biostudies-literatureDas Gupta SNIBIB NIH HHSSusan G. Komen for the CureAlvin J. Siteman Cancer CenterU.S. Department of DefenseNCI NIH HHSNational Institutes of HealthNIH HHS188https://www.ebi.ac.uk/biostudies/studies/S-EPMC12557909biostudies-literatureUnknown27(1)<h4>Background</h4>Early-adulthood adiposity is associated with breast cancer risk. This study aims to evaluate the associations of early-adulthood adiposity with an emerging biomarker of breast cancer risk, breast parenchymal complexity, within premenopausal women.<h4>Methods</h4>We retrospectively analyzed early-adulthood adiposity data and screening digital mammograms (DMs) of 326 premenopausal women (age in years; median: 48; range: 34-58) recruited at our institution. Adiposity measures were either assessed by trained personnel or self-reported by participants. DMs were analyzed using a well-validated computational imaging pipeline consisting of automated breast segmentation and radiomic feature extraction modules. The extracted radiomic features were fused into woman-specific breast Breast cancer research : BCRAssociations of early-adulthood adiposity with breast parenchymal tissue complexity within premenopausal women.PMC12557909R01CA246592T32 EB014855ASP241266346R01 CA286120R01 CA246592R01CA286120CCR15332379BC230591T32-EB014855-126118Das Gupta SToriola ATHernandez Lopez JGetz KGastounioti ABennett DLfalseAssociations of early-adulthood adiposity with breast parenchymal tissue complexity within premenopausal women.<h4>Background</h4>Early-adulthood adiposity is associated with breast cancer risk. This study aims to evaluate the associations of early-adulthood adiposity with an emerging biomarker of breast cancer risk, breast parenchymal complexity, within premenopausal women.<h4>Methods</h4>We retrospectively analyzed early-adulthood adiposity data and screening digital mammograms (DMs) of 326 premenopausal women (age in years; median: 48; range: 34-58) recruited at our institution. Adiposity measures were either assessed by trained personnel or self-reported by participants. DMs were analyzed using a well-validated computational imaging pipeline consisting of automated breast segmentation and radiomic feature extraction modules. The extracted radiomic features were fused into woman-specific breast 2025-01-01T00:00:00Z2025 Oct2026-06-06T15:01:59.782Z2026-06-01T03:10:34.742ZS-EPMC125579094114622810.1186/s13058-025-02144-0