Volume 54, Issue 3, Pages 601-611 (September 2008) Development, Validation, and Head-to-Head Comparison of Logistic Regression-Based Nomograms and Artificial Neural Network Models Predicting Prostate Cancer on Initial Extended Biopsy  Satoru Kawakami, Noboru Numao, Yuhei Okubo, Fumitaka Koga, Shinya Yamamoto, Kazutaka Saito, Yasuhisa Fujii, Junji Yonese, Hitoshi Masuda, Kazunori Kihara, Iwao Fukui  European Urology  Volume 54, Issue 3, Pages 601-611 (September 2008) DOI: 10.1016/j.eururo.2008.01.017 Copyright © 2008 European Association of Urology Terms and Conditions

Fig. 1 Transverse, sagittal, and coronal projections of the three-dimensional 26-core biopsy (3D26PBx), a combination of (A) transperineal 14-core biopsy (TP14PBx) and (B) transrectal 12-core biopsy. (C) Three-dimensional 22-core biopsy (3D22PBx) and three-dimensional 14-core biopsy (3D14PBx) are subsets of the 3D26PBx. European Urology 2008 54, 601-611DOI: (10.1016/j.eururo.2008.01.017) Copyright © 2008 European Association of Urology Terms and Conditions

Fig. 2 Logistic regression-based nomogram 1 (A) and nomogram 2 (B) predicting initial extended biopsy outcome. To obtain predicted probability of prostate cancer on initial extended biopsy, locate patient values at each axis. Draw a vertical line upward to the “Points” axis to determine the points of the variable. Sum the points for all variables and locate the sum on the “Total points” axis. Draw a vertical line down to the “Probability of prostate cancer on initial biopsy” axis to find the patient's probability of having prostate cancer on initial extended biopsy. DRE=digital rectal examination (1=suspicious, 0=normal); PSA=prostate-specific antigen; fPSA: free PSA; TRUS: transrectal ultrasound findings (1=suspicious, 0=normal). European Urology 2008 54, 601-611DOI: (10.1016/j.eururo.2008.01.017) Copyright © 2008 European Association of Urology Terms and Conditions

Fig. 3 Schematic diagrams of artificial neural network (ANN) models predicting initial extended biopsy outcome. (A) ANN1 considers age, digital rectal examination (DRE) findings, prostate-specific antigen (PSA), and free PSA (fPSA) as input variables. (B) ANN2 further considers transrectal ultrasound (TRUS) findings and prostate volume as additional input variables. European Urology 2008 54, 601-611DOI: (10.1016/j.eururo.2008.01.017) Copyright © 2008 European Association of Urology Terms and Conditions

Fig. 4 Calibration plots with local regression nonparametric smoothing lines of the predicting models on the external validation data set. (A) Comparison between nomogram 1 (colored) and ANN1 (black). (B) Comparison between nomogram 2 (colored) and ANN2 (black). Perfect predictions correspond to the 45̊ dotted line. Points estimated below the 45̊ line represent over-prediction, whereas those above the 45̊ line represent under-prediction. Area under the receiver operating characteristics curve (AUC) values and the sum of square of the residuals (SSR) of the calibration plot are shown. European Urology 2008 54, 601-611DOI: (10.1016/j.eururo.2008.01.017) Copyright © 2008 European Association of Urology Terms and Conditions