ArticleBritish journal of cancer2021
Quantitative breast density analysis to predict interval and node-positive cancers in pursuit of improved screening protocols: a case-control study.
Article in British journal of cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Biological basis and clinical translation prospects of circulating cell-free DNA in precision management of breast cancer (Review).Oncology letters · 2026Review
- Development and evaluation of artificial intelligence tools to estimate volumetric breast density from processed 2D mammograms.BJR artificial intelligence · 2026Article
- Breast density and risk of breast cancer.International journal of cancer · 2023Article
- Proton density water fraction as a reproducible MR-based measurement of breast density.Magnetic resonance in medicine · 2022Article
- Automated Breast Density Measurement in Mammography for Cancer Risk Stratification.Radiology. Imaging cancer · 2021Article
- OPTIMAM Mammography Image Database: A Large-Scale Resource of Mammography Images and Clinical Data.Radiology. Artificial intelligence · 2021Article
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10 authors.
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Abstract
backgroundThis 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.
methodsThis 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.
resultsFGV, 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).
conclusionFGV, 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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