Quantitative breast density analysis to predict interval and node-positive cancers in pursuit of improved screening protocols: a case–control study
Menée à partir de données portant sur 1 204 femmes âgées de 47 à 73 ans dont 599 atteintes d'un cancer du sein et 605 témoins, cette étude évalue l'association entre des biomarqueurs d'imagerie (volume fibroglandulaire, densité mammaire volumétrique, échelle visuelle analogique évaluant la densité mammaire, degré de densité) pour prédire le risque de cancer de l'intervalle ou de cancer avec envahissement ganglionnaire
Background : This study investigates whether quantitative breast density (BD) serves as an imaging biomarker for more intensive breast cancer screening by predicting interval, and node-positive cancers.
Methods : This case–control study of 1204 women aged 47–73 includes 599 cancer cases (302 screen-detected, 297 interval; 239 node-positive, 360 node-negative) and 605 controls. Automated BD software calculated fibroglandular volume (FGV), volumetric breast density (VBD) and density grade (DG). A radiologist assessed BD using a visual analogue scale (VAS) from 0 to 100. Logistic regression and area under the receiver operating characteristic curves (AUC) determined whether BD could predict mode of detection (screen-detected or interval); node-negative cancers; node-positive cancers, and all cancers vs. controls.
Results : FGV, VBD, VAS, and DG all discriminated interval cancers (all p < 0.01) from controls. Only FGV-quartile discriminated screen-detected cancers (p < 0.01). Based on AUC, FGV discriminated all cancer types better than VBD or VAS. FGV showed a significantly greater discrimination of interval cancers, AUC = 0.65, than of screen-detected cancers, AUC = 0.61 (p < 0.01) as did VBD (0.63 and 0.53, respectively, p < 0.001).
Conclusion : FGV, VBD, VAS and DG discriminate interval cancers from controls, reflecting some masking risk. Only FGV discriminates screen-detected cancers perhaps adding a unique component of breast cancer risk.
British Journal of Cancer , article en libre accès, 2021