2025/09/10 by Zhuo Xiao, Xiao, Zhuo, Qinglong Yao +18 · 1 voice
Computer Science · Medicine · Physics and Astronomy · #Advanced Radiotherapy Techniques #Artificial Intelligence (cs.AI) #Artificial Intelligence in Healthcare and Education #FOS: Computer and information sciences #FOS: Physical sciences #Medical Physics (physics.med-ph) #Radiomics and Machine Learning in Medical Imaging #cs.AI #physics.med-ph
paper · pdf · doi:10.48550/arxiv.2509.08407
openalex publication_date 2025/09/10 · arxiv published 2025/09/10 · arxiv updated 2025/09/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Seed implant brachytherapy (SIBT) is an effective cancer treatment modality; however, clinical planning often relies on manual adjustment of objective function weights, leading to inefficiencies and suboptimal results. This study proposes an adaptive weight optimization framework for SIBT planning, driven by large language models (LLMs). A locally deployed DeepSeek-R1 LLM is integrated with an automatic planning algorithm in an iterative loop. Starting with fixed weights, the LLM evaluates plan quality and recommends new weights in the next iteration. This process continues until convergence criteria are met, after which the LLM conducts a comprehensive evaluation to identify the optimal plan. A clinical knowledge base, constructed and queried via retrieval-augmented generation (RAG), enhances the model's domain-specific reasoning. The proposed method was validated on 23 patient cases, showing that the LLM-assisted approach produces plans that are comparable to or exceeding clinically approved and fixed-weight plans, in terms of dose homogeneity for the clinical target volume (CTV) and sparing of organs at risk (OARs). The study demonstrates the potential use of LLMs in SIBT planning automation.