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Author Matsui, Yoshiyuki ♦ Utsunomiya, Noriaki ♦ Ichioka, Kentaro ♦ Ueda, Norihumi ♦ Yoshimura, Koji ♦ Terai, Akito ♦ Arai, Yoichi
Source CiteSeerX
Content type Text
File Format PDF
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Predictive Accuracy ♦ Artificial Neural Network Analysis ♦ Prostate Biopsy ♦ Japanese Population ♦ Prostate Cancer ♦ Ng Ml ♦ Unnecessary Biopsy ♦ Sensitivity Level ♦ Anna Model ♦ Prostate-specific Antigen ♦ Lr Analysis ♦ Total Psa Ratio ♦ Patient Age ♦ Volume-related Parameter ♦ Input Variable ♦ Thoseof Ln ♦ Conventional Psa ♦ Chief Complaint ♦ Presumed Circle Area Ratio ♦ Psa Value ♦ Digital Rectal Examination ♦ Many Case ♦ Negative Predictive Value ♦ Total Psa ♦ Twenty-eight Patient ♦ Curve Analysis ♦ Receiver Operating Characteristic ♦ Logistic Regression ♦ Prostate Volume
Abstract 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, transitionzonevolume(TZ), PSAdensity (PSAD)andPSA-TZdensity (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 ofANNA2wassignificantly greater than thoseof ln(PSA),PSAD,PSATZand free to totalPSA 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
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Publisher Date 2004-01-01