Improving Pretreatment Risk Prognostication in Localized Prostate Cancer
Menée à partir de données portant sur une cohorte internationale de 19 684 patients atteints d'un cancer de la prostate non métastatique traité par prostatectomie radicale ou radiothérapie en combinaison ou non avec un traitement anti-androgénique (âge médian : 64 ans ; durée médiane de suivi : 71,8 mois), cette étude évalue la performance d'un système de score, basé sur des données clinico-pathologiques (âge, stade de la tumeur, score de Gleason, niveau sérique du PSA avant traitement, ...), pour prédire la mortalité spécifique
In an elegantly executed analysis by Dess et al in this issue of JAMA Oncology, the international staging collaboration for cancer of the prostate (STAR-CAP) proposes a contemporary pretreatment, predictive staging system that accomplishes 2 important goals: (1) it meets the criteria set forth by the Precision Medicine Core committee of the American Joint Committee on Cancer (AJCC); and (2) it improves on the predictive accuracy of prior models and staging systems. The use of multivariable, adjusted predictive tools was pioneered by Kattan and colleagues with the introduction of the pretreatment nomogram in prostate cancer. These instruments focused primarily on posttreatment recurrence, which is an end point that does not always correlate with prostate cancer–specific mortality. Many of these early predictive tools also relied on discovery data sets that reflected less diverse, urban academic practices that are not always generalizable to other populations, such as Black men. Therefore, a staging classification system that is based on statistical modeling and can be used to predict the risk of prostate cancer-specific mortality for a racially diverse cohort of men diagnosed with clinically localized prostate cancer is greatly needed.
JAMA Oncology , éditorial, 2019