Initial biopsy outcome prediction--head-to-head comparison of a logistic regression-based nomogram versus artificial neural network.
OBJECTIVE
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.
Chun
Felix K-H
FK
Cancer Prognostics and Health Outcomes Unit, University of Montreal, Montreal, Quebec, Canada.
Graefen
Markus
M
Briganti
Alberto
A
Gallina
Andrea
A
Hopp
Julia
J
Kattan
Michael W
MW
Huland
Hartwig
H
Karakiewicz
Pierre I
PI
D000328:Adult; D000368:Aged; D001707:Biopsy, Needle; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D016571:Neural Networks, Computer; D049451:Nomograms; D011237:Predictive Value of Tests; D011467:Prostate / Q000473:pathology*; D011471:Prostatic Neoplasms / Q000175:diagnosis*; D012680:Sensitivity and Specificity