title: A clinical model for identifying the short-term risk of breast cancer.
abstract: 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.
authors.affiliation: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Box 281, Stockholm, 171 77, Sweden. mikael.eriksson@ki.se.
authors.forename: Mikael
authors.initials: M
authors.lastname: Eriksson
authors.affiliation: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Box 281, Stockholm, 171 77, Sweden.
authors.forename: Kamila
authors.initials: K
authors.lastname: Czene
authors.affiliation: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Box 281, Stockholm, 171 77, Sweden.
authors.forename: Yudi
authors.initials: Y
authors.lastname: Pawitan
authors.affiliation: Department of Radiology, South General Hospital, 118 83, Stockholm, Sweden.
authors.forename: Karin
authors.initials: K
authors.lastname: Leifland
authors.affiliation: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Box 281, Stockholm, 171 77, Sweden.
authors.forename: Hatef
authors.initials: H
authors.lastname: Darabi
authors.affiliation: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Box 281, Stockholm, 171 77, Sweden.|Department of Oncology, South General Hospital, 118 83, Stockholm, Sweden.
authors.forename: Per
authors.initials: P
authors.lastname: Hall
mesh_terms: D000328:Adult; D000368:Aged; D019540:Area Under Curve; D000071060:Breast Density; D001943:Breast Neoplasms / Q000175:diagnosis / Q000453:epidemiology* / Q000517:prevention & control; D016022:Case-Control Studies; D003198:Computer Simulation; D055088:Early Detection of Cancer; D005260:Female; D020249:Hormone Replacement Therapy / Q000009:adverse effects; D006801:Humans; D008327:Mammography; D008403:Mass Screening; D008875:Middle Aged; D008962:Models, Theoretical; D012306:Risk; D012307:Risk Factors; D013548:Sweden / Q000453:epidemiology
keywords: Breast cancer; Computer-aided detection; Mammographic density; Masses; Microcalcification; Prevention; Risk prediction