1. Effects of vitamin D supplementation on a deep learning-based mammographic evaluation in SWOG S0812.
- Author
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McGuinness, Julia, Anderson, Garnet, Mutasa, Simukayi, Hershman, Dawn, Terry, Mary, Tehranifar, Parisa, Lew, Danika, Yee, Monica, Brown, Eric, Kairouz, Sebastien, Kuwajerwala, Nafisa, Bevers, Therese, Doster, John, Zarwan, Corrine, Kruper, Laura, Minasian, Lori, Ford, Leslie, Arun, Banu, Neuhouser, Marian, Goodman, Gary, Brown, Patrick, Ha, Richard, and Crew, Katherine
- Subjects
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.
- Published
- 2024