Pan Jin-Feng, Su Rui, Cao Jian-Zhou, Zhao Zhen-Ya, Ren Da-Wei, Ye Sha-Zhou, Huang Rui-da, Tao Zhu-Lei, Yu Cheng-Ling, Jiang Jun-Hui, Ma Qi
Medical School, Ningbo University, Ningbo, China.
Comprehensive Urogenital Cancer Center, Ningbo First Hospital, The Affiliated Hospital of Ningbo University, Ningbo, China.
Front Oncol. 2021 Sep 13;11:740868. doi: 10.3389/fonc.2021.740868. eCollection 2021.
The purpose of this study is to explore the value of combining bpMRI and clinical indicators in the diagnosis of clinically significant prostate cancer (csPCa), and developing a prediction model and Nomogram to guide clinical decision-making.
We retrospectively analyzed 530 patients who underwent prostate biopsy due to elevated serum prostate specific antigen (PSA) levels and/or suspicious digital rectal examination (DRE). Enrolled patients were randomly assigned to the training group ( = 371, 70%) and validation group ( = 159, 30%). All patients underwent prostate bpMRI examination, and T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) sequences were collected before biopsy and were scored, which were respectively named T2WI score and DWI score according to Prostate Imaging Reporting and Data System version 2 (PI-RADS v.2) scoring protocol, and then PI-RADS scoring was performed. We defined a new bpMRI-based parameter named Total score (Total score = T2WI score + DWI score). PI-RADS score and Total score were separately included in the multivariate analysis of the training group to determine independent predictors for csPCa and establish prediction models. Then, prediction models and clinical indicators were compared by analyzing the area under the curve (AUC) and decision curves. A Nomogram for predicting csPCa was established using data from the training group.
In the training group, 160 (43.1%) patients had prostate cancer (PCa), including 128 (34.5%) with csPCa. Multivariate regression analysis showed that the PI-RADS score, Total score, f/tPSA, and PSA density (PSAD) were independent predictors of csPCa. The prediction model that was defined by Total score, f/tPSA, and PSAD had the highest discriminatory power of csPCa (AUC = 0.931), and the diagnostic sensitivity and specificity were 85.1% and 87.5%, respectively. Decision curve analysis (DCA) showed that the prediction model achieved an optimal overall net benefit in both the training group and the validation group. In addition, the Nomogram predicted csPCa revealed good estimation when compared with clinical indicators.
The prediction model and Nomogram based on bpMRI and clinical indicators exhibit a satisfactory predictive value and improved risk stratification for csPCa, which could be used for clinical biopsy decision-making.
本研究旨在探讨结合多参数磁共振成像（bpMRI）与临床指标在临床显著性前列腺癌（csPCa）诊断中的价值，并建立预测模型和列线图以指导临床决策。
我们回顾性分析了530例因血清前列腺特异性抗原（PSA）水平升高和/或直肠指检（DRE）可疑而接受前列腺活检的患者。纳入的患者被随机分配到训练组（n = 371，70%）和验证组（n = 159，30%）。所有患者均接受前列腺bpMRI检查，在活检前采集T2加权成像（T2WI）和扩散加权成像（DWI）序列并进行评分，根据前列腺影像报告和数据系统第2版（PI-RADS v.2）评分协议分别命名为T2WI评分和DWI评分，然后进行PI-RADS评分。我们定义了一个基于bpMRI的新参数，称为总分（总分 = T2WI评分 + DWI评分）。将PI-RADS评分和总分分别纳入训练组的多因素分析中，以确定csPCa的独立预测因素并建立预测模型。然后，通过分析曲线下面积（AUC）和决策曲线来比较预测模型和临床指标。使用训练组的数据建立了预测csPCa的列线图。
在训练组中，160例（43.1%）患者患有前列腺癌（PCa），其中128例（34.5%）为csPCa。多因素回归分析显示，PI-RADS评分、总分、游离/总PSA（f/tPSA）和PSA密度（PSAD）是csPCa的独立预测因素。由总分、f/tPSA和PSAD定义的预测模型对csPCa具有最高的鉴别力（AUC = 0.931），诊断敏感性和特异性分别为85.1%和87.5%。决策曲线分析（DCA）表明，该预测模型在训练组和验证组中均实现了最佳的总体净效益。此外，与临床指标相比，列线图对csPCa的预测显示出良好的估计效果。
基于bpMRI和临床指标的预测模型和列线图对csPCa具有令人满意的预测价值，并改善了风险分层，可用于临床活检决策。
