Purpose: Mortality from bladder cancer (BC) increases exponentially once it invades the muscle, with inherent challenges delineating at the population level. We sought to develop and validate a natural language processing (NLP) model for automatically identifying patients with muscle-invasive bladder cancer (MIBC).
Methods: All patients with a Current Procedural Terminology code for transurethral resection of bladder tumor (TURBT; n = 76,060) were selected from the Department of Veterans Affairs (VA) database. A sample of 600 patients (with 2,337 full-text notes) who had TURBT and confirmed pathology results were selected for NLP model development and validation. The NLP performance was assessed by calculating the sensitivity, specificity, positive predictive value, negative predictive value, F1 score, and overall accuracy at the individual note and patient levels.
Results: In the validation cohort, the NLP model had average overall accuracies of 94% and 96% at the note and patient levels. Specifically, the F1 score and overall accuracy for predicting muscle invasion at the patient level were 0.87% and 96%, respectively. The model classified nonmuscle-invasive bladder cancer (NMIBC) with overall accuracies of 90% and 93% at the note and patient levels. When applying the model to 71,200 patients VA-wide, the model classified 13,642 (19%) as having MIBC and 47,595 (66%) as NMIBC and was able to identify invasion status for 96% of patients with TURBT at the population level. Inherent limitations include a relatively small training set, given the size of the VA population.
Conclusion: This NLP model, with high accuracy, may be a practical tool for efficiently identifying BC invasion status and aid in population-based BC research.