Bianchi Lorenzo, Schiavina Riccardo, Borghesi Marco, Bianchi Federico Mineo, Briganti Alberto, Carini Marco, Terrone Carlo, Mottrie Alex, Gacci Mauro, Gontero Paolo, Imbimbo Ciro, Marchioro Giansilvio, Milanese Giulio, Mirone Vincenzo, Montorsi Francesco, Morgia Giuseppe, Novara Giacomo, Porreca Angelo, Volpe Alessandro, Brunocilla Eugenio
Department of Urology, University of Bologna, Bologna, Italy.
Unit of Urology/Division of Oncology, URI, IRCCS San Raffaele Hospital, Milan, Italy.
Int J Urol. 2018 Jun;25(6):574-581. doi: 10.1111/iju.13565. Epub 2018 Apr 6.
To assess the predictive accuracy and the clinical value of a recent nomogram predicting cancer-specific mortality-free survival after surgery in pN1 prostate cancer patients through an external validation.
We evaluated 518 prostate cancer patients treated with radical prostatectomy and pelvic lymph node dissection with evidence of nodal metastases at final pathology, at 10 tertiary centers. External validation was carried out using regression coefficients of the previously published nomogram. The performance characteristics of the model were assessed by quantifying predictive accuracy, according to the area under the curve in the receiver operating characteristic curve and model calibration. Furthermore, we systematically analyzed the specificity, sensitivity, positive predictive value and negative predictive value for each nomogram-derived probability cut-off. Finally, we implemented decision curve analysis, in order to quantify the nomogram's clinical value in routine practice.
External validation showed inferior predictive accuracy as referred to in the internal validation (65.8% vs 83.3%, respectively). The discrimination (area under the curve) of the multivariable model was 66.7% (95% CI 60.1-73.0%) by testing with receiver operating characteristic curve analysis. The calibration plot showed an overestimation throughout the range of predicted cancer-specific mortality-free survival rates probabilities. However, in decision curve analysis, the nomogram's use showed a net benefit when compared with the scenarios of treating all patients or none.
In an external setting, the nomogram showed inferior predictive accuracy and suboptimal calibration characteristics as compared to that reported in the original population. However, decision curve analysis showed a clinical net benefit, suggesting a clinical implication to correctly manage pN1 prostate cancer patients after surgery.
通过外部验证评估一种近期的列线图预测pN1期前列腺癌患者术后无癌症特异性死亡生存的预测准确性和临床价值。
我们在10个三级中心评估了518例接受根治性前列腺切除术和盆腔淋巴结清扫术且最终病理显示有淋巴结转移证据的前列腺癌患者。使用先前发表的列线图的回归系数进行外部验证。根据受试者工作特征曲线下面积和模型校准来量化预测准确性,评估模型的性能特征。此外,我们系统分析了每个列线图得出的概率截断值的特异性、敏感性、阳性预测值和阴性预测值。最后,我们进行决策曲线分析,以量化列线图在常规实践中的临床价值。
外部验证显示预测准确性低于内部验证(分别为65.8%和83.3%)。通过受试者工作特征曲线分析测试,多变量模型的辨别力(曲线下面积)为66.7%(95%可信区间60.1 - 73.0%)。校准图显示在预测的无癌症特异性死亡生存率概率范围内存在高估。然而,在决策曲线分析中,与治疗所有患者或不治疗的情况相比,列线图的使用显示出净效益。
在外部环境中,与原始人群报告的情况相比,列线图显示出较低的预测准确性和次优的校准特征。然而,决策曲线分析显示出临床净效益,表明对正确管理术后pN1期前列腺癌患者具有临床意义。