Examination of fully automated mammographic density measures using LIBRA and breast cancer risk in a cohort of 21,000 non-Hispanic white women
Abstract Background Breast 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...
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2023-08-01
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| Series: | Breast Cancer Research |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13058-023-01685-6 |
| _version_ | 1827632663599513600 |
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| author | Laurel A. Habel Stacey E. Alexeeff Ninah Achacoso Vignesh A. Arasu Aimilia Gastounioti Lawrence Gerstley Robert J. Klein Rhea Y. Liang Jafi A. Lipson Walter Mankowski Laurie R. Margolies Joseph H. Rothstein Daniel L. Rubin Li Shen Adriana Sistig Xiaoyu Song Marvella A. Villaseñor Mark Westley Alice S. Whittemore Martin J. Yaffe Pei Wang Despina Kontos Weiva Sieh |
| author_facet | Laurel A. Habel Stacey E. Alexeeff Ninah Achacoso Vignesh A. Arasu Aimilia Gastounioti Lawrence Gerstley Robert J. Klein Rhea Y. Liang Jafi A. Lipson Walter Mankowski Laurie R. Margolies Joseph H. Rothstein Daniel L. Rubin Li Shen Adriana Sistig Xiaoyu Song Marvella A. Villaseñor Mark Westley Alice S. Whittemore Martin J. Yaffe Pei Wang Despina Kontos Weiva Sieh |
| author_sort | Laurel A. Habel |
| collection | DOAJ |
| description | Abstract Background Breast 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. Methods We 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. Results The 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. Conclusion Associations 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. |
| first_indexed | 2024-03-09T14:48:43Z |
| format | Article |
| id | doaj.art-c80b8409fd6744bfba5b869ab41e08e7 |
| institution | Directory Open Access Journal |
| issn | 1465-542X |
| language | English |
| last_indexed | 2024-03-09T14:48:43Z |
| publishDate | 2023-08-01 |
| publisher | BMC |
| record_format | Article |
| series | Breast Cancer Research |
| spelling | doaj.art-c80b8409fd6744bfba5b869ab41e08e72023-11-26T14:37:43ZengBMCBreast Cancer Research1465-542X2023-08-012511910.1186/s13058-023-01685-6Examination of fully automated mammographic density measures using LIBRA and breast cancer risk in a cohort of 21,000 non-Hispanic white womenLaurel A. Habel0Stacey E. Alexeeff1Ninah Achacoso2Vignesh A. Arasu3Aimilia Gastounioti4Lawrence Gerstley5Robert J. Klein6Rhea Y. Liang7Jafi A. Lipson8Walter Mankowski9Laurie R. Margolies10Joseph H. Rothstein11Daniel L. Rubin12Li Shen13Adriana Sistig14Xiaoyu Song15Marvella A. Villaseñor16Mark Westley17Alice S. Whittemore18Martin J. Yaffe19Pei Wang20Despina Kontos21Weiva Sieh22Division of Research, Kaiser Permanente Northern CaliforniaDivision of Research, Kaiser Permanente Northern CaliforniaDivision of Research, Kaiser Permanente Northern CaliforniaDivision of Research, Kaiser Permanente Northern CaliforniaMallinckrodt Institute of Radiology, Washington University School of MedicineDivision of Research, Kaiser Permanente Northern CaliforniaDepartment of Genetics and Genomic Sciences, Icahn School of Medicine at Mount SinaiDepartment of Radiology, Stanford University School of MedicineDepartment of Radiology, Stanford University School of MedicineDepartment of Radiology, University of Pennsylvania Perelman School of MedicineDepartment of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount SinaiDepartment of Genetics and Genomic Sciences, Icahn School of Medicine at Mount SinaiDepartment of Radiology, Stanford University School of MedicineDepartment of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount SinaiDepartment of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount SinaiTisch Cancer Institute, Icahn School of Medicine at Mount SinaiDivision of Research, Kaiser Permanente Northern CaliforniaDivision of Research, Kaiser Permanente Northern CaliforniaDepartment of Biomedical Data Science, Stanford University School of MedicineSunnybrook Research Institute and Department of Medical Biophysics, University of TorontoDepartment of Genetics and Genomic Sciences, Icahn School of Medicine at Mount SinaiDepartment of Radiology, University of Pennsylvania Perelman School of MedicineDepartment of Genetics and Genomic Sciences, Icahn School of Medicine at Mount SinaiAbstract Background Breast 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. Methods We 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. Results The 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. Conclusion Associations 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.https://doi.org/10.1186/s13058-023-01685-6Breast cancerMammographyMammographic densityRisk factorsEpidemiology |
| spellingShingle | Laurel A. Habel Stacey E. Alexeeff Ninah Achacoso Vignesh A. Arasu Aimilia Gastounioti Lawrence Gerstley Robert J. Klein Rhea Y. Liang Jafi A. Lipson Walter Mankowski Laurie R. Margolies Joseph H. Rothstein Daniel L. Rubin Li Shen Adriana Sistig Xiaoyu Song Marvella A. Villaseñor Mark Westley Alice S. Whittemore Martin J. Yaffe Pei Wang Despina Kontos Weiva Sieh 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 Breast cancer Mammography Mammographic density Risk factors Epidemiology |
| title | Examination of fully automated mammographic density measures using LIBRA and breast cancer risk in a cohort of 21,000 non-Hispanic white women |
| title_full | Examination of fully automated mammographic density measures using LIBRA and breast cancer risk in a cohort of 21,000 non-Hispanic white women |
| title_fullStr | Examination of fully automated mammographic density measures using LIBRA and breast cancer risk in a cohort of 21,000 non-Hispanic white women |
| title_full_unstemmed | Examination of fully automated mammographic density measures using LIBRA and breast cancer risk in a cohort of 21,000 non-Hispanic white women |
| title_short | Examination of fully automated mammographic density measures using LIBRA and breast cancer risk in a cohort of 21,000 non-Hispanic white women |
| title_sort | examination of fully automated mammographic density measures using libra and breast cancer risk in a cohort of 21 000 non hispanic white women |
| topic | Breast cancer Mammography Mammographic density Risk factors Epidemiology |
| url | https://doi.org/10.1186/s13058-023-01685-6 |
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