人工智能辅助读取非对比前列腺MRI:在PROSA试验中查人群中的应用和与专家解释的一致性
Emanuele Messina1, Antonella Borrelli1, Francesca Mezzapesa1
1Department of Radiological Sciences, Oncology and Pathology, Sapienza University/Policlinico Umberto I, Rome, Italy.
European radiology
|February 13, 2026
概括
人工智能 (AI) 显著提高了经验较少的放射科医生在前列腺癌 (PCa) 查中使用双参数MRI (bpMRI) 的诊断性能. 人工智能协助提高了一致性和准确性,有助于早期检测临床意义上的PCa.
科学领域:
- 放射学 放射学是一门学科.
- 医疗成像医学成像
- 医疗保健中的人工智能
背景情况:
- 双参数MRI (bpMRI) 是临床显著前列腺癌 (csPCa) 查的一个有前途的工具.
- 人工智能 (AI) 显示出在支持放射科医生的潜力,特别是那些经验较少的放射科医生.
研究的目的:
- 评估人工智能软件在支持经验较少的放射科医生使用bpMRI进行csPCa查方面的有效性.
- 评估AI对诊断性能和前列腺MRI解释一致性的影响.
主要方法:
- 来自PROSA试验的499个bpMRI扫描的回顾性分析 (759名男性有资格进行PCa查).
- 由专家放射科医生,经验较少的读者,单独使用人工智能软件和经验较少的读者在人工智能支持下进行独立评估.
- 通过ROC曲线和读者间协议 (Cohen's kappa) 评估诊断性能,以专家解释为参考标准.
主要成果:
- 人工智能辅助的经验较少的阅读者实现了最高的诊断性能 (AUC 0.969),超过了单独使用人工智能和无助阅读者.
- 人工智能辅助显著改善了读者之间的协议 (κ从0.64到0.84),并减少了模两可的PI-RADS 3病例.
- 人工智能支持增强了与专家解释的准确一致,从32.5%增加到54.5%,并减少了诊断不一致.
结论:
- 人工智能有效地支持经验较少的放射科医生在bpMRI解释中进行csPCa查,提高诊断性能和一致性.
- 将人工智能集成到放射学工作流程中可以减轻报告负担,优先考虑可疑病例,提高查效率.
- 人工智能协助为大量PCa选设置提供了关键优势,促进了公平的早期检测.
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