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

Felix K H Chun, Markus Graefen, Alberto Briganti, Andrea Gallina, Julia Hopp, Michael W. Kattan, Hartwig Huland, Pierre I. Karakiewicz

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)1236-1243
Number of pages8
JournalEuropean Urology
Volume51
Issue number5
DOIs
Publication statusPublished - May 2007

Keywords

  • Artificial neural network
  • External validation
  • Head-to-head comparison
  • Initial prostate biopsy
  • Nomogram
  • Prostate cancer

ASJC Scopus subject areas

  • Urology

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