Mo Li-Cai, Zhang Xian-Jun, Zheng Hai-Hong, Huang Xiao-Peng, Zheng Lin, Zhou Zhi-Rui, Wang Jia-Jia
Department of Urology, Taizhou Hospital of Zhejiang Province affiliated with Wenzhou Medical University, Linhai, Taizhou, Zhejiang, China.
Department of Pathology, Taizhou Hospital of Zhejiang Province affiliated with Wenzhou Medical University, Linhai, Taizhou, Zhejiang, China.
Front Oncol. 2022 Nov 29;12:1068893. doi: 10.3389/fonc.2022.1068893. eCollection 2022.
On prostate biopsy, multiparametric magnetic resonance imaging (mpMRI) and the Prostate Health Index (PHI) have allowed prediction of clinically significant prostate cancer (csPCa).
To predict the likelihood of csPCa, we created a nomogram based on a multivariate model that included PHI and mpMRI. We assessed 315 males who were scheduled for prostate biopsies.
We used the Prostate Imaging Reporting and Data System version 2 (PI-RADS V2) to assess mpMRI and optimize PHI testing prior to biopsy. Univariate analysis showed that csPCa may be identified by PHI with a cut-off value of 77.77, PHID with 2.36, and PI-RADS with 3 as the best threshold. Multivariable logistic models for predicting csPCa were developed using PI-RADS, free PSA (fPSA), PHI, and prostate volume. A multivariate model that included PI-RADS, fPSA, PHI, and prostate volume had the best accuracy (AUC: 0.882). Decision curve analysis (DCA), which was carried out to verify the nomogram's clinical applicability, showed an ideal advantage (13.35% higher than the model that include PI-RADS only).
In conclusion, the nomogram based on PHI and mpMRI is a valuable tool for predicting csPCa while avoiding unnecessary biopsy as much as possible.
在前列腺活检中，多参数磁共振成像（mpMRI）和前列腺健康指数（PHI）有助于预测临床显著性前列腺癌（csPCa）。
为了预测csPCa的可能性，我们基于包含PHI和mpMRI的多变量模型创建了一个列线图。我们评估了315名计划进行前列腺活检的男性。
我们使用前列腺影像报告和数据系统第2版（PI-RADS V2）来评估mpMRI，并在活检前优化PHI检测。单变量分析表明，PHI以77.77为临界值、PHID以2.36为临界值、PI-RADS以3为最佳阈值时可识别csPCa。使用PI-RADS、游离前列腺特异抗原（fPSA）、PHI和前列腺体积建立了预测csPCa的多变量逻辑模型。包含PI-RADS、fPSA、PHI和前列腺体积的多变量模型具有最佳准确性（AUC：0.882）。为验证列线图的临床适用性而进行的决策曲线分析（DCA）显示出理想的优势（比仅包含PI-RADS的模型高13.35%）。
总之，基于PHI和mpMRI的列线图是预测csPCa的有价值工具，同时尽可能避免不必要的活检。
