Effects of vitamin D supplementation on a deep learning-based mammographic evaluation in SWOG S0812.
JNCI Cancer Spectr
; 8(4)2024 Jul 01.
Article
de En
| MEDLINE
| ID: mdl-38814817
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 20â000 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.
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Sujet principal:
Tumeurs du sein
/
Mammographie
/
Cholécalciférol
/
Compléments alimentaires
/
Densité mammaire
/
Apprentissage profond
Limites:
Adult
/
Female
/
Humans
/
Middle aged
Langue:
En
Journal:
JNCI Cancer Spectr
Année:
2024
Type de document:
Article
Pays d'affiliation:
États-Unis d'Amérique
Pays de publication:
Royaume-Uni