Guo Shikuan, Ren Jing, Meng Qingze, Zhang Boyuan, Jiao Jianhua, Han Donghui, Wu Peng, Ma Shuaijun, Zhang Jing, Xing Nianzeng, Qin Weijun, Kang Fei, Zhang Jingliang
Department of Urology, Xijing Hospital, Fourth Military Medical University, No.127, Changle West Road, Xincheng District, Xi'an, Shaanxi, 710032, China.
Department of Urology, No.988 Hospital of Joint Logistic Support Force, Zhengzhou, Henan, 450042, China.
Eur J Nucl Med Mol Imaging. 2025 Jan;52(2):756-765. doi: 10.1007/s00259-024-06916-2. Epub 2024 Sep 12.
An MRI-based risk calculator (RC) has been recommended for diagnosing clinically significant prostate cancer (csPCa). PSMA PET/CT can detect lesions that are not visible on MRI, and the addition of PSMA PET/CT to MRI may improve diagnostic performance. The aim of this study was to incorporate the PRIMARY score or SUVmax derived from [Ga]Ga-PSMA-11 PET/CT into the RC and compare these models with MRI-based RC to assess whether this can further reduce unnecessary biopsies.
A total of 683 consecutive biopsy-naïve men who underwent both [Ga]Ga-PSMA-11 PET/CT and MRI before biopsy were temporally divided into a development cohort (n = 552) and a temporal validation cohort (n = 131). Three logistic regression RCs were developed and compared: MRI-RC, MRI-SUVmax-RC and MRI-PRIMARY-RC. Discrimination, calibration, and clinical utility were evaluated. The primary outcome was the clinical utility of the risk calculators for detecting csPCa and reducing the number of negative biopsies.
The prevalence of csPCa was 47.5% (262/552) in the development cohort and 41.9% (55/131) in the temporal validation cohort. In the development cohort, the AUC of MRI-PRIMARY-RC was significantly higher than that of MRI-RC (0.924 vs. 0.868, p < 0.001) and MRI-SUVmax-RC (0.924 vs. 0.904, p = 0.002). In the temporal validation cohort, MRI-PRIMARY-RC also showed the best discriminative ability with an AUC of 0.921 (95% CI: 0.873-0.969). Bootstrapped calibration curves revealed that the model fit was acceptable. MRI-PRIMARY-RC exhibited near-perfect calibration within the range of 0-40%. DCA showed that MRI-PRIMARY-RC had the greatest net benefit for detecting csPCa compared with MRI-RC and MRI-SUVmax-RC at a risk threshold of 5-40% for csPCa in both the development and validation cohorts.
The addition of the PRIMARY score to MRI-based multivariable model improved the accuracy of risk stratification prior to biopsy. Our novel MRI-PRIMARY prediction model is a promising approach for reducing unnecessary biopsies and improving the early detection of csPCa.
已推荐使用基于磁共振成像（MRI）的风险计算器（RC）来诊断具有临床意义的前列腺癌（csPCa）。前列腺特异性膜抗原（PSMA）正电子发射断层扫描/计算机断层扫描（PET/CT）能够检测出MRI上不可见的病变，将PSMA PET/CT与MRI相结合可能会提高诊断性能。本研究的目的是将源自[镓]Ga-PSMA-11 PET/CT的PRIMARY评分或最大标准化摄取值（SUVmax）纳入风险计算器，并将这些模型与基于MRI的风险计算器进行比较，以评估这是否能进一步减少不必要的活检。
共有683名连续的未经活检的男性在活检前接受了[镓]Ga-PSMA-11 PET/CT和MRI检查，按时间顺序分为一个开发队列（n = 552）和一个时间验证队列（n = 131）。开发并比较了三个逻辑回归风险计算器：MRI-RC、MRI-SUVmax-RC和MRI-PRIMARY-RC。评估了区分度、校准度和临床实用性。主要结果是风险计算器在检测csPCa和减少阴性活检数量方面的临床实用性。
在开发队列中，csPCa的患病率为47.5%（262/552），在时间验证队列中为41.9%（55/131）。在开发队列中，MRI-PRIMARY-RC的曲线下面积（AUC）显著高于MRI-RC（0.924对0.868，p < 0.001）和MRI-SUVmax-RC（0.924对0.904，p = 0.002）。在时间验证队列中，MRI-PRIMARY-RC也显示出最佳的区分能力，AUC为0.921（95%置信区间：0.873 - 0.969）。自抽样校准曲线显示模型拟合度可接受。MRI-PRIMARY-RC在0 - 40%的范围内表现出近乎完美的校准。决策曲线分析（DCA）表明，在开发队列和验证队列中，对于csPCa风险阈值为5 - 40%时，与MRI-RC和MRI-SUVmax-RC相比，MRI-PRIMARY-RC在检测csPCa方面具有最大的净效益。
在基于MRI的多变量模型中加入PRIMARY评分提高了活检前风险分层的准确性。我们新的MRI-PRIMARY预测模型是一种有前景的方法，可减少不必要的活检并改善csPCa的早期检测。
