Effects of vitamin D supplementation on a deep learning-based mammographic evaluation in SWOG S0812.
Document Type
Article
Publication Date
7-1-2024
Publication Title
JNCI cancer spectrum
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.
Volume
8
Issue
4
First Page
kae042
Recommended Citation
McGuinness JE, Anderson GL, Mutasa S, Hershman DL, Terry MB, Tehranifar P, et al [Brown EA, Kuwajerwala N] Effects of vitamin D supplementation on a deep learning-based mammographic evaluation in SWOG S0812. JNCI Cancer Spectr. 2024 Jul 1;8(4):pkae042. doi: 10.1093/jncics/pkae042. PMID: 38814817
DOI
10.1093/jncics/pkae042
ISSN
2515-5091
PubMed ID
38814817
