph2025

Fine-Tuning Open-Source Large Language Models to Improve Their Performance on Radiation Oncology Tasks: A Feasibility Study to Investigate Their Potential Clinical Applications in Radiation Oncology

Peilong Wang, Zhengliang Liu, Yiwei Li +12

Background: The radiation oncology clinical practice involves many steps relying on the dynamic interplay of abundant text data. Large language models have displayed remarkable cap…

physics.med-ph2025★ 4 cited

Evaluating The Performance of Using Large Language Models to Automate Summarization of CT Simulation Orders in Radiation Oncology

Meiyun Cao, Shaw Hu, Jason Sharp +10

Purpose: This study aims to use a large language model (LLM) to automate the generation of summaries from the CT simulation orders and evaluate its performance. Materials and Metho…