Artificial neural networks in the diagnosis and prognosis of prostate cancer: a pilot study.

There is controversy about how prostate cancer screening tests should best be used because of the false-negative and false-positive results. There also is controversy about prostate cancer treatment because of errors in tumor staging, uncertainty about treatment efficacy and the variable natural history of the disease. We sought to determine in a pilot study whether artificial neural networks would be helpful to predict biopsy results in men with abnormal screening test(s) and to predict treatment outcome after radical prostatectomy. To predict biopsy results, we extracted data from a prostate specific antigen (PSA) based screening study data base in 1,787 men with a serum PSA concentration of more than 4.0 ng./ml. (approximately 40% of the men also had suspicious findings on digital rectal examination). To predict cancer recurrence after radical prostatectomy, we extracted data from a random sample of 240 patients selected from a data base of men who had undergone radical prostatectomy. The neural network predicted the biopsy result with 87% overall accuracy, and its output threshold could be adjusted to achieve the desired tradeoff between sensitivity and specificity. It also predicted tumor recurrence with 90% overall accuracy. We conclude that trained neural networks may be useful in decision making for prostate cancer patients.