• A Tewari and P Narayan.
• University of Florida and Department of Veterans Affairs Medical Center, Gainesville, USA.
• J. Urol. 1998 Aug 1; 160 (2): 430-6.

PurposeAn estimated $1.5 billion is spent annually for direct medical expenses and an additional $2.5 billion for indirect costs for the management of prostate cancer. Today there are several procedures for staging prostate cancer, including lymph node dissection. Despite these procedures, the accuracy of predicting extracapsular disease remains low (range 37 to 63, mean 45%). Use of multiple staging procedures adds significantly to the costs of managing prostate cancer. Recently artificial intelligence based neural networks have become available for medical applications. Unlike traditional statistical methods, these networks do not assume linearity or homogeneity of variance and, thus, they are more accurate for clinical data. We applied this concept to staging localized prostate cancer and devised an algorithm that can be used for prostate cancer staging.Materials And MethodsOur study comprised 1,200 men with clinically organ confined prostate cancer who underwent preoperative staging using serum prostate specific antigen, systematic biopsy and Gleason scoring before radical prostatectomy and lymphadenectomy. The performance of the neural network was validated for a subset of patients and network predictions were compared with actual pathological stage. Mean patient age was 62.9 years, mean serum prostate specific antigen 8.1 ng./ml. and mean biopsy Gleason 6. Of the patients 55% had organ confined disease, 27% positive margins, 8% seminal vesicle involvement and 7% lymph node disease. Of margin positive patients 30% also had seminal vesicle involvement, while of seminal vesicle positive patients 50% also had positive margins.ResultsThe sensitivity of the network was 81 to 100%, and specificity was 72 to 75% for various predictions of margin, seminal vesicle and lymph node involvement. The negative predictive values tended to be relatively high for all 3 features (range 92 to 100%). The neural network missed only 8% of patients with margin positive disease, and 2% with lymph node and 0% with seminal vesicle involvement.ConclusionsOur study suggests that neural networks may be useful as an initial staging tool for detection of extracapsular extension in patients with clinically organ confined prostate cancer. These networks preclude unnecessary staging tests for 63% of patients with clinically organ confined prostate cancer.

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