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在双参数MRI上检测临床显著前列腺癌的综合临床和放射学变量全面的评分系统:与多参数MRI的多中心比较:多参数MRI的多中心比较

Liqin Yang¹, Zhenwei Ding², Xu Wang³

¹Hangzhou Traditional Chinese Medicine Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, China.

Abdominal radiology (New York)

|June 19, 2025

概括

结合临床和放射学数据的新评分系统在活检前准确预测临床上显著的前列腺癌 (csPCa) 风险. 这种工具提高了积极的预测价值和放射科医生的同意,帮助临床决策.

科学领域:

泌尿器科泌尿器科泌尿器科泌尿器科
放射学放射学是一门学科.
在瘤学瘤学.

背景情况:

在活检之前准确预测临床显著的前列腺癌 (csPCa) 对患者管理至关重要.
像前列腺成像报告和数据系统 (PI-RADS) 这样的现有方法在预测准确性和读者间协议方面存在局限性.
需要一个有效的评分系统,整合不同的临床和放射学标记.

研究的目的:

开发和验证一种新的评分系统,用于在活检之前预测csPCa的可能性.
根据开发的评分系统,将患者分为不同的风险类别.
将新系统的性能与现有的放射性评估进行比较.

主要方法:

788名患者的回顾性分析,分为衍生和验证队列.
评估成像变量,包括PI-RADS v2.1,简化的PI-RADS (S-PI-RADS),前列腺体积 (PV),损伤体积 (LV) 和直径比 (LD/SD).
将临床数据 (%fPSA) 和衍生变量 (aPSA = tPSA × (LV/PV)) 集成到多变量回归模型中,以创建分数系统.

主要成果:

开发的评分系统,整合了%fPSA,S-PI-RADS,aPSA和LD/SD,显示出强大的预测性能 (AUC:0.891-0.897),与PI-RADS相比.
与PI-RADS和S-PI-RADS相比,评分系统在所有队列中实现了优异的正预测值 (PPV).
与PI-RADS (κ=0.643) 相比,该系统显示出强大的读者间一致性 (κ=0.808),并将风险分为四个类别 (3.8%至90.3%的观察到风险).

结论:

新的评分系统有效地预测了csPCa的可能性,并以高准确度分层风险.
该系统的诊断性能与PI-RADS相当,但PPV和读者间可靠性得到了改进.
这种评分系统在前列腺癌诊断中具有实践临床应用的巨大潜力.

关键词:

临床上显著的前列腺癌.前列腺成像报告和数据系统评分系统评分系统这是一个简化的PI-RADS.

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