[The utility of artificial neural networks in the prediction of prostate cancer on transrectal biopsy].
OBJECTIVE
To determine whether the development of an artificial neural network (ANN) made up of clinical variables allows for the prediction of prostate biopsy (PB) outcome.


METHODS
Patients (n=953) underwent PB at the Arquitecto Marcide Hospital in Ferrol (Spain), between january 2000 and june 2005. The variables studied were age, PSA, digital rectal examination (DRE) and prostate volume, data for all of which were available in 843 cases. In order to determine factors related to prostate cancer (PC) diagnosis, a logistic regression analysis and a feed-forward neural network were developed, including three hidden layer nodes and an output node, representing the probability of PC. Both models were constructed from a random sample of n=643 patients (derivation set). The predictive capacity was assessed with the remaining 200 patients (validation set), by means of ROC curves and the area under the curve (AUC).


RESULTS
PC was detected in 500 (59.3%) cases. Adjusting for age, PSA, digital rectal examination and prostate volume, in a multivariate logistic regression model it was observed that all the variables were independent predictors of PC. The AUC were 0.693 for PSA, 0.707 for prostate volume, 0.815 for logistic regression and 0.819 for ANN. The predictive capacity of the ANN was significantly higher than that of the PSA (p=0.002) and prostate volume (p<0,001) and similar to that of logistic regression (p=0.760).


CONCLUSIONS
The ANN shows a PC prediction capacity that is significantly higher than unimodal diagnosis methods, and similar to that of logistic regression.
Rodríguez Alonso
A
A
Servicio de Urologia, Hospital Arquitecto Marcide - Profesor Novoa Santos, Ferrol, La Coruña. arodri68@terra.es
Pértega Díaz
S
S
González Blanco
A
A
Pita Feŕnandez
S
S
Suárez Pascual
G
G
Cuerpo Pérez
M A
MA
D000368:Aged; D000369:Aged, 80 and over; D001706:Biopsy / Q000379:methods; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D011471:Prostatic Neoplasms / Q000473:pathology*; D012007:Rectum