用3929对真实数据训练，自回归生成3D肝脏MRI结构化报告。
MRI2Rep: Autoregressive Structured Report Generation for 3D Liver MRI
- 基于自回归框架，将自由文本报告转为结构化诊断序列。
- 病例级敏感度76.0%，病灶级F1达29.4%，显著优于基线模型。
- 首次实现端到端的3D肝脏MRI LI-RADS结构化报告生成，适合临床辅助。
3D MRI报告的手动撰写耗时费力，而由于体积复杂性和配对数据稀缺，针对3D肝脏MRI的端到端结构化报告生成仍研究不足。我们提出MRI2Rep，一种自回归框架用于肝脏MRI报告生成。基于10年单机构采集的3,929对真实世界MRI-报告数据，报告到标签归一化（RLC）模块将自由文本报告转换为无病变标注的结构化、闭词汇诊断序列。在保留测试集上，MRI2Rep实现76.0%病例级敏感度、29.4%病灶级F1，远超适配的医学视觉语言基线（最高8.3%），肝水平准确率达82.4%。盲法阅片研究中，两名放射科医生分别评定75%和70%的AI生成报告为临床可接受，原报告分别为95%和100%。基于大模型的自动评估器LLM-Eval以更严格标准评定61.8%的报告可接受，支持其作为保守代理使用。据我们所知，这是首个针对3D肝脏MRI的端到端LI-RADS结构化报告系统。
原文摘要 · Abstract (English)
Manual reporting of 3D MRI studies is time-consuming, yet end-to-end structured report generation for 3D liver MRI remains underexplored due to volumetric complexity and scarce paired data. We propose MRI2Rep, an autoregressive framework for liver MRI report generation. From 3,929 real-world MRI-report pairs acquired over a 10-year single-institution cohort, a Report-to-Label Canonicalization (RLC) module converts free-text reports into structured, closed-vocabulary diagnostic sequences without lesion-level annotations. On a held-out test set, MRI2Rep achieves 76.0% case-level sensitivity, 29.4% lesion-level F1, compared with no more than 8.3% for adapted medical vision-language baselines, and 82.4% liver-level accuracy. In a blinded reader study, two radiologists rated 75% and 70% of AI-generated reports as clinically acceptable, compared with 95% and 100% for original reports. Our automated LLM-based judge, LLM-Eval, rated 61.8% of AI-generated reports as acceptable, applying a stricter standard and supporting its use as a conservative proxy. To our knowledge, this is the first end-to-end LI-RADS-structured reporting system for 3D liver MRI.
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