© 2004 Foundation for Promotion of Cancer Research
The Use of Artificial Neural Network Analysis to Improve the Predictive Accuracy of Prostate Biopsy in the Japanese Population
1 Department of Urology, Kurashiki Central Hospital, Kurashiki, Okayama, Japan and 2 Department of Urology, Tohoku University, School of Medicine, Sendai
For reprints and all correspondence: Yoshiyuki Matsui, Department of Urology, Faculty of Medicine, Kyoto University, Shogoin Kawahara-cho 54, Sakyo-ku, Kyoto 606-8507, Japan. E-mail: ym1108{at}kuhp.kyoto-u.ac.jp
Received May 20, 2004; accepted August 9, 2004
Objective: We examined the efficacy of an artificial neural network analysis (ANNA) based on parameters available from previously existing examinations for improving the predictive accuracy of prostate cancer screening in the Japanese population.
Methods: Two hundred and twenty-eight patients with prostate-specific antigen (PSA) of 2–10 ng/ml were enrolled in this study. Two artificial neural network analysis (ANNA) models were constructed: ANNA1 with patient age, total PSA, free to total PSA ratio, prostate volume, transition zone volume (TZ), PSA density (PSAD) and PSA-TZ density (PSATZ) as input variables, and ANNA2 with presumed circle area ratio (PCAR), digital rectal examination (DRE) findings and chief complaint added as variables. The predictive accuracies of the ANNA models were compared with conventional PSA and volume-related parameters and a logistic regression (LR) model by receiver operating characteristic (ROC) curve analysis.
Results: Of 228 patients, 58 (25.5%) were diagnosed with prostate cancer. While ANNA2 had a slightly larger area under the curve (AUC) than ANNA1 (0.782 versus 0.793, P = 0.8477), the AUC of ANNA2 was significantly greater than those of ln(PSA), PSAD, PSATZ and free to total PSA ratio (P = 0.0004, 0.0230, 0.0304, and 0.0037, respectively). The accuracy of ANNA2 was significantly better than that of LR analysis at 90 and 95% sensitivity levels (P = 0.0051 and P < 0.0001, respectively). At 95% sensitivity level, ANNA2 reduced unnecessary biopsies by 40.0% with a negative predictive value of 95.7%.
Conclusions: To determine the indication of prostate biopsy for PSA value in the range of 2–10 ng/ml, the ANNA model has the possibility to reduce unnecessary biopsies without missing many cases of cancers.
Key Words: prostate cancer • artificial neural network analysis • prostate biopsy • logistic regression analysis
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