aceA Large Language Model Framework for Seed Implant Brachytherapy: Adaptive Weight Optimization via Retrieval-Augmented Generation
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 recom…
In a retrospective check on 23 head-and-neck patients, LLM-guided weight tuning kept tumor coverage while cutting excessive target dose compared with fixed-weight planning.
Validation was retrospective on 23 cases, and organ-at-risk contours were available for only eight patients, where results were only favorable numerical trends; the authors position the tool as assistive under clinician supervision.
Evidence
- Peer-reviewedAnnals of the New York Academy of Sciences2026-11-01
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Truvace Impact Record TRV-2026-2324, v1: “A Large Language Model Framework for Seed Implant Brachytherapy: Adaptive Weight Optimization via Retrieval-Augmented Generation.” Truvace, 2026-10-31. /record/TRV-2026-2324 (accessed at citation time). sha256 a845872635cec620…
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