Do prostate cancer risk models improve the predictive accuracy of PSA screening? A meta-analysis.
BACKGROUND
Despite the extensive development of risk prediction models to aid patient decision-making on prostate screening, it is unknown whether these models could improve predictive accuracy of PSA testing to detect prostate cancer (PCa). The objective of this study was to perform a systematic review to identify PCa risk models and to assess the model's performance to predict PCa by conducting a meta-analysis.


METHODS
A systematic literature search of Medline was conducted to identify PCa predictive risk models that used at least two variables, of which one of the variables was prostate-specific antigen (PSA) level. Model performance (discrimination and calibration) was assessed. Prediction models validated in ≥5 study populations and reported area under the curve (AUC) for prediction of any or clinically significant PCa were eligible for meta-analysis. Summary AUC and 95% CIs were calculated using a random-effects model.


RESULTS
The systematic review identified 127 unique PCa prediction models; however, only six models met study criteria for meta-analysis for predicting any PCa: Prostataclass, Finne, Karakiewcz, Prostate Cancer Prevention Trial (PCPT), Chun, and the European Randomized Study of Screening for Prostate Cancer Risk Calculator 3 (ERSPC RC3). Summary AUC estimates show that PCPT does not differ from PSA testing (0.66) despite performing better in studies validating both PSA and PCPT. Predictive accuracy to discriminate PCa increases with Finne (AUC = 0.74), Karakiewcz (AUC = 0.74), Chun (AUC = 0.76) and ERSPC RC3 and Prostataclass have the highest discriminative value (AUC = 0.79), which is equivalent to doubling the sensitivity of PSA testing (44% versus 21%) without loss of specificity. The discriminative accuracy of PCPT to detect clinically significant PCa was AUC = 0.71. Calibration measures of the models were poorly reported.


CONCLUSIONS
Risk prediction models improve the predictive accuracy of PSA testing to detect PCa. Future developments in the use of PCa risk models should evaluate its clinical effectiveness in practice.
Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK. Electronic address: k.louie@qmul.ac.uk.
K S
KS
Louie
Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK; Joseph Fourier University-Grenoble 1, CNRS, TIMC-IMAG UMR 5525, Grenoble; Medical Evaluation Unit, Grenoble University Hospital, Grenoble, France.
A
A
Seigneurin
Department of Urology, University College Hospital London and St Bartholomew's Hospital London and Centre for Experimental Cancer Medicine, Bart's Cancer Institute, London; The Clinical Effectiveness Unit, The Royal College of Surgeons of England, London, UK.
P
P
Cathcart
Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK.
P
P
Sasieni
D019540:Area Under Curve; D001706:Biopsy; D003661:Decision Support Techniques; D016002:Discriminant Analysis; D006801:Humans; D007610:Kallikreins / Q000097:blood*; D008297:Male; D008954:Models, Biological; D015233:Models, Statistical; D011237:Predictive Value of Tests; D011379:Prognosis; D017430:Prostate-Specific Antigen / Q000097:blood*; D011471:Prostatic Neoplasms / Q000097:blood* / Q000473:pathology / Q000628:therapy; D012372:ROC Curve; D018570:Risk Assessment; D012307:Risk Factors
C549870:KLK3 protein, human; D007610:Kallikreins; D017430:Prostate-Specific Antigen
meta-analysis; prostate cancer; risk calculators; risk prediction models; screening