Examination of fully automated mammographic density measures using LIBRA and breast cancer risk in a cohort of 21,000 non-Hispanic white women

Breast Cancer Research

Research

Adriana Sistig¹  Li Shen²  Alice S. Whittemore³  Laurie R. Margolies⁴  Weiva Sieh⁵  Joseph H. Rothstein⁵  Robert J. Klein⁶  Pei Wang⁶  Jafi A. Lipson⁷  Rhea Y. Liang⁷  Daniel L. Rubin⁸  Despina Kontos⁹  Walter Mankowski⁹  Laurel A. Habel^1,10  Mark Westley^1,10  Lawrence Gerstley^1,10  Marvella A. Villaseñor^1,10  Stacey E. Alexeeff^1,10  Ninah Achacoso^1,10  Vignesh A. Arasu^1,11  Aimilia Gastounioti^1,12  Martin J. Yaffe^1,13  Xiaoyu Song^1,14 

[1] Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA;Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA;Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA;Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA;Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA;Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA;Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA;Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA;Department of Epidemiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA;Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA;Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA;Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA;Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA;Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA;Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA;Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA;Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA;Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA;Department of Radiology, Kaiser Permanente Northern California, Vallejo, CA, USA;Mallinckrodt Institute of Radiology, Washington University School of Medicine, Saint Louis, MO, USA;Sunnybrook Research Institute and Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada;Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA;Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA;

关键词: Breast cancer;    Mammography;    Mammographic density;    Risk factors;    Epidemiology;   

DOI  :  10.1186/s13058-023-01685-6

 received in 2023-04-05, accepted in 2023-07-09,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundBreast density is strongly associated with breast cancer risk. Fully automated quantitative density assessment methods have recently been developed that could facilitate large-scale studies, although data on associations with long-term breast cancer risk are limited. We examined LIBRA assessments and breast cancer risk and compared results to prior assessments using Cumulus, an established computer-assisted method requiring manual thresholding.MethodsWe conducted a cohort study among 21,150 non-Hispanic white female participants of the Research Program in Genes, Environment and Health of Kaiser Permanente Northern California who were 40–74 years at enrollment, followed for up to 10 years, and had archived processed screening mammograms acquired on Hologic or General Electric full-field digital mammography (FFDM) machines and prior Cumulus density assessments available for analysis. Dense area (DA), non-dense area (NDA), and percent density (PD) were assessed using LIBRA software. Cox regression was used to estimate hazard ratios (HRs) for breast cancer associated with DA, NDA and PD modeled continuously in standard deviation (SD) increments, adjusting for age, mammogram year, body mass index, parity, first-degree family history of breast cancer, and menopausal hormone use. We also examined differences by machine type and breast view.ResultsThe adjusted HRs for breast cancer associated with each SD increment of DA, NDA and PD were 1.36 (95% confidence interval, 1.18–1.57), 0.85 (0.77–0.93) and 1.44 (1.26–1.66) for LIBRA and 1.44 (1.33–1.55), 0.81 (0.74–0.89) and 1.54 (1.34–1.77) for Cumulus, respectively. LIBRA results were generally similar by machine type and breast view, although associations were strongest for Hologic machines and mediolateral oblique views. Results were also similar during the first 2 years, 2–5 years and 5–10 years after the baseline mammogram.ConclusionAssociations with breast cancer risk were generally similar for LIBRA and Cumulus density measures and were sustained for up to 10 years. These findings support the suitability of fully automated LIBRA assessments on processed FFDM images for large-scale research on breast density and cancer risk.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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