| Autor: |
McGuinness, Julia E, Anderson, Garnet L, Mutasa, Simukayi, Hershman, Dawn L, Terry, Mary Beth, Tehranifar, Parisa, Lew, Danika L, Yee, Monica, Brown, Eric A, Kairouz, Sebastien S, Kuwajerwala, Nafisa, Bevers, Therese B, Doster, John E, Zarwan, Corrine, Kruper, Laura, Minasian, Lori M, Ford, Leslie, Arun, Banu, Neuhouser, Marian L, Goodman, Gary E |
| Předmět: |
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| Zdroj: |
JNCI Cancer Spectrum; Aug2024, Vol. 8 Issue 4, p1-5, 5p |
| Abstrakt: |
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. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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