Addressing Racial and Ethnic Differences in Diagnostic Resolution of Abnormal Mammographic Findings
Menée aux Etats-Unis à partir de données portant sur 45 186 femmes dont les résultats d'une ou plusieurs mammographies de dépistage se sont avérés anormaux (âge : de 40 à 79 ans), cette étude évalue, en fonction de l'origine ethnique, l'association entre des facteurs socio-démographiques ou médicaux (âge, niveau socio-économique du voisinage, antécédents familiaux de cancer du sein, densité mammaire, ...) et le délai entre la découverte d'anomalies lors de la mammographie de dépistage et la biopsie
Breast cancer is the most common nonskin cancer and second leading cause of cancer death in US women. Over 280 000 new cases of invasive breast cancer are expected to be diagnosed in 2022, and over 43 000 women are expected to die from the disease during this same period. While early detection though routine screening contributes to improved breast cancer–specific outcomes, delays in diagnostic resolution of abnormal mammographic findings may diminish the benefits of early breast cancer detection resulting in lower disease-specific and overall survival.In this issue of JAMA Oncology, Lawson and colleagues report the results of a prospective cohort study to evaluate multilevel predictors of time to biopsy after abnormal screening mammography by race and ethnicity. The study included 45 186 women between the ages of 40 and 79 years with a recommendation for biopsy following an abnormal screening mammogram between 2009 and 2019 through the Breast Cancer Surveillance Consortium (BCSC), a collaborative network of 6 active breast imaging registries and 2 historic registries. Exposures included woman-, neighborhood-, and health care–level factors. Unadjusted and adjusted relative risk (RR) of a woman not having a biopsy within 30, 60, and 90 days following an abnormal screening mammogram was evaluated using sequential log-binomial regression models, and unadjusted and adjusted median time to biopsy was assessed using accelerated failure time models.
JAMA Oncology , commentaire, 2021