Effects of vitamin D supplementation on a deep learning-based mammographic evaluation in SWOG S0812.
Publication Title
JNCI Cancer Spectr
Document Type
Article
Publication Date
7-1-2024
Keywords
washington; swedish; Humans; Female; Mammography; Deep Learning; Breast Neoplasms; Dietary Supplements; Breast Density; Middle Aged; Cholecalciferol; Adult; Vitamin D; Premenopause; Neural Networks, Computer; Risk Assessment
Abstract
Deep learning-based mammographic evaluations could noninvasively assess response to breast cancer chemoprevention. We evaluated change in a convolutional neural network-based breast cancer risk model applied to mammograms among women enrolled in SWOG S0812, which randomly assigned 208 premenopausal high-risk women to receive oral vitamin D3 20 000 IU weekly or placebo for 12 months. We applied the convolutional neural network model to mammograms collected at baseline (n = 109), 12 months (n = 97), and 24 months (n = 67) and compared changes in convolutional neural network-based risk score between treatment groups. Change in convolutional neural network-based risk score was not statistically significantly different between vitamin D and placebo groups at 12 months (0.005 vs 0.002, P = .875) or at 24 months (0.020 vs 0.001, P = .563). The findings are consistent with the primary analysis of S0812, which did not demonstrate statistically significant changes in mammographic density with vitamin D supplementation compared with placebo. There is an ongoing need to evaluate biomarkers of response to novel breast cancer chemopreventive agents.
Area of Special Interest
Cancer
Area of Special Interest
Women & Children
Specialty/Research Institute
Oncology
Specialty/Research Institute
Diagnostic Imaging
DOI
10.1093/jncics/pkae042
