PSA and new biomarkers within multivariate models to improve early detection of prostate cancer.
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.
Stephan
Carsten
C
Department of Urology, Charité - Universitätsmedizin Berlin, Campus Charité Mitte, Charitéplatz 1, D-10098 Berlin, Germany. carsten.stephan@charite.de
Cammann
Henning
H
Meyer
Hellmuth-A
HA
Lein
Michael
M
Jung
Klaus
K
D014408:Biomarkers, Tumor / Q000097:blood*; D006801:Humans; D016015:Logistic Models; D008297:Male; D016571:Neural Networks, Computer; D017430:Prostate-Specific Antigen / Q000097:blood*; D011471:Prostatic Neoplasms / Q000097:blood / Q000175:diagnosis*
D014408:Biomarkers, Tumor; D017430:Prostate-Specific Antigen