A Nomogram Derived by Combination of Demographic and Biomarker Data Improves the Noninvasive Evaluation of Patients at Risk for Bladder Cancer.

Improvements in the non-invasive clinical evaluation of patients at risk for bladder cancer (BCa) would be of benefit both to individuals and to healthcare systems. We investigated the potential utility of a hybrid nomogram that combined key demographic features with the results of a multiplex urinary biomarker assay in hopes of identifying patients at risk of harboring BCa. Logistic regression analysis was used to model the probability of BCa burden in a cohort of 686 subjects (394 with BCa) using key demographic features alone, biomarker data alone and the combination of demographic features and key biomarker data. We examined discrimination, calibration and decision curve analysis techniques to evaluate prediction model performance. Area under the receiver operating characteristic curve (AUROC) analyses revealed that demographic features alone predicted tumor burden with an accuracy of 0.806 [95% CI: 0.76–0.85], while biomarker data had an accuracy of 0.835 [95% CI: 0.80–0.87]. The addition of molecular data into the nomogram improved the predictive performance to 0.891 [95% CI: 0.86–0.92]. Decision curve analyses showed that the hybrid nomogram performed better than demographic or biomarker data alone. A nomogram construction strategy that combines key demographic features with biomarker data may facilitate the accurate, non-invasive evaluation of patients at risk of harboring BCa. Further research is needed to evaluate the BCa risk nomogram for potential clinical utility. The application of such a nomogram may better inform the decision to perform invasive diagnostic procedures.

对膀胱癌（BCa）高危患者进行无创临床评估的改进对个人和医疗保健系统都有益。我们研究了一种混合列线图的潜在用途，该列线图将关键人口统计学特征与一种多尿液生物标志物检测结果相结合，希望能识别出有患膀胱癌风险的患者。 采用逻辑回归分析，仅使用关键人口统计学特征、仅使用生物标志物数据以及将人口统计学特征和关键生物标志物数据相结合，对686名受试者（394名患有膀胱癌）队列中膀胱癌负荷的概率进行建模。我们通过检验判别力、校准和决策曲线分析技术来评估预测模型的性能。 受试者工作特征曲线下面积（AUROC）分析显示，仅人口统计学特征预测肿瘤负荷的准确率为0.806[95%置信区间：0.76 - 0.85]，而生物标志物数据的准确率为0.835[95%置信区间：0.80 - 0.87]。将分子数据加入列线图后，预测性能提高到0.891[95%置信区间：0.86 - 0.92]。决策曲线分析表明，混合列线图比单独使用人口统计学特征或生物标志物数据的效果更好。 一种将关键人口统计学特征与生物标志物数据相结合的列线图构建策略可能有助于对有患膀胱癌风险的患者进行准确的无创评估。需要进一步研究以评估膀胱癌风险列线图的潜在临床应用价值。 这种列线图的应用可能会更好地为是否进行侵入性诊断程序的决策提供依据。