Novel nomograms to predict muscle invasion and lymph node metastasis in upper tract urothelial carcinoma.

Overview

abstract

OBJECTIVES: To develop accurate preoperative nomograms for prediction of muscle-invasive disease and lymph node metastasis in upper tract urothelial carcinoma (UTUC), to assist surgeons in risk stratifying patients and help guide treatment decisions. MATERIALS/METHODS: The National Cancer Database was used to identify all patients from 2004 to 2016 with UTUC who underwent extirpative surgery and lymphadenectomy. Univariate and multivariate logistic regression was performed to identify variables predicting muscle-invasive and node-positive disease. The data set was split 80:20 into a derivation and validation cohort and used to generate and test two nomograms. Nomograms were assessed using area under the curve (AUC) and calibration plots. RESULTS: A total of 6,143 patients met inclusion criteria. Predictors of muscle-invasive disease were age, grade, lymphovascular invasion (LVI), tumor size, and positive clinical lymph node status. Predictors of node-positive disease were grade, LVI, tumor size, and positive clinical lymph node status. The accuracy of the final nomogram predicting muscle-invasive disease was 80.0% (AUC 0.800, corrected C-index 0.813), and the accuracy of the nomogram predicting node-positive disease was 87.8% (AUC 0.878, corrected C-index 0.887). CONCLUSIONS: With data readily available after imaging and biopsy (age, tumor grade, LVI status, tumor size, and clinical lymph node status), we developed the first preoperative nomograms to quantitatively predict muscle-invasive disease and lymph node metastasis in UTUC, with an accuracy of 80.0% and 87.8% respectively. This information could be helpful to assist surgeons with pre-operative risk stratification.