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人工智能衍生过渡区PSA密度作为一个选工具,以减少不必要的前列腺系统活检在MRI阴性男性
Jiaheng Shang1,2,3, Jingyun Wu4, Ruiyi Deng1,2,3
1Department of Urology, Peking University First Hospital, Beijing, China.
Insights into imaging
|February 10, 2026
概括
过渡区PSA密度 (TZ-PSAD) 在检测有临床意义的前列腺癌 (csPCa) 时比常规PSA密度 (PSAD) 更有效. 这种人工智能驱动的指标有助于优化活检决策,减少不必要的程序.
科学领域:
- 泌尿病学和瘤学 泌尿和瘤学
- 医学成像和诊断 医学成像和诊断
- 医疗保健中的人工智能
背景情况:
- 准确检测临床显著的前列腺癌 (csPCa) 对于患者的管理至关重要.
- 检查前MRI扫描结果呈阴性的患者,对于识别cspca来说是一个诊断挑战.
- 传统的PSA密度 (PSAD) 在预测该队列中的csPCa方面存在局限.
研究的目的:
- 为了评估过渡区PSA密度 (TZ-PSAD) 与常规PSAD用于csPCa检测的预测性能.
- 评估人工智能驱动的前列腺区域细分对计算TZ-PSAD的有用性.
- 确定TZ-PSAD是否可以改善MRI阴性患者的风险分层并减少不必要的前列腺活检.
主要方法:
- 一组606名患有阴性预活检MRI发现的患者接受了通过直肠超声波引导的系统活检.
- 人工智能 (AI) 软件用于自动前列腺和区域体积测量.
- 计算了TZ-PSAD和PSAD,并使用ROC分析比较了它们的诊断性能;多变量分析确定了csPCa的预测因素.
主要成果:
- 与PSAD (AUC=0.686) 相比,TZ-PSAD在检测csPCa方面显示出明显优异的区分能力 (AUC=0.718).
- 0.35 ng/mL/cc的最佳TZ-PSAD值确定了一个高风险组 (20.1%的csPCa检测率),超过了0.2 ng/mL/cc的PSAD值.
- 多变量分析证实TZ-PSAD是成像不可见的csPCa的强有力的独立预测器.
结论:
- 使用人工智能驱动的细分计算的TZ-PSAD在预测阴性MRI男性的csPCa方面明显优于传统的PSAD.
- TZ-PSAD作为一种有效的分类工具,可以更好地分层风险,并可能减少不必要的活检.
- 这种人工智能辅助的方法优化了前列腺癌诊断和在具有挑战性的病例患者管理.
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