标题： RadGPT：构建三维图像文本肿瘤数据集 显示英文标题

标题： RadGPT: Constructing 3D Image-Text Tumor Datasets

摘要： 每年在美国进行的CT扫描超过8500万次，为放射科医生创建与肿瘤相关的报告是一项具有挑战性和耗时的任务。 为了解决这一需求，我们提出了RadGPT，这是一种解剖感知的视觉-语言AI代理，用于从CT扫描生成详细报告。 RadGPT首先分割肿瘤，包括良性囊肿和恶性肿瘤及其周围的解剖结构，然后将这些信息转换为结构化报告和叙述性报告。 这些报告提供了肿瘤大小、形状、位置、衰减、体积以及与周围血管和器官的相互作用。 在未见过的医院进行的广泛评估表明，RadGPT可以生成准确的报告，对小肿瘤（<2厘米）检测的灵敏度/特异性分别为：肝脏肿瘤80/73%，肾脏肿瘤92/78%，胰腺肿瘤77/77%。 对于大肿瘤，灵敏度范围从89%到97%。 结果显著超过了腹部CT报告生成的最先进水平。 RadGPT为17个公共数据集生成了报告。 通过放射科医生的审查和修改，我们确保了报告的准确性，并创建了第一个公开可用的图像-文本3D医学数据集，包含来自9,262次CT扫描的180万条文本标记和270万张图像，其中包括2,947次肿瘤扫描/报告的8,562次肿瘤实例。 我们的报告可以：（1）在八个肝叶子段和三个胰腺子段中定位肿瘤，并逐体素标注；（2）在260份报告中确定胰腺肿瘤阶段（T1-T4）；以及（3）对多个肿瘤进行单独分析——这在人工报告中很少见。 重要的是，948份报告是针对早期肿瘤的。 显示英文摘要

摘要： With over 85 million CT scans performed annually in the United States, creating tumor-related reports is a challenging and time-consuming task for radiologists. To address this need, we present RadGPT, an Anatomy-Aware Vision-Language AI Agent for generating detailed reports from CT scans. RadGPT first segments tumors, including benign cysts and malignant tumors, and their surrounding anatomical structures, then transforms this information into both structured reports and narrative reports. These reports provide tumor size, shape, location, attenuation, volume, and interactions with surrounding blood vessels and organs. Extensive evaluation on unseen hospitals shows that RadGPT can produce accurate reports, with high sensitivity/specificity for small tumor (<2 cm) detection: 80/73% for liver tumors, 92/78% for kidney tumors, and 77/77% for pancreatic tumors. For large tumors, sensitivity ranges from 89% to 97%. The results significantly surpass the state-of-the-art in abdominal CT report generation. RadGPT generated reports for 17 public datasets. Through radiologist review and refinement, we have ensured the reports' accuracy, and created the first publicly available image-text 3D medical dataset, comprising over 1.8 million text tokens and 2.7 million images from 9,262 CT scans, including 2,947 tumor scans/reports of 8,562 tumor instances. Our reports can: (1) localize tumors in eight liver sub-segments and three pancreatic sub-segments annotated per-voxel; (2) determine pancreatic tumor stage (T1-T4) in 260 reports; and (3) present individual analyses of multiple tumors--rare in human-made reports. Importantly, 948 of the reports are for early-stage tumors.