Initial Biopsy Outcome Prediction-Head-to-Head Comparison of a Logistic Regression-Based Nomogram versus Artificial Neural Network

Objectives: Nomograms and artificial neural networks (ANNs) represent alternative methodologic approaches to predict the probability of prostate cancer on initial biopsy. We hypothesized that, in a head-to-head comparison, one of the approaches might demonstrate better accuracy and performance characteristics than the other. Methods: A previously published nomogram, which relies on age, digital rectal examination, serum prostate-specific antigen (PSA), and percent-free PSA, and an ANN, which relies on the same predictors plus prostate volume, were applied to a cohort of 3980 men, who were subjected to multicore systematic prostate biopsy. The accuracy and the performance characteristics were compared between these two approaches. Results: The accuracy of the nomogram was 71% versus 67% for the ANN (p = 0.0001). Graphical exploration of the performance characteristics demonstrated virtually perfect predictions for the nomogram. Conversely, the ANN underestimated the observed rate of prostate cancer. Conclusions: A 4% increase in predictive accuracy implies that the use of the nomogram instead of the ANN will result in 40 additional patients who will be correctly classified between benign and cancer.