Quantitative breast density analysis to predict interval and node-positive cancers in pursuit of improved screening protocols: a case–control study
Warren, Lucy M.
Wilkinson, Louise S.
Wallis, Matthew G.
Young, Kenneth C.
British Journal of Cancer
Nature Publishing Group UK
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Burnside, E. S., Warren, L. M., Myles, J., Wilkinson, L. S., Wallis, M. G., Patel, M., Smith, R. A., et al. (2021). Quantitative breast density analysis to predict interval and node-positive cancers in pursuit of improved screening protocols: a case–control study. British Journal of Cancer, 125 (6), 884-892. https://doi.org/10.1038/s41416-021-01466-y
Funder: Policy Research Unit in Cancer Awareness, Screening and early Diagnosis, PR-PRU-1217-21601
Funder: American Cancer Society NHPDCSGBR-GBRLONG Policy Research Unit in Cancer Awareness, Screening and early Diagnosis, PR-PRU-1217-21601
Abstract: 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.
Article, /631/67/1347, /692/499, article
Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.) (K24CA194251)
External DOI: https://doi.org/10.1038/s41416-021-01466-y
This record's URL: https://www.repository.cam.ac.uk/handle/1810/327992