Effects of Vitamin D Supplementation on a Deep Learning-Based Mammographic Evaluation in SWOG S0812

Authors

Julia E McGuinness
Garnet L Anderson
Simukayi Mutasa
Dawn L Hershman
Mary Beth Terry
Parisa Tehranifar
Danika L Lew
Monica Yee
Eric A Brown
Sebastien S Kairouz
Nafisa Kuwajerwala
Therese B Bevers
John E Doster
Corrine Zarwan
Laura Kruper
Lori M Minasian
Leslie Ford
Banu Arun
Marian L Neuhouser
Gary E Goodman
Powel H Brown
Richard Ha
Katherine D Crew

Publication Date

7-1-2024

Journal

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.

Keywords

Humans, Female, Mammography, Deep Learning, Breast Neoplasms, Dietary Supplements, Breast Density, Middle Aged, Cholecalciferol, Adult, Vitamin D, Premenopause, Neural Networks, Computer, Risk Assessment

Comments

PMID: 38814817