Department of Urology, Xiangya Hospital, Central South University, Changsha, China.
Department of Pathology, Xiangya Hospital, Central South University, Changsha, China.
Urol Oncol. 2020 Jul;38(7):641.e19-641.e29. doi: 10.1016/j.urolonc.2020.04.015. Epub 2020 May 7.
Accurate preoperative prediction of inguinal lymph node metastasis (LNM) aids in clinical decision making, especially for patients with penile cancer with clinically negative lymph nodes. We aim to develop a nomogram to predict the preoperative risk of LNM by incorporating clinicopathologic features and tumor biomarkers.
Eighty-four patients with penile cancer with clinically negative lymph nodes were enrolled. The programmed death ligand 1 (PD-L1) expression profile was detected by immunohistochemistry. The neutrophil-to-lymphocyte ratio (NLR) was calculated based on parameters of a routine blood examination. Multivariate logistic regression analysis was utilized to construct predictive nomograms for LNM based on data of 64 patients. The nomogram performance was assessed for calibration, discrimination, and clinical use.
Tumor grade, lymphovascular invasion, PD-L1, and NLR were independent predictors of LNM. Then, 4 prediction models were constructed. Clinical model included tumor grade and lymphovascular invasion. NLR model was built by adding the NLR to clinical model. PD-L1 model was built by adding the PD-L1 to clinical model. Finally, a combined model was built by adding both PD-L1 and NLR to clinical model. Combined model showed the best performance compared with other models. It showed good discrimination with a C-index of 0.89, and good calibration. In addition, decision curve analysis suggested that model 4 was clinically useful.
We developed a nomogram that incorporated tumor grade, lymphovascular invasion, PD-L1, and NLR that could be conveniently used to predict the preoperative individualized risk of inguinal LNM in patients with penile cancer.
准确预测腹股沟淋巴结转移（LNM）有助于临床决策，尤其是对于临床淋巴结阴性的阴茎癌患者。我们旨在通过纳入临床病理特征和肿瘤生物标志物来开发一种列线图来预测 LNM 的术前风险。
纳入 84 例临床淋巴结阴性的阴茎癌患者。通过免疫组织化学检测程序性死亡配体 1（PD-L1）的表达谱。根据常规血液检查的参数计算中性粒细胞与淋巴细胞比值（NLR）。利用多变量逻辑回归分析，基于 64 例患者的数据构建 LNM 的预测列线图。评估列线图的校准、区分和临床应用性能。
肿瘤分级、脉管侵犯、PD-L1 和 NLR 是 LNM 的独立预测因子。然后构建了 4 个预测模型。临床模型包括肿瘤分级和脉管侵犯。NLR 模型是通过在临床模型中加入 NLR 构建的。PD-L1 模型是通过在临床模型中加入 PD-L1 构建的。最后，通过在临床模型中加入 PD-L1 和 NLR 构建联合模型。与其他模型相比，联合模型显示出最佳性能。它显示出良好的区分度，C 指数为 0.89，且校准良好。此外，决策曲线分析表明模型 4 具有临床实用性。
我们开发了一种列线图，纳入了肿瘤分级、脉管侵犯、PD-L1 和 NLR，可方便地用于预测阴茎癌患者腹股沟 LNM 的术前个体化风险。
