Division of Urology, IEO - European Institute of Oncology, IRCCS, Milan, Italy.
Division of Urology, IEO - European Institute of Oncology, IRCCS, Milan, Italy.
Urol Oncol. 2021 Jul;39(7):431.e15-431.e22. doi: 10.1016/j.urolonc.2020.11.040. Epub 2021 Jan 8.
To develop a novel risk tool that allows the prediction of lymph node invasion (LNI) among patients with prostate cancer (PCa) treated with robot-assisted radical prostatectomy (RARP) and extended pelvic lymph node dissection (ePLND).
We retrospectively identified 742 patients treated with RARP + ePLND at a single center between 2012 and 2018. All patients underwent multiparametric magnetic resonance imaging (mpMRI) and were diagnosed with targeted biopsies. First, the nomogram published by Briganti et al. was validated in our cohort. Second, three novel multivariable logistic regression models predicting LNI were developed: (1) a complete model fitted with PSA, ISUP grade groups, percentage of positive cores (PCP), extracapsular extension (ECE), and Prostate Imaging Reporting and Data System (PI-RADS) score; (2) a simplified model where ECE score was not included (model 1); and (3) a simplified model where PI-RADS score was not included (model 2). The predictive accuracy of the models was assessed with the receiver operating characteristic-derived area under the curve (AUC). Calibration plots and decision curve analyses were used.
Overall, 149 patients (20%) had LNI. In multivariable logistic regression models, PSA (OR: 1.03; P= 0.001), ISUP grade groups (OR: 1.33; P= 0.001), PCP (OR: 1.01; P= 0.01), and ECE score (ECE 4 vs. 3 OR: 2.99; ECE 5 vs. 3 OR: 6.97; P< 0.001) were associated with higher rates of LNI. The AUC of the Briganti et al. model was 74%. Conversely, the AUC of model 1 vs. model 2 vs. complete model was, respectively, 78% vs. 81% vs. 81%. Simplified model 1 (ECE score only) was then chosen as the best performing model. A nomogram to calculate the individual probability of LNI, based on model 1 was created. Setting our cut-off at 5% we missed only 2.6% of LNI patients.
We developed a novel nomogram that combines PSA, ISUP grade groups, PCP, and mpMRI-derived ECE score to predict the probability of LNI at final pathology in RARP candidates. The application of a nomogram derived cut-off of 5% allows to avoid a consistent number of ePLND procedures, missing only 2.6% of LNI patients. External validation of our model is needed.
开发一种新的风险工具，以预测接受机器人辅助根治性前列腺切除术（RARP）和扩展盆腔淋巴结清扫术（ePLND）治疗的前列腺癌（PCa）患者的淋巴结侵犯（LNI）。
我们回顾性地确定了 2012 年至 2018 年期间在一家中心接受 RARP+ePLND 治疗的 742 名患者。所有患者均接受多参数磁共振成像（mpMRI）检查，并进行靶向活检。首先，验证了 Briganti 等人发表的列线图在我们的队列中的有效性。其次，开发了三种新的多变量逻辑回归模型来预测 LNI：（1）完全模型，纳入 PSA、ISUP 分级组、阳性核心百分比（PCP）、包膜外扩展（ECE）和前列腺影像报告和数据系统（PI-RADS）评分；（2）不包括 ECE 评分的简化模型（模型 1）；（3）不包括 PI-RADS 评分的简化模型（模型 2）。通过接收者操作特征曲线（AUC）下的面积评估模型的预测准确性。使用校准图和决策曲线分析。
总体而言，149 名患者（20%）发生 LNI。多变量逻辑回归模型中，PSA（OR：1.03；P=0.001）、ISUP 分级组（OR：1.33；P=0.001）、PCP（OR：1.01；P=0.01）和 ECE 评分（ECE 4 与 3，OR：2.99；ECE 5 与 3，OR：6.97；P<0.001）与更高的 LNI 发生率相关。 Briganti 等人模型的 AUC 为 74%。相反，模型 1 与模型 2 与完整模型的 AUC 分别为 78%、81%和 81%。然后选择简化模型 1（仅 ECE 评分）作为表现最佳的模型。根据模型 1 创建了一个用于计算 LNI 个体概率的列线图。我们设定截止值为 5%，仅漏诊了 2.6%的 LNI 患者。
我们开发了一种新的列线图，该图结合了 PSA、ISUP 分级组、PCP 和基于 mpMRI 的 ECE 评分，以预测 RARP 候选者最终病理中 LNI 的概率。应用列线图衍生的 5%截止值可以避免进行大量的 ePLND 手术，仅漏诊 2.6%的 LNI 患者。需要对我们的模型进行外部验证。
