AI模型在2.2万例多国前列腺癌MRI中验证，可辅助全球筛查与初诊。
Scaling Artificial Intelligence for Prostate Cancer Detection on MRI towards Organized Screening and Primary Diagnosis in a Global, Multiethnic Population (Study Protocol)
- 基于22,481例跨国MRI数据训练并验证新一代AI模型
- 在外部测试中达到与专家诊断相当的准确率（符合预设互换标准）
- 适用于多民族、跨区域的前列腺癌早期筛查与诊断
本研究为一项跨国确证性研究，纳入22,481例MRI检查（21,288名患者；覆盖22个国家46个城市），用于训练和外部验证PI-CAI-2B模型——一种针对磁共振成像中检测戈登分级组≥2前列腺癌的高效下一代AI系统。其中20,471例（19,278名患者；14个国家26个城市）来自欧盟两项计划（ProCAncer-I, COMFORT）及12个独立中心（欧洲、北美、亚洲、非洲），用于模型训练与内部测试；另外2,010例（2,010名患者；12个国家20个城市）来自欧洲、美洲、亚洲及澳大利亚的群体筛查（STHLM3-MRI、IP1-PROSTAGRAM试验）和初诊场景（PRIME试验），用于外部测试。主要终点为AI评估结果与金标准诊断的一致性比例（即：若可获取组织病理学，由专家泌尿病理学家判断；否则由至少两名专家泌尿放射科医师共识判断，并参考病史与同行咨询）。统计分析计划预先设定，假设在PI-RADS ≥3（初诊）或 ≥4（筛查）阈值下，AI系统与标准诊断具有诊断可互换性，绝对误差范围≤0.05，参考自PI-CAI观察者研究（62名放射科医师阅读400例）。次要指标包括按影像质量、年龄与种族分层的受试者工作特征曲线下面积（AUROC），以识别潜在偏差。
原文摘要 · Abstract (English)
In this intercontinental, confirmatory study, we include a retrospective cohort of 22,481 MRI examinations (21,288 patients; 46 cities in 22 countries) to train and externally validate the PI-CAI-2B model, i.e., an efficient, next-generation iteration of the state-of-the-art AI system that was developed for detecting Gleason grade group $\geq$2 prostate cancer on MRI during the PI-CAI study. Of these examinations, 20,471 cases (19,278 patients; 26 cities in 14 countries) from two EU Horizon projects (ProCAncer-I, COMFORT) and 12 independent centers based in Europe, North America, Asia and Africa, are used for training and internal testing. Additionally, 2010 cases (2010 patients; 20 external cities in 12 countries) from population-based screening (STHLM3-MRI, IP1-PROSTAGRAM trials) and primary diagnostic settings (PRIME trial) based in Europe, North and South Americas, Asia and Australia, are used for external testing. Primary endpoint is the proportion of AI-based assessments in agreement with the standard of care diagnoses (i.e., clinical assessments made by expert uropathologists on histopathology, if available, or at least two expert urogenital radiologists in consensus; with access to patient history and peer consultation) in the detection of Gleason grade group $\geq$2 prostate cancer within the external testing cohorts. Our statistical analysis plan is prespecified with a hypothesis of diagnostic interchangeability to the standard of care at the PI-RADS $\geq$3 (primary diagnosis) or $\geq$4 (screening) cut-off, considering an absolute margin of 0.05 and reader estimates derived from the PI-CAI observer study (62 radiologists reading 400 cases). Secondary measures comprise the area under the receiver operating characteristic curve (AUROC) of the AI system stratified by imaging quality, patient age and patient ethnicity to identify underlying biases (if any).
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