Cheng Chunliang, Liu Jinhui, Yi Xiaoping, Yin Hongling, Qiu Dongxu, Zhang Jinwei, Chen Jinbo, Hu Jiao, Li Huihuang, Li Mingyong, Zu Xiongbing, Tang Yongxiang, Gao Xiaomei, Hu Shuo, Cai Yi
Department of Urology, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.
Department of Radiology, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.
Transl Androl Urol. 2023 Jul 31;12(7):1115-1126. doi: 10.21037/tau-22-832. Epub 2023 Jul 21.
There are some limitations in the commonly used methods for the detection of prostate cancer. There is a lack of nomograms based on multiparametric magnetic resonance imaging (mpMRI) and Ga-prostate-specific membrane antigen (PSMA) positron emission tomography-computed tomography (PET-CT) for the prediction of prostate cancer. The study seeks to compare the performance of mpMRI and Ga-PSMA PET-CT, and design a novel predictive model capable of predicting clinically significant prostate cancer (csPCa) before biopsy based on a combination of Ga-PSMA PET-CT, mpMRI, and patient clinical parameters.
From September 2020 to June 2021, we prospectively enrolled 112 consecutive patients with no prior history of prostate cancer who underwent both Ga-PSMA PET-CT and mpMRI prior to biopsy at our clinical center. Univariate and multivariate regression analyses were used to identify predictors of csPCa, with a predictive model and its nomogram incorporating Ga-PSMA PET-CT, mpMRI, and the clinical predictors then being generated. The constructed model was evaluated using receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis, and further validated with the internal and external cohorts.
The model incorporated prostate-specific antigen density (PSAd), Prostate Imaging Reporting and Data System (PI-RADS) category, and maximum standardized uptake value (SUVmax), and it exhibited excellent predictive efficacy when applying to evaluate both training and validation cohorts [area under the curve (AUC): 0.936 and 0.940, respectively]. Compared with SUVmax alone, the model demonstrated excellent diagnostic performance with improved specificity (0.910, 95% CI: 0.824-0.963) and positive predictive values (0.811, 95% CI: 0.648-0.920). Calibration curve and decision curve analysis further confirmed that the model exhibited a high degree of clinical net benefit and low error rate.
The constructed model in this study was capable of accurately predicting csPCa prior to biopsy with excellent discriminative ability. As such, this model has the potential to be an effective non-invasive approach for the diagnosis of csPCa.
前列腺癌常用检测方法存在一些局限性。缺乏基于多参数磁共振成像（mpMRI）和镓-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）的列线图用于预测前列腺癌。本研究旨在比较mpMRI和镓-PSMA PET-CT的性能，并基于镓-PSMA PET-CT、mpMRI和患者临床参数的组合设计一种能够在活检前预测临床显著前列腺癌（csPCa）的新型预测模型。
2020年9月至2021年6月，我们前瞻性纳入了112例既往无前列腺癌病史的连续患者，这些患者在我们临床中心活检前均接受了镓-PSMA PET-CT和mpMRI检查。采用单因素和多因素回归分析确定csPCa的预测因素，随后生成包含镓-PSMA PET-CT、mpMRI和临床预测因素的预测模型及其列线图。使用受试者操作特征（ROC）曲线、校准曲线和决策曲线分析对构建的模型进行评估，并在内部和外部队列中进一步验证。
该模型纳入了前列腺特异性抗原密度（PSAd）、前列腺影像报告和数据系统（PI-RADS）类别以及最大标准化摄取值（SUVmax），在应用于评估训练队列和验证队列时均表现出优异的预测效能[曲线下面积（AUC）分别为0.936和0.940]。与单独的SUVmax相比，该模型具有优异的诊断性能，特异性提高（0.910，95%CI：0.824-0.963），阳性预测值提高（0.811，95%CI：0.648-0.920）。校准曲线和决策曲线分析进一步证实该模型具有高度的临床净效益和低错误率。
本研究构建的模型能够在活检前准确预测csPCa，具有优异的判别能力。因此，该模型有可能成为诊断csPCa的一种有效的非侵入性方法。
