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Our objective was to determine whether multivariate algorithms based on serum total PSA, the free proportion of PSA, age, digital rectal examination and prostate volume can reduce the rate of false-positive PSA results in prostate cancer screening more effectively than the proportion of free PSA alone at 95% sensitivity. A total of 1,775 consecutive 55-to 67-year-old men with a serum PSA of 4 -10 g/l in the European Randomized Study of Screening for Prostate Cancer were included. To predict the presence of cancer, multivariate algorithms were constructed using logistic regression (LR) and a multilayer perceptron neural network with Bayesian regularization (BR-MLP). A prospective setting was simulated by dividing the data set chronologically into one set for training and validation (67%, n ‫؍‬ 1,183) and one test set (33%, n ‫؍‬ 592). The diagnostic models were calibrated using the training set to obtain 95% sensitivity. When applied to the test set, the LR model, the BR-MLP model and the proportion of free PSA reached 92%, 87% and 94% sensitivity and Key words: prostate cancer; screening; logistic regression; neural network; prostate-specific antigenProstate cancer is the cancer with the highest incidence among men in most industrialized countries. 1 PSA is increasingly used for early detection and screening, even though it has not been shown to reduce prostate cancer mortality and may lead to overdiagnosis of the disease. A principal problem with prostate cancer screening is that only approximately a third of men with elevated serum PSA (Ն4 g/l) have cancer in prostate biopsies. Most false-positive PSA results are caused by benign prostatic hyperplasia (BPH). False-positive results cause unnecessary prostate biopsies, generating anxiety, discomfort and costs. 2,3 Various approaches have been suggested to identify, among screening positive men, a low-risk group that could be spared unnecessary prostate biopsies. The proportion of free PSA in serum is lower in men with prostate cancer than in those with BPH, 4 -7 and determination of the proportion of free PSA has been shown to reduce Ͼ30% of false-positive PSA results at 90% sensitivity. 8,9 Prostate volume is usually smaller in prostate cancer cases than in other men with elevated serum PSA, 10 -12 and the ratio between total PSA and prostate volume (PSA density) can also be used to reduce false-positive PSA results. 9 Logistic regression (LR) and neural networks have been applied to produce diagnostic algorithms based mainly on total PSA, free PSA, prostate volume and digital rectal examination (DRE). Combined use of several variables has been shown to reduce the number of false-positive PSA results more efficiently than single use of the proportion of free PSA. [12][13][14][15][16][17][18][19] However, LR or neural network algorithms for prostate cancer detection have not been studied prospectively; i.e., use of algorithm outcomes for making real-life biopsy decisions has not been reported. We simulated a prospective setting by using earlier subjects as trai...

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