一站式平台实现放疗MRI扩散成像的自动处理与可追溯解读。
An integrated diffusion-weighted imaging processing and interpretation platform for MR-guided radiotherapy
- 用深度学习统一处理畸变、降噪及扩散参数拟合。
- 专家评分平均4.65，93%评价达4分以上，临床实用性强。
- 生成结论可溯源至文献章节，适合放疗医生快速决策。
磁共振引导直线加速器（MR-Linac）可在每次放疗中获取扩散加权成像（DWI），但低信噪比数据转化为临床决策需可靠定量处理与整合矛盾文献的解读。本文介绍并评估一个集成式网络平台，从原始MR-Linac DWI数据出发，经深度学习处理（畸变校正、去噪、IVIM/ADC拟合）后，通过纵向病灶分析和检索增强生成（RAG）解释模块，实现结构化、文献支持的临床解读。该模块基于双层知识库（结构化文献目录+全文行索引），可将每个结论回溯至具体文献、段落与行范围，并调用确定性工具完成计算。两名独立专家对九例胶质母细胞瘤患者的报告进行评分（1-5分），涵盖临床推理合理性、文献引用质量与整体临床价值三方面。54项评分中，平均分为4.65±0.80，93% ≥4分；三项指标均值分别为4.6、4.5、4.8，85%的评分对一致。结果表明，该平台能整合放疗MRI-DWI后处理与可追溯的专家级解读，同时揭示大模型推理在放疗领域应用中的验证必要性。
原文摘要 · Abstract (English)
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.
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