Do prostate cancer risk models improve the predictive accuracy of PSA screening? A meta-analysis

Louie, Karly S.; Seigneurin, Arnaud; Cathcart, Paul; Sasieni, Peter

doi:10.1093/annonc/mdu525

Cited by 160 publications

(147 citation statements)

References 63 publications

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“…Risk calculators developed from cohort studies may also be useful in reducing the number of unnecessary biopsies. None have clearly shown superiority over another or can be considered as optimal [20].…”

Section: Classificationmentioning

confidence: 99%

EAU-ESTRO-SIOG Guidelines on Prostate Cancer. Part 1: Screening, Diagnosis, and Local Treatment with Curative Intent

et al. 2017

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Section: Classificationmentioning

confidence: 99%

EAU-ESTRO-SIOG Guidelines on Prostate Cancer. Part 1: Screening, Diagnosis, and Local Treatment with Curative Intent

et al. 2017

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“…Using the SNP data reported in their Table 2 and applying a validated simulation algorithm (4), we estimated that the AUC of the polygenic risk score would be 0.64. If confirmed by their data, this AUC would be lower than other models, including the prediction model from the Prostate Cancer Prevention Trial, which AUC was 0.66 for any prostate cancer and 0.71 for clinical significant prostate cancer (5).…”

mentioning

confidence: 76%

Risk Analysis of Prostate Cancer in PRACTICAL Consortium—Letter

Martens

Kers

Janssens

2016

Cancer Epidemiology, Biomarkers &Amp; Prevention

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colleagues investigated the predictive ability of a polygenic risk score and observed that the risk of men in the top 1% of the distribution was 30.6-fold compared with men in the bottom 1% and 4.2-fold compared with the median risk (1). The authors conclude that "genetic risk profiling using SNPs could be useful in defining men at high risk for the disease for targeted prevention and screening programs." Yet, such conclusion warrants a formal assessment of calibration and discriminative ability.First, assessment of calibration is essential because the reported risks were not based on empirical observations but calculated from a risk model that was built assuming multiplicative effects between the SNPs. The authors verified whether allelic effects within each SNP could be considered as multiplicative, but not whether multiplicativity between SNPs could be assumed. Multiplicative models are known to under-and overestimate risks at the extremes of the risk distribution, especially when they include a large number of SNPs (2, 3). Although the authors mentioned that "the predicted ORs for the top 1% and the bottom 1% of the population, based on a log-linear model, did not differ from that observed," this needs to be evidenced by a formal calibration analysis of the entire risk distribution and of the extremes if these are of special interest.Second, the discriminative ability of the model should be assessed by examining how well the predicted risks distinguish between men who did or did not develop prostate cancer, quantified by the area under the receiver operating characteristic curve (AUC), to compare its performance with other models. Using the SNP data reported in their Table 2 and applying a validated simulation algorithm (4), we estimated that the AUC of the polygenic risk score would be 0.64. If confirmed by their data, this AUC would be lower than other models, including the prediction model from the Prostate Cancer Prevention Trial, which AUC was 0.66 for any prostate cancer and 0.71 for clinical significant prostate cancer (5).Finally, the predictive performance is generally highest in the population in which the prediction model is developed, because the coefficients of the model are fitted to the data. The researchers have enough data to split their sample in two and perform both the development and validation analyses in one study. Independent validation of both calibration and discrimination will likely lead to a more modest perspective of the predictive performance of the polygenic risk score. Disclosure of Potential Conflicts of InterestNo potential conflicts of interest were disclosed.

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“…88 One well-performed systematic review and metaanalysis has examined the diagnostic accuracy of multiple prostate cancer risk nomograms; 88 however, most of the available nomograms remained untested or inadequately validated. In the six nomograms with adequate validation across several study populations, the discrimination properties for prostate cancer detection were moderate (AUC 0.66-0.79) and most did not assess calibration.…”

Section: Prostate Risk Calculatorsmentioning

confidence: 99%

“…In the six nomograms with adequate validation across several study populations, the discrimination properties for prostate cancer detection were moderate (AUC 0.66-0.79) and most did not assess calibration. 88 In addition, most nomograms were not validated for the prediction of clinically significant disease.…”

Section: Prostate Risk Calculatorsmentioning

confidence: 99%

“…Although all of these calculators can be used to estimate the risk of harbouring clinically significant prostate cancer prior to prostate biopsy, they again display only moderate predictive accuracy, which varies across different study populations. 88,[93][94][95][96] Nonetheless, their online availability, ease of use, and improvement upon PSA alone make them attractive adjuncts when counselling patients considering undergoing prostate biopsy. Thus, prostate risk calculators can be used to estimate the risk of clinically significant prostate cancer in men presenting with an elevated PSA.…”

Section: Prostate Risk Calculatorsmentioning

confidence: 99%

See 1 more Smart Citation

Canadian Urological Association recommendations on prostate cancer screening and early diagnosis

Rendon¹,

Mason

Marzouk³

et al. 2017

CUAJ

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Do prostate cancer risk models improve the predictive accuracy of PSA screening? A meta-analysis

Abstract: 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.

Cited by 160 publications

References 63 publications

EAU-ESTRO-SIOG Guidelines on Prostate Cancer. Part 1: Screening, Diagnosis, and Local Treatment with Curative Intent

EAU-ESTRO-SIOG Guidelines on Prostate Cancer. Part 1: Screening, Diagnosis, and Local Treatment with Curative Intent

Risk Analysis of Prostate Cancer in PRACTICAL Consortium—Letter

Canadian Urological Association recommendations on prostate cancer screening and early diagnosis

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