A Pre-operative Nomogram for Prediction of Lymph Node Metastasis in Bladder Urothelial Carcinoma - Lu, Xiaofan; Wang, Yang; Jiang, Liyun; Gao, Jun; Zhu, Yue; Hu, Wenjun; Wang, Jiashuo; Ruan, Xinjia; Xu, Zhengbao; Meng, Xiaowei; Zhang, Bing; Yan, Fangrong (Frontiers in Oncology, 2019)
The status of lymph node (LN) metastases plays a decisive role in the selection of surgical procedures and postoperative treatment. Several histopathologic as predictors of LN metastasis are commonly available postoperatively. Medical imaging improved preoperative diagnosis, but the results are not fully satisfactory due to substantial false positives. Thus, a reliable and robust method for preoperative assessment of LN status is urgently required. We developed a prediction model in a training set from TCGA-BLCA cohort including 196 bladder urothelial carcinoma samples with confirmed LN metastasis status. LASSO regression was harnessed for dimension reduction, feature selection and LNM-signature building. Multivariable logistic regression was used to develop the predicting model, incorporating the LNM-signature and a genomic mutation of MLL2, and was presented with a LNM-nomogram. The performance of the nomogram was assessed with respect to its calibration, discrimination and clinical usefulness. Internal validation evaluated by testing set of TCGA-cohort and independent validation were assessed by two independent cohorts. The LNM-signature, which consisted of 48 selected features, was significantly associated with LN status (p<0.005 for both training and testing set of TCGA-cohort). Predictors contained in the individualized prediction nomogram included the LNM-signature and MLL2 mutation status. The model demonstrated good discrimination, with an AUC of 98.7% (85.3% for testing set) and good calibration with p=0.973 (0.485 for testing set) in Hosmer-Lemeshow goodness of fit test. Decision curve analysis manifested that the LNM-nomogram was clinically useful. This study presents a preoperative nomogram incorporating a LNM-signature and a genomic mutation, which can be conveniently utilized to facilitate preoperative individualized prediction of LN metastasis in patients with bladder urothelial carcinoma.