• Dépistage, diagnostic, pronostic

  • Essais de technologies et de biomarqueurs dans un contexte clinique

  • Sein

Breast Density and Benign Breast Disease: Risk Assessment to Identify Women at High Risk of Breast Cancer

A partir des données 2000-2010 des registres américains des cancers et des données statistiques démographiques de l'année 2010 (1 135 977 femmes ; âge : 35 à 74 ans ; durée moyenne de suivi : 6,9 ans), cette étude évalue la performance d'un modèle mathématique, prenant en compte la présence ou non d'une maladie bénigne du sein ainsi que la densité mammaire, pour prédire le risque de cancer du sein

Purpose : Women with proliferative breast lesions are candidates for primary prevention, but few risk models incorporate benign findings to assess breast cancer risk. We incorporated benign breast disease (BBD) diagnoses into the Breast Cancer Surveillance Consortium (BCSC) risk model, the only breast cancer risk assessment tool that uses breast density.

Methods : We developed and validated a competing-risk model using 2000 to 2010 SEER data for breast cancer incidence and 2010 vital statistics to adjust for the competing risk of death. We used Cox proportional hazards regression to estimate the relative hazards for age, race/ethnicity, family history of breast cancer, history of breast biopsy, BBD diagnoses, and breast density in the BCSC.

Results : We included 1,135,977 women age 35 to 74 years undergoing mammography with no history of breast cancer; 17% of the women had a prior breast biopsy. During a mean follow-up of 6.9 years, 17,908 women were diagnosed with invasive breast cancer. The BCSC BBD model slightly overpredicted risk (expected-to-observed ratio, 1.04; 95% CI, 1.03 to 1.06) and had modest discriminatory accuracy (area under the receiver operator characteristic curve, 0.665). Among women with proliferative findings, adding BBD to the model increased the proportion of women with an estimated 5-year risk of 3% or higher from 9.3% to 27.8% (P < .001).

Conclusion : The BCSC BBD model accurately estimates women's risk for breast cancer using breast density and BBD diagnoses. Greater numbers of high-risk women eligible for primary prevention after BBD diagnosis are identified using the BCSC BBD model.

Journal of Clinical Oncology , résumé, 2015

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