Identifying regions of interest in mammogram images

Screening mammography is the primary preventive strategy for early detection of breast cancer and an essential input to breast cancer risk prediction and application of prevention/risk management guidelines. Identifying regions of interest within mammogram images that are associated with 5- or 10-ye...

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Bibliographic Details
Published in:Statistical methods in medical research 2023-05, Vol.32 (5), p.895-903
Main Authors: Jiang, Shu, Cao, Jiguo, Colditz, Graham A.
Format: Article
Language:English
Subjects:
Bivariate analysis
Breast cancer
Breast Neoplasms - diagnosis
Domains
Early Detection of Cancer
Female
Humans
Mammography
Mammography - methods
Medical screening
Original s
Penalty function
Prevention
Proportional Hazards Models
Risk
Risk management
Spline functions
Statistical models
Triangulation
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Summary:Screening mammography is the primary preventive strategy for early detection of breast cancer and an essential input to breast cancer risk prediction and application of prevention/risk management guidelines. Identifying regions of interest within mammogram images that are associated with 5- or 10-year breast cancer risk is therefore clinically meaningful. The problem is complicated by the irregular boundary issue posed by the semi-circular domain of the breast area within mammograms. Accommodating the irregular domain is especially crucial when identifying regions of interest, as the true signal comes only from the semi-circular domain of the breast region, and noise elsewhere. We address these challenges by introducing a proportional hazards model with imaging predictors characterized by bivariate splines over triangulation. The model sparsity is enforced with the group lasso penalty function. We apply the proposed method to the motivating Joanne Knight Breast Health Cohort to illustrate important risk patterns and show that the proposed method is able to achieve higher discriminatory performance.
ISSN:0962-2802
1477-0334
DOI:10.1177/09622802231160551