The online version of this article (doi:10.1186/s13058-017-0820-y) contains supplementary material, which is available to authorized users.
Most mammography screening programs are not individualized. To efficiently screen for breast cancer, the individual risk of the disease should be determined. We describe a model that could be used at most mammography screening units without adding substantial cost.
The study was based on the Karma cohort, which included 70,877 participants. Mammograms were collected up to 3 years following the baseline mammogram. A prediction protocol was developed using mammographic density, computer-aided detection of microcalcifications and masses, use of hormone replacement therapy (HRT), family history of breast cancer, menopausal status, age, and body mass index. Relative risks were calculated using conditional logistic regression. Absolute risks were calculated using the iCARE protocol.
Comparing women at highest and lowest mammographic density yielded a fivefold higher risk of breast cancer for women at highest density. When adding microcalcifications and masses to the model, high-risk women had a nearly ninefold higher risk of breast cancer than those at lowest risk. In the full model, taking HRT use, family history of breast cancer, and menopausal status into consideration, the AUC reached 0.71.
Measures of mammographic features and information on HRT use, family history of breast cancer, and menopausal status enabled early identification of women within the mammography screening program at such a high risk of breast cancer that additional examinations are warranted. In contrast, women at low risk could probably be screened less intensively.
Additional file 1: Table S1. Relative risk of developing breast cancer in relation to mammographic density, number microcalcifications and number masses. Table S2. Relative risks on developing breast cancer in relation to tumor invasiveness and mode of detection. Table S3. Final model including main effects of risk factors, beta coefficients, standard errors and p-values. Table S4. Number of breast cancer cases diagnosed during study follow-up stratified by predicted risks at baseline in the Karma cohort. Supplementary Method 1. Supplementary Method 2. Supplementary Method 3. (DOCX 59 kb)13058_2017_820_MOESM1_ESM.docx
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