Conference on Prognostic Factors and Staging in Cancer Management: Contributions of Artificial Neural Networks and Other Statistical Methods

Received December 18, 2000; accepted January 3, 2001. BACKGROUND. Transrectal prostate biopsy decisions often have been based on absolute cutoff values for total and free prostate-specific antigen (PSA). The authors decided that it would be more appropriate to develop risk profiles for the individual patient to allow him to decide whether to undergo a prostate biopsy. METHODS. To develop risk profiles, the authors first used multivariate logistic regression analysis to analyze 2054 males who were part of the Tyrol (Austria) PSA Screening Project. Second, artificial neural network (ANN) analyses were performed using data from 3474 males who also were part of the Tyrol PSA Screening Project and who had undergone prostate biopsy. These analyses were compared with standard cutoff levels of specificity for the detection of prostate carcinoma. RESULTS. To the authors’ knowledge, this was the first time that multivariate logistic regression analysis was used to decide whether to perform prostate biopsies based on risk profiles rather than on single cutoff levels. For the detection of prostate carcinoma, at sensitivity levels of 90 –95%, the ANN was 150 –200% more specific than the standard cutoff points. For screened volunteers with total PSA levels below 4 ng/mL, ANN showed a lower cancer predictive ability in comparison with volunteers with total PSA levels above 4 ng/mL. However, the ANN was approximately 150 –200% more specific than the standard cutoff levels in both groups. CONCLUSIONS. At high sensitivity levels, ANN increased the specificity for prostate carcinoma detection in a PSA-based screened population. The improvement in specificity between standard cutoff levels and ANN ranged between 150 –200% and was not affected by the presence of benign prostatic hyperplasia or prostatitis. Cancer 2001;91:1667–72. © 2001 American Cancer Society.