Development and Validation of Prognostic Nomograms to Predict Overall and Cancer-Specific Survival for Patients with Adenocarcinoma of the Urinary Bladder: A Population-Based Study

Adenocarcinoma of the bladder (ACB) rarely occurs but is associated with poor outcome. We aim to establish reliable nomograms for estimating cancer-specific survival (CSS) and overall survival (OS) of ACB patients. ACB patients were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database (2004–2015). A total of 1,149 patients were randomly divided into training cohort (<i>n</i> = 692) and validation cohort (<i>n</i> = 457). Multivariate Cox proportional hazards regression models were employed to identify independent prognostic factors. Nomograms predicting OS and CSS were constructed utilizing screened factors. The performance of nomograms was internally and externally validated by calibration curves, the receiver operating characteristic (ROC) curves, concordance index (C-index), and decision curve analysis (DCA). OS nomogram incorporated age, race, histologic grade, American Joint Committee of Cancer (AJCC) stage, metastasis, surgery, chemotherapy, and tumor size. The C-indices were 0.754 (95% CI: 0.732–0.775) for training set and 0.743 (95% CI: 0.712–0.767) for validation set. Meanwhile, the calibration plots for 3- and 5-year OS displayed fine concordance between actual and predicted outcomes. In addition, higher areas under the curve (AUCs) were seen in training cohort (3-year: 0.799 vs. 0.630; 5-year: 0.797 vs. 0.648) and validation cohort (3-year: 0.802 vs. 0.662; 5-year: 0.752 vs. 0.660). Finally, DCA curves of the nomograms exhibited larger net benefits than AJCC stage. CSS nomogram showed similar results. Our study constructed and validated nomograms with improved discriminative abilities and clinical benefits to predict the survival outcomes of ACB patients. The models might assist clinicians in optimizing therapeutic management on individual levels.

膀胱腺癌（Adenocarcinoma of the bladder, ACB）发病率较低，但预后较差。本研究旨在构建可靠的列线图，以预测ACB患者的癌症特异性生存（cancer-specific survival, CSS）与总生存（overall survival, OS）。我们从监测、流行病学与最终结果（Surveillance, Epidemiology, and End Results, SEER）数据库（2004–2015年）中检索ACB患者数据，共纳入1149例患者，按随机分组原则分为训练队列（n=692）与验证队列（n=457）。采用多变量Cox比例风险回归模型筛选独立预后因素，基于筛选出的因素构建预测OS与CSS的列线图。通过校准曲线、受试者工作特征（Receiver Operating Characteristic, ROC）曲线、一致性指数（concordance index, C-index）及决策曲线分析（Decision Curve Analysis, DCA）对列线图的性能进行内部与外部验证。OS列线图纳入了年龄、种族、组织学分级、美国癌症联合委员会（American Joint Committee of Cancer, AJCC）分期、转移情况、手术治疗、化疗及肿瘤大小。训练集的C-index为0.754（95%CI：0.732–0.775），验证集为0.743（95%CI：0.712–0.767）。同时，3年与5年OS的校准曲线显示实际生存与预测生存结果具有良好的一致性。此外，训练队列的曲线下面积（areas under the curve, AUCs）更高（3年：0.799 vs. 0.630；5年：0.797 vs. 0.648），验证队列亦如此（3年：0.802 vs. 0.662；5年：0.752 vs. 0.660）。最终，列线图的决策曲线显示其净获益均优于AJCC分期。CSS列线图亦得到相似结果。本研究构建并验证了具有良好区分能力与临床价值的列线图，可用于预测ACB患者的生存结局，该模型或可帮助临床医生实现个体化的治疗方案优化。