Assessing breast cancer risk within the general screening population: developing a breast cancer risk model to identify higher risk women at mammographic screening

• Published in: European radiology Vol. 30; no. 10; pp. 5417 - 5426
• Main Authors: Abdolell, Mohamed, Payne, Jennifer I., Caines, Judy, Tsuruda, Kaitlyn, Barnes, Penny J., Talbot, Pam J., Tong, Olivia, Brown, Peter, Rivers-Bowerman, Michael, Iles, Sian
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2020; Springer Nature B.V
• Subjects: Adult; Age; Aged; Biopsy; Biopsy, Large-Core Needle; Breast; Breast - diagnostic imaging; Breast - pathology; Breast cancer; Breast Density; Breast Neoplasms - diagnostic imaging; Breast Neoplasms - pathology; Case-Control Studies; Classifiers; Data collection; Diagnostic Radiology; Early Detection of Cancer - methods; Estimates; Family medical history; Female; Genetic Predisposition to Disease; Genetics; Health risks; Humans; Imaging; Internal Medicine; Interventional Radiology; Mammography; Mass Screening - methods; Medicine; Medicine & Public Health; Middle Aged; Neuroradiology; Performance enhancement; Population; Radiology; Retrospective Studies; Risk analysis; Risk assessment; Risk Assessment - methods; Risk Factors; ROC Curve; Screening; Ultrasound; Breast; Breast density; Breast neoplasms; Mammography; Risk assessment
• ISSN: 0938-7994; 1432-1084
• DOI: 10.1007/s00330-020-06901-x

Objectives To develop a breast cancer risk model to identify women at mammographic screening who are at higher risk of breast cancer within the general screening population. Methods This retrospective nested case-control study used data from a population-based breast screening program (2009–2015). All women aged 40–75 diagnosed with screen-detected or interval breast cancer ( n  = 1882) were frequency-matched 3:1 on age and screen-year with women without screen-detected breast cancer ( n  = 5888). Image-derived risk factors from the screening mammogram (percent mammographic density [PMD], breast volume, age) were combined with core biopsy history, first-degree family history, and other clinical risk factors in risk models. Model performance was assessed using the area under the receiver operating characteristic curve (AUC). Classifiers assigning women to low- versus high-risk deciles were derived from risk models. Agreement between classifiers was assessed using a weighted kappa. Results The AUC was 0.597 for a risk model including only image-derived risk factors. The successive addition of core biopsy and family history significantly improved performance (AUC = 0.660, p  < 0.001 and AUC = 0.664, p  = 0.04, respectively). Adding the three remaining risk factors did not further improve performance (AUC = 0.665, p  = 0.45). There was almost perfect agreement (kappa = 0.97) between risk assessments based on a classifier derived from image-derived risk factors, core biopsy, and family history compared with those derived from a model including all available risk factors. Conclusions Women in the general screening population can be risk-stratified at time of screen using a simple model based on age, PMD, breast volume, and biopsy and family history. Key Points • A breast cancer risk model based on three image-derived risk factors as well as core biopsy and first-degree family history can provide current risk estimates at time of screen. • Risk estimates generated from a combination of image-derived risk factors, core biopsy history, and first-degree family history may be more valid than risk estimates that rely on extensive self-reported risk factors. • A simple breast cancer risk model can avoid extensive clinical risk factor data collection.