发表机构
University of Texas Southwestern Medical Center; University of California San Francisco（德克萨斯大学西南医学中心; 加利福尼亚大学旧金山分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究开发了一体化网络平台，整合MR-Linac的DWI后处理与可追溯的RAG临床解释，经专家评分验证其在胶质母细胞瘤病例中具有高临床实用性。
AI 中文摘要
背景：磁共振引导直线加速器（MR-Linacs）可在每次治疗分次时采集扩散加权成像（DWI），但将这些低信噪比采集数据转化为临床决策，既需要可靠的定量处理，也需要协调零散且常相互矛盾的文献的解释。目的：描述并评估一个一体化的基于网络的平台，该平台可将原始MR-Linac DWI转化为基于结构化文献的临床解释，并通过独立专家评分评估其检索增强生成（RAG）解释模块。方法：该平台将深度学习处理流程（包含失真校正、去噪及体素内不相干运动（IVIM）/表观扩散系数（ADC）拟合）与纵向感兴趣区分析、RAG解释智能体相结合。该智能体在两层知识基础上推理：经整理的出版物（结构化目录索引加按行索引的全文），将算术运算委托给确定性工具，且设计为将每个陈述追溯至源文献、章节及行范围。一名医学物理师和一名医师对该智能体针对9例纵向胶质母细胞瘤病例的报告，在三个指标上按1-5分制独立评分：临床推理合理性、文献引用质量及整体临床实用性。结果：在54项评分中，合并均值为4.65±0.80，93%的评分≥4；各指标均值分别为4.6（推理）、4.5（引用）、4.8（实用性），且85%的配对评分在1分范围内一致。结论：单一平台可将MR-Linac DWI后处理与可追溯、经专家评估的临床解释相结合，同时强调验证放射肿瘤学中大型语言模型（LLM）生成的推理所需的保障措施。
英文摘要
Background: Magnetic resonance imaging-guided linear accelerators (MR-Linacs) allow diffusion-weighted imaging (DWI) to be acquired at every treatment fraction, but converting these low-signal-to-noise-ratio acquisitions into clinical decisions requires both reliable quantitative processing and an interpretation that reconciles a scattered and often contradictory literature. Purpose: To describe and evaluate an integrated, web-based platform that carries raw MR-Linac DWI to a structured, literature-grounded clinical interpretation, and to assess its retrieval-augmented generation (RAG) interpretation module by independent expert rating. Methods: The platform couples a deep-learning processing pipeline, comprising distortion correction, denoising, and intravoxel incoherent motion (IVIM)/apparent diffusion coefficient (ADC) fitting, with longitudinal region-of-interest analysis and a RAG interpretation agent. The agent reasons over a two-layer knowledge base of curated publications (a structured catalog index plus line-indexed full text), delegates arithmetic to deterministic tools, and is designed to trace each statement to a source document, section, and line range. One medical physicist and one physician independently rated the agent's reports for nine longitudinal glioblastoma cases on a 1-5 scale across three metrics: clinical-reasoning soundness, literature-citation quality, and overall clinical utility. Results: Across 54 ratings, the pooled mean was 4.65 +/- 0.80, with 93% of ratings >= 4; metric means were 4.6 (reasoning), 4.5 (citation), and 4.8 (utility), and raters agreed within one point on 85% of paired ratings. Conclusions: A single platform can integrate MR-Linac DWI post-processing with traceable, expert-evaluated clinical interpretation, while highlighting the safeguards needed to verify LLM-generated reasoning in radiation oncology.
Comments29 pages
