Center of Minimally Invasive Treatment for Tumor, Department of Medical Ultrasound, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China.
Ultrasound Research and Education Institute, Clinical Research Center for Interventional Medicine, Tongji University School of Medicine, Shanghai, China.
Br J Radiol. 2022 Sep 1;95(1138):20220209. doi: 10.1259/bjr.20220209. Epub 2022 Aug 17.
To develop a nomogram prediction model based on Prostate Imaging Reporting and Data System v.2.1 (PI-RADS v2.1) and contrast-enhanced ultrasound (CEUS) for predicting prostate cancer (PCa) and clinically significant prostate cancer (csPCa) in males with prostate-specific antigen (PSA) 4-10 ng ml to avoid unnecessary biopsy.
A total of 490 patients who underwent prostate biopsy for PSA 4-10 ng ml were enrolled and randomly divided into a pilot cohort (70%) and a validation cohort (30%). Univariate and multivariate logistic regression models were constructed to select potential predictors of PCa and csPCa, and a nomogram was created. The area under receiver operating characteristic (ROC) curve (AUC) was calculated, and compared using DeLong's test. The diagnostic performance and unnecessary biopsy rate of the nomogram prediction model were also assessed. Hosmer-Lemeshow goodness-of-fit test was employed to test for model fitness.
The multivariate analysis revealed that features independently associated with PCa and csPCa were age, PI-RADS score and CEUS manifestations. Incorporating these factors, the nomogram achieved good discrimination performance of AUC 0.843 for PCa, 0.876 for csPCa in the pilot cohort, and 0.818 for PCa, 0.857 for csPCa in the validation cohort, respectively, and had well-fitted calibration curves. And the diagnostic performance of the nomogram was comparable to the model including all the parameters ( > 0.05). Besides, the nomogram prediction model yielded meaningful reduction in unnecessary biopsy rate (from 74.8 to 21.1% in PCa, and from 83.7 to 5.4% in csPCa).
The nomogram prediction model based on age, PI-RADS v2.1 and CEUS achieved an optimal prediction of PCa and csPCa. Using this model, the PCa risk for an individual patient can be estimated, which can lead to a rational biopsy choice.
This study gives an account of improving pre-biopsy risk stratification in males with "gray zone" PSA level through PI-RADS v2.1 and CEUS.
基于前列腺影像报告和数据系统第 2.1 版（PI-RADS v2.1）和对比增强超声（CEUS），建立预测前列腺癌（PCa）和临床显著前列腺癌（csPCa）的列线图预测模型，以避免对前列腺特异性抗原（PSA）在 4-10ng/ml 的男性进行不必要的前列腺活检。
共纳入 490 例 PSA 为 4-10ng/ml 行前列腺活检的患者，将其随机分为试点队列（70%）和验证队列（30%）。采用单因素和多因素逻辑回归模型筛选 PCa 和 csPCa 的潜在预测因素，并建立列线图。计算受试者工作特征（ROC）曲线下面积（AUC），并采用 DeLong 检验进行比较。评估列线图预测模型的诊断性能和不必要活检率。采用 Hosmer-Lemeshow 拟合优度检验评估模型拟合度。
多因素分析显示，与 PCa 和 csPCa 独立相关的特征是年龄、PI-RADS 评分和 CEUS 表现。纳入这些因素后，列线图在试点队列中对 PCa 和 csPCa 的鉴别性能良好，AUC 分别为 0.843 和 0.876，在验证队列中分别为 0.818 和 0.857，且校准曲线拟合良好。并且该列线图的诊断性能与包含所有参数的模型相当（>0.05）。此外，该列线图预测模型可显著降低不必要的活检率（PCa 从 74.8%降至 21.1%，csPCa 从 83.7%降至 5.4%）。
基于年龄、PI-RADS v2.1 和 CEUS 的列线图预测模型可实现对 PCa 和 csPCa 的最佳预测。使用该模型可以评估个体患者的 PCa 风险，从而可以做出合理的活检选择。
本研究通过 PI-RADS v2.1 和 CEUS 报告了如何改善“灰色地带”PSA 水平男性的前列腺活检前风险分层。
