PSA and new biomarkers within multivariate models to improve early detection of prostate cancer

Abstract 

This review gives an overview of the use of prostate-specific antigen (PSA) and percent free-PSA (%fPSA)-based artificial neural networks (ANNs) and logistic regression models (LR) to reduce unnecessary prostate biopsies. There is a clear advantage in including clinical data such as age, digital rectal examination and transrectal ultrasound (TRUS) variables like prostate volume and PSA density as additional factors to tPSA and %fPSA within ANNs and LR models. There is also positive impact of tPSA and fPSA assays on the outcome of ANNs. New markers provide additional value within ANNs but to prove their clinical usefulness further testing is necessary.

Abbreviations: ANN, artificial neural network, AUC, area under receiver operating characteristic curve, BPH, benign prostatic hyperplasia, DRE, digital rectal examination, fPSA, free (non-complexed) PSA, iANN, immulite-based ANN, MLP, multilayer perceptron, nANN, new (with the new assay developed) ANN, LR, logistic regression, PCa, prostate cancer, PSA, prostate-specific antigen, PSAD, PSA density (ratio of PSA to prostate volume), PSAD-TZ, transition zone density, %fPSA, percent free-PSA, tPSA, total PSA, TRUS, transrectal ultrasound

Keywords: Prostate cancer, Prostate-specific antigen, Receiver operating characteristic curve, Artificial neural network, Logistic regression, Prostate biopsy