Predicting risk of bladder cancer using clinical and demographic information from prostate, lung, colorectal, and ovarian cancer (PLCO) screening trial participants
Menée sur 149 542 participants à un essai de dépistage du cancer de l'ovaire, de la prostate, du côlon-rectum ou du poumon (âge : 55 à 74 ans, durée médiane de suivi : 12 ans), cette étude évalue la valeur d'un nomogramme, développé à partir de données cliniques et sociodémographiques, pour prédire le risque de cancer de la vessie (1 383 cas dont 259 femmes), notamment de haut grade (464 cas)
Background : Effective screening and prevention strategies for bladder cancer require accurate risk stratification models. We developed models to predict the risk of bladder cancer based on clinical and sociodemographic factors in participants in the Prostate, Lung, Colon, Ovarian Cancer (PLCO) screening trial.
Methods : Baseline clinical and sociodemographic data were obtained from 149,542 PLCO participants aged 55-74 without a prior history of bladder cancer. Cox proportional hazards models were used to predict the risk of all and high-grade bladder cancers (ABC and HGBC) from baseline information. We used the risk model to design a hypothetical bladder cancer mortality prevention trial.
Results : Over a median follow-up of 12 years, 1124 men and 259 women developed bladder cancer (including 392 and 72 with HGBC, respectively). The incidence was 133.6 and 29.6 cases per 100,000 person-years, respectively. Nomograms to predict the risk of ABC and HGBC were constructed and the c-indices were 0.746 and 0.759, respectively. Age, race, education, smoking (intensity and duration), comorbidity, prostatitis, syphilis, and hormone replacement therapy use were statistically significant predictors in the models. We show that our risk model can be used to design a BC mortality prevention trial half the size of a trial designed without risk stratification.
Conclusion : Models to predict the risk of ABC and HGBC have been developed and validated.
Impact : Using the upper 40th percentile from the HGBC model, a suitable cohort for a screening or chemoprevention trial could be identified, although the size and follow-up of such a trial would be costly.
Cancer Epidemiology Biomarkers & Prevention , résumé, 2013