1. Quantitative breast density analysis to predict interval and node-positive cancers in pursuit of improved screening protocols: a case–control study
- Author
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Matthew G. Wallis, Mishal N. Patel, Louise S. Wilkinson, Kenneth C. Young, Jonathan P. Myles, Lucy M. Warren, Elizabeth S. Burnside, Nathalie J. Massat, Stephen W. Duffy, Robert A. Smith, Burnside, Elizabeth S [0000-0002-6600-435X], Smith, Robert A [0000-0003-3344-2238], Massat, Nathalie J [0000-0002-1095-994X], Duffy, Stephen W [0000-0003-4901-7922], and Apollo - University of Cambridge Repository
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Oncology ,Cancer Research ,medicine.medical_specialty ,Visual Analog Scale ,Imaging biomarker ,Visual analogue scale ,Breast Neoplasms ,Logistic regression ,Risk Assessment ,Article ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,Breast cancer screening ,0302 clinical medicine ,Breast cancer ,Internal medicine ,Neoplasms ,medicine ,Humans ,Early Detection of Cancer ,Aged ,Randomized Controlled Trials as Topic ,Breast Density ,Receiver operating characteristic ,medicine.diagnostic_test ,business.industry ,Case-control study ,Cancer ,Middle Aged ,medicine.disease ,Risk factors ,Case-Control Studies ,030220 oncology & carcinogenesis ,Female ,business ,Mammography - Abstract
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, 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.
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- 2021