The prediction of cancer-specific mortality in T1 non-muscle-invasive bladder cancer: comparison of logistic regression and artificial neural network: a SEER population-based study

Abstract

Abstract Purpose To identify the risk factors for 5-year cancer-specific (CSS) and overall survival (OS) and to compare the accuracy of logistic regression (LR) and artificial neural network (ANN) in the prediction of survival outcomes in T1 non-muscle-invasive bladder cancer. Methods This is a population-based analysis using the Surveillance, Epidemiology, and End Results database. Patients with T1 bladder cancer (BC) who underwent transurethral resection of the tumour (TURBT) between 2004 and 2015 were included in the analysis. The predictive abilities of LR and ANN were compared. Results Overall 32,060 patients with T1 BC were randomly assigned to training and validation cohorts in the proportion of 70:30. There were 5691 (17.75%) cancer-specific deaths and 18,485 (57.7%) all-cause deaths within a median of 116 months of follow-up (IQR 80–153). Multivariable analysis with LR revealed that age, race, tumour grade, histology variant, the primary character, location and size of the tumour, marital status, and annual income constitute independent risk factors for CSS. In the validation cohort, LR and ANN yielded 79.5% and 79.4% accuracy in 5-year CSS prediction respectively. The area under the ROC curve for CSS predictions reached 73.4% and 72.5% for LR and ANN respectively. Conclusions Available risk factors might be useful to estimate the risk of CSS and OS and thus facilitate optimal treatment choice. The accuracy of survival prediction is still moderate. T1 BC with adverse features requires more aggressive treatment after initial TURBT.