Laboratory of Urology, Department of Urology, Fujian Union Hospital, Fujian Medical University, Fuzhou, Fujian, China.
Department of Nursing, Laboratory of Urology, Department of Urology, Fujian Union Hospital, Fujian Medical University, Fuzhou, Fujian, China.
PLoS One. 2019 Nov 19;14(11):e0218645. doi: 10.1371/journal.pone.0218645. eCollection 2019.
Prostate biopsies are frequently performed to screen for prostate cancer (PCa) with complications such as infections and bleeding. To reduce unnecessary biopsies, here we designed an improved predictive model of MRI-based prostate volume and associated zone-adjusted prostate-specific antigen (PSA) concentrations for diagnosing PCa and risk stratification. Multiparametric MRI administered to 422 consecutive patients before initial transrectal ultrasonography-guided 13-core prostate biopsies from January 2012 to March 2018 at Fujian Medical University Union Hospital. Univariate and multivariate logistic regression analyses and determination of the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was performed to evaluate and integrate the predictors of PCa and high-risk prostate cancer (HR-PCa). The detection rates of PCa was 43.84% (185/422). And the detection rates of HR-PCa was 71.35% (132/185) in PCa patients. Multivariate analysis revealed that prostate volume(PV), PSA density(PSAD), transitional zone volume(TZV), PSA density of the transitional zone(PSADTZ), and MR were independent predictors of PCa and HR-PCa. PSA, peripheral zone volume(PZV) and PSA density of the peripheral zone(PSADPZ) were independent predictors of PCa but not HR-PCa. The AUC of our best predictive model including PSA + PV + PSAD + MR + TZV or PSA + PV + PSAD + MR + PZV was 0.906 for PCa. The AUC of the best predictive model of PV + PSAD + MR + TZV was 0.893 for HR-PCa. In conclusion, our results will likely improve the detection rate of prostate cancer, avoiding unnecessary prostate biopsies, and for evaluating risk stratification.
前列腺活检常用于筛查前列腺癌（PCa），但存在感染和出血等并发症。为了减少不必要的活检，我们设计了一种改进的基于 MRI 的前列腺体积和相关区域调整前列腺特异性抗原（PSA）浓度的预测模型，用于诊断 PCa 和风险分层。
2012 年 1 月至 2018 年 3 月，在福建医科大学附属协和医院，对 422 例连续患者进行了多参数 MRI 检查，然后进行了经直肠超声引导下的 13 核前列腺活检。采用单变量和多变量逻辑回归分析，并确定受试者工作特征（ROC）曲线的曲线下面积（AUC），以评估和整合 PCa 和高危前列腺癌（HR-PCa）的预测因素。422 例患者中，PCa 的检出率为 43.84%（185/422），PCa 患者中 HR-PCa 的检出率为 71.35%（132/185）。多变量分析显示，前列腺体积（PV）、PSA 密度（PSAD）、移行区体积（TZV）、移行区 PSA 密度（PSADTZ）和 MRI 是 PCa 和 HR-PCa 的独立预测因素。PSA、外周区体积（PZV）和外周区 PSA 密度（PSADPZ）是 PCa 的独立预测因素，但不是 HR-PCa 的独立预测因素。包括 PSA+PV+PSAD+MR+TZV 或 PSA+PV+PSAD+MR+PZV 的最佳预测模型的 AUC 为 0.906，用于诊断 PCa。PV+PSAD+MR+TZV 最佳预测模型的 AUC 为 0.893，用于诊断 HR-PCa。总之，我们的研究结果可能会提高前列腺癌的检出率，避免不必要的前列腺活检，并用于评估风险分层。
