发表机构
Weill Cornell Medicine; University of Western Australia; Indiana University; Regenstrief Institute; Yale University; University of California, Los Angeles（威尔康乃尔医学院; 西澳大利亚大学; 印第安纳大学; 瑞根斯特里夫研究所; 耶鲁大学; 加州大学洛杉矶分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对自动生成胸部CT报告的挑战，提出MonteRET框架，它整合多种特征，通过知识检索和报告重写代理完善报告。经训练和评估，该框架在多方面提升了报告质量，相比其他方法有显著优势，获放射科住院医师青睐。
AI 中文摘要
自动生成胸部CT报告具有挑战性，因为临床忠实报告需要对整个容积的理解和对局部解剖学发现的准确描述。我们开发并回顾性评估了MonteRET，这是一个用于生成胸部CT发现部分的区域感知检索增强框架。它整合了全局CT特征与区域级解剖学表示，利用预测的医疗状况和区域级视觉语言对齐检索临床相关知识，并通过知识引导的报告重写代理完善初始报告。我们在来自RadGenome-ChestCT的24,128次CT扫描的公共队列上训练模型，并在1,564次CT扫描的公共RadGenome-ChestCT测试集和来自纽约长老会/威尔康奈尔医学中心的82次CT扫描的外部队列上进行评估。与匹配的基线和几种先进方法相比，MonteRET提高了报告质量、语义相似度和临床疗效，召回率提升显著，放射科住院医师的人工专家评估也更青睐MonteRET。
英文摘要
Automated chest CT report generation remains challenging because clinically faithful reporting requires both whole-volume understanding and accurate description of localized anatomical findings. Here we developed and retrospectively evaluated MonteRET, a region-aware retrieval-enhanced framework for generating chest CT findings sections. MonteRET integrates global CT features with region-level anatomical representations, retrieves clinically relevant knowledge using predicted medical conditions and region-level vision-language alignment, and refines initial reports through a knowledge-guided report rewriting agent. We trained our model on a public cohort with 24,128 CT scans from RadGenome-ChestCT. We evaluated MonteRET on the public RadGenome-ChestCT test set of 1,564 CT scans and an external cohort of 82 CT scans from NewYork-Presbyterian/Weill Cornell Medical Center. MonteRET improved report quality, semantic similarity, and clinical efficacy compared with a matched baseline and several state-of-the-art methods. Gains were most pronounced for recall, suggesting fewer omitted findings. Human expert evaluation by radiology residents also favored MonteRET.
