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

Posted by Krista Whitehouse on October 30, 2020 in Breast
Assessing breast cancer risk
Assessing breast cancer risk

New publicaton from Dr. Mohamed Abdolell, Dr. Jennifer Payne, Dr. Peter Brown, Dr. Michael Rivers-Bowerman, Dr. Sian Iles. See full text.

Abstract

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.


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