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Lohani KR, Nibbe AM, Vierkant RA, Pacheco-Spann LM, Seymour LR, Vachon CM, Sherman ME, Winham SJ, Degnim AC, Hill DA. Understanding Benign Breast Disease and Subsequent Breast Cancer in Hispanic White Females: A Step Closer to Evidence-Based Management. Cancer Epidemiol Biomarkers Prev 2024; 33:1107-1113. [PMID: 38838262 DOI: 10.1158/1055-9965.epi-24-0204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 04/24/2024] [Accepted: 05/30/2024] [Indexed: 06/07/2024] Open
Abstract
INTRODUCTION Although Hispanic White (HW) females have a lower incidence of breast cancer than non-Hispanic White (NHW) females, breast cancer risk is unclear for HW females after benign breast disease (BBD). METHODS We compared BBD characteristics and subsequent breast cancer risk among HW and NHW females in New Mexico using a population-based collection of benign breast biopsies (1996-2007). BBD was categorized as nonproliferative disease (NPD), proliferative disease without atypia (PDWA), or atypical hyperplasia (AH). Breast cancer risk was assessed as absolute risk (AR) using cumulative incidence and RR by comparing the number of breast cancer events in BBDs to non-BBD. RESULTS This study included 3,684 HW and 6,587 NHW females with BBD. HW females had similar proportions of NPD (58.6% vs. 54.3%), PDWA (21.4% vs. 23.5%), and AH (3.6% vs. 3.3%) as NHW females. Breast cancer risk among all females with BBD was higher than population-based expected rates (RR, 1.87) and was similar for HW and NHW subgroups (RR = 1.99 vs. 1.84). As expected, breast cancer risk increased with increasing BBD severity, both overall [RR, 1.81 (NPD), 1.85 (PDWA), and 3.10 (AH)] and in the HW and NHW subgroups. Adjusted AR of breast cancer at 5 years also increased with the severity of BBD (HW vs. NHW; NPD: 1.4% vs. 2.1%; PDWA: 1.5% vs. 2.7%; AH: 6% vs. 4.8%). CONCLUSIONS We found similar breast cancer RRs and ARs in HW and NHW. Risk counseling should ensure that HW females receive breast cancer clinical management warranted by their similar absolute risks. IMPACT The present population-based provides evidence for the clinical management of HW females with BBD for the prevention of breast cancer.
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Affiliation(s)
- Kush R Lohani
- Division of Breast and Melanoma Surgical Oncology, Department of Surgery, Mayo Clinic, Rochester, Minnesota
| | - Andrea M Nibbe
- Division of Epidemiology, Biostatistics, and Preventive Medicine, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, New Mexico
| | - Robert A Vierkant
- Division of Clinical Trials and Biostatistics, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota
| | - Laura M Pacheco-Spann
- Division of Epidemiology, Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, Florida
| | - Lisa R Seymour
- Department of Surgery Research, Mayo Clinic, Rochester, Minnesota
| | - Celine M Vachon
- Division of Epidemiology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota
| | - Mark E Sherman
- Division of Epidemiology, Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, Florida
| | - Stacey J Winham
- Division of Computational Biology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota
| | - Amy C Degnim
- Division of Breast and Melanoma Surgical Oncology, Department of Surgery, Mayo Clinic, Rochester, Minnesota
| | - Deirdre A Hill
- Division of Epidemiology, Biostatistics, and Preventive Medicine, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, New Mexico
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Sprague BL, Ichikawa L, Eavey J, Lowry KP, Rauscher GH, O'Meara ES, Miglioretti DL, Lee JM, Stout NK, Herschorn SD, Perry H, Weaver DL, Kerlikowske K. Performance of Supplemental US Screening in Women with Dense Breasts and Varying Breast Cancer Risk: Results from the Breast Cancer Surveillance Consortium. Radiology 2024; 312:e232380. [PMID: 39105648 DOI: 10.1148/radiol.232380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/07/2024]
Abstract
Background It is unclear whether breast US screening outcomes for women with dense breasts vary with levels of breast cancer risk. Purpose To evaluate US screening outcomes for female patients with dense breasts and different estimated breast cancer risk levels. Materials and Methods This retrospective observational study used data from US screening examinations in female patients with heterogeneously or extremely dense breasts conducted from January 2014 to October 2020 at 24 radiology facilities within three Breast Cancer Surveillance Consortium (BCSC) registries. The primary outcomes were the cancer detection rate, false-positive biopsy recommendation rate, and positive predictive value of biopsies performed (PPV3). Risk classification of participants was performed using established BCSC risk prediction models of estimated 6-year advanced breast cancer risk and 5-year invasive breast cancer risk. Differences in high- versus low- or average-risk categories were assessed using a generalized linear model. Results In total, 34 791 US screening examinations from 26 489 female patients (mean age at screening, 53.9 years ± 9.0 [SD]) were included. The overall cancer detection rate per 1000 examinations was 2.0 (95% CI: 1.6, 2.4) and was higher in patients with high versus low or average risk of 6-year advanced breast cancer (5.5 [95% CI: 3.5, 8.6] vs 1.3 [95% CI: 1.0, 1.8], respectively; P = .003). The overall false-positive biopsy recommendation rate per 1000 examinations was 29.6 (95% CI: 22.6, 38.6) and was higher in patients with high versus low or average 6-year advanced breast cancer risk (37.0 [95% CI: 28.2, 48.4] vs 28.1 [95% CI: 20.9, 37.8], respectively; P = .04). The overall PPV3 was 6.9% (67 of 975; 95% CI: 5.3, 8.9) and was higher in patients with high versus low or average 6-year advanced cancer risk (15.0% [15 of 100; 95% CI: 9.9, 22.2] vs 4.9% [30 of 615; 95% CI: 3.3, 7.2]; P = .01). Similar patterns in outcomes were observed by 5-year invasive breast cancer risk. Conclusion The cancer detection rate and PPV3 of supplemental US screening increased with the estimated risk of advanced and invasive breast cancer. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Helbich and Kapetas in this issue.
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Affiliation(s)
- Brian L Sprague
- From the Department of Surgery, Office of Health Promotion Research, University of Vermont Larner College of Medicine, 1 S Prospect St, UHC Bldg Rm 4425, Burlington, VT 05405 (B.L.S.); Department of Radiology, University of Vermont Larner College of Medicine, Burlington, Vt (B.L.S., S.D.H., H.P.); University of Vermont Cancer Center, University of Vermont Larner College of Medicine, Burlington, Vt (B.L.S., S.D.H., H.P., D.L.W.); Kaiser Permanente Washington Health Research Institute, Seattle, Wash (L.I., J.E., E.S.O., D.L.M.); Department of Radiology, University of Washington and Fred Hutchinson Cancer Center, Seattle, Wash (K.P.L., J.M.L.); Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago, Chicago, Ill (G.H.R.); Division of Biostatistics, Department of Public Health Sciences, University of California Davis, Davis, Calif (D.L.M.); Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Mass (N.K.S.); Department of Pathology & Laboratory Medicine, University of Vermont Larner College of Medicine, Burlington, Vt (D.L.W.); Departments of Medicine and Epidemiology and Biostatistics, University of California San Francisco, San Francisco, Calif (K.K.); and Department of Veterans Affairs, General Internal Medicine Section, University of California San Francisco, San Francisco, Calif (K.K.)
| | - Laura Ichikawa
- From the Department of Surgery, Office of Health Promotion Research, University of Vermont Larner College of Medicine, 1 S Prospect St, UHC Bldg Rm 4425, Burlington, VT 05405 (B.L.S.); Department of Radiology, University of Vermont Larner College of Medicine, Burlington, Vt (B.L.S., S.D.H., H.P.); University of Vermont Cancer Center, University of Vermont Larner College of Medicine, Burlington, Vt (B.L.S., S.D.H., H.P., D.L.W.); Kaiser Permanente Washington Health Research Institute, Seattle, Wash (L.I., J.E., E.S.O., D.L.M.); Department of Radiology, University of Washington and Fred Hutchinson Cancer Center, Seattle, Wash (K.P.L., J.M.L.); Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago, Chicago, Ill (G.H.R.); Division of Biostatistics, Department of Public Health Sciences, University of California Davis, Davis, Calif (D.L.M.); Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Mass (N.K.S.); Department of Pathology & Laboratory Medicine, University of Vermont Larner College of Medicine, Burlington, Vt (D.L.W.); Departments of Medicine and Epidemiology and Biostatistics, University of California San Francisco, San Francisco, Calif (K.K.); and Department of Veterans Affairs, General Internal Medicine Section, University of California San Francisco, San Francisco, Calif (K.K.)
| | - Joanna Eavey
- From the Department of Surgery, Office of Health Promotion Research, University of Vermont Larner College of Medicine, 1 S Prospect St, UHC Bldg Rm 4425, Burlington, VT 05405 (B.L.S.); Department of Radiology, University of Vermont Larner College of Medicine, Burlington, Vt (B.L.S., S.D.H., H.P.); University of Vermont Cancer Center, University of Vermont Larner College of Medicine, Burlington, Vt (B.L.S., S.D.H., H.P., D.L.W.); Kaiser Permanente Washington Health Research Institute, Seattle, Wash (L.I., J.E., E.S.O., D.L.M.); Department of Radiology, University of Washington and Fred Hutchinson Cancer Center, Seattle, Wash (K.P.L., J.M.L.); Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago, Chicago, Ill (G.H.R.); Division of Biostatistics, Department of Public Health Sciences, University of California Davis, Davis, Calif (D.L.M.); Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Mass (N.K.S.); Department of Pathology & Laboratory Medicine, University of Vermont Larner College of Medicine, Burlington, Vt (D.L.W.); Departments of Medicine and Epidemiology and Biostatistics, University of California San Francisco, San Francisco, Calif (K.K.); and Department of Veterans Affairs, General Internal Medicine Section, University of California San Francisco, San Francisco, Calif (K.K.)
| | - Kathryn P Lowry
- From the Department of Surgery, Office of Health Promotion Research, University of Vermont Larner College of Medicine, 1 S Prospect St, UHC Bldg Rm 4425, Burlington, VT 05405 (B.L.S.); Department of Radiology, University of Vermont Larner College of Medicine, Burlington, Vt (B.L.S., S.D.H., H.P.); University of Vermont Cancer Center, University of Vermont Larner College of Medicine, Burlington, Vt (B.L.S., S.D.H., H.P., D.L.W.); Kaiser Permanente Washington Health Research Institute, Seattle, Wash (L.I., J.E., E.S.O., D.L.M.); Department of Radiology, University of Washington and Fred Hutchinson Cancer Center, Seattle, Wash (K.P.L., J.M.L.); Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago, Chicago, Ill (G.H.R.); Division of Biostatistics, Department of Public Health Sciences, University of California Davis, Davis, Calif (D.L.M.); Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Mass (N.K.S.); Department of Pathology & Laboratory Medicine, University of Vermont Larner College of Medicine, Burlington, Vt (D.L.W.); Departments of Medicine and Epidemiology and Biostatistics, University of California San Francisco, San Francisco, Calif (K.K.); and Department of Veterans Affairs, General Internal Medicine Section, University of California San Francisco, San Francisco, Calif (K.K.)
| | - Garth H Rauscher
- From the Department of Surgery, Office of Health Promotion Research, University of Vermont Larner College of Medicine, 1 S Prospect St, UHC Bldg Rm 4425, Burlington, VT 05405 (B.L.S.); Department of Radiology, University of Vermont Larner College of Medicine, Burlington, Vt (B.L.S., S.D.H., H.P.); University of Vermont Cancer Center, University of Vermont Larner College of Medicine, Burlington, Vt (B.L.S., S.D.H., H.P., D.L.W.); Kaiser Permanente Washington Health Research Institute, Seattle, Wash (L.I., J.E., E.S.O., D.L.M.); Department of Radiology, University of Washington and Fred Hutchinson Cancer Center, Seattle, Wash (K.P.L., J.M.L.); Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago, Chicago, Ill (G.H.R.); Division of Biostatistics, Department of Public Health Sciences, University of California Davis, Davis, Calif (D.L.M.); Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Mass (N.K.S.); Department of Pathology & Laboratory Medicine, University of Vermont Larner College of Medicine, Burlington, Vt (D.L.W.); Departments of Medicine and Epidemiology and Biostatistics, University of California San Francisco, San Francisco, Calif (K.K.); and Department of Veterans Affairs, General Internal Medicine Section, University of California San Francisco, San Francisco, Calif (K.K.)
| | - Ellen S O'Meara
- From the Department of Surgery, Office of Health Promotion Research, University of Vermont Larner College of Medicine, 1 S Prospect St, UHC Bldg Rm 4425, Burlington, VT 05405 (B.L.S.); Department of Radiology, University of Vermont Larner College of Medicine, Burlington, Vt (B.L.S., S.D.H., H.P.); University of Vermont Cancer Center, University of Vermont Larner College of Medicine, Burlington, Vt (B.L.S., S.D.H., H.P., D.L.W.); Kaiser Permanente Washington Health Research Institute, Seattle, Wash (L.I., J.E., E.S.O., D.L.M.); Department of Radiology, University of Washington and Fred Hutchinson Cancer Center, Seattle, Wash (K.P.L., J.M.L.); Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago, Chicago, Ill (G.H.R.); Division of Biostatistics, Department of Public Health Sciences, University of California Davis, Davis, Calif (D.L.M.); Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Mass (N.K.S.); Department of Pathology & Laboratory Medicine, University of Vermont Larner College of Medicine, Burlington, Vt (D.L.W.); Departments of Medicine and Epidemiology and Biostatistics, University of California San Francisco, San Francisco, Calif (K.K.); and Department of Veterans Affairs, General Internal Medicine Section, University of California San Francisco, San Francisco, Calif (K.K.)
| | - Diana L Miglioretti
- From the Department of Surgery, Office of Health Promotion Research, University of Vermont Larner College of Medicine, 1 S Prospect St, UHC Bldg Rm 4425, Burlington, VT 05405 (B.L.S.); Department of Radiology, University of Vermont Larner College of Medicine, Burlington, Vt (B.L.S., S.D.H., H.P.); University of Vermont Cancer Center, University of Vermont Larner College of Medicine, Burlington, Vt (B.L.S., S.D.H., H.P., D.L.W.); Kaiser Permanente Washington Health Research Institute, Seattle, Wash (L.I., J.E., E.S.O., D.L.M.); Department of Radiology, University of Washington and Fred Hutchinson Cancer Center, Seattle, Wash (K.P.L., J.M.L.); Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago, Chicago, Ill (G.H.R.); Division of Biostatistics, Department of Public Health Sciences, University of California Davis, Davis, Calif (D.L.M.); Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Mass (N.K.S.); Department of Pathology & Laboratory Medicine, University of Vermont Larner College of Medicine, Burlington, Vt (D.L.W.); Departments of Medicine and Epidemiology and Biostatistics, University of California San Francisco, San Francisco, Calif (K.K.); and Department of Veterans Affairs, General Internal Medicine Section, University of California San Francisco, San Francisco, Calif (K.K.)
| | - Janie M Lee
- From the Department of Surgery, Office of Health Promotion Research, University of Vermont Larner College of Medicine, 1 S Prospect St, UHC Bldg Rm 4425, Burlington, VT 05405 (B.L.S.); Department of Radiology, University of Vermont Larner College of Medicine, Burlington, Vt (B.L.S., S.D.H., H.P.); University of Vermont Cancer Center, University of Vermont Larner College of Medicine, Burlington, Vt (B.L.S., S.D.H., H.P., D.L.W.); Kaiser Permanente Washington Health Research Institute, Seattle, Wash (L.I., J.E., E.S.O., D.L.M.); Department of Radiology, University of Washington and Fred Hutchinson Cancer Center, Seattle, Wash (K.P.L., J.M.L.); Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago, Chicago, Ill (G.H.R.); Division of Biostatistics, Department of Public Health Sciences, University of California Davis, Davis, Calif (D.L.M.); Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Mass (N.K.S.); Department of Pathology & Laboratory Medicine, University of Vermont Larner College of Medicine, Burlington, Vt (D.L.W.); Departments of Medicine and Epidemiology and Biostatistics, University of California San Francisco, San Francisco, Calif (K.K.); and Department of Veterans Affairs, General Internal Medicine Section, University of California San Francisco, San Francisco, Calif (K.K.)
| | - Natasha K Stout
- From the Department of Surgery, Office of Health Promotion Research, University of Vermont Larner College of Medicine, 1 S Prospect St, UHC Bldg Rm 4425, Burlington, VT 05405 (B.L.S.); Department of Radiology, University of Vermont Larner College of Medicine, Burlington, Vt (B.L.S., S.D.H., H.P.); University of Vermont Cancer Center, University of Vermont Larner College of Medicine, Burlington, Vt (B.L.S., S.D.H., H.P., D.L.W.); Kaiser Permanente Washington Health Research Institute, Seattle, Wash (L.I., J.E., E.S.O., D.L.M.); Department of Radiology, University of Washington and Fred Hutchinson Cancer Center, Seattle, Wash (K.P.L., J.M.L.); Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago, Chicago, Ill (G.H.R.); Division of Biostatistics, Department of Public Health Sciences, University of California Davis, Davis, Calif (D.L.M.); Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Mass (N.K.S.); Department of Pathology & Laboratory Medicine, University of Vermont Larner College of Medicine, Burlington, Vt (D.L.W.); Departments of Medicine and Epidemiology and Biostatistics, University of California San Francisco, San Francisco, Calif (K.K.); and Department of Veterans Affairs, General Internal Medicine Section, University of California San Francisco, San Francisco, Calif (K.K.)
| | - Sally D Herschorn
- From the Department of Surgery, Office of Health Promotion Research, University of Vermont Larner College of Medicine, 1 S Prospect St, UHC Bldg Rm 4425, Burlington, VT 05405 (B.L.S.); Department of Radiology, University of Vermont Larner College of Medicine, Burlington, Vt (B.L.S., S.D.H., H.P.); University of Vermont Cancer Center, University of Vermont Larner College of Medicine, Burlington, Vt (B.L.S., S.D.H., H.P., D.L.W.); Kaiser Permanente Washington Health Research Institute, Seattle, Wash (L.I., J.E., E.S.O., D.L.M.); Department of Radiology, University of Washington and Fred Hutchinson Cancer Center, Seattle, Wash (K.P.L., J.M.L.); Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago, Chicago, Ill (G.H.R.); Division of Biostatistics, Department of Public Health Sciences, University of California Davis, Davis, Calif (D.L.M.); Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Mass (N.K.S.); Department of Pathology & Laboratory Medicine, University of Vermont Larner College of Medicine, Burlington, Vt (D.L.W.); Departments of Medicine and Epidemiology and Biostatistics, University of California San Francisco, San Francisco, Calif (K.K.); and Department of Veterans Affairs, General Internal Medicine Section, University of California San Francisco, San Francisco, Calif (K.K.)
| | - Hannah Perry
- From the Department of Surgery, Office of Health Promotion Research, University of Vermont Larner College of Medicine, 1 S Prospect St, UHC Bldg Rm 4425, Burlington, VT 05405 (B.L.S.); Department of Radiology, University of Vermont Larner College of Medicine, Burlington, Vt (B.L.S., S.D.H., H.P.); University of Vermont Cancer Center, University of Vermont Larner College of Medicine, Burlington, Vt (B.L.S., S.D.H., H.P., D.L.W.); Kaiser Permanente Washington Health Research Institute, Seattle, Wash (L.I., J.E., E.S.O., D.L.M.); Department of Radiology, University of Washington and Fred Hutchinson Cancer Center, Seattle, Wash (K.P.L., J.M.L.); Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago, Chicago, Ill (G.H.R.); Division of Biostatistics, Department of Public Health Sciences, University of California Davis, Davis, Calif (D.L.M.); Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Mass (N.K.S.); Department of Pathology & Laboratory Medicine, University of Vermont Larner College of Medicine, Burlington, Vt (D.L.W.); Departments of Medicine and Epidemiology and Biostatistics, University of California San Francisco, San Francisco, Calif (K.K.); and Department of Veterans Affairs, General Internal Medicine Section, University of California San Francisco, San Francisco, Calif (K.K.)
| | - Donald L Weaver
- From the Department of Surgery, Office of Health Promotion Research, University of Vermont Larner College of Medicine, 1 S Prospect St, UHC Bldg Rm 4425, Burlington, VT 05405 (B.L.S.); Department of Radiology, University of Vermont Larner College of Medicine, Burlington, Vt (B.L.S., S.D.H., H.P.); University of Vermont Cancer Center, University of Vermont Larner College of Medicine, Burlington, Vt (B.L.S., S.D.H., H.P., D.L.W.); Kaiser Permanente Washington Health Research Institute, Seattle, Wash (L.I., J.E., E.S.O., D.L.M.); Department of Radiology, University of Washington and Fred Hutchinson Cancer Center, Seattle, Wash (K.P.L., J.M.L.); Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago, Chicago, Ill (G.H.R.); Division of Biostatistics, Department of Public Health Sciences, University of California Davis, Davis, Calif (D.L.M.); Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Mass (N.K.S.); Department of Pathology & Laboratory Medicine, University of Vermont Larner College of Medicine, Burlington, Vt (D.L.W.); Departments of Medicine and Epidemiology and Biostatistics, University of California San Francisco, San Francisco, Calif (K.K.); and Department of Veterans Affairs, General Internal Medicine Section, University of California San Francisco, San Francisco, Calif (K.K.)
| | - Karla Kerlikowske
- From the Department of Surgery, Office of Health Promotion Research, University of Vermont Larner College of Medicine, 1 S Prospect St, UHC Bldg Rm 4425, Burlington, VT 05405 (B.L.S.); Department of Radiology, University of Vermont Larner College of Medicine, Burlington, Vt (B.L.S., S.D.H., H.P.); University of Vermont Cancer Center, University of Vermont Larner College of Medicine, Burlington, Vt (B.L.S., S.D.H., H.P., D.L.W.); Kaiser Permanente Washington Health Research Institute, Seattle, Wash (L.I., J.E., E.S.O., D.L.M.); Department of Radiology, University of Washington and Fred Hutchinson Cancer Center, Seattle, Wash (K.P.L., J.M.L.); Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago, Chicago, Ill (G.H.R.); Division of Biostatistics, Department of Public Health Sciences, University of California Davis, Davis, Calif (D.L.M.); Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Mass (N.K.S.); Department of Pathology & Laboratory Medicine, University of Vermont Larner College of Medicine, Burlington, Vt (D.L.W.); Departments of Medicine and Epidemiology and Biostatistics, University of California San Francisco, San Francisco, Calif (K.K.); and Department of Veterans Affairs, General Internal Medicine Section, University of California San Francisco, San Francisco, Calif (K.K.)
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Lee JM, Ichikawa L, Kerlikowske K, Buist DSM, Lee CI, Sprague BL, Henderson LM, Onega T, Wernli KJ, Lowry KP, Stout NK, Tosteson ANA, Miglioretti DL. Relative Timing of Mammography and MRI for Breast Cancer Screening: Impact on Performance Evaluation. J Am Coll Radiol 2024:S1546-1440(24)00597-0. [PMID: 38969253 DOI: 10.1016/j.jacr.2024.06.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 06/15/2024] [Accepted: 06/24/2024] [Indexed: 07/07/2024]
Abstract
OBJECTIVE Mammography and MRI screening typically occur in combination or in alternating sequence. We compared multimodality screening performance accounting for the relative timing of mammography and MRI and overlapping follow-up periods. METHODS We identified 8,260 screening mammograms performed 2005 to 2017 in the Breast Cancer Surveillance Consortium, paired with screening MRIs within ±90 days (combined screening) or 91 to 270 days (alternating screening). Performance for combined screening (cancer detection rate [CDR] per 1,000 examinations and sensitivity) was calculated with 1-year follow-up for each modality, and with a single follow-up period treating the two tests as a single test. Alternating screening performance was calculated with 1-year follow-up for each modality and also with follow-up ending at the next screen if within 1 year (truncated follow-up). RESULTS For 3,810 combined screening pairs, CDR per 1,000 screens was 6.8 (95% confidence interval [CI]: 4.6-10.0) for mammography and 12.3 (95% CI: 9.3-16.4) for MRI as separate tests compared with 13.1 (95% CI: 10.0-17.3) as a single combined test. Sensitivity of each test was 48.1% (35.0%-61.5%) for mammography and 79.7% (95% CI: 67.7%-88.0%) for MRI compared with 96.2% (95% CI: 85.9%-99.0%) for combined screening. For 4,450 alternating screening pairs, mammography CDR per 1,000 screens changed from 3.6 (95% CI: 2.2-5.9) to zero with truncated follow-up; sensitivity was incalculable (denominator = 0). MRI CDR per 1,000 screens changed from 12.1 (95% CI 9.3-15.8) to 11.7 (95% CI: 8.9-15.3) with truncated follow-up; sensitivity changed from 75.0% (95% CI 63.8%-83.6%) to 86.7% (95% CI 75.5%-93.2%). DISCUSSION Updating auditing approaches to account for combined and alternating screening sequencing and to address outcome attribution issues arising from overlapping follow-up periods can improve the accuracy of multimodality screening performance evaluation.
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Affiliation(s)
- Janie M Lee
- Section Chief of Breast Imaging, Department of Radiology, University of Washington School of Medicine, Seattle, Washington; Director of Breast Imaging, Fred Hutchinson Cancer Center, Seattle, Washington.
| | - Laura Ichikawa
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington
| | - Karla Kerlikowske
- General Internal Medicine Section, Department of Veterans Affairs, University of California, San Francisco, San Francisco, California; Women's Health Comprehensive Clinic, San Francisco Veterans Affairs Health Care System, San Francisco, California; Director of the San Francisco Mammography Registry and Co-Director at the Veteran's Administration Advanced Fellowship in Women's Health San Francisco; Departments of Medicine and Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California
| | - Diana S M Buist
- Department of Health Systems Science, Kaiser Permanente Bernard J. Tyson School of Medicine, Pasadena, California
| | - Christoph I Lee
- Director of the Northwest Screening and Cancer Outcomes Research Enterprise, Department of Radiology, University of Washington School of Medicine, Seattle, Washington; Deputy Editor of JACR; Fred Hutchinson Cancer Center, Seattle, Washington
| | - Brian L Sprague
- University of Vermont Cancer Center, University of Vermont Larner College of Medicine, Burlington, Vermont; Director of the Vermont Breast Cancer Surveillance System; University of Vermont Cancer Center Cancer Population Science (CPS) Program Co-Leader
| | - Louise M Henderson
- Department of Radiology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, North Carolina; Director of the Carolina Mammography Registry and Co-Leader of UNC Lineberger Cancer Epidemiology Research Program
| | - Tracy Onega
- Senior Director of Population Sciences, Department of Population Health Sciences, and the Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah
| | - Karen J Wernli
- Senior Scientific Investigator, Kaiser Permanente Washington Health Research Institute, Seattle, Washington
| | - Kathryn P Lowry
- Fred Hutchinson Cancer Center, Seattle, Washington; Associate Director of the Northwest Screening and Cancer Outcomes Research Enterprise, Department of Radiology, University of Washington School of Medicine, Seattle, Washington
| | - Natasha K Stout
- Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, Boston, Massachusetts; During the study period: Associate Professor, Population Medicine, Harvard Medical School
| | - Anna N A Tosteson
- Director of the Comparative Effectiveness Research Program, The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire; Associate Director for Population Sciences, Dartmouth Cancer Center; Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire
| | - Diana L Miglioretti
- Professor and Division Chief of Biostatistics, Biostatistics and Population Sciences and Health Disparities Program, University of California, Davis, Comprehensive Cancer Center, Davis, California
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Park EK, Lee H, Kim M, Kim T, Kim J, Kim KH, Kooi T, Chang Y, Ryu S. Artificial Intelligence-Powered Imaging Biomarker Based on Mammography for Breast Cancer Risk Prediction. Diagnostics (Basel) 2024; 14:1212. [PMID: 38928628 PMCID: PMC11202482 DOI: 10.3390/diagnostics14121212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2024] [Revised: 06/04/2024] [Accepted: 06/05/2024] [Indexed: 06/28/2024] Open
Abstract
The purposes of this study were to develop an artificial intelligence (AI) model for future breast cancer risk prediction based on mammographic images, investigate the feasibility of the AI model, and compare the AI model, clinical statistical risk models, and Mirai, a state of-the art deep learning algorithm based on screening mammograms for 1-5-year breast cancer risk prediction. We trained and developed a deep learning model using a total of 36,995 serial mammographic examinations from 21,438 women (cancer-enriched mammograms, 17.5%). To determine the feasibility of the AI prediction model, mammograms and detailed clinical information were collected. C-indices and area under the receiver operating characteristic curves (AUCs) for 1-5-year outcomes were obtained. We compared the AUCs of our AI prediction model, Mirai, and clinical statistical risk models, including the Tyrer-Cuzick (TC) model and Gail model, using DeLong's test. A total of 16,894 mammograms were independently collected for external validation, of which 4002 were followed by a cancer diagnosis within 5 years. Our AI prediction model obtained a C-index of 0.76, with AUCs of 0.90, 0.84, 0.81, 0.78, and 0.81, to predict the 1-5-year risks. Our AI prediction model showed significantly higher AUCs than those of the TC model (AUC: 0.57; p < 0.001) and Gail model (AUC: 0.52; p < 0.001), and achieved similar performance to Mirai. The deep learning AI model using mammograms and AI-powered imaging biomarkers has substantial potential to advance accurate breast cancer risk prediction.
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Affiliation(s)
- Eun Kyung Park
- Department of Radiology, We Comfortable Clinic, Seoul 07327, Republic of Korea
| | - Hyeonsoo Lee
- Lunit Inc., Seoul 06241, Republic of Korea; (H.L.); (M.K.); (T.K.); (J.K.); (K.H.K.); (T.K.)
| | - Minjeong Kim
- Lunit Inc., Seoul 06241, Republic of Korea; (H.L.); (M.K.); (T.K.); (J.K.); (K.H.K.); (T.K.)
| | - Taesoo Kim
- Lunit Inc., Seoul 06241, Republic of Korea; (H.L.); (M.K.); (T.K.); (J.K.); (K.H.K.); (T.K.)
| | - Junha Kim
- Lunit Inc., Seoul 06241, Republic of Korea; (H.L.); (M.K.); (T.K.); (J.K.); (K.H.K.); (T.K.)
| | - Ki Hwan Kim
- Lunit Inc., Seoul 06241, Republic of Korea; (H.L.); (M.K.); (T.K.); (J.K.); (K.H.K.); (T.K.)
| | - Thijs Kooi
- Lunit Inc., Seoul 06241, Republic of Korea; (H.L.); (M.K.); (T.K.); (J.K.); (K.H.K.); (T.K.)
| | - Yoosoo Chang
- Center of Cohort Studies, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul 04514, Republic of Korea; (Y.C.); (S.R.)
- Department of Occupational and Environmental Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul 03181, Republic of Korea
- Department of Clinical Research Design & Evaluation, Samsung Advanced Institute for Health Science & Technology (SAIHST), Sungkyunkwan University, Seoul 06355, Republic of Korea
| | - Seungho Ryu
- Center of Cohort Studies, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul 04514, Republic of Korea; (Y.C.); (S.R.)
- Department of Occupational and Environmental Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul 03181, Republic of Korea
- Department of Clinical Research Design & Evaluation, Samsung Advanced Institute for Health Science & Technology (SAIHST), Sungkyunkwan University, Seoul 06355, Republic of Korea
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Hubbard RA, Su YR, Bowles EJA, Ichikawa L, Kerlikowske K, Lowry KP, Miglioretti DL, Tosteson ANA, Wernli KJ, Lee JM. Predicting five-year interval second breast cancer risk in women with prior breast cancer. J Natl Cancer Inst 2024; 116:929-937. [PMID: 38466940 PMCID: PMC11160498 DOI: 10.1093/jnci/djae063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 02/22/2024] [Accepted: 03/07/2024] [Indexed: 03/13/2024] Open
Abstract
BACKGROUND Annual surveillance mammography is recommended for women with a personal history of breast cancer. Risk prediction models that estimate mammography failures such as interval second breast cancers could help to tailor surveillance imaging regimens to women's individual risk profiles. METHODS In a cohort of women with a history of breast cancer receiving surveillance mammography in the Breast Cancer Surveillance Consortium in 1996-2019, we used Least Absolute Shrinkage and Selection Operator (LASSO)-penalized regression to estimate the probability of an interval second cancer (invasive cancer or ductal carcinoma in situ) in the 1 year after a negative surveillance mammogram. Based on predicted risks from this one-year risk model, we generated cumulative risks of an interval second cancer for the five-year period after each mammogram. Model performance was evaluated using cross-validation in the overall cohort and within race and ethnicity strata. RESULTS In 173 290 surveillance mammograms, we observed 496 interval cancers. One-year risk models were well-calibrated (expected/observed ratio = 1.00) with good accuracy (area under the receiver operating characteristic curve = 0.64). Model performance was similar across race and ethnicity groups. The median five-year cumulative risk was 1.20% (interquartile range 0.93%-1.63%). Median five-year risks were highest in women who were under age 40 or pre- or perimenopausal at diagnosis and those with estrogen receptor-negative primary breast cancers. CONCLUSIONS Our risk model identified women at high risk of interval second breast cancers who may benefit from additional surveillance imaging modalities. Risk models should be evaluated to determine if risk-guided supplemental surveillance imaging improves early detection and decreases surveillance failures.
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Affiliation(s)
- Rebecca A Hubbard
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Yu-Ru Su
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA, USA
| | - Erin J A Bowles
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA, USA
| | - Laura Ichikawa
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA, USA
| | - Karla Kerlikowske
- Departments of Medicine and Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
- General Internal Medicine Section, Department of Veterans Affairs, University of California, San Francisco, CA, USA
| | - Kathryn P Lowry
- Department of Radiology, University of Washington and Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Diana L Miglioretti
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA, USA
- Division of Biostatistics, Department of Public Health Sciences, University of California Davis, Davis, CA, USA
| | - Anna N A Tosteson
- The Dartmouth Institute for Health Policy and Clinical Practice and Dartmouth Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - Karen J Wernli
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA, USA
| | - Janie M Lee
- Department of Radiology, University of Washington and Fred Hutchinson Cancer Center, Seattle, WA, USA
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Sprague BL, Nowak SA, Ahern TP, Herschorn SD, Kaufman PA, Odde C, Perry H, Sowden MM, Vacek PM, Weaver DL. Long-term Mammography Screening Trends and Predictors of Return to Screening after the COVID-19 Pandemic: Results from a Statewide Registry. Radiol Imaging Cancer 2024; 6:e230161. [PMID: 38578209 PMCID: PMC11148837 DOI: 10.1148/rycan.230161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 01/09/2024] [Accepted: 02/27/2024] [Indexed: 04/06/2024]
Abstract
Purpose To evaluate long-term trends in mammography screening rates and identify sociodemographic and breast cancer risk characteristics associated with return to screening after the COVID-19 pandemic. Materials and Methods In this retrospective study, statewide screening mammography data of 222 384 female individuals aged 40 years or older (mean age, 58.8 years ± 11.7 [SD]) from the Vermont Breast Cancer Surveillance System were evaluated to generate descriptive statistics and Joinpoint models to characterize screening patterns during 2000-2022. Log-binomial regression models estimated associations of sociodemographic and risk characteristics with post-COVID-19 pandemic return to screening. Results The proportion of female individuals in Vermont aged 50-74 years with a screening mammogram obtained in the previous 2 years declined from a prepandemic level of 61.3% (95% CI: 61.1%, 61.6%) in 2019 to 56.0% (95% CI: 55.7%, 56.3%) in 2021 before rebounding to 60.7% (95% CI: 60.4%, 61.0%) in 2022. Screening adherence in 2022 remained substantially lower than that observed during the 2007-2010 apex of screening adherence (66.1%-67.0%). Joinpoint models estimated an annual percent change of -1.1% (95% CI: -1.5%, -0.8%) during 2010-2022. Among the cohort of 95 644 individuals screened during January 2018-March 2020, the probability of returning to screening during 2020-2022 varied by age (eg, risk ratio [RR] = 0.94 [95% CI: 0.93, 0.95] for age 40-44 vs age 60-64 years), race and ethnicity (RR = 0.84 [95% CI: 0.78, 0.90] for Black vs White individuals), education (RR = 0.84 [95% CI: 0.81, 0.86] for less than high school degree vs college degree), and by 5-year breast cancer risk (RR = 1.06 [95% CI: 1.04, 1.08] for very high vs average risk). Conclusion Despite a rebound to near prepandemic levels, Vermont mammography screening rates have steadily declined since 2010, with certain sociodemographic groups less likely to return to screening after the pandemic. Keywords: Mammography, Breast, Health Policy and Practice, Neoplasms-Primary, Epidemiology, Screening Supplemental material is available for this article. © RSNA, 2024.
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Affiliation(s)
- Brian L. Sprague
- From the Department of Surgery (B.L.S., T.P.A., M.M.S.), Department
of Radiology (B.L.S., S.D.H., H.P.), University of Vermont Cancer Center
(B.L.S., S.A.N., T.P.A., S.D.H., P.A.K., H.P., M.M.S., P.M.V., D.L.W.),
Department of Pathology and Laboratory Medicine (S.A.N., D.L.W.), Department of
Medicine (P.A.K.), and Department of Biostatistics (P.M.V.), University of
Vermont Larner College of Medicine, 1 S Prospect St, UHC Bldg Rm 4425,
Burlington, VT 05401; and Bachelor of Individualized Studies Program, College of
Liberal Arts, University of Minnesota, Minneapolis, Minn (C.O.)
| | - Sarah A. Nowak
- From the Department of Surgery (B.L.S., T.P.A., M.M.S.), Department
of Radiology (B.L.S., S.D.H., H.P.), University of Vermont Cancer Center
(B.L.S., S.A.N., T.P.A., S.D.H., P.A.K., H.P., M.M.S., P.M.V., D.L.W.),
Department of Pathology and Laboratory Medicine (S.A.N., D.L.W.), Department of
Medicine (P.A.K.), and Department of Biostatistics (P.M.V.), University of
Vermont Larner College of Medicine, 1 S Prospect St, UHC Bldg Rm 4425,
Burlington, VT 05401; and Bachelor of Individualized Studies Program, College of
Liberal Arts, University of Minnesota, Minneapolis, Minn (C.O.)
| | - Thomas P. Ahern
- From the Department of Surgery (B.L.S., T.P.A., M.M.S.), Department
of Radiology (B.L.S., S.D.H., H.P.), University of Vermont Cancer Center
(B.L.S., S.A.N., T.P.A., S.D.H., P.A.K., H.P., M.M.S., P.M.V., D.L.W.),
Department of Pathology and Laboratory Medicine (S.A.N., D.L.W.), Department of
Medicine (P.A.K.), and Department of Biostatistics (P.M.V.), University of
Vermont Larner College of Medicine, 1 S Prospect St, UHC Bldg Rm 4425,
Burlington, VT 05401; and Bachelor of Individualized Studies Program, College of
Liberal Arts, University of Minnesota, Minneapolis, Minn (C.O.)
| | - Sally D. Herschorn
- From the Department of Surgery (B.L.S., T.P.A., M.M.S.), Department
of Radiology (B.L.S., S.D.H., H.P.), University of Vermont Cancer Center
(B.L.S., S.A.N., T.P.A., S.D.H., P.A.K., H.P., M.M.S., P.M.V., D.L.W.),
Department of Pathology and Laboratory Medicine (S.A.N., D.L.W.), Department of
Medicine (P.A.K.), and Department of Biostatistics (P.M.V.), University of
Vermont Larner College of Medicine, 1 S Prospect St, UHC Bldg Rm 4425,
Burlington, VT 05401; and Bachelor of Individualized Studies Program, College of
Liberal Arts, University of Minnesota, Minneapolis, Minn (C.O.)
| | - Peter A. Kaufman
- From the Department of Surgery (B.L.S., T.P.A., M.M.S.), Department
of Radiology (B.L.S., S.D.H., H.P.), University of Vermont Cancer Center
(B.L.S., S.A.N., T.P.A., S.D.H., P.A.K., H.P., M.M.S., P.M.V., D.L.W.),
Department of Pathology and Laboratory Medicine (S.A.N., D.L.W.), Department of
Medicine (P.A.K.), and Department of Biostatistics (P.M.V.), University of
Vermont Larner College of Medicine, 1 S Prospect St, UHC Bldg Rm 4425,
Burlington, VT 05401; and Bachelor of Individualized Studies Program, College of
Liberal Arts, University of Minnesota, Minneapolis, Minn (C.O.)
| | - Catherine Odde
- From the Department of Surgery (B.L.S., T.P.A., M.M.S.), Department
of Radiology (B.L.S., S.D.H., H.P.), University of Vermont Cancer Center
(B.L.S., S.A.N., T.P.A., S.D.H., P.A.K., H.P., M.M.S., P.M.V., D.L.W.),
Department of Pathology and Laboratory Medicine (S.A.N., D.L.W.), Department of
Medicine (P.A.K.), and Department of Biostatistics (P.M.V.), University of
Vermont Larner College of Medicine, 1 S Prospect St, UHC Bldg Rm 4425,
Burlington, VT 05401; and Bachelor of Individualized Studies Program, College of
Liberal Arts, University of Minnesota, Minneapolis, Minn (C.O.)
| | - Hannah Perry
- From the Department of Surgery (B.L.S., T.P.A., M.M.S.), Department
of Radiology (B.L.S., S.D.H., H.P.), University of Vermont Cancer Center
(B.L.S., S.A.N., T.P.A., S.D.H., P.A.K., H.P., M.M.S., P.M.V., D.L.W.),
Department of Pathology and Laboratory Medicine (S.A.N., D.L.W.), Department of
Medicine (P.A.K.), and Department of Biostatistics (P.M.V.), University of
Vermont Larner College of Medicine, 1 S Prospect St, UHC Bldg Rm 4425,
Burlington, VT 05401; and Bachelor of Individualized Studies Program, College of
Liberal Arts, University of Minnesota, Minneapolis, Minn (C.O.)
| | - Michelle M. Sowden
- From the Department of Surgery (B.L.S., T.P.A., M.M.S.), Department
of Radiology (B.L.S., S.D.H., H.P.), University of Vermont Cancer Center
(B.L.S., S.A.N., T.P.A., S.D.H., P.A.K., H.P., M.M.S., P.M.V., D.L.W.),
Department of Pathology and Laboratory Medicine (S.A.N., D.L.W.), Department of
Medicine (P.A.K.), and Department of Biostatistics (P.M.V.), University of
Vermont Larner College of Medicine, 1 S Prospect St, UHC Bldg Rm 4425,
Burlington, VT 05401; and Bachelor of Individualized Studies Program, College of
Liberal Arts, University of Minnesota, Minneapolis, Minn (C.O.)
| | - Pamela M. Vacek
- From the Department of Surgery (B.L.S., T.P.A., M.M.S.), Department
of Radiology (B.L.S., S.D.H., H.P.), University of Vermont Cancer Center
(B.L.S., S.A.N., T.P.A., S.D.H., P.A.K., H.P., M.M.S., P.M.V., D.L.W.),
Department of Pathology and Laboratory Medicine (S.A.N., D.L.W.), Department of
Medicine (P.A.K.), and Department of Biostatistics (P.M.V.), University of
Vermont Larner College of Medicine, 1 S Prospect St, UHC Bldg Rm 4425,
Burlington, VT 05401; and Bachelor of Individualized Studies Program, College of
Liberal Arts, University of Minnesota, Minneapolis, Minn (C.O.)
| | - Donald L. Weaver
- From the Department of Surgery (B.L.S., T.P.A., M.M.S.), Department
of Radiology (B.L.S., S.D.H., H.P.), University of Vermont Cancer Center
(B.L.S., S.A.N., T.P.A., S.D.H., P.A.K., H.P., M.M.S., P.M.V., D.L.W.),
Department of Pathology and Laboratory Medicine (S.A.N., D.L.W.), Department of
Medicine (P.A.K.), and Department of Biostatistics (P.M.V.), University of
Vermont Larner College of Medicine, 1 S Prospect St, UHC Bldg Rm 4425,
Burlington, VT 05401; and Bachelor of Individualized Studies Program, College of
Liberal Arts, University of Minnesota, Minneapolis, Minn (C.O.)
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Kim HJ, Kim HH, Kim KH, Lee JS, Choi WJ, Chae EY, Shin HJ, Cha JH, Shim WH. Use of a commercial artificial intelligence-based mammography analysis software for improving breast ultrasound interpretations. Eur Radiol 2024:10.1007/s00330-024-10718-3. [PMID: 38570382 DOI: 10.1007/s00330-024-10718-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 02/22/2024] [Accepted: 03/13/2024] [Indexed: 04/05/2024]
Abstract
OBJECTIVES To evaluate the use of a commercial artificial intelligence (AI)-based mammography analysis software for improving the interpretations of breast ultrasound (US)-detected lesions. METHODS A retrospective analysis was performed on 1109 breasts that underwent both mammography and US-guided breast biopsy. The AI software processed mammograms and provided an AI score ranging from 0 to 100 for each breast, indicating the likelihood of malignancy. The performance of the AI score in differentiating mammograms with benign outcomes from those revealing cancers following US-guided breast biopsy was evaluated. In addition, prediction models for benign outcomes were constructed based on clinical and imaging characteristics with and without AI scores, using logistic regression analysis. RESULTS The AI software had an area under the receiver operating characteristics curve (AUROC) of 0.79 (95% CI, 0.79-0.82) in differentiating between benign and cancer cases. The prediction models that did not include AI scores (non-AI model), only used AI scores (AI-only model), and included AI scores (integrated model) had AUROCs of 0.79 (95% CI, 0.75-0.83), 0.78 (95% CI, 0.74-0.82), and 0.85 (95% CI, 0.81-0.88) in the development cohort, and 0.75 (95% CI, 0.68-0.81), 0.82 (95% CI, 0.76-0.88), and 0.84 (95% CI, 0.79-0.90) in the validation cohort, respectively. The integrated model outperformed the non-AI model in the development and validation cohorts (p < 0.001 for both). CONCLUSION The commercial AI-based mammography analysis software could be a valuable adjunct to clinical decision-making for managing US-detected breast lesions. CLINICAL RELEVANCE STATEMENT The commercial AI-based mammography analysis software could potentially reduce unnecessary biopsies and improve patient outcomes. KEY POINTS • Breast US has high rates of false-positive interpretations. • A commercial AI-based mammography analysis software could distinguish mammograms having benign outcomes from those revealing cancers after US-guided breast biopsy. • A commercial AI-based mammography analysis software may improve interpretations for breast US-detected lesions.
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Affiliation(s)
- Hee Jeong Kim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-Ro 43-Gil, Songpa-Gu, Seoul, 05505, South Korea
| | - Hak Hee Kim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-Ro 43-Gil, Songpa-Gu, Seoul, 05505, South Korea.
| | - Ki Hwan Kim
- Lunit Inc., 15F, 27, Teheran-Ro 2-Gil, Gangnam-Gu, Seoul, 06241, South Korea
| | - Ji Sung Lee
- Department of Clinical Epidemiology and Biostatistics, Asan Medical Center, University of Ulsan College, Ulsan, South Korea
| | - Woo Jung Choi
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-Ro 43-Gil, Songpa-Gu, Seoul, 05505, South Korea
| | - Eun Young Chae
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-Ro 43-Gil, Songpa-Gu, Seoul, 05505, South Korea
| | - Hee Jung Shin
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-Ro 43-Gil, Songpa-Gu, Seoul, 05505, South Korea
| | - Joo Hee Cha
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-Ro 43-Gil, Songpa-Gu, Seoul, 05505, South Korea
| | - Woo Hyun Shim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-Ro 43-Gil, Songpa-Gu, Seoul, 05505, South Korea
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Shimizu A, Iwabuchi Y, Tsukada J, Nakahara T, Sakurai R, Tonda K, Jinzaki M. Correlation between breast cancer and background parenchymal uptake on 18F-fluorodeoxyglucose positron emission tomography. Eur J Radiol 2024; 173:111378. [PMID: 38382424 DOI: 10.1016/j.ejrad.2024.111378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 02/05/2024] [Accepted: 02/16/2024] [Indexed: 02/23/2024]
Abstract
PURPOSE This study aimed to investigate differences in background parenchymal uptake (BPU) between patients with and without breast cancer using 18F-fluorodeoxyglucose positron emission tomography. METHODS Female patients (n = 130, 62.9 ± 12.7 years) with newly diagnosed breast cancer and 50 healthy participants (59.6 ± 13.3 years) without breast cancer were retrospectively included. BPU was evaluated using the maximum standardized uptake value. Data on participant age, body mass index, blood glucose level, and menopausal status were collected from medical records. Breast density was evaluated using mammography. Logistic regression analysis and receiver operating characteristic curves were used to examine the correlation between breast cancer and various characteristic factors, including BPU. RESULTS The BPU of patients with breast cancer was significantly higher than that of controls (P < 0.001). The results of logistic regression analysis regarding the presence of breast cancer demonstrated that BPU and menopausal status showed higher odds ratios of 13.6 and 4.25, respectively. The area under the receiver operating characteristic curve for BPU was 0.751. CONCLUSIONS Patients with breast cancer showed higher 18F-fluorodeoxyglucose-BPU. Glucose metabolism of mammary glands may correlate with the development of breast cancer.
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Affiliation(s)
- Atsushi Shimizu
- Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjyuku-ku, Tokyo 160-8582, Japan
| | - Yu Iwabuchi
- Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjyuku-ku, Tokyo 160-8582, Japan.
| | - Jitsuro Tsukada
- Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjyuku-ku, Tokyo 160-8582, Japan
| | - Takehiro Nakahara
- Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjyuku-ku, Tokyo 160-8582, Japan
| | - Ryosuke Sakurai
- Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjyuku-ku, Tokyo 160-8582, Japan; Department of Radiology, Dokkyo Medical University, 880 Kitakobayashi, Mibu, Shimotsuga-gun, Tochigi 321-0293, Japan
| | - Kai Tonda
- Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjyuku-ku, Tokyo 160-8582, Japan
| | - Masahiro Jinzaki
- Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjyuku-ku, Tokyo 160-8582, Japan
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Gard CC, Tice JA, Miglioretti DL, Sprague BL, Bissell MC, Henderson LM, Kerlikowske K. Extending the Breast Cancer Surveillance Consortium Model of Invasive Breast Cancer. J Clin Oncol 2024; 42:779-789. [PMID: 37976443 PMCID: PMC10906584 DOI: 10.1200/jco.22.02470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Revised: 08/08/2023] [Accepted: 09/18/2023] [Indexed: 11/19/2023] Open
Abstract
PURPOSE We extended the Breast Cancer Surveillance Consortium (BCSC) version 2 (v2) model of invasive breast cancer risk to include BMI, extended family history of breast cancer, and age at first live birth (version 3 [v3]) to better inform appropriate breast cancer prevention therapies and risk-based screening. METHODS We used Cox proportional hazards regression to estimate the age- and race- and ethnicity-specific relative hazards for family history of breast cancer, breast density, history of benign breast biopsy, BMI, and age at first live birth for invasive breast cancer in the BCSC cohort. We evaluated calibration using the ratio of expected-to-observed (E/O) invasive breast cancers in the cohort and discrimination using the area under the receiver operating characteristic curve (AUROC). RESULTS We analyzed data from 1,455,493 women age 35-79 years without a history of breast cancer. During a mean follow-up of 7.3 years, 30,266 women were diagnosed with invasive breast cancer. The BCSC v3 model had an E/O of 1.03 (95% CI, 1.01 to 1.04) and an AUROC of 0.646 for 5-year risk. Compared with the v2 model, discrimination of the v3 model improved most in Asian, White, and Black women. Among women with a BMI of 30.0-34.9 kg/m2, the true-positive rate in women with an estimated 5-year risk of 3% or higher increased from 10.0% (v2) to 19.8% (v3) and the improvement was greater among women with a BMI of ≥35 kg/m2 (7.6%-19.8%). CONCLUSION The BCSC v3 model updates an already well-calibrated and validated breast cancer risk assessment tool to include additional important risk factors. The inclusion of BMI was associated with the largest improvement in estimated risk for individual women.
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Affiliation(s)
- Charlotte C. Gard
- Department of Economics, Applied Statistics, and International Business, New Mexico State University, Las Cruces, NM
| | - Jeffrey A. Tice
- Division of General Internal Medicine, Department of Medicine, University of California, San Francisco, San Francisco, CA
| | - Diana L. Miglioretti
- University of California, Davis, Davis, CA
- Kaiser Permanente Washington Health Research Institute, Seattle, WA
| | - Brian L. Sprague
- Department of Surgery, University of Vermont Cancer Center, Burlington, VT
- Department of Radiology, University of Vermont Cancer Center, Burlington, VT
| | | | | | - Karla Kerlikowske
- General Internal Medicine Section, Department of Veteran Affairs, University of California, San Francisco, San Francisco, CA
- Departments of Medicine and Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA
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Kamil D, Wojcik KM, Smith L, Zhang J, Wilson OWA, Butera G, Jayasekera J. A Scoping Review of Personalized, Interactive, Web-Based Clinical Decision Tools Available for Breast Cancer Prevention and Screening in the United States. MDM Policy Pract 2024; 9:23814683241236511. [PMID: 38500600 PMCID: PMC10946080 DOI: 10.1177/23814683241236511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 02/04/2024] [Indexed: 03/20/2024] Open
Abstract
Introduction. Personalized web-based clinical decision tools for breast cancer prevention and screening could address knowledge gaps, enhance patient autonomy in shared decision-making, and promote equitable care. The purpose of this review was to present evidence on the availability, usability, feasibility, acceptability, quality, and uptake of breast cancer prevention and screening tools to support their integration into clinical care. Methods. We used the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews Checklist to conduct this review. We searched 6 databases to identify literature on the development, validation, usability, feasibility, acceptability testing, and uptake of the tools into practice settings. Quality assessment for each tool was conducted using the International Patient Decision Aid Standard instrument, with quality scores ranging from 0 to 63 (lowest-highest). Results. We identified 10 tools for breast cancer prevention and 9 tools for screening. The tools included individual (e.g., age), clinical (e.g., genomic risk factors), and health behavior (e.g., alcohol use) characteristics. Fourteen tools included race/ethnicity, but no tool incorporated contextual factors (e.g., insurance, access) associated with breast cancer. All tools were internally or externally validated. Six tools had undergone usability testing in samples including White (median, 71%; range, 9%-96%), insured (99%; 97%-100%) women, with college education or higher (60%; 27%-100%). All of the tools were developed and tested in academic settings. Seven (37%) tools showed potential evidence of uptake in clinical practice. The tools had an average quality assessment score of 21 (range, 9-39). Conclusions. There is limited evidence on testing and uptake of breast cancer prevention and screening tools in diverse clinical settings. The development, testing, and integration of tools in academic and nonacademic settings could potentially improve uptake and equitable access to these tools. Highlights There were 19 personalized, interactive, Web-based decision tools for breast cancer prevention and screening.Breast cancer outcomes were personalized based on individual clinical characteristics (e.g., age, medical history), genomic risk factors (e.g., BRCA1/2), race and ethnicity, and health behaviors (e.g., smoking). The tools did not include contextual factors (e.g., insurance status, access to screening facilities) that could potentially contribute to breast cancer outcomes.Validation, usability, acceptability, and feasibility testing were conducted mostly among White and/or insured patients with some college education (or higher) in academic settings. There was limited evidence on testing and uptake of the tools in nonacademic clinical settings.
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Affiliation(s)
- Dalya Kamil
- Health Equity and Decision Sciences Research Laboratory, Division of Intramural Research, National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, USA
| | - Kaitlyn M. Wojcik
- Health Equity and Decision Sciences Research Laboratory, Division of Intramural Research, National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, USA
| | - Laney Smith
- Frederick P. Whiddon College of Medicine, Mobile, AL, USA
| | | | - Oliver W. A. Wilson
- Health Equity and Decision Sciences Research Laboratory, Division of Intramural Research, National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, USA
| | - Gisela Butera
- Office of Research Services, National Institutes of Health Library, Bethesda, MD, USA
| | - Jinani Jayasekera
- Health Equity and Decision Sciences Research Laboratory, Division of Intramural Research, National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, USA
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11
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Gard CC, Lange J, Miglioretti DL, O’Meara ES, Lee CI, Etzioni R. Risk of cancer versus risk of cancer diagnosis? Accounting for diagnostic bias in predictions of breast cancer risk by race and ethnicity. J Med Screen 2023; 30:209-216. [PMID: 37306245 PMCID: PMC10713859 DOI: 10.1177/09691413231180028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
OBJECTIVES Cancer risk prediction may be subject to detection bias if utilization of screening is related to cancer risk factors. We examine detection bias when predicting breast cancer risk by race/ethnicity. METHODS We used screening and diagnosis histories from the Breast Cancer Surveillance Consortium to estimate risk of breast cancer onset and calculated relative risk of onset and diagnosis for each racial/ethnic group compared with non-Hispanic White women. RESULTS Of 104,073 women aged 40-54 receiving their first screening mammogram at a Breast Cancer Surveillance Consortium facility between 2000 and 2018, 10.2% (n = 10,634) identified as Asian, 10.9% (n = 11,292) as Hispanic, and 8.4% (n = 8719) as non-Hispanic Black. Hispanic and non-Hispanic Black women had slightly lower screening frequencies but biopsy rates following a positive mammogram were similar across groups. Risk of cancer diagnosis was similar for non-Hispanic Black and White women (relative risk vs non-Hispanic White = 0.90, 95% CI 0.65 to 1.14) but was lower for Asian (relative risk = 0.70, 95% CI 0.56 to 0.97) and Hispanic women (relative risk = 0.82, 95% CI 0.62 to 1.08). Relative risks of disease onset were 0.78 (95% CI 0.68 to 0.88), 0.70 (95% CI 0.59 to 0.83), and 0.95 (95% CI 0.84 to 1.09) for Asian, Hispanic, and non-Hispanic Black women, respectively. CONCLUSIONS Racial/ethnic differences in mammography and biopsy utilization did not induce substantial detection bias; relative risks of disease onset were similar to or modestly different than relative risks of diagnosis. Asian and Hispanic women have lower risks of developing breast cancer than non-Hispanic Black and White women, who have similar risks.
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Affiliation(s)
- Charlotte C. Gard
- Department of Economics, Applied Statistics, and International Business, New Mexico State University, Las Cruces, NM, USA
| | - Jane Lange
- Knight Cancer Institute, Oregon Health and Science University, Portland, OR, USA
| | - Diana L. Miglioretti
- Department of Public Health Sciences, University of California Davis School of Medicine, Davis, CA, USA
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Ellen S. O’Meara
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Christoph I. Lee
- Department of Radiology, University of Washington School of Medicine, Seattle, WA, USA
- Department of Health Services, University of Washington School of Public Health, Seattle, WA, USA
- Hutchinson Institute for Cancer Outcomes Research, Seattle, WA, USA
| | - Ruth Etzioni
- Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Biostatistics, University of Washington School of Public Health, Seattle, WA, USA
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12
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Nyante SJ, Abraham L, Bowles EJA, Lee CI, Kerlikowske K, Miglioretti DL, Sprague BL, Henderson LM. Racial and Ethnic Variation in Diagnostic Mammography Performance among Women Reporting a Breast Lump. Cancer Epidemiol Biomarkers Prev 2023; 32:1542-1551. [PMID: 37440458 PMCID: PMC10790330 DOI: 10.1158/1055-9965.epi-23-0289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 06/12/2023] [Accepted: 07/11/2023] [Indexed: 07/15/2023] Open
Abstract
BACKGROUND We evaluated diagnostic mammography among women with a breast lump to determine whether performance varied across racial and ethnic groups. METHODS This study included 51,014 diagnostic mammograms performed between 2005 and 2018 in the Breast Cancer Surveillance Consortium among Asian/Pacific Islander (12%), Black (7%), Hispanic/Latina (6%), and White (75%) women reporting a lump. Breast cancers occurring within 1 year were ascertained from cancer registry linkages. Multivariable regression was used to adjust performance statistic comparisons for breast cancer risk factors, mammogram modality, demographics, additional imaging, and imaging facility. RESULTS Cancer detection rates were highest among Asian/Pacific Islander [per 1,000 exams, 84.2 (95% confidence interval (CI): 72.0-98.2)] and Black women [81.4 (95% CI: 69.4-95.2)] and lowest among Hispanic/Latina women [42.9 (95% CI: 34.2-53.6)]. Positive predictive values (PPV) were higher among Black [37.0% (95% CI: 31.2-43.3)] and White [37.0% (95% CI: 30.0-44.6)] women and lowest among Hispanic/Latina women [22.0% (95% CI: 17.2-27.7)]. False-positive results were most common among Asian/Pacific Islander women [per 1,000 exams, 183.9 (95% CI: 126.7-259.2)] and lowest among White women [112.4 (95% CI: 86.1-145.5)]. After adjustment, false-positive and cancer detection rates remained higher for Asian/Pacific Islander and Black women (vs. Hispanic/Latina and White). Adjusted PPV was highest among Asian/Pacific Islander women. CONCLUSIONS Among women with a lump, Asian/Pacific Islander and Black women were more likely to have cancer detected and more likely to receive a false-positive result compared with White and Hispanic/Latina women. IMPACT Strategies for optimizing diagnostic mammography among women with a lump may vary by racial/ethnic group, but additional factors that influence performance differences need to be identified. See related In the Spotlight, p. 1479.
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Affiliation(s)
- Sarah J. Nyante
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Linn Abraham
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA
| | - Erin J. Aiello Bowles
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA
| | - Christoph I. Lee
- Department of Radiology, University of Washington School of Medicine; Department of Health Services, University of Washington School of Public Health; Fred Hutchinson Cancer Center, Seattle, WA
| | - Karla Kerlikowske
- Departments of Medicine and Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA
| | - Diana L. Miglioretti
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA
- Department of Public Health Sciences, University of California, Davis, Davis, CA
| | - Brian L. Sprague
- Department of Surgery and University of Vermont Cancer Center, University of Vermont, Burlington, VT
| | - Louise M. Henderson
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC
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13
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Kerlikowske K, Bissell MCS, Sprague BL, Tice JA, Tossas KY, Bowles EJA, Ho TQH, Keegan THM, Miglioretti DL. Impact of BMI on Prevalence of Dense Breasts by Race and Ethnicity. Cancer Epidemiol Biomarkers Prev 2023; 32:1524-1530. [PMID: 37284771 PMCID: PMC10701641 DOI: 10.1158/1055-9965.epi-23-0049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 03/18/2023] [Accepted: 04/25/2023] [Indexed: 06/08/2023] Open
Abstract
BACKGROUND Density notification laws require notifying women of dense breasts with dense breast prevalence varying by race/ethnicity. We evaluated whether differences in body mass index (BMI) account for differences in dense breasts prevalence by race/ethnicity. METHODS Prevalence of dense breasts (heterogeneously or extremely dense) according to Breast Imaging Reporting and Data System and obesity (BMI > 30 kg/m2) were estimated from 2,667,207 mammography examinations among 866,033 women in the Breast Cancer Surveillance Consortium (BCSC) from January 2005 through April 2021. Prevalence ratios (PR) for dense breasts relative to overall prevalence by race/ethnicity were estimated by standardizing race/ethnicity prevalence in the BCSC to the 2020 U.S. population, and adjusting for age, menopausal status, and BMI using logistic regression. RESULTS Dense breasts were most prevalent among Asian women (66.0%) followed by non-Hispanic/Latina (NH) White (45.5%), Hispanic/Latina (45.3%), and NH Black (37.0%) women. Obesity was most prevalent in Black women (58.4%) followed by Hispanic/Latina (39.3%), NH White (30.6%), and Asian (8.5%) women. The adjusted prevalence of dense breasts was 19% higher [PR = 1.19; 95% confidence interval (CI), 1.19-1.20] in Asian women, 8% higher (PR = 1.08; 95% CI, 1.07-1.08) in Black women, the same in Hispanic/Latina women (PR = 1.00; 95% CI, 0.99-1.01), and 4% lower (PR = 0.96; 95% CI, 0.96-0.97) in NH White women relative to the overall prevalence. CONCLUSIONS Clinically important differences in breast density prevalence are present across racial/ethnic groups after accounting for age, menopausal status, and BMI. IMPACT If breast density is the sole criterion used to notify women of dense breasts and discuss supplemental screening it may result in implementing inequitable screening strategies across racial/ethnic groups. See related In the Spotlight, p. 1479.
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Affiliation(s)
- Karla Kerlikowske
- Departments of Medicine and Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
- General Internal Medicine Section, Department of Veterans Affairs, University of California, San Francisco, CA, USA
| | - Michael C. S. Bissell
- Division of Biostatistics, Department of Public Health Sciences, University of California Davis School of Medicine, Davis, CA, USA
| | - Brian L. Sprague
- Departments of Surgery and Radiology, Office of Health Promotion Research, Larner College of Medicine at the University of Vermont and University of Vermont Cancer Center, Burlington, VT, USA
| | - Jeffrey A. Tice
- Division of General Internal Medicine, Department of Medicine, University of California, San Francisco, CA, USA
| | - Katherine Y. Tossas
- Department of Health Behavior and Policy, School of Medicine, and Massey Cancer Center, Virginia Commonwealth University, Richmond VA, USA
| | - Erin J. A. Bowles
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA, USA
| | - Thao-Quyen H. Ho
- Department of Training and Scientific Research, University Medical Center, Ho Chi Minh city, Vietnam
- Breast Imaging Unit, Diagnostic Imaging Center, Tam Anh General Hospital, Ho Chi Minh City, Vietnam
| | - Theresa H. M. Keegan
- Center for Oncology Hematology Outcomes Research and Training (COHORT) and Division of Hematology and Oncology, University of California Davis School of Medicine, Sacramento, CA, USA
| | - Diana L. Miglioretti
- Division of Biostatistics, Department of Public Health Sciences, University of California Davis School of Medicine, Davis, CA, USA
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA, USA
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14
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Stevens ER, Caverly T, Butler JM, Kukhareva P, Richardson S, Mann DM, Kawamoto K. Considerations for using predictive models that include race as an input variable: The case study of lung cancer screening. J Biomed Inform 2023; 147:104525. [PMID: 37844677 PMCID: PMC11221602 DOI: 10.1016/j.jbi.2023.104525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 10/06/2023] [Accepted: 10/13/2023] [Indexed: 10/18/2023]
Abstract
Indiscriminate use of predictive models incorporating race can reinforce biases present in source data and lead to an exacerbation of health disparities. In some countries, such as the United States, there is therefore a push to remove race from prediction models; however, there are still many prediction models that use race as an input. Biomedical informaticists who are given the responsibility of using these predictive models in healthcare environments are likely to be faced with questions like how to deal with race covariates in these models. Thus, there is a need for a pragmatic framework to help model users think through how to include race in their chosen model so as to avoid inadvertently exacerbating disparities. In this paper, we use the case study of lung cancer screening to propose a simple framework to guide how model users can approach the use (or non-use) of race inputs in the predictive models they are tasked with leveraging in electronic health records and clinical workflows.
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Affiliation(s)
- Elizabeth R Stevens
- Department of Population Health, NYU Grossman School of Medicine, New York, NY, United States.
| | - Tanner Caverly
- University of Michigan Medical School, Ann Arbor, MI, United States
| | - Jorie M Butler
- Department of Biomedical Informatics, University of Utah Health, Salt Lake City, UT, United States
| | - Polina Kukhareva
- Department of Biomedical Informatics, University of Utah Health, Salt Lake City, UT, United States
| | - Safiya Richardson
- Department of Population Health, NYU Grossman School of Medicine, New York, NY, United States
| | - Devin M Mann
- Department of Population Health, NYU Grossman School of Medicine, New York, NY, United States; Medical Center Information Technology, NYU Langone Health, New York, NY, United States
| | - Kensaku Kawamoto
- Department of Biomedical Informatics, University of Utah Health, Salt Lake City, UT, United States
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15
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Ahn JS, Shin S, Yang SA, Park EK, Kim KH, Cho SI, Ock CY, Kim S. Artificial Intelligence in Breast Cancer Diagnosis and Personalized Medicine. J Breast Cancer 2023; 26:405-435. [PMID: 37926067 PMCID: PMC10625863 DOI: 10.4048/jbc.2023.26.e45] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 09/25/2023] [Accepted: 10/06/2023] [Indexed: 11/07/2023] Open
Abstract
Breast cancer is a significant cause of cancer-related mortality in women worldwide. Early and precise diagnosis is crucial, and clinical outcomes can be markedly enhanced. The rise of artificial intelligence (AI) has ushered in a new era, notably in image analysis, paving the way for major advancements in breast cancer diagnosis and individualized treatment regimens. In the diagnostic workflow for patients with breast cancer, the role of AI encompasses screening, diagnosis, staging, biomarker evaluation, prognostication, and therapeutic response prediction. Although its potential is immense, its complete integration into clinical practice is challenging. Particularly, these challenges include the imperatives for extensive clinical validation, model generalizability, navigating the "black-box" conundrum, and pragmatic considerations of embedding AI into everyday clinical environments. In this review, we comprehensively explored the diverse applications of AI in breast cancer care, underlining its transformative promise and existing impediments. In radiology, we specifically address AI in mammography, tomosynthesis, risk prediction models, and supplementary imaging methods, including magnetic resonance imaging and ultrasound. In pathology, our focus is on AI applications for pathologic diagnosis, evaluation of biomarkers, and predictions related to genetic alterations, treatment response, and prognosis in the context of breast cancer diagnosis and treatment. Our discussion underscores the transformative potential of AI in breast cancer management and emphasizes the importance of focused research to realize the full spectrum of benefits of AI in patient care.
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Affiliation(s)
| | | | | | | | | | | | | | - Seokhwi Kim
- Department of Pathology, Ajou University School of Medicine, Suwon, Korea
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Korea.
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16
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Abstract
Multiple tools exist to assess a patient's breast cancer risk. The choice of risk model depends on the patient's risk factors and how the calculation will impact care. High-risk patients-those with a lifetime breast cancer risk of ≥20%-are, for instance, eligible for supplemental screening with breast magnetic resonance imaging. Those with an elevated short-term breast cancer risk (frequently defined as a 5-year risk ≥1.66%) should be offered endocrine prophylaxis. High-risk patients should also receive guidance on modification of lifestyle factors that affect breast cancer risk.
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Affiliation(s)
- Amy E Cyr
- Department of Medicine, Washington University, Box 8056, 660 South Euclid Avenue, Saint Louis, MO 63110, USA.
| | - Kaitlyn Kennard
- Department of Surgery, Washington University, Box 8051, 660 South Euclid Avenue, Saint louis, MO 63110, USA
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17
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Herington J, McCradden MD, Creel K, Boellaard R, Jones EC, Jha AK, Rahmim A, Scott PJH, Sunderland JJ, Wahl RL, Zuehlsdorff S, Saboury B. Ethical Considerations for Artificial Intelligence in Medical Imaging: Deployment and Governance. J Nucl Med 2023; 64:1509-1515. [PMID: 37620051 DOI: 10.2967/jnumed.123.266110] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 07/11/2023] [Indexed: 08/26/2023] Open
Abstract
The deployment of artificial intelligence (AI) has the potential to make nuclear medicine and medical imaging faster, cheaper, and both more effective and more accessible. This is possible, however, only if clinicians and patients feel that these AI medical devices (AIMDs) are trustworthy. Highlighting the need to ensure health justice by fairly distributing benefits and burdens while respecting individual patients' rights, the AI Task Force of the Society of Nuclear Medicine and Molecular Imaging has identified 4 major ethical risks that arise during the deployment of AIMD: autonomy of patients and clinicians, transparency of clinical performance and limitations, fairness toward marginalized populations, and accountability of physicians and developers. We provide preliminary recommendations for governing these ethical risks to realize the promise of AIMD for patients and populations.
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Affiliation(s)
- Jonathan Herington
- Department of Health Humanities and Bioethics and Department of Philosophy, University of Rochester, Rochester, New York
| | - Melissa D McCradden
- Department of Bioethics, Hospital for Sick Children, and Dana Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Kathleen Creel
- Department of Philosophy and Religion and Khoury College of Computer Sciences, Northeastern University, Boston, Massachusetts
| | - Ronald Boellaard
- Department of Radiology and Nuclear Medicine, Cancer Centre Amsterdam, Amsterdam University Medical Centres, Amsterdam, The Netherlands
| | - Elizabeth C Jones
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Maryland
| | - Abhinav K Jha
- Department of Biomedical Engineering and Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, Missouri
| | - Arman Rahmim
- Departments of Radiology and Physics, University of British Columbia, Vancouver, British Columbia, Canada
| | - Peter J H Scott
- Department of Radiology, University of Michigan Medical School, Ann Arbor, Michigan
| | - John J Sunderland
- Departments of Radiology and Physics, University of Iowa, Iowa City, Iowa
| | - Richard L Wahl
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, Missouri; and
| | | | - Babak Saboury
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Maryland;
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18
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Strandberg R, Czene K, Hall P, Humphreys K. Novel predictions of invasive breast cancer risk in mammography screening cohorts. Stat Med 2023; 42:3816-3837. [PMID: 37337390 DOI: 10.1002/sim.9834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 05/23/2023] [Accepted: 06/04/2023] [Indexed: 06/21/2023]
Abstract
Mammography screening programs are aimed at reducing mortality due to breast cancer by detecting tumors at an early stage. There is currently interest in moving away from the age-based screening programs, and toward personalized screening based on individual risk factors. To accomplish this, risk prediction models for breast cancer are needed to determine who should be screened, and when. We develop a novel approach using a (random effects) continuous growth model, which we apply to a large population-based, Swedish screening cohort. Unlike existing breast cancer prediction models, this approach explicitly incorporates each woman's individual screening visits in the prediction. It jointly models invasive breast cancer tumor onset, tumor growth rate, symptomatic detection rate, and screening sensitivity. In addition to predicting the overall risk of invasive breast cancer, this model can make separate predictions regarding specific tumor sizes, and the mode of detection (eg, detected at screening, or through symptoms between screenings). It can also predict how these risks change depending on whether or not a woman will attend her next screening. In our study, we predict, given a future diagnosis, that the probability of having a tumor less than (as opposed to greater than) 10-mm diameter, at detection, will be, on average, 2.6 times higher if a woman in the cohort attends their next screening. This indicates that the model can be used to evaluate the short-term benefit of screening attendance, at an individual level.
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Affiliation(s)
- Rickard Strandberg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Swedish eScience Research Centre (SeRC), Karolinska Institutet, Stockholm, Sweden
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Keith Humphreys
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Swedish eScience Research Centre (SeRC), Karolinska Institutet, Stockholm, Sweden
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19
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Louro J, Román M, Moshina N, Olstad CF, Larsen M, Sagstad S, Castells X, Hofvind S. Personalized Breast Cancer Screening: A Risk Prediction Model Based on Women Attending BreastScreen Norway. Cancers (Basel) 2023; 15:4517. [PMID: 37760486 PMCID: PMC10526465 DOI: 10.3390/cancers15184517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 09/06/2023] [Accepted: 09/09/2023] [Indexed: 09/29/2023] Open
Abstract
BACKGROUND We aimed to develop and validate a model predicting breast cancer risk for women targeted by breast cancer screening. METHOD This retrospective cohort study included 57,411 women screened at least once in BreastScreen Norway during the period from 2007 to 2019. The prediction model included information about age, mammographic density, family history of breast cancer, body mass index, age at menarche, alcohol consumption, exercise, pregnancy, hormone replacement therapy, and benign breast disease. We calculated a 4-year absolute breast cancer risk estimates for women and in risk groups by quartiles. The Bootstrap resampling method was used for internal validation of the model (E/O ratio). The area under the curve (AUC) was estimated with a 95% confidence interval (CI). RESULTS The 4-year predicted risk of breast cancer ranged from 0.22-7.33%, while 95% of the population had a risk of 0.55-2.31%. The thresholds for the quartiles of the risk groups, with 25% of the population in each group, were 0.82%, 1.10%, and 1.47%. Overall, the model slightly overestimated the risk with an E/O ratio of 1.10 (95% CI: 1.09-1.11) and the AUC was 62.6% (95% CI: 60.5-65.0%). CONCLUSIONS This 4-year risk prediction model showed differences in the risk of breast cancer, supporting personalized screening for breast cancer in women aged 50-69 years.
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Affiliation(s)
- Javier Louro
- Department of Epidemiology and Evaluation, Hospital del Mar Medical Research Institute, 08003 Barcelona, Spain; (J.L.); (M.R.); (X.C.)
- Network for Research on Chronicity, Primary Care, and Health Promotion (RICAPPS), 48902 Barakaldo, Spain
| | - Marta Román
- Department of Epidemiology and Evaluation, Hospital del Mar Medical Research Institute, 08003 Barcelona, Spain; (J.L.); (M.R.); (X.C.)
- Network for Research on Chronicity, Primary Care, and Health Promotion (RICAPPS), 48902 Barakaldo, Spain
| | - Nataliia Moshina
- Section for Breast Cancer Screening, Cancer Registry of Norway, 0304 Oslo, Norway; (N.M.); (C.F.O.); (M.L.); (S.S.)
| | - Camilla F. Olstad
- Section for Breast Cancer Screening, Cancer Registry of Norway, 0304 Oslo, Norway; (N.M.); (C.F.O.); (M.L.); (S.S.)
| | - Marthe Larsen
- Section for Breast Cancer Screening, Cancer Registry of Norway, 0304 Oslo, Norway; (N.M.); (C.F.O.); (M.L.); (S.S.)
| | - Silje Sagstad
- Section for Breast Cancer Screening, Cancer Registry of Norway, 0304 Oslo, Norway; (N.M.); (C.F.O.); (M.L.); (S.S.)
| | - Xavier Castells
- Department of Epidemiology and Evaluation, Hospital del Mar Medical Research Institute, 08003 Barcelona, Spain; (J.L.); (M.R.); (X.C.)
- Network for Research on Chronicity, Primary Care, and Health Promotion (RICAPPS), 48902 Barakaldo, Spain
| | - Solveig Hofvind
- Section for Breast Cancer Screening, Cancer Registry of Norway, 0304 Oslo, Norway; (N.M.); (C.F.O.); (M.L.); (S.S.)
- Department of Health and Care Sciences, Faculty of Health Sciences, UiT, The Arctic University of Norway, 9037 Tromsø, Norway
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20
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Sprague BL, Ichikawa L, Eavey J, Lowry KP, Rauscher G, O’Meara ES, Miglioretti DL, Chen S, Lee JM, Stout NK, Mandelblatt JS, Alsheik N, Herschorn SD, Perry H, Weaver DL, Kerlikowske K. Breast cancer risk characteristics of women undergoing whole-breast ultrasound screening versus mammography alone. Cancer 2023; 129:2456-2468. [PMID: 37303202 PMCID: PMC10506533 DOI: 10.1002/cncr.34768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 02/06/2023] [Accepted: 02/24/2023] [Indexed: 06/13/2023]
Abstract
BACKGROUND There are no consensus guidelines for supplemental breast cancer screening with whole-breast ultrasound. However, criteria for women at high risk of mammography screening failures (interval invasive cancer or advanced cancer) have been identified. Mammography screening failure risk was evaluated among women undergoing supplemental ultrasound screening in clinical practice compared with women undergoing mammography alone. METHODS A total of 38,166 screening ultrasounds and 825,360 screening mammograms without supplemental screening were identified during 2014-2020 within three Breast Cancer Surveillance Consortium (BCSC) registries. Risk of interval invasive cancer and advanced cancer were determined using BCSC prediction models. High interval invasive breast cancer risk was defined as heterogeneously dense breasts and BCSC 5-year breast cancer risk ≥2.5% or extremely dense breasts and BCSC 5-year breast cancer risk ≥1.67%. Intermediate/high advanced cancer risk was defined as BCSC 6-year advanced breast cancer risk ≥0.38%. RESULTS A total of 95.3% of 38,166 ultrasounds were among women with heterogeneously or extremely dense breasts, compared with 41.8% of 825,360 screening mammograms without supplemental screening (p < .0001). Among women with dense breasts, high interval invasive breast cancer risk was prevalent in 23.7% of screening ultrasounds compared with 18.5% of screening mammograms without supplemental imaging (adjusted odds ratio, 1.35; 95% CI, 1.30-1.39); intermediate/high advanced cancer risk was prevalent in 32.0% of screening ultrasounds versus 30.5% of screening mammograms without supplemental screening (adjusted odds ratio, 0.91; 95% CI, 0.89-0.94). CONCLUSIONS Ultrasound screening was highly targeted to women with dense breasts, but only a modest proportion were at high mammography screening failure risk. A clinically significant proportion of women undergoing mammography screening alone were at high mammography screening failure risk.
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Affiliation(s)
- Brian L. Sprague
- Office of Health Promotion Research, Department of Surgery, University of Vermont Larner College of Medicine, Burlington, VT
- Department of Radiology, University of Vermont Larner College of Medicine, Burlington, VT
- University of Vermont Cancer Center, University of Vermont Larner College of Medicine, Burlington, VT
| | - Laura Ichikawa
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente WA, Seattle, Washington
| | - Joanna Eavey
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente WA, Seattle, Washington
| | - Kathryn P. Lowry
- Department of Radiology, University of Washington and Seattle Cancer Care Alliance, Seattle, WA
| | - Garth Rauscher
- Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago, Chicago, IL
| | - Ellen S. O’Meara
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente WA, Seattle, Washington
| | - Diana L. Miglioretti
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente WA, Seattle, Washington
- Division of Biostatistics, Department of Public Health Sciences, University of California Davis, Davis, CA
| | - Shuai Chen
- Division of Biostatistics, Department of Public Health Sciences, University of California Davis, Davis, CA
| | - Janie M. Lee
- Department of Radiology, University of Washington and Seattle Cancer Care Alliance, Seattle, WA
| | - Natasha K. Stout
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA
| | - Jeanne S. Mandelblatt
- Department of Oncology, Georgetown University Medical Center, Georgetown University, Washington, DC, USA
| | - Nila Alsheik
- Advocate Caldwell Breast Center, Advocate Lutheran General Hospital, 1700 Luther Lane, Park Ridge, IL
| | - Sally D. Herschorn
- Department of Radiology, University of Vermont Larner College of Medicine, Burlington, VT
- University of Vermont Cancer Center, University of Vermont Larner College of Medicine, Burlington, VT
| | - Hannah Perry
- Department of Radiology, University of Vermont Larner College of Medicine, Burlington, VT
- University of Vermont Cancer Center, University of Vermont Larner College of Medicine, Burlington, VT
| | - Donald L. Weaver
- University of Vermont Cancer Center, University of Vermont Larner College of Medicine, Burlington, VT
- Department of Pathology & Laboratory Medicine, University of Vermont Larner College of Medicine, Burlington, VT
| | - Karla Kerlikowske
- Departments of Medicine and Epidemiology and Biostatistics, University of California, San Francisco, CA
- General Internal Medicine Section, Department of Veterans Affairs, University of California, San Francisco, CA
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21
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Vachon CM, Scott CG, Norman AD, Khanani SA, Jensen MR, Hruska CB, Brandt KR, Winham SJ, Kerlikowske K. Impact of Artificial Intelligence System and Volumetric Density on Risk Prediction of Interval, Screen-Detected, and Advanced Breast Cancer. J Clin Oncol 2023; 41:3172-3183. [PMID: 37104728 PMCID: PMC10256336 DOI: 10.1200/jco.22.01153] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 12/13/2022] [Accepted: 02/24/2023] [Indexed: 04/29/2023] Open
Abstract
PURPOSE Artificial intelligence (AI) algorithms improve breast cancer detection on mammography, but their contribution to long-term risk prediction for advanced and interval cancers is unknown. METHODS We identified 2,412 women with invasive breast cancer and 4,995 controls matched on age, race, and date of mammogram, from two US mammography cohorts, who had two-dimensional full-field digital mammograms performed 2-5.5 years before cancer diagnosis. We assessed Breast Imaging Reporting and Data System density, an AI malignancy score (1-10), and volumetric density measures. We used conditional logistic regression to estimate odds ratios (ORs), 95% CIs, adjusted for age and BMI, and C-statistics (AUC) to describe the association of AI score with invasive cancer and its contribution to models with breast density measures. Likelihood ratio tests (LRTs) and bootstrapping methods were used to compare model performance. RESULTS On mammograms between 2-5.5 years prior to cancer, a one unit increase in AI score was associated with 20% greater odds of invasive breast cancer (OR, 1.20; 95% CI, 1.17 to 1.22; AUC, 0.63; 95% CI, 0.62 to 0.64) and was similarly predictive of interval (OR, 1.20; 95% CI, 1.13 to 1.27; AUC, 0.63) and advanced cancers (OR, 1.23; 95% CI, 1.16 to 1.31; AUC, 0.64) and in dense (OR, 1.18; 95% CI, 1.15 to 1.22; AUC, 0.66) breasts. AI score improved prediction of all cancer types in models with density measures (PLRT values < .001); discrimination improved for advanced cancer (ie, AUC for dense volume increased from 0.624 to 0.679, Δ AUC 0.065, P = .01) but did not reach statistical significance for interval cancer. CONCLUSION AI imaging algorithms coupled with breast density independently contribute to long-term risk prediction of invasive breast cancers, in particular, advanced cancer.
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Affiliation(s)
- Celine M. Vachon
- Division of Epidemiology, Department Quantitative Sciences, Mayo Clinic, Rochester, MN
| | - Christopher G. Scott
- Division of Clinical Trials and Biostatistics, Department Quantitative Sciences, Mayo Clinic, Rochester, MN
| | - Aaron D. Norman
- Division of Epidemiology, Department Quantitative Sciences, Mayo Clinic, Rochester, MN
| | - Sadia A. Khanani
- Division of Breast Imaging, Department of Radiology, Mayo Clinic, Rochester, MN
| | - Matthew R. Jensen
- Division of Clinical Trials and Biostatistics, Department Quantitative Sciences, Mayo Clinic, Rochester, MN
| | - Carrie B. Hruska
- Division of Breast Imaging, Department of Radiology, Mayo Clinic, Rochester, MN
| | - Kathleen R. Brandt
- Division of Breast Imaging, Department of Radiology, Mayo Clinic, Rochester, MN
| | - Stacey J. Winham
- Division of Computational Biology, Department Quantitative Sciences, Mayo Clinic, Rochester, MN
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22
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Arasu VA, Habel LA, Achacoso NS, Buist DSM, Cord JB, Esserman LJ, Hylton NM, Glymour MM, Kornak J, Kushi LH, Lewis DA, Liu VX, Lydon CM, Miglioretti DL, Navarro DA, Pu A, Shen L, Sieh W, Yoon HC, Lee C. Comparison of Mammography AI Algorithms with a Clinical Risk Model for 5-year Breast Cancer Risk Prediction: An Observational Study. Radiology 2023; 307:e222733. [PMID: 37278627 PMCID: PMC10315521 DOI: 10.1148/radiol.222733] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 04/05/2023] [Accepted: 04/18/2023] [Indexed: 06/07/2023]
Abstract
Background Although several clinical breast cancer risk models are used to guide screening and prevention, they have only moderate discrimination. Purpose To compare selected existing mammography artificial intelligence (AI) algorithms and the Breast Cancer Surveillance Consortium (BCSC) risk model for prediction of 5-year risk. Materials and Methods This retrospective case-cohort study included data in women with a negative screening mammographic examination (no visible evidence of cancer) in 2016, who were followed until 2021 at Kaiser Permanente Northern California. Women with prior breast cancer or a highly penetrant gene mutation were excluded. Of the 324 009 eligible women, a random subcohort was selected, regardless of cancer status, to which all additional patients with breast cancer were added. The index screening mammographic examination was used as input for five AI algorithms to generate continuous scores that were compared with the BCSC clinical risk score. Risk estimates for incident breast cancer 0 to 5 years after the initial mammographic examination were calculated using a time-dependent area under the receiver operating characteristic curve (AUC). Results The subcohort included 13 628 patients, of whom 193 had incident cancer. Incident cancers in eligible patients (additional 4391 of 324 009) were also included. For incident cancers at 0 to 5 years, the time-dependent AUC for BCSC was 0.61 (95% CI: 0.60, 0.62). AI algorithms had higher time-dependent AUCs than did BCSC, ranging from 0.63 to 0.67 (Bonferroni-adjusted P < .0016). Time-dependent AUCs for combined BCSC and AI models were slightly higher than AI alone (AI with BCSC time-dependent AUC range, 0.66-0.68; Bonferroni-adjusted P < .0016). Conclusion When using a negative screening examination, AI algorithms performed better than the BCSC risk model for predicting breast cancer risk at 0 to 5 years. Combined AI and BCSC models further improved prediction. © RSNA, 2023 Supplemental material is available for this article.
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Affiliation(s)
- Vignesh A. Arasu
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
| | - Laurel A. Habel
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
| | - Ninah S. Achacoso
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
| | - Diana S. M. Buist
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
| | - Jason B. Cord
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
| | - Laura J. Esserman
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
| | - Nola M. Hylton
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
| | - M. Maria Glymour
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
| | - John Kornak
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
| | - Lawrence H. Kushi
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
| | - Donald A. Lewis
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
| | - Vincent X. Liu
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
| | - Caitlin M. Lydon
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
| | - Diana L. Miglioretti
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
| | - Daniel A. Navarro
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
| | - Albert Pu
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
| | - Li Shen
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
| | - Weiva Sieh
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
| | - Hyo-Chun Yoon
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
| | - Catherine Lee
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
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23
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Sprague BL, Coley RY, Lowry KP, Kerlikowske K, Henderson LM, Su YR, Lee CI, Onega T, Bowles EJA, Herschorn SD, diFlorio-Alexander RM, Miglioretti DL. Digital Breast Tomosynthesis versus Digital Mammography Screening Performance on Successive Screening Rounds from the Breast Cancer Surveillance Consortium. Radiology 2023; 307:e223142. [PMID: 37249433 PMCID: PMC10315524 DOI: 10.1148/radiol.223142] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 03/23/2023] [Accepted: 03/29/2023] [Indexed: 05/31/2023]
Abstract
Background Prior cross-sectional studies have observed that breast cancer screening with digital breast tomosynthesis (DBT) has a lower recall rate and higher cancer detection rate compared with digital mammography (DM). Purpose To evaluate breast cancer screening outcomes with DBT versus DM on successive screening rounds. Materials and Methods In this retrospective cohort study, data from 58 breast imaging facilities in the Breast Cancer Surveillance Consortium were collected. Analysis included women aged 40-79 years undergoing DBT or DM screening from 2011 to 2020. Absolute differences in screening outcomes by modality and screening round were estimated during the study period by using generalized estimating equations with marginal standardization to adjust for differences in women's risk characteristics across modality and round. Results A total of 523 485 DBT examinations (mean age of women, 58.7 years ± 9.7 [SD]) and 1 008 123 DM examinations (mean age, 58.4 years ± 9.8) among 504 863 women were evaluated. DBT and DM recall rates decreased with successive screening round, but absolute recall rates in each round were significantly lower with DBT versus DM (round 1 difference, -3.3% [95% CI: -4.6, -2.1] [P < .001]; round 2 difference, -1.8% [95% CI: -2.9, -0.7] [P = .003]; round 3 or above difference, -1.2% [95% CI: -2.4, -0.1] [P = .03]). DBT had significantly higher cancer detection (difference, 0.6 per 1000 examinations [95% CI: 0.2, 1.1]; P = .009) compared with DM only for round 3 and above. There were no significant differences in interval cancer rate (round 1 difference, 0.00 per 1000 examinations [95% CI: -0.24, 0.30] [P = .96]; round 2 or above difference, 0.04 [95% CI: -0.19, 0.31] [P = .76]) or total advanced cancer rate (round 1 difference, 0.00 per 1000 examinations [95% CI: -0.15, 0.19] [P = .94]; round 2 or above difference, -0.06 [95% CI: -0.18, 0.11] [P = .43]). Conclusion DBT had lower recall rates and could help detect more cancers than DM across three screening rounds, with no difference in interval or advanced cancer rates. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Skaane in this issue.
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Affiliation(s)
- Brian L. Sprague
- From the Departments of Surgery (B.L.S.) and Radiology (B.L.S., S.D.H.), University of Vermont Cancer Center, University of Vermont Larner College of Medicine, UHC Bldg, Room 4425, 1 S Prospect St, Burlington, VT 05401; Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Wash (R.Y.C., Y.R.S., E.J.A.B., D.L.M.); Department of Radiology, University of Washington, Fred Hutchinson Cancer Center, Seattle, Wash (K.P.L., C.I.L.); Departments of Medicine and Epidemiology and Biostatistics, University of California–San Francisco, San Francisco, Calif (K.K.); Department of Radiology, University of North Carolina, Chapel Hill, NC (L.M.H.); Department of Population Health Sciences, University of Utah, Huntsman Cancer Institute, Salt Lake City, Utah (T.O.); Department of Radiology, Giesel School of Medicine at Dartmouth, Lebanon, NH (R.M.d.F.A.); and Division of Biostatistics, Department of Public Health Sciences, University of California–Davis, Davis, Calif (D.L.M.)
| | - Rebecca Yates Coley
- From the Departments of Surgery (B.L.S.) and Radiology (B.L.S., S.D.H.), University of Vermont Cancer Center, University of Vermont Larner College of Medicine, UHC Bldg, Room 4425, 1 S Prospect St, Burlington, VT 05401; Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Wash (R.Y.C., Y.R.S., E.J.A.B., D.L.M.); Department of Radiology, University of Washington, Fred Hutchinson Cancer Center, Seattle, Wash (K.P.L., C.I.L.); Departments of Medicine and Epidemiology and Biostatistics, University of California–San Francisco, San Francisco, Calif (K.K.); Department of Radiology, University of North Carolina, Chapel Hill, NC (L.M.H.); Department of Population Health Sciences, University of Utah, Huntsman Cancer Institute, Salt Lake City, Utah (T.O.); Department of Radiology, Giesel School of Medicine at Dartmouth, Lebanon, NH (R.M.d.F.A.); and Division of Biostatistics, Department of Public Health Sciences, University of California–Davis, Davis, Calif (D.L.M.)
| | - Kathryn P. Lowry
- From the Departments of Surgery (B.L.S.) and Radiology (B.L.S., S.D.H.), University of Vermont Cancer Center, University of Vermont Larner College of Medicine, UHC Bldg, Room 4425, 1 S Prospect St, Burlington, VT 05401; Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Wash (R.Y.C., Y.R.S., E.J.A.B., D.L.M.); Department of Radiology, University of Washington, Fred Hutchinson Cancer Center, Seattle, Wash (K.P.L., C.I.L.); Departments of Medicine and Epidemiology and Biostatistics, University of California–San Francisco, San Francisco, Calif (K.K.); Department of Radiology, University of North Carolina, Chapel Hill, NC (L.M.H.); Department of Population Health Sciences, University of Utah, Huntsman Cancer Institute, Salt Lake City, Utah (T.O.); Department of Radiology, Giesel School of Medicine at Dartmouth, Lebanon, NH (R.M.d.F.A.); and Division of Biostatistics, Department of Public Health Sciences, University of California–Davis, Davis, Calif (D.L.M.)
| | - Karla Kerlikowske
- From the Departments of Surgery (B.L.S.) and Radiology (B.L.S., S.D.H.), University of Vermont Cancer Center, University of Vermont Larner College of Medicine, UHC Bldg, Room 4425, 1 S Prospect St, Burlington, VT 05401; Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Wash (R.Y.C., Y.R.S., E.J.A.B., D.L.M.); Department of Radiology, University of Washington, Fred Hutchinson Cancer Center, Seattle, Wash (K.P.L., C.I.L.); Departments of Medicine and Epidemiology and Biostatistics, University of California–San Francisco, San Francisco, Calif (K.K.); Department of Radiology, University of North Carolina, Chapel Hill, NC (L.M.H.); Department of Population Health Sciences, University of Utah, Huntsman Cancer Institute, Salt Lake City, Utah (T.O.); Department of Radiology, Giesel School of Medicine at Dartmouth, Lebanon, NH (R.M.d.F.A.); and Division of Biostatistics, Department of Public Health Sciences, University of California–Davis, Davis, Calif (D.L.M.)
| | - Louise M. Henderson
- From the Departments of Surgery (B.L.S.) and Radiology (B.L.S., S.D.H.), University of Vermont Cancer Center, University of Vermont Larner College of Medicine, UHC Bldg, Room 4425, 1 S Prospect St, Burlington, VT 05401; Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Wash (R.Y.C., Y.R.S., E.J.A.B., D.L.M.); Department of Radiology, University of Washington, Fred Hutchinson Cancer Center, Seattle, Wash (K.P.L., C.I.L.); Departments of Medicine and Epidemiology and Biostatistics, University of California–San Francisco, San Francisco, Calif (K.K.); Department of Radiology, University of North Carolina, Chapel Hill, NC (L.M.H.); Department of Population Health Sciences, University of Utah, Huntsman Cancer Institute, Salt Lake City, Utah (T.O.); Department of Radiology, Giesel School of Medicine at Dartmouth, Lebanon, NH (R.M.d.F.A.); and Division of Biostatistics, Department of Public Health Sciences, University of California–Davis, Davis, Calif (D.L.M.)
| | - Yu-Ru Su
- From the Departments of Surgery (B.L.S.) and Radiology (B.L.S., S.D.H.), University of Vermont Cancer Center, University of Vermont Larner College of Medicine, UHC Bldg, Room 4425, 1 S Prospect St, Burlington, VT 05401; Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Wash (R.Y.C., Y.R.S., E.J.A.B., D.L.M.); Department of Radiology, University of Washington, Fred Hutchinson Cancer Center, Seattle, Wash (K.P.L., C.I.L.); Departments of Medicine and Epidemiology and Biostatistics, University of California–San Francisco, San Francisco, Calif (K.K.); Department of Radiology, University of North Carolina, Chapel Hill, NC (L.M.H.); Department of Population Health Sciences, University of Utah, Huntsman Cancer Institute, Salt Lake City, Utah (T.O.); Department of Radiology, Giesel School of Medicine at Dartmouth, Lebanon, NH (R.M.d.F.A.); and Division of Biostatistics, Department of Public Health Sciences, University of California–Davis, Davis, Calif (D.L.M.)
| | - Christoph I. Lee
- From the Departments of Surgery (B.L.S.) and Radiology (B.L.S., S.D.H.), University of Vermont Cancer Center, University of Vermont Larner College of Medicine, UHC Bldg, Room 4425, 1 S Prospect St, Burlington, VT 05401; Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Wash (R.Y.C., Y.R.S., E.J.A.B., D.L.M.); Department of Radiology, University of Washington, Fred Hutchinson Cancer Center, Seattle, Wash (K.P.L., C.I.L.); Departments of Medicine and Epidemiology and Biostatistics, University of California–San Francisco, San Francisco, Calif (K.K.); Department of Radiology, University of North Carolina, Chapel Hill, NC (L.M.H.); Department of Population Health Sciences, University of Utah, Huntsman Cancer Institute, Salt Lake City, Utah (T.O.); Department of Radiology, Giesel School of Medicine at Dartmouth, Lebanon, NH (R.M.d.F.A.); and Division of Biostatistics, Department of Public Health Sciences, University of California–Davis, Davis, Calif (D.L.M.)
| | - Tracy Onega
- From the Departments of Surgery (B.L.S.) and Radiology (B.L.S., S.D.H.), University of Vermont Cancer Center, University of Vermont Larner College of Medicine, UHC Bldg, Room 4425, 1 S Prospect St, Burlington, VT 05401; Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Wash (R.Y.C., Y.R.S., E.J.A.B., D.L.M.); Department of Radiology, University of Washington, Fred Hutchinson Cancer Center, Seattle, Wash (K.P.L., C.I.L.); Departments of Medicine and Epidemiology and Biostatistics, University of California–San Francisco, San Francisco, Calif (K.K.); Department of Radiology, University of North Carolina, Chapel Hill, NC (L.M.H.); Department of Population Health Sciences, University of Utah, Huntsman Cancer Institute, Salt Lake City, Utah (T.O.); Department of Radiology, Giesel School of Medicine at Dartmouth, Lebanon, NH (R.M.d.F.A.); and Division of Biostatistics, Department of Public Health Sciences, University of California–Davis, Davis, Calif (D.L.M.)
| | - Erin J. A. Bowles
- From the Departments of Surgery (B.L.S.) and Radiology (B.L.S., S.D.H.), University of Vermont Cancer Center, University of Vermont Larner College of Medicine, UHC Bldg, Room 4425, 1 S Prospect St, Burlington, VT 05401; Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Wash (R.Y.C., Y.R.S., E.J.A.B., D.L.M.); Department of Radiology, University of Washington, Fred Hutchinson Cancer Center, Seattle, Wash (K.P.L., C.I.L.); Departments of Medicine and Epidemiology and Biostatistics, University of California–San Francisco, San Francisco, Calif (K.K.); Department of Radiology, University of North Carolina, Chapel Hill, NC (L.M.H.); Department of Population Health Sciences, University of Utah, Huntsman Cancer Institute, Salt Lake City, Utah (T.O.); Department of Radiology, Giesel School of Medicine at Dartmouth, Lebanon, NH (R.M.d.F.A.); and Division of Biostatistics, Department of Public Health Sciences, University of California–Davis, Davis, Calif (D.L.M.)
| | - Sally D. Herschorn
- From the Departments of Surgery (B.L.S.) and Radiology (B.L.S., S.D.H.), University of Vermont Cancer Center, University of Vermont Larner College of Medicine, UHC Bldg, Room 4425, 1 S Prospect St, Burlington, VT 05401; Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Wash (R.Y.C., Y.R.S., E.J.A.B., D.L.M.); Department of Radiology, University of Washington, Fred Hutchinson Cancer Center, Seattle, Wash (K.P.L., C.I.L.); Departments of Medicine and Epidemiology and Biostatistics, University of California–San Francisco, San Francisco, Calif (K.K.); Department of Radiology, University of North Carolina, Chapel Hill, NC (L.M.H.); Department of Population Health Sciences, University of Utah, Huntsman Cancer Institute, Salt Lake City, Utah (T.O.); Department of Radiology, Giesel School of Medicine at Dartmouth, Lebanon, NH (R.M.d.F.A.); and Division of Biostatistics, Department of Public Health Sciences, University of California–Davis, Davis, Calif (D.L.M.)
| | - Roberta M. diFlorio-Alexander
- From the Departments of Surgery (B.L.S.) and Radiology (B.L.S., S.D.H.), University of Vermont Cancer Center, University of Vermont Larner College of Medicine, UHC Bldg, Room 4425, 1 S Prospect St, Burlington, VT 05401; Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Wash (R.Y.C., Y.R.S., E.J.A.B., D.L.M.); Department of Radiology, University of Washington, Fred Hutchinson Cancer Center, Seattle, Wash (K.P.L., C.I.L.); Departments of Medicine and Epidemiology and Biostatistics, University of California–San Francisco, San Francisco, Calif (K.K.); Department of Radiology, University of North Carolina, Chapel Hill, NC (L.M.H.); Department of Population Health Sciences, University of Utah, Huntsman Cancer Institute, Salt Lake City, Utah (T.O.); Department of Radiology, Giesel School of Medicine at Dartmouth, Lebanon, NH (R.M.d.F.A.); and Division of Biostatistics, Department of Public Health Sciences, University of California–Davis, Davis, Calif (D.L.M.)
| | - Diana L. Miglioretti
- From the Departments of Surgery (B.L.S.) and Radiology (B.L.S., S.D.H.), University of Vermont Cancer Center, University of Vermont Larner College of Medicine, UHC Bldg, Room 4425, 1 S Prospect St, Burlington, VT 05401; Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Wash (R.Y.C., Y.R.S., E.J.A.B., D.L.M.); Department of Radiology, University of Washington, Fred Hutchinson Cancer Center, Seattle, Wash (K.P.L., C.I.L.); Departments of Medicine and Epidemiology and Biostatistics, University of California–San Francisco, San Francisco, Calif (K.K.); Department of Radiology, University of North Carolina, Chapel Hill, NC (L.M.H.); Department of Population Health Sciences, University of Utah, Huntsman Cancer Institute, Salt Lake City, Utah (T.O.); Department of Radiology, Giesel School of Medicine at Dartmouth, Lebanon, NH (R.M.d.F.A.); and Division of Biostatistics, Department of Public Health Sciences, University of California–Davis, Davis, Calif (D.L.M.)
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24
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Ho TQH, Bissell MCS, Lee CI, Lee JM, Sprague BL, Tosteson ANA, Wernli KJ, Henderson LM, Kerlikowske K, Miglioretti DL. Prioritizing Screening Mammograms for Immediate Interpretation and Diagnostic Evaluation on the Basis of Risk for Recall. J Am Coll Radiol 2023; 20:299-310. [PMID: 36273501 PMCID: PMC10044471 DOI: 10.1016/j.jacr.2022.09.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Revised: 09/08/2022] [Accepted: 09/19/2022] [Indexed: 11/11/2022]
Abstract
PURPOSE The aim of this study was to develop a prioritization strategy for scheduling immediate screening mammographic interpretation and possible diagnostic evaluation. METHODS A population-based cohort with screening mammograms performed from 2012 to 2020 at 126 radiology facilities from 7 Breast Cancer Surveillance Consortium registries was identified. Classification trees identified combinations of clinical history (age, BI-RADS® density, time since prior mammogram, history of false-positive recall or biopsy result), screening modality (digital mammography, digital breast tomosynthesis), and facility characteristics (profit status, location, screening volume, practice type, academic affiliation) that grouped screening mammograms by recall rate, with ≥12/100 considered high and ≥16/100 very high. An efficiency ratio was estimated as the percentage of recalls divided by the percentage of mammograms. RESULTS The study cohort included 2,674,051 screening mammograms in 925,777 women, with 235,569 recalls. The most important predictor of recall was time since prior mammogram, followed by age, history of false-positive recall, breast density, history of benign biopsy, and screening modality. Recall rates were very high for baseline mammograms (21.3/100; 95% confidence interval, 19.7-23.0) and high for women with ≥5 years since prior mammogram (15.1/100; 95% confidence interval, 14.3-16.1). The 9.2% of mammograms in subgroups with very high and high recall rates accounted for 19.2% of recalls, an efficiency ratio of 2.1 compared with a random approach. Adding women <50 years of age with dense breasts accounted for 20.3% of mammograms and 33.9% of recalls (efficiency ratio = 1.7). Results including facility-level characteristics were similar. CONCLUSIONS Prioritizing women with baseline mammograms or ≥5 years since prior mammogram for immediate interpretation and possible diagnostic evaluation could considerably reduce the number of women needing to return for diagnostic imaging at another visit.
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Affiliation(s)
- Thao-Quyen H Ho
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, School of Medicine, Davis, California; Breast Imaging Unit, Diagnostic Imaging Center, Tam Anh General Hospital, Ho Chi Minh City, Vietnam; Department of Training and Scientific Research, University Medical Center, Ho Chi Minh City, Vietnam
| | - Michael C S Bissell
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, School of Medicine, Davis, California
| | - Christoph I Lee
- Breast Imaging, Department of Radiology, University of Washington School of Medicine, Seattle, Washington; Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle, Washington; Hutchinson Institute for Cancer Outcomes Research, Seattle, Washington; Northwest Screening and Cancer Outcomes Research Enterprise, University of Washington, Seattle, Washington; Deputy Editor, JACR
| | - Janie M Lee
- Breast Imaging, Department of Radiology, University of Washington School of Medicine, Seattle, Washington; Hutchinson Institute for Cancer Outcomes Research, Seattle, Washington; Breast Imaging, Fred Hutchinson Cancer Center, Seattle, Washington
| | - Brian L Sprague
- Department of Surgery, Office of Health Promotion Research, Larner College of Medicine at the University of Vermont and Co-Leader, Cancer Control and Population Health Sciences Program, University of Vermont Cancer Center, Burlington, Vermont
| | - Anna N A Tosteson
- The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth and Associate Director for Population Sciences, Dartmouth Cancer Center, Lebanon, New Hampshire
| | - Karen J Wernli
- Kaiser Permanente Bernard J. Tyson School of Medicine, Pasadena, California; Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Washington
| | - Louise M Henderson
- Department of Radiology, University of North Carolina, Chapel Hill, North Carolina; Cancer Epidemiology Program, Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, North Carolina
| | - Karla Kerlikowske
- Departments of Medicine and Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California; General Internal Medicine Section, Department of Veterans Affairs, University of California, San Francisco, San Francisco, California; Women's Health Comprehensive Clinic, and Director, Advanced Postdoctoral Fellowship in Women's Health, San Francisco Veterans Affairs Health Care System, San Francisco, California
| | - Diana L Miglioretti
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, School of Medicine, Davis, California; Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Washington; Biostatistics and Population Sciences and Health Disparities Program, University of California, Davis, Comprehensive Cancer Center, Davis, California.
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Tran TXM, Kim S, Song H, Lee E, Park B. Association of Longitudinal Mammographic Breast Density Changes with Subsequent Breast Cancer Risk. Radiology 2023; 306:e220291. [PMID: 36125380 DOI: 10.1148/radiol.220291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Background Although Breast Imaging Reporting and Data System (BI-RADS) density classification has been used to assess future breast cancer risk, its reliability and validity are still debated in literature. Purpose To determine the association between overall longitudinal changes in mammographic breast density and breast cancer risk stratified by menopausal status. Materials and Methods In a retrospective cohort study using the Korean National Health Insurance Service database, women aged at least 40 years without a history of cancer who underwent three consecutive biennial mammographic screenings in 2009-2014 were followed up through December 2020. Participants were divided according to baseline breast density: fatty (BI-RADS categories a, b) versus dense (BI-RADS categories c, d) and then into subgroups on the basis of changes from the first to second and from second to third screenings. Women without change in breast density were used as the reference group. Main outcomes were incident breast cancer events, both invasive breast cancer and ductal carcinoma in situ. Cox proportion hazard regression was used to calculate the hazard ratio (HR) with adjustment for other covariables. Results Among 2 253 963 women (mean age, 59 years ± 9) there were 22 439 detected breast cancers. Premenopausal women with fatty breasts at the first screening had a higher risk of breast cancer as density increased in the second and third screenings (fatty-to-dense HR, 1.45 [95% CI: 1.27, 1.65]; dense-to-fatty HR, 1.53 [95% CI: 1.34, 1.74]; dense-to-dense HR, 1.93 [95% CI: 1.75, 2.13]). In premenopausal women with dense breasts at baseline, those in whom density continuously decreased had a 0.62-fold lower risk (95% CI: 0.56, 0.69). Similar results were observed in postmenopausal women, remaining significant after adjustment for baseline breast density or changes in body mass index (fatty-to-dense HR, 1.50 [95% CI: 1.39, 1.62]; dense-to-fatty HR, 1.42 [95% CI: 1.31, 1.53]; dense-to-dense HR, 1.62 [95% CI: 1.51, 1.75]). Conclusion In both premenopausal and postmenopausal women undergoing three consecutive biennial mammographic screenings, a consecutive increase in breast density augmented the future breast cancer risk whereas a continuous decrease was associated with a lower risk. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Kataoka et al in this issue.
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Affiliation(s)
- Thi Xuan Mai Tran
- From the Departments of Preventive Medicine (T.X.M.T., B.P.) and Health Sciences (S.K.), Hanyang University College of Medicine, 222 Wangsimni-ro, Seongdong-gu, Seoul 04763, Republic of Korea; Department of Epidemiology and Biostatistics, Graduate School of Public Health, Hanyang University, Seoul, Republic of Korea (H.S.); Department of Radiology, Soonchunhyang University Bucheon Hospital, Soonchunhyang University College of Medicine, Bucheon, Korea (E.L.)
| | - Soyeoun Kim
- From the Departments of Preventive Medicine (T.X.M.T., B.P.) and Health Sciences (S.K.), Hanyang University College of Medicine, 222 Wangsimni-ro, Seongdong-gu, Seoul 04763, Republic of Korea; Department of Epidemiology and Biostatistics, Graduate School of Public Health, Hanyang University, Seoul, Republic of Korea (H.S.); Department of Radiology, Soonchunhyang University Bucheon Hospital, Soonchunhyang University College of Medicine, Bucheon, Korea (E.L.)
| | - Huiyeon Song
- From the Departments of Preventive Medicine (T.X.M.T., B.P.) and Health Sciences (S.K.), Hanyang University College of Medicine, 222 Wangsimni-ro, Seongdong-gu, Seoul 04763, Republic of Korea; Department of Epidemiology and Biostatistics, Graduate School of Public Health, Hanyang University, Seoul, Republic of Korea (H.S.); Department of Radiology, Soonchunhyang University Bucheon Hospital, Soonchunhyang University College of Medicine, Bucheon, Korea (E.L.)
| | - Eunhye Lee
- From the Departments of Preventive Medicine (T.X.M.T., B.P.) and Health Sciences (S.K.), Hanyang University College of Medicine, 222 Wangsimni-ro, Seongdong-gu, Seoul 04763, Republic of Korea; Department of Epidemiology and Biostatistics, Graduate School of Public Health, Hanyang University, Seoul, Republic of Korea (H.S.); Department of Radiology, Soonchunhyang University Bucheon Hospital, Soonchunhyang University College of Medicine, Bucheon, Korea (E.L.)
| | - Boyoung Park
- From the Departments of Preventive Medicine (T.X.M.T., B.P.) and Health Sciences (S.K.), Hanyang University College of Medicine, 222 Wangsimni-ro, Seongdong-gu, Seoul 04763, Republic of Korea; Department of Epidemiology and Biostatistics, Graduate School of Public Health, Hanyang University, Seoul, Republic of Korea (H.S.); Department of Radiology, Soonchunhyang University Bucheon Hospital, Soonchunhyang University College of Medicine, Bucheon, Korea (E.L.)
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Conley CC, Wernli KJ, Knerr S, Li T, Leppig K, Ehrlich K, Farrell D, Gao H, Bowles EJA, Graham AL, Luta G, Jayasekera J, Mandelblatt JS, Schwartz MD, O'Neill SC. Using Protection Motivation Theory to Predict Intentions for Breast Cancer Risk Management: Intervention Mechanisms from a Randomized Controlled Trial. JOURNAL OF CANCER EDUCATION : THE OFFICIAL JOURNAL OF THE AMERICAN ASSOCIATION FOR CANCER EDUCATION 2023; 38:292-300. [PMID: 34813048 PMCID: PMC9124715 DOI: 10.1007/s13187-021-02114-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 11/01/2021] [Indexed: 06/13/2023]
Abstract
The purpose of this study is to evaluate the direct and indirect effects of a web-based, Protection Motivation Theory (PMT)-informed breast cancer education and decision support tool on intentions for risk-reducing medication and breast MRI among high-risk women. Women with ≥ 1.67% 5-year breast cancer risk (N = 995) were randomized to (1) control or (2) the PMT-informed intervention. Six weeks post-intervention, 924 (93% retention) self-reported PMT constructs and behavioral intentions. Bootstrapped mediations evaluated the direct effect of the intervention on behavioral intentions and the mediating role of PMT constructs. There was no direct intervention effect on intentions for risk-reducing medication or MRI (p's ≥ 0.12). There were significant indirect effects on risk-reducing medication intentions via perceived risk, self-efficacy, and response efficacy, and on MRI intentions via perceived risk and response efficacy (p's ≤ 0.04). The PMT-informed intervention effected behavioral intentions via perceived breast cancer risk, self-efficacy, and response efficacy. Future research should extend these findings from intentions to behavior. ClinicalTrials.gov Identifier: NCT03029286 (date of registration: January 24, 2017).
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Affiliation(s)
- Claire C Conley
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, 2115 Wisconsin Avenue NW, Suite 300, Washington, DC, 20007, USA
| | - Karen J Wernli
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Sarah Knerr
- Department of Health Services, University of Washington, Seattle, WA, USA
| | - Tengfei Li
- Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University, Washington, DC, USA
| | | | - Kelly Ehrlich
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | | | - Hongyuan Gao
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Erin J A Bowles
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Amanda L Graham
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, 2115 Wisconsin Avenue NW, Suite 300, Washington, DC, 20007, USA
- Truth Initiative, Washington, DC, USA
| | - George Luta
- Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University, Washington, DC, USA
| | - Jinani Jayasekera
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, 2115 Wisconsin Avenue NW, Suite 300, Washington, DC, 20007, USA
| | - Jeanne S Mandelblatt
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, 2115 Wisconsin Avenue NW, Suite 300, Washington, DC, 20007, USA
| | - Marc D Schwartz
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, 2115 Wisconsin Avenue NW, Suite 300, Washington, DC, 20007, USA
| | - Suzanne C O'Neill
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, 2115 Wisconsin Avenue NW, Suite 300, Washington, DC, 20007, USA.
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Sprague BL, Chen S, Miglioretti DL, Gard CC, Tice JA, Hubbard RA, Aiello Bowles EJ, Kaufman PA, Kerlikowske K. Cumulative 6-Year Risk of Screen-Detected Ductal Carcinoma In Situ by Screening Frequency. JAMA Netw Open 2023; 6:e230166. [PMID: 36808238 PMCID: PMC9941892 DOI: 10.1001/jamanetworkopen.2023.0166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/22/2023] Open
Abstract
IMPORTANCE Detection of ductal carcinoma in situ (DCIS) by mammography screening is a controversial outcome with potential benefits and harms. The association of mammography screening interval and woman's risk factors with the likelihood of DCIS detection after multiple screening rounds is poorly understood. OBJECTIVE To develop a 6-year risk prediction model for screen-detected DCIS according to mammography screening interval and women's risk factors. DESIGN, SETTING, AND PARTICIPANTS This Breast Cancer Surveillance Consortium cohort study assessed women aged 40 to 74 years undergoing mammography screening (digital mammography or digital breast tomosynthesis) from January 1, 2005, to December 31, 2020, at breast imaging facilities within 6 geographically diverse registries of the consortium. Data were analyzed between February and June 2022. EXPOSURES Screening interval (annual, biennial, or triennial), age, menopausal status, race and ethnicity, family history of breast cancer, benign breast biopsy history, breast density, body mass index, age at first birth, and false-positive mammography history. MAIN OUTCOMES AND MEASURES Screen-detected DCIS defined as a DCIS diagnosis within 12 months after a positive screening mammography result, with no concurrent invasive disease. RESULTS A total of 916 931 women (median [IQR] age at baseline, 54 [46-62] years; 12% Asian, 9% Black, 5% Hispanic/Latina, 69% White, 2% other or multiple races, and 4% missing) met the eligibility criteria, with 3757 screen-detected DCIS diagnoses. Screening round-specific risk estimates from multivariable logistic regression were well calibrated (expected-observed ratio, 1.00; 95% CI, 0.97-1.03) with a cross-validated area under the receiver operating characteristic curve of 0.639 (95% CI, 0.630-0.648). Cumulative 6-year risk of screen-detected DCIS estimated from screening round-specific risk estimates, accounting for competing risks of death and invasive cancer, varied widely by all included risk factors. Cumulative 6-year screen-detected DCIS risk increased with age and shorter screening interval. Among women aged 40 to 49 years, the mean 6-year screen-detected DCIS risk was 0.30% (IQR, 0.21%-0.37%) for annual screening, 0.21% (IQR, 0.14%-0.26%) for biennial screening, and 0.17% (IQR, 0.12%-0.22%) for triennial screening. Among women aged 70 to 74 years, the mean cumulative risks were 0.58% (IQR, 0.41%-0.69%) after 6 annual screens, 0.40% (IQR, 0.28%-0.48%) for 3 biennial screens, and 0.33% (IQR, 0.23%-0.39%) after 2 triennial screens. CONCLUSIONS AND RELEVANCE In this cohort study, 6-year screen-detected DCIS risk was higher with annual screening compared with biennial or triennial screening intervals. Estimates from the prediction model, along with risk estimates of other screening benefits and harms, could help inform policy makers' discussions of screening strategies.
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Affiliation(s)
- Brian L. Sprague
- Office of Health Promotion Research, University of Vermont, Burlington
- Department of Surgery, University of Vermont, Burlington
- University of Vermont Cancer Center, Burlington
| | - Shuai Chen
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis
| | - Diana L. Miglioretti
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle
| | - Charlotte C. Gard
- Department of Economics, Applied Statistics, and International Business, New Mexico State University, Las Cruces
| | - Jeffrey A. Tice
- Division of General Internal Medicine, Department of Medicine, University of California, San Francisco
| | - Rebecca A. Hubbard
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia
| | - Erin J. Aiello Bowles
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle
| | - Peter A. Kaufman
- Division of Hematology/Oncology, University of Vermont Cancer Center, Burlington
| | - Karla Kerlikowske
- Department of Medicine, University of California, San Francisco
- Department of Epidemiology and Biostatistics, University of California, San Francisco
- General Internal Medicine Section, Department of Veterans Affairs, University of California, San Francisco
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Reddy VA, Sarin R, Panda D, Hanitha R, Jain J, Chatterjee S, Annapurneswari S, Saipillai MZ, Gupta S, Khan E, Bhattacharya J, Bhandari TPS, Prasad SVSS, Swain M. A Multi-centric retrospective study into the epidemiological distribution of breast cancer patients in India. J Cancer Res Ther 2023; 19:S869-S876. [PMID: 38384068 DOI: 10.4103/jcrt.jcrt_1876_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 10/01/2022] [Indexed: 02/23/2024]
Abstract
BACKGROUND A multicentric private hospital-based retrospective study was conducted to understand the epidemiology of breast cancer in terms of demographics and clinical characteristics (staging and hormone receptor status) at the time of diagnosis. METHODS The data for 5,688 female breast cancer patients were collected from the hospital and clinical records of four study centres. All statistical analysis was performed using Microsoft Excel 2016 and R software. Survival was estimated by the Kaplan-Meier method and compared by the log-rank test. A P value of <.05 was considered statistically significant. RESULTS The mean and median age of the study population was 52.6 (± 12.4) years and 53.0 (range 51-54 across the four centers) years, respectively. About 68% of patients were in the age category of 41 65 years, 17.6% were <40 years old among whom 23.4% of patients reported a positive family history. Most of the patients (66.3%) were diagnosed at an early stage (Stage I and II). The 3-year OS probability was 100%, 97.5%, 94.1%, and 74.7% for TNM Stages I, II, III, and IV, respectively. The 3-year RFS was 95.7%, 95.5%, 84.5%, and 49% for TNM Stages I, II, III, and IV, respectively. CONCLUSION The present study highlights the epidemiological distribution of breast cancer patients. It emphasizes the importance of disease awareness among the urban and educated female population as most patients were diagnosed at earlier stages and demonstrated higher OS and RFS than reported in government registries.
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Affiliation(s)
- Vijay Anand Reddy
- Apollo Health City Jubilee Hills, Jubilee Hills, Hyderabad, Telangana, India
| | - Ramesh Sarin
- Indraprastha Apollo Hospitals, Sarita Vihar, Delhi, India
| | - Dipanjan Panda
- Indraprastha Apollo Hospitals, Sarita Vihar, Delhi, India
| | - Rnm Hanitha
- Sapien Biosciences Private Limited, AIMSR Building, Apollo Health City, Jubilee Hills, Hyderabad, Telangana, India
| | - Jugnu Jain
- Sapien Biosciences Private Limited, AIMSR Building, Apollo Health City, Jubilee Hills, Hyderabad, Telangana, India
| | - Soma Chatterjee
- Sapien Biosciences Private Limited, AIMSR Building, Apollo Health City, Jubilee Hills, Hyderabad, Telangana, India
| | - S Annapurneswari
- Apollo Cancer Center No. 320, Anna Salai, Thiru Vi Ka Kudiyiruppu, Teynampet, Chennai, Tamil Nadu, India
| | - Mohamed Zehran Saipillai
- Apollo Cancer Center No. 320, Anna Salai, Thiru Vi Ka Kudiyiruppu, Teynampet, Chennai, Tamil Nadu, India
| | - Shaikat Gupta
- Apollo Multispecialty Hospitals, Kolkata, West Bengal, India
| | - Enamuddin Khan
- Apollo Multispecialty Hospitals, Kolkata, West Bengal, India
| | | | | | - S V S S Prasad
- Apollo Health City Jubilee Hills, Jubilee Hills, Hyderabad, Telangana, India
| | - Meenakshi Swain
- Apollo Health City Jubilee Hills, Jubilee Hills, Hyderabad, Telangana, India
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Zaoui M, Morel M, Louadj L, Ferrand N, Lamazière A, Uzan C, Canlorbe G, Atlan M, Sabbah M. Adipocytes secretome from normal and tumor breast favor breast cancer invasion by metabolic reprogramming. Clin Transl Oncol 2022; 25:1389-1401. [PMID: 36520383 DOI: 10.1007/s12094-022-03035-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 11/29/2022] [Indexed: 12/23/2022]
Abstract
BACKGROUND Adipose tissue is a major component of breast stroma. This study focused on delineating the effects of adipose stem cells (ASCs) derived from breast of healthy women and cancer patients with normal or tumor breast cells. METHODS The ASCs were induced to differentiate into adipocytes, and the subsequent adipocyte conditioned media (ACM) were evaluated for their fatty acid profile, adipokine secretion and influence on proliferation, migration and invasion on tumoral (MCF-7 and SUM159) and normal (HMEC) human breast cell lines. RESULTS An enrichment of arachidonic acid was observed in ACM from tumor tissues. Adipose tissues from tumor free secrete twice as much leptin than those from proximal or distal to the tumor. All ACMs display proliferative activity and favor invasiveness of SUM159 cells compared to MCF-7 and HMEC. All ACMs induced lipid droplets accumulation in MCF-7 cells and increased CD36 expression in tumor cells. CONCLUSION We conclude that among secreted factors analyzed, only arachidonic acid and leptin levels did discriminate ASCs from tumor-bearing and tumor-free breasts emphasizing the importance that other cell types could contribute to the adipose tissue secretome in a tumor context.
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Affiliation(s)
- Maurice Zaoui
- Team Cancer Biology and Therapeutics, Centre de Recherche Saint-Antoine (CRSA), Institut Universitaire de Cancérologie, Sorbonne University, INSERM UMR_S 938, 75012, Paris, France
| | - Mehdi Morel
- Team Cancer Biology and Therapeutics, Centre de Recherche Saint-Antoine (CRSA), Institut Universitaire de Cancérologie, Sorbonne University, INSERM UMR_S 938, 75012, Paris, France
| | - Lila Louadj
- Team Cancer Biology and Therapeutics, Centre de Recherche Saint-Antoine (CRSA), Institut Universitaire de Cancérologie, Sorbonne University, INSERM UMR_S 938, 75012, Paris, France
| | - Nathalie Ferrand
- Team Cancer Biology and Therapeutics, Centre de Recherche Saint-Antoine (CRSA), Institut Universitaire de Cancérologie, Sorbonne University, INSERM UMR_S 938, 75012, Paris, France
| | - Antonin Lamazière
- UMR 70203, Laboratory of Biomolecules, Institut National de La Santé Et de La Recherche Médicale (INSERM), École Normale Supérieure, AP-HP, 75012, Paris, France
| | - Catherine Uzan
- Team Cancer Biology and Therapeutics, Centre de Recherche Saint-Antoine (CRSA), Institut Universitaire de Cancérologie, Sorbonne University, INSERM UMR_S 938, 75012, Paris, France
- Department of Gynecological and Breast Surgery and Oncology, Assistance Publique des Hôpitaux de Paris (AP-HP)Pitié-Salpêtrière University Hospital, 75013, Paris, France
| | - Geoffroy Canlorbe
- Team Cancer Biology and Therapeutics, Centre de Recherche Saint-Antoine (CRSA), Institut Universitaire de Cancérologie, Sorbonne University, INSERM UMR_S 938, 75012, Paris, France
- Department of Gynecological and Breast Surgery and Oncology, Assistance Publique des Hôpitaux de Paris (AP-HP)Pitié-Salpêtrière University Hospital, 75013, Paris, France
| | - Michael Atlan
- Department of Plastic Surgery, Reconstructive, Aesthetic, Microsurgery and Tissue Regeneration, Tenon Hospital, Institut Universitaire de Cancérologie, AP-HP, 75020, Paris, France
| | - Michèle Sabbah
- Team Cancer Biology and Therapeutics, Centre de Recherche Saint-Antoine (CRSA), Institut Universitaire de Cancérologie, Sorbonne University, INSERM UMR_S 938, 75012, Paris, France.
- Centre National de la Recherche Scientifique (CNRS), 75012, Paris, France.
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Mehta TS, Lourenco AP, Niell BL, Bennett DL, Brown A, Chetlen A, Freer P, Ivansco LK, Jochelson MS, Klein KA, Malak SF, McCrary M, Mullins D, Neal CH, Newell MS, Ulaner GA, Moy L. ACR Appropriateness Criteria® Imaging After Breast Surgery. J Am Coll Radiol 2022; 19:S341-S356. [PMID: 36436961 DOI: 10.1016/j.jacr.2022.09.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 09/01/2022] [Indexed: 11/27/2022]
Abstract
Given that 20% to 40% of women who have percutaneous breast biopsy subsequently undergo breast surgery, knowledge of imaging women with a history of benign (including high-risk) disease or breast cancer is important. For women who had surgery for nonmalignant pathology, the surveillance recommendations are determined by their overall risk. Higher-than-average risk women with a history of benign surgery may require screening mammography starting at an earlier age before 40 and may benefit from screening MRI. For women with breast cancer who have undergone initial excision and have positive margins, imaging with diagnostic mammography or MRI can sometimes guide additional surgical planning. Women who have completed breast conservation therapy for cancer should get annual mammography and may benefit from the addition of MRI or ultrasound to their surveillance regimen. The ACR Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances in which peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.
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Affiliation(s)
- Tejas S Mehta
- Director of Diversity, Equity Inclusion and Population Health in Radiology, UMass Memorial Medical Center, Worchester, Massachusetts.
| | - Ana P Lourenco
- Panel Chair; Residency Program Director, Alpert Medical School of Brown University, Providence, Rhode Island
| | - Bethany L Niell
- Panel Vice-Chair; Section Chief of Breast Imaging, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida; Commission Government Relations Chair
| | - Debbie L Bennett
- Section Chief - Breast Imaging, Mallinckrodt Institute of Radiology/Washington University School of Medicine, Saint Louis, Missouri
| | - Ann Brown
- Assistant Section Chief, University of Cincinnati, Cincinnati, Ohio
| | - Alison Chetlen
- Vice Chair of Education, Division Chief Breast Imaging, Penn State Health Hershey Medical Center, Hershey, Pennsylvania
| | - Phoebe Freer
- Section Chief, Breast Imaging, University of Utah/Huntsman Cancer Institute, Salt Lake City, Utah; ACR/SCBI Screening Leadership Group Inaugural Class
| | - Lillian K Ivansco
- Assistant Chief, Department of Radiology, Section Chief for Breast Imaging and Quality, Co-Chair, Breast Imaging Sourcing and Standards Team, Kaiser Permanente Georgia, Atlanta, Georgia
| | - Maxine S Jochelson
- Chief of the Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, New York
| | | | | | - Marion McCrary
- Associate Director of Duke GME Coaching, Duke Signature Care, Durham, North Carolina; American College of Physicians; Governor-Elect, American College of Physicians, North Carolina Chapter
| | - David Mullins
- Chief of Staff, Princeton Community Hospital, Princeton, West Virginia; American College of Surgeons
| | | | - Mary S Newell
- Emory University Hospital, Atlanta, Georgia; RADS Committee
| | - Gary A Ulaner
- Hoag Family Cancer Institute, Newport Beach, California and University of Southern California, Los Angeles, California; Commission on Nuclear Medicine and Molecular Imaging
| | - Linda Moy
- Specialty Chair, NYU Clinical Cancer Center, New York, New York
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Aminpour N, Sogunro O, Towfighi P, Park BU, Boisvert M. Clinical management of myoid hamartomas of the breast: A case report and literature review. Heliyon 2022; 8:e11723. [DOI: 10.1016/j.heliyon.2022.e11723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 07/25/2022] [Accepted: 11/10/2022] [Indexed: 11/18/2022] Open
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Tamayo LI, Perez F, Perez A, Hernandez M, Martinez A, Huang X, Zavala VA, Ziv E, Neuhausen SL, Carvajal-Carmona LG, Duron Y, Fejerman L. Cancer screening and breast cancer family history in Spanish-speaking Hispanic/Latina women in California. Front Oncol 2022; 12:940162. [PMID: 36387260 PMCID: PMC9643826 DOI: 10.3389/fonc.2022.940162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 09/28/2022] [Indexed: 01/25/2023] Open
Abstract
Background Breast cancer is the most common cancer among women in the U.S. and the leading cause of cancer death among Hispanics/Latinas (H/L). H/L are less likely than Non-H/L White (NHW) women to be diagnosed in the early stages of this disease. Approximately 5-10% of breast cancer can be attributed to inherited genetic mutations in high penetrance genes such as BRCA1/2. Women with pathogenic variants in these genes have a 40-80% lifetime risk of breast cancer. Past studies have shown that genetic counseling can help women and their families make informed decisions about genetic testing and early cancer detection or risk-reduction strategies. However, H/L are 3.9-4.8 times less likely to undergo genetic testing than NHW women. We developed a program to outreach and educate the H/L community about hereditary breast cancer, targeting monolingual Spanish-speaking individuals in California. Through this program, we have assessed cancer screening behavior and identified women who might benefit from genetic counseling in a population that is usually excluded from cancer research and care. Materials and Methods The "Tu Historia Cuenta" program is a promotores-based virtual outreach and education program including the cities of San Francisco, Sacramento, and Los Angeles. Participants responded to three surveys: a demographic survey, a breast cancer family history survey, and a feedback survey. Survey responses were described for participants and compared by area where the program took place using chi-square, Fisher exact tests, and t tests. Multinomial logistic regression models were used for multivariate analyses. Results and Conclusion We enrolled 1042 women, 892 completed the cancer family history survey and 62 (7%) provided responses compatible with referral to genetic counseling. We identified 272 women (42.8% ages 40 to 74 years) who were due for mammograms, 250 women (24.7% ages 25 to 65 years) due for Papanicolaou test, and 189 women (71.6% ages 50+) due for colorectal cancer screening. These results highlight the need of additional support for programs that spread awareness about cancer risk and facilitate access to resources, specifically within the H/L community.
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Affiliation(s)
- Lizeth I. Tamayo
- Department of Public Health Sciences, The University of Chicago, Chicago, IL, United States
| | - Fabian Perez
- Department of Public Health Sciences, University of California, Davis, Davis, CA, United States
| | - Angelica Perez
- Department of Public Health Sciences, University of California, Davis, Davis, CA, United States
| | | | | | - Xiaosong Huang
- Department of Public Health Sciences, University of California, Davis, Davis, CA, United States
| | - Valentina A. Zavala
- Department of Public Health Sciences, University of California, Davis, Davis, CA, United States
| | - Elad Ziv
- Department of General Internal Medicine, University of California, San Francisco, San Francisco, CA, United States
| | - Susan L. Neuhausen
- Department of Population Sciences, Beckman Research Institute of City of Hope, Duarte, CA, United States
| | - Luis G. Carvajal-Carmona
- Department of Biochemistry and Molecular Medicine, University of California, Davis, Davis, CA, United States,Comprehensive Cancer Center, University of California Davis, Sacramento, CA, United States
| | - Ysabel Duron
- The Latino Cancer Institute, San Jose, CA, United States
| | - Laura Fejerman
- Department of Public Health Sciences, University of California, Davis, Davis, CA, United States,Comprehensive Cancer Center, University of California Davis, Sacramento, CA, United States,*Correspondence: Laura Fejerman,
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Ergun MA, Hajjar A, Alagoz O, Rampurwala M. Optimal breast cancer risk reduction policies tailored to personal risk level. Health Care Manag Sci 2022; 25:363-388. [PMID: 35687269 PMCID: PMC10445480 DOI: 10.1007/s10729-022-09596-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2021] [Accepted: 03/17/2022] [Indexed: 11/04/2022]
Abstract
Depending on personal and hereditary factors, each woman has a different risk of developing breast cancer, one of the leading causes of death for women. For women with a high-risk of breast cancer, their risk can be reduced by two main therapeutic approaches: 1) preventive treatments such as hormonal therapies (i.e., tamoxifen, raloxifene, exemestane); or 2) a risk reduction surgery (i.e., mastectomy). Existing national clinical guidelines either fail to incorporate or have limited use of the personal risk of developing breast cancer in their proposed risk reduction strategies. As a result, they do not provide enough resolution on the benefit-risk trade-off of an intervention policy as personal risk changes. In addressing this problem, we develop a discrete-time, finite-horizon Markov decision process (MDP) model with the objective of maximizing the patient's total expected quality-adjusted life years. We find several useful insights some of which contradict the existing national breast cancer risk reduction recommendations. For example, we find that mastectomy is the optimal choice for the border-line high-risk women who are between ages 22 and 38. Additionally, in contrast to the National Comprehensive Cancer Network recommendations, we find that exemestane is a plausible, in fact, the best, option for high-risk postmenopausal women.
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Affiliation(s)
- Mehmet A Ergun
- Department of Industrial and Systems Engineering, University of Wisconsin-Madison, 3242 Mechanical Engineering Building, 1513 University Avenue, Madison, WI, 53706, USA
- Department of Industrial Engineering, Istanbul Technical University, Istanbul, Turkey
| | - Ali Hajjar
- Harvard Medical School, Boston, Massachusetts, Boston, USA
- Massachusetts General Hospital Institute for Technology Assessment, Boston, USA
| | - Oguzhan Alagoz
- Department of Industrial and Systems Engineering, University of Wisconsin-Madison, 3242 Mechanical Engineering Building, 1513 University Avenue, Madison, WI, 53706, USA.
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Laza-Vásquez C, Martínez-Alonso M, Forné-Izquierdo C, Vilaplana-Mayoral J, Cruz-Esteve I, Sánchez-López I, Reñé-Reñé M, Cazorla-Sánchez C, Hernández-Andreu M, Galindo-Ortego G, Llorens-Gabandé M, Pons-Rodríguez A, Rué M. Feasibility and Acceptability of Personalized Breast Cancer Screening (DECIDO Study): A Single-Arm Proof-of-Concept Trial. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:10426. [PMID: 36012059 PMCID: PMC9407798 DOI: 10.3390/ijerph191610426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Revised: 08/11/2022] [Accepted: 08/18/2022] [Indexed: 06/15/2023]
Abstract
The aim of this study was to assess the acceptability and feasibility of offering risk-based breast cancer screening and its integration into regular clinical practice. A single-arm proof-of-concept trial was conducted with a sample of 387 women aged 40-50 years residing in the city of Lleida (Spain). The study intervention consisted of breast cancer risk estimation, risk communication and screening recommendations, and a follow-up. A polygenic risk score with 83 single nucleotide polymorphisms was used to update the Breast Cancer Surveillance Consortium risk model and estimate the 5-year absolute risk of breast cancer. The women expressed a positive attitude towards varying the frequency of breast screening according to individual risk and, especially, more frequently inviting women at higher-than-average risk. A lower intensity screening for women at lower risk was not as welcome, although half of the participants would accept it. Knowledge of the benefits and harms of breast screening was low, especially with regard to false positives and overdiagnosis. The women expressed a high understanding of individual risk and screening recommendations. The participants' intention to participate in risk-based screening and satisfaction at 1-year were very high.
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Affiliation(s)
- Celmira Laza-Vásquez
- Department of Nursing and Physiotherapy and Health Care Research Group (GRECS), IRBLleida—Institut de Recerca Biomèdica de Lleida, University of Lleida, 25198 Lleida, Spain
| | - Montserrat Martínez-Alonso
- IRBLleida—Institut de Recerca Biomèdica de Lleida, Department of Basic Medical Sciences, University of Lleida, 25198 Lleida, Spain
| | - Carles Forné-Izquierdo
- Department of Basic Medical Sciences, University of Lleida, 25198 Lleida, Spain
- Heorfy Consulting, 25007 Lleida, Spain
| | - Jordi Vilaplana-Mayoral
- Department of Computing and Industrial Engineering, University of Lleida, 25001 Lleida, Spain
| | - Inés Cruz-Esteve
- Primer de Maig Basic Health Area, Catalan Institute of Health, 25003 Lleida, Spain
| | | | - Mercè Reñé-Reñé
- Department of Radiology, Arnau de Vilanova University Hospital, 25198 Lleida, Spain
| | | | | | | | | | - Anna Pons-Rodríguez
- Example Basic Health Area, Catalan Institute of Health, 25006 Lleida, Spain
- Health PhD Program, University of Lleida, 25198 Lleida, Spain
| | - Montserrat Rué
- IRBLleida—Institut de Recerca Biomèdica de Lleida, Department of Basic Medical Sciences, University of Lleida, 25198 Lleida, Spain
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Chalfant JS, Hoyt AC. Breast Density: Current Knowledge, Assessment Methods, and Clinical Implications. JOURNAL OF BREAST IMAGING 2022; 4:357-370. [PMID: 38416979 DOI: 10.1093/jbi/wbac028] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Indexed: 03/01/2024]
Abstract
Breast density is an accepted independent risk factor for the future development of breast cancer, and greater breast density has the potential to mask malignancies on mammography, thus lowering the sensitivity of screening mammography. The risk associated with dense breast tissue has been shown to be modifiable with changes in breast density. Numerous studies have sought to identify factors that influence breast density, including age, genetic, racial/ethnic, prepubertal, adolescent, lifestyle, environmental, hormonal, and reproductive history factors. Qualitative, semiquantitative, and quantitative methods of breast density assessment have been developed, but to date there is no consensus assessment method or reference standard for breast density. Breast density has been incorporated into breast cancer risk models, and there is growing consciousness of the clinical implications of dense breast tissue in both the medical community and public arena. Efforts to improve breast cancer screening sensitivity for women with dense breasts have led to increased attention to supplemental screening methods in recent years, prompting the American College of Radiology to publish Appropriateness Criteria for supplemental screening based on breast density.
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Affiliation(s)
- James S Chalfant
- David Geffen School of Medicine at University of California, Los Angeles, Department of Radiological Sciences, Santa Monica, CA, USA
| | - Anne C Hoyt
- David Geffen School of Medicine at University of California, Los Angeles, Department of Radiological Sciences, Santa Monica, CA, USA
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Nyante SJ, Abraham L, Bowles EJA, Lee CI, Kerlikowske K, Miglioretti DL, Sprague BL, Henderson LM. Diagnostic Mammography Performance across Racial and Ethnic Groups in a National Network of Community-Based Breast Imaging Facilities. Cancer Epidemiol Biomarkers Prev 2022; 31:1324-1333. [PMID: 35712862 PMCID: PMC9272467 DOI: 10.1158/1055-9965.epi-21-1379] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 03/16/2022] [Accepted: 04/26/2022] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND We evaluated differences in diagnostic mammography performance based on women's race/ethnicity. METHODS This cohort study included 267,868 diagnostic mammograms performed to evaluate screening mammogram findings at 98 facilities in the Breast Cancer Surveillance Consortium between 2005 and 2017. Mammogram assessments were recorded prospectively and breast cancers occurring within one year were ascertained. Performance statistics were calculated with 95% confidence intervals (CI) for each racial/ethnic group. Multivariable regression was used to control for personal characteristics and imaging facility. RESULTS Among non-Hispanic White (70%), non-Hispanic Black (13%), Asian/Pacific Islander (10%), and Hispanic (7%) women, the invasive cancer detection rate (iCDR, per 1,000 mammograms) and positive predictive value (PPV2) were highest among non-Hispanic White women (iCDR, 35.8; 95% CI, 35.0-36.7; PPV2, 27.8; 95% CI, 27.3-28.3) and lowest among Hispanic women (iCDR, 22.3; 95% CI, 20.2-24.6; PPV2, 19.4; 95% CI, 18.0-20.9). Short interval follow-up recommendations were most common among non-Hispanic Black women [(31.0%; 95% CI, 30.6%-31.5%) vs. other groups, range, 16.6%-23.6%]. False-positive biopsy recommendations were most common among Asian/Pacific Islander women [per 1,000 mammograms: 169.2; 95% CI, 164.8-173.7) vs. other groups, range, 126.5-136.1]. Some differences were explained by adjusting for receipt of diagnostic ultrasound or MRI for iCDR and imaging facility for short-interval follow-up. Other differences changed little after adjustment. CONCLUSIONS Diagnostic mammography performance varied across racial/ethnic groups. Addressing characteristics related to imaging facility and access, rather than personal characteristics, may help reduce some of these disparities. IMPACT Diagnostic mammography performance studies should include racially and ethnically diverse populations to provide an accurate view of the population-level effects.
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Affiliation(s)
- Sarah J. Nyante
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Linn Abraham
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA
| | - Erin J. Aiello Bowles
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA
| | - Christoph I. Lee
- Department of Radiology, University of Washington School of Medicine; Department of Health Services, University of Washington School of Public Health, Seattle, WA
| | - Karla Kerlikowske
- Departments of Medicine and Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA
| | - Diana L. Miglioretti
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA
- Department of Public Health Sciences, University of California, Davis, Davis, CA
| | - Brian L. Sprague
- Department of Surgery and University of Vermont Cancer Center, University of Vermont, Burlington, VT
| | - Louise M. Henderson
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC
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Kerlikowske K, Su YR, Sprague BL, Tosteson ANA, Buist DSM, Onega T, Henderson LM, Alsheik N, Bissell MCS, O’Meara ES, Lee CI, Miglioretti DL. Association of Screening With Digital Breast Tomosynthesis vs Digital Mammography With Risk of Interval Invasive and Advanced Breast Cancer. JAMA 2022; 327:2220-2230. [PMID: 35699706 PMCID: PMC9198754 DOI: 10.1001/jama.2022.7672] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 04/21/2022] [Indexed: 12/15/2022]
Abstract
Importance Digital breast tomosynthesis (DBT) was developed with the expectation of improving cancer detection in women with dense breasts. Studies are needed to evaluate interval invasive and advanced breast cancer rates, intermediary outcomes related to breast cancer mortality, by breast density and breast cancer risk. Objective To evaluate whether DBT screening is associated with a lower likelihood of interval invasive cancer and advanced breast cancer compared with digital mammography by extent of breast density and breast cancer risk. Design, Setting, and Participants Cohort study of 504 427 women aged 40 to 79 years who underwent 1 003 900 screening digital mammography and 375 189 screening DBT examinations from 2011 through 2018 at 44 US Breast Cancer Surveillance Consortium (BCSC) facilities with follow-up for cancer diagnoses through 2019 by linkage to state or regional cancer registries. Exposures Breast Imaging Reporting and Data System (BI-RADS) breast density; BCSC 5-year breast cancer risk. Main Outcomes and Measures Rates per 1000 examinations of interval invasive cancer within 12 months of screening mammography and advanced breast cancer (prognostic pathologic stage II or higher) within 12 months of screening mammography, both estimated with inverse probability weighting. Results Among 504 427 women in the study population, the median age at time of mammography was 58 years (IQR, 50-65 years). Interval invasive cancer rates per 1000 examinations were not significantly different for DBT vs digital mammography (overall, 0.57 vs 0.61, respectively; difference, -0.04; 95% CI, -0.14 to 0.06; P = .43) or among all the 836 250 examinations with BCSC 5-year risk less than 1.67% (low to average-risk) or all the 413 061 examinations with BCSC 5-year risk of 1.67% or higher (high risk) across breast density categories. Advanced cancer rates were not significantly different for DBT vs digital mammography among women at low to average risk or at high risk with almost entirely fatty, scattered fibroglandular densities, or heterogeneously dense breasts. Advanced cancer rates per 1000 examinations were significantly lower for DBT vs digital mammography for the 3.6% of women with extremely dense breasts and at high risk of breast cancer (13 291 examinations in the DBT group and 31 300 in the digital mammography group; 0.27 vs 0.80 per 1000 examinations; difference, -0.53; 95% CI, -0.97 to -0.10) but not for women at low to average risk (10 611 examinations in the DBT group and 37 796 in the digital mammography group; 0.54 vs 0.42 per 1000 examinations; difference, 0.12; 95% CI, -0.09 to 0.32). Conclusions and Relevance Screening with DBT vs digital mammography was not associated with a significant difference in risk of interval invasive cancer and was associated with a significantly lower risk of advanced breast cancer among the 3.6% of women with extremely dense breasts and at high risk of breast cancer. No significant difference was observed in the 96.4% of women with nondense breasts, heterogeneously dense breasts, or with extremely dense breasts not at high risk.
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Affiliation(s)
- Karla Kerlikowske
- Departments of Medicine and Epidemiology and Biostatistics, University of California, San Francisco
- General Internal Medicine Section, Department of Veterans Affairs, University of California, San Francisco
| | - Yu-Ru Su
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle
| | - Brian L. Sprague
- Departments of Surgery and Radiology, University of Vermont, Burlington
| | - Anna N. A. Tosteson
- The Dartmouth Institute for Health Policy and Clinical Practice and Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire
| | - Diana S. M. Buist
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle
| | - Tracy Onega
- Department of Population Health Sciences, University of Utah, Salt Lake City
- Huntsman Cancer Institute, Salt Lake City, Utah
| | | | - Nila Alsheik
- School of Public Health, Division of Epidemiology and Biostatistics, University of Illinois at Chicago
| | | | - Ellen S. O’Meara
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle
| | | | - Diana L. Miglioretti
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle
- Department of Public Health Sciences, University of California, Davis
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Eroglu I, Sevilimedu V, Park A, King TA, Pilewskie ML. Accuracy of the Breast Cancer Surveillance Consortium Model Among Women with LCIS. Breast Cancer Res Treat 2022; 194:257-264. [PMID: 35595928 DOI: 10.1007/s10549-021-06499-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2021] [Accepted: 12/20/2021] [Indexed: 11/26/2022]
Abstract
PURPOSE The Breast Cancer Surveillance Consortium (BCSC) model predicts risk of invasive breast cancer risk based on age, race, family history, breast density, and history of benign breast disease, including lobular carcinoma in situ (LCIS). However, validation studies for this model included few women with LCIS. We sought to evaluate the accuracy of the BCSC model among this cohort. METHODS Women with LCIS diagnosed between 1983 and 2017 were identified from a prospectively maintained database. The BCSC score was calculated; those with prior breast cancer, unknown breast density, age < 35 years or > 74 years, or with history of chemoprevention use were excluded. The Kaplan-Meier method was used to estimate incidence rates. Time-dependent receiver operating characteristic (ROC) analysis was used to analyze the discriminative capacity of the model. RESULTS 1302 women with LCIS were included. At a median follow-up of 7 years, 152 women (12%) developed invasive cancer (6 with bilateral disease). Cumulative incidences of invasive breast cancer were 7.1% (95% CI 5.6-8.7) and 13.3% (95% CI 10.9-15.6), respectively, and the median BCSC risk scores were 4.9 and 10.4, respectively, at 5 and 10 years. The median 10-year BCSC score was significantly lower than the 10-year Tyrer-Cuzick score (10.4 vs 20.8, p < 0.001). The ROC curve scores (AUC) for BCSC at 5 and 10 years were 0.59 (95% CI 0.52-0.66) and 0.58 (95% CI 0.52-0.64), respectively. CONCLUSION The BCSC model has moderate accuracy in predicting invasive breast cancer risk among women with LCIS with fair discrimination for risk prediction between individuals.
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Affiliation(s)
- Idil Eroglu
- Weill Cornell Medical College, New York, NY, USA
- Breast Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, 300 East 66th Street, New York, NY, 10065, USA
| | - Varadan Sevilimedu
- Biostatistics Service, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Anna Park
- Breast Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, 300 East 66th Street, New York, NY, 10065, USA
| | - Tari A King
- Division of Breast Surgery, Department of Surgery, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Breast Oncology Program, Dana-Farber Cancer Institute/Brigham and Women's Cancer Center, Boston, MA, USA
| | - Melissa L Pilewskie
- Breast Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, 300 East 66th Street, New York, NY, 10065, USA.
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Kim S, Tran TXM, Song H, Ryu S, Chang Y, Park B. Mammographic Breast Density, Benign Breast Disease, and Subsequent Breast Cancer Risk in 3.9 Million Korean Women. Radiology 2022; 304:534-541. [PMID: 35579518 DOI: 10.1148/radiol.212727] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Background Mammographic breast density and benign breast disease are strong risk factors for breast cancer. Accordingly, women with both risk factors may have a markedly high risk for developing breast cancer. Purpose To investigate the risk of breast cancer associated with the combination of mammographic density and benign breast disease in Korean women, where population-based mammographic breast cancer screening is provided for all women aged at least 40 years. Materials and Methods This is a retrospective analysis of data from a nationwide breast cancer screening program linked with the national cancer registry. The study included Korean women between 40-74 years of age screened for breast cancer between January 2009 and December 2010 and observed up to December 2020 (median follow-up of 10.6 years). Benign breast disease and breast density were extracted from mammography screening results. Cox proportional hazard regression analysis was used to calculate adjusted hazard ratios (HRs) for breast cancer risk. Results In this study, 3 911 348 women (mean age, 53 years ± 9 [SD]) were analyzed. During follow-up (median, 10.6 years), 58 321 women developed breast cancer. At screening, 10 729 (18.4%) cases of benign breast disease were detected among women who developed breast cancer. Women with extremely dense breasts (Breast Imaging Reporting and Data System [BI-RADS] density category D) and benign breast disease had a greater risk of breast cancer when compared with women presenting with fatty breast (BI-RADS density category A) and those without benign breast disease (HR, 2.75; 95% CI: 2.63, 2.88; P < .001). Women with benign breast disease and fatty breasts (HR, 1.49; 95% CI: 1.40, 1.58; P < .001) and women with extremely dense breasts and without benign breast disease (HR, 2.28; 95% CI: 2.20, 2.35; P < .001) also had an elevated breast cancer risk compared with women with fatty breast tissue. Conclusion Women with dense breasts and benign breast disease at screening mammography had an elevated risk of future breast cancer. © RSNA, 2022 Online supplemental material is available for this article.
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Affiliation(s)
- Soyeoun Kim
- From the Departments of Health Sciences (S.K.) and Preventive Medicine (T.X.M.T., B.P.), Hanyang University College of Medicine, 222 Wangsimni-ro, Seongdong-gu, Seoul 04763, Republic of Korea; Department of Epidemiology and Biostatistics, Graduate School of Public Health, Hanyang University, Seoul, Republic of Korea (H.S.); Center for Cohort Studies and Department of Occupational and Environmental Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea (S.R., Y.C.); and Department of Clinical Research Design and Evaluation, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul, Republic of Korea (S.R., Y.C.)
| | - Thi Xuan Mai Tran
- From the Departments of Health Sciences (S.K.) and Preventive Medicine (T.X.M.T., B.P.), Hanyang University College of Medicine, 222 Wangsimni-ro, Seongdong-gu, Seoul 04763, Republic of Korea; Department of Epidemiology and Biostatistics, Graduate School of Public Health, Hanyang University, Seoul, Republic of Korea (H.S.); Center for Cohort Studies and Department of Occupational and Environmental Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea (S.R., Y.C.); and Department of Clinical Research Design and Evaluation, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul, Republic of Korea (S.R., Y.C.)
| | - Huiyeon Song
- From the Departments of Health Sciences (S.K.) and Preventive Medicine (T.X.M.T., B.P.), Hanyang University College of Medicine, 222 Wangsimni-ro, Seongdong-gu, Seoul 04763, Republic of Korea; Department of Epidemiology and Biostatistics, Graduate School of Public Health, Hanyang University, Seoul, Republic of Korea (H.S.); Center for Cohort Studies and Department of Occupational and Environmental Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea (S.R., Y.C.); and Department of Clinical Research Design and Evaluation, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul, Republic of Korea (S.R., Y.C.)
| | - Seungho Ryu
- From the Departments of Health Sciences (S.K.) and Preventive Medicine (T.X.M.T., B.P.), Hanyang University College of Medicine, 222 Wangsimni-ro, Seongdong-gu, Seoul 04763, Republic of Korea; Department of Epidemiology and Biostatistics, Graduate School of Public Health, Hanyang University, Seoul, Republic of Korea (H.S.); Center for Cohort Studies and Department of Occupational and Environmental Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea (S.R., Y.C.); and Department of Clinical Research Design and Evaluation, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul, Republic of Korea (S.R., Y.C.)
| | - Yoosoo Chang
- From the Departments of Health Sciences (S.K.) and Preventive Medicine (T.X.M.T., B.P.), Hanyang University College of Medicine, 222 Wangsimni-ro, Seongdong-gu, Seoul 04763, Republic of Korea; Department of Epidemiology and Biostatistics, Graduate School of Public Health, Hanyang University, Seoul, Republic of Korea (H.S.); Center for Cohort Studies and Department of Occupational and Environmental Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea (S.R., Y.C.); and Department of Clinical Research Design and Evaluation, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul, Republic of Korea (S.R., Y.C.)
| | - Boyoung Park
- From the Departments of Health Sciences (S.K.) and Preventive Medicine (T.X.M.T., B.P.), Hanyang University College of Medicine, 222 Wangsimni-ro, Seongdong-gu, Seoul 04763, Republic of Korea; Department of Epidemiology and Biostatistics, Graduate School of Public Health, Hanyang University, Seoul, Republic of Korea (H.S.); Center for Cohort Studies and Department of Occupational and Environmental Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea (S.R., Y.C.); and Department of Clinical Research Design and Evaluation, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul, Republic of Korea (S.R., Y.C.)
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Kerlikowske K, Chen S, Golmakani MK, Sprague BL, Tice JA, Tosteson ANA, Rauscher GH, Henderson LM, Buist DSM, Lee JM, Gard CC, Miglioretti DL. Cumulative Advanced Breast Cancer Risk Prediction Model Developed in a Screening Mammography Population. J Natl Cancer Inst 2022; 114:676-685. [PMID: 35026019 PMCID: PMC9086807 DOI: 10.1093/jnci/djac008] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 10/14/2021] [Accepted: 01/10/2022] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND Estimating advanced breast cancer risk in women undergoing annual or biennial mammography could identify women who may benefit from less or more intensive screening. We developed an actionable model to predict cumulative 6-year advanced cancer (prognostic pathologic stage II or higher) risk according to screening interval. METHODS We included 931 186 women aged 40-74 years in the Breast Cancer Surveillance Consortium undergoing 2 542 382 annual (prior mammogram within 11-18 months) or 752 049 biennial (prior within 19-30 months) screening mammograms. The prediction model includes age, race and ethnicity, body mass index, breast density, family history of breast cancer, and prior breast biopsy subdivided by menopausal status and screening interval. We used fivefold cross-validation to internally validate model performance. We defined higher than 95th percentile as high risk (>0.658%), higher than 75th percentile to 95th or less percentile as intermediate risk (0.380%-0.658%), and 75th or less percentile as low to average risk (<0.380%). RESULTS Obesity, high breast density, and proliferative disease with atypia were strongly associated with advanced cancer. The model is well calibrated and has an area under the receiver operating characteristics curve of 0.682 (95% confidence interval = 0.670 to 0.694). Based on women's predicted advanced cancer risk under annual and biennial screening, 69.1% had low or average risk regardless of screening interval, 12.4% intermediate risk with biennial screening and average risk with annual screening, and 17.4% intermediate or high risk regardless of screening interval. CONCLUSION Most women have low or average advanced cancer risk and can undergo biennial screening. Intermediate-risk women may consider annual screening, and high-risk women may consider supplemental imaging in addition to annual screening.
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Affiliation(s)
- Karla Kerlikowske
- Department of Medicine and Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
- General Internal Medicine Section, Department of Veterans Affairs, University of California, San Francisco, CA, USA
| | - Shuai Chen
- Department of Public Health Sciences, University of California, Davis, CA, USA
| | | | - Brian L Sprague
- Department of Surgery and Radiology, University of Vermont, Burlington, VT, USA
| | - Jeffrey A Tice
- Department of Medicine and Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | - Anna N A Tosteson
- The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
- Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - Garth H Rauscher
- School of Public Health, Division of Epidemiology and Biostatistics, University of Illinois at Chicago, Chicago, IL, USA
| | - Louise M Henderson
- Department of Radiology, University of North Carolina, Chapel Hill, NC, USA
| | - Diana S M Buist
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA, USA
| | - Janie M Lee
- Department of Radiology, University of Washington, and Seattle Cancer Care Alliance, Seattle, WA, USA
| | - Charlotte C Gard
- Department of Economics, Applied Statistics, and International Business, New Mexico State University, Las Cruces, NM, USA
| | - Diana L Miglioretti
- Department of Public Health Sciences, University of California, Davis, CA, USA
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA, USA
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Pons-Rodriguez A, Marzo-Castillejo M, Cruz-Esteve I, Galindo-Ortego G, Hernández-Leal MJ, Rué M. [Moving toward personalized breast cancer screening: The role of Primary Care]. Aten Primaria 2022; 54:102288. [PMID: 35477080 PMCID: PMC9061619 DOI: 10.1016/j.aprim.2022.102288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 01/11/2022] [Indexed: 11/02/2022] Open
Abstract
Breast cancer is the leading cause of death in the world among women. The Spanish National Health System (SNHS) introduced population-based breast cancer screening in 2006. As in most European programs, risk is identified on the basis of age and a mammogram is offered every two years to women aged 50-69 years. Scientific evidence is moving toward personalized screening, based on individual risk. This article presents the clinical trials that will evaluate the efficacy of personalized screening and some studies carried out in our environment on the effect of informing women of the benefits and adverse effects of screening or the acceptability and feasibility of offering personalized screening, in the Shared Decision Making context. The Preventive Activities and Health Promotion Program can help transform screening in our SNHS.
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Affiliation(s)
| | - Mercè Marzo-Castillejo
- Unitat de Suport a la Recerca Metropolitana Sud, IDIAP Jordi Gol, Direcció d'Atenció Primària Costa de Ponent, Institut Català de la Salut, Barcelona, España
| | | | | | - Maria José Hernández-Leal
- Departament d'Economia, Universitat Rovira i Virgili, Reus, España; Centre de Recerca en Economia i Sostenibilitat (ECO-SOS), Tarragona, España; Grup de Recerca en Anàlisi Estadística i Econòmica en Salut (GRAEES), Lleida y Reus, España
| | - Montserrat Rué
- Grup de Recerca en Anàlisi Estadística i Econòmica en Salut (GRAEES), Lleida y Reus, España; Departament de Ciències Mèdiques Bàsiques, Universitat de Lleida - Institut de Recerca Biomèdica de Lleida (IRB Lleida), Lleida, España.
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Tran TXM, Kim S, Song H, Park B. Mammographic breast density, body mass index and risk of breast cancer in Korean women aged 75 years and older. Int J Cancer 2022; 151:869-877. [PMID: 35460071 DOI: 10.1002/ijc.34038] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 03/26/2022] [Accepted: 04/08/2022] [Indexed: 01/23/2023]
Abstract
Mammographic breast density and body mass index (BMI) are strong risk factors of breast cancer, but few studies have investigated these factors in older women. Our study assessed the association between breast density, BMI and the breast cancer risk among women aged ≥75 years. We included women who underwent breast cancer screening between 2009 and 2014 and were followed up until 2020. Breast density was measured using Breast Imaging Reporting and Data System. BMI was classified into three groups: <23, 23 to <25 and ≥25. Cox proportional hazards models were used to estimate the association of breast density and BMI with breast cancer risk. In 483 564 women, 1885 developed breast cancer. The 5-year incidence increased with an increase in breast density and BMI. Increase in breast density was associated with an increased breast cancer risk in all BMI categories: among women with BMI <23, those with heterogeneous/extreme density had a 2.98-fold (95% CI: 2.23-3.80) increased risk of breast cancer compared to those with entirely fatty breasts. An increase in BMI was associated with increased breast cancer risk in women with the same breast density in all density categories. When the combined associations of breast density and BMI on the risk of breast cancer were considered, women with a BMI ≥25 and heterogeneous/extreme breast density had a 5.35-fold (95% CI: 4.26-6.72) increased risk of breast cancer compared to women with a BMI <23 and fatty breasts. Women aged ≥75 years, with dense breasts, regardless of BMI status, might benefit from a tailored screening strategy for early detection of breast cancer.
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Affiliation(s)
- Thi Xuan Mai Tran
- Department of Preventive Medicine, Hanyang University College of Medicine, Seoul, Republic of Korea
| | - Soyeoun Kim
- Department of Health Sciences, Hanyang University College of Medicine, Seoul, Republic of Korea
| | - Huiyeon Song
- Department of Epidemiology and Biostatistics, Graduate School of Public Health, Hanyang University, Seoul, Republic of Korea
| | - Boyoung Park
- Department of Preventive Medicine, Hanyang University College of Medicine, Seoul, Republic of Korea
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Clift AK, Dodwell D, Lord S, Petrou S, Brady SM, Collins GS, Hippisley-Cox J. The current status of risk-stratified breast screening. Br J Cancer 2022; 126:533-550. [PMID: 34703006 PMCID: PMC8854575 DOI: 10.1038/s41416-021-01550-3] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 08/25/2021] [Accepted: 09/14/2021] [Indexed: 12/23/2022] Open
Abstract
Apart from high-risk scenarios such as the presence of highly penetrant genetic mutations, breast screening typically comprises mammography or tomosynthesis strategies defined by age. However, age-based screening ignores the range of breast cancer risks that individual women may possess and is antithetical to the ambitions of personalised early detection. Whilst screening mammography reduces breast cancer mortality, this is at the risk of potentially significant harms including overdiagnosis with overtreatment, and psychological morbidity associated with false positives. In risk-stratified screening, individualised risk assessment may inform screening intensity/interval, starting age, imaging modality used, or even decisions not to screen. However, clear evidence for its benefits and harms needs to be established. In this scoping review, the authors summarise the established and emerging evidence regarding several critical dependencies for successful risk-stratified breast screening: risk prediction model performance, epidemiological studies, retrospective clinical evaluations, health economic evaluations and qualitative research on feasibility and acceptability. Family history, breast density or reproductive factors are not on their own suitable for precisely estimating risk and risk prediction models increasingly incorporate combinations of demographic, clinical, genetic and imaging-related parameters. Clinical evaluations of risk-stratified screening are currently limited. Epidemiological evidence is sparse, and randomised trials only began in recent years.
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Affiliation(s)
- Ash Kieran Clift
- Cancer Research UK Oxford Centre, Department of Oncology, University of Oxford, Oxford, UK.
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
| | - David Dodwell
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Simon Lord
- Department of Oncology, University of Oxford, Oxford, UK
| | - Stavros Petrou
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | | | - Gary S Collins
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology & Musculoskeletal Sciences, University of Oxford, Oxford, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - Julia Hippisley-Cox
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
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Advani S, Abraham L, Buist DS, Kerlikowske K, Miglioretti DL, Sprague BL, Henderson LM, Onega T, Schousboe JT, Demb J, Zhang D, Walter LC, Lee CI, Braithwaite D, O’Meara ES. Breast biopsy patterns and findings among older women undergoing screening mammography: The role of age and comorbidity. J Geriatr Oncol 2022; 13:161-169. [PMID: 34896059 PMCID: PMC9450010 DOI: 10.1016/j.jgo.2021.11.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 10/06/2021] [Accepted: 11/29/2021] [Indexed: 11/24/2022]
Abstract
INTRODUCTION Limited evidence exists on the impact of age and comorbidity on biopsy rates and findings among older women. MATERIALS AND METHODS We used data from 170,657 women ages 66-94 enrolled in the United States Breast Cancer Surveillance Consortium (BCSC). We estimated one-year rates of biopsy by type (any, fine-needle aspiration (FNA), core or surgical) and yield of the most invasive biopsy finding (benign, ductal carcinoma in situ (DCIS) and invasive breast cancer) by age and comorbidity. Statistical significance was assessed using Wald statistics comparing coefficients estimated from logistic regression models adjusted for age, comorbidity, BCSC registry, and interaction between age and comorbidity. RESULTS Of 524,860 screening mammograms, 9830 biopsies were performed following 7930 exams (1.5%) within one year, specifically 5589 core biopsies (1.1%), 3422 (0.7%) surgical biopsies and 819 FNAs (0.2%). Biopsy rates per 1000 screens decreased with age (66-74:15.7, 95%CI:14.8-16.8), 75-84:14.5(13.5-15.6), 85-94:13.2(11.3,15.4), ptrend < 0.001) and increased with Charlson Comorbidity Score (CCS = 0:14.4 (13.5-15.3), CCS = 1:16.6 (15.2-18.1), CCS ≥2:19.0 (16.9-21.5), ptrend < 0.001).Biopsy rates increased with CCS at ages 66-74 and 75-84 but not 85-94. Core and surgical biopsy rates increased with CCS at ages 66-74 only. For each biopsy type, the yield of invasive breast cancer increased with age irrespective of comorbidity. DISCUSSION Women aged 66-84 with significant comorbidity in a breast cancer screening population had higher breast biopsy rates and similar rates of invasive breast cancer diagnosis than their counterparts with lower comorbidity. A considerable proportion of these diagnoses may represent overdiagnoses, given the high competing risk of death from non-breast-cancer causes among older women.
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Affiliation(s)
- Shailesh Advani
- Department of Oncology, Georgetown University, Washington, DC
| | - Linn Abraham
- Kaiser Permanente Washington Health Research Institute, Seattle, WA
| | - Diana S.M. Buist
- Kaiser Permanente Washington Health Research Institute, Seattle, WA
| | - Karla Kerlikowske
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA,Department of Medicine, University of California, San Francisco, San Francisco, CA
| | - Diana L. Miglioretti
- Department of Public Health Sciences, School of Medicine, University of California, Davis, Davis, CA
| | - Brian L. Sprague
- Department of Surgery, University of Vermont Larner College of Medicine, Burlington, VT
| | | | - Tracy Onega
- Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, NH
| | | | - Joshua Demb
- Division of Gastroenterology, Department of Internal Medicine, School of Medicine, University of California, San Diego, La Jolla, CA
| | - Dongyu Zhang
- Department of Epidemiology, University of Florida, Gainesville, FL
| | - Louise C. Walter
- Department of Medicine, University of California, San Francisco, San Francisco, CA
| | - Christoph I. Lee
- Department of Radiology, University of Washington School of Medicine; Department of Health Services, University of Washington School of Public Health, Seattle, WA
| | - Dejana Braithwaite
- Department of Epidemiology, University of Florida, Gainesville, FL, United States of America; University of Florida Health Cancer Center, Gainesville, FL, United States of America; Department of Aging and Geriatric Research, University of Florida, Gainesville, FL, United States of America.
| | - Ellen S. O’Meara
- Kaiser Permanente Washington Health Research Institute, Seattle, WA
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Román M, Louro J, Posso M, Vidal C, Bargalló X, Vázquez I, Quintana MJ, Alcántara R, Saladié F, del Riego J, Peñalva L, Sala M, Castells X. Long-Term Risk of Breast Cancer after Diagnosis of Benign Breast Disease by Screening Mammography. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19052625. [PMID: 35270331 PMCID: PMC8909630 DOI: 10.3390/ijerph19052625] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 02/11/2022] [Accepted: 02/18/2022] [Indexed: 12/04/2022]
Abstract
Assessing the long-term risk of breast cancer after diagnosis of benign breast disease by mammography is of utmost importance to design personalised screening strategies. We analysed individual-level data from 778,306 women aged 50–69 years with at least one mammographic screening participation in any of ten breast cancer screening centers in Spain from 1996 to 2015, and followed-up until 2017. We used Poisson regression to compare the rates of incident breast cancer among women with and without benign breast disease. During a median follow-up of 7.6 years, 11,708 (1.5%) women had an incident of breast cancer and 17,827 (2.3%) had a benign breast disease. The risk of breast cancer was 1.77 times higher among women with benign breast disease than among those without (95% CI: 1.61 to 1.95). The relative risk increased to 1.99 among women followed for less than four years, and remained elevated for two decades, with relative risk 1.96 (95% CI: 1.32 to 2.92) for those followed from 12 to 20 years. Benign breast disease is a long-term risk factor for breast cancer. Women with benign breast disease could benefit from closer surveillance and personalized screening strategies.
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Affiliation(s)
- Marta Román
- Epidemiology and Evaluation Department, IMIM (Hospital del Mar Medical Research Institute), 08003 Barcelona, Spain; (J.L.); (M.P.); (M.S.)
- Research Network on Health Services in Chronic Diseases (REDISSEC), 08003 Barcelona, Spain
- Correspondence: (M.R.); (X.C.)
| | - Javier Louro
- Epidemiology and Evaluation Department, IMIM (Hospital del Mar Medical Research Institute), 08003 Barcelona, Spain; (J.L.); (M.P.); (M.S.)
- Research Network on Health Services in Chronic Diseases (REDISSEC), 08003 Barcelona, Spain
| | - Margarita Posso
- Epidemiology and Evaluation Department, IMIM (Hospital del Mar Medical Research Institute), 08003 Barcelona, Spain; (J.L.); (M.P.); (M.S.)
- Research Network on Health Services in Chronic Diseases (REDISSEC), 08003 Barcelona, Spain
| | - Carmen Vidal
- Cancer Prevention and Monitoring Program, Catalan Institute of Oncology (ICO), 08908 Barcelona, Spain;
| | - Xavier Bargalló
- Department of Radiology, Hospital Clinic, 08036 Barcelona, Spain;
| | - Ivonne Vázquez
- Pathology Department, IMIM (Hospital del Mar Medical Research Institute), 08003 Barcelona, Spain;
| | - María Jesús Quintana
- Department of Clinical Epidemiology and Public Health, University Hospital de la Santa Creu i Sant Pau, 08025 Barcelona, Spain;
| | - Rodrigo Alcántara
- Radiology Department, IMIM (Hospital del Mar Medical Research Institute), 08003 Barcelona, Spain;
| | - Francina Saladié
- Epidemiology and Cancer Prevention Service, Hospital Universitari Sant Joan de Reus, IISPV, 43204 Reus, Spain;
| | - Javier del Riego
- Department of Radiology, Parc Taulí University Hospital-UAB, 08208 Sabadell, Spain;
| | - Lupe Peñalva
- Breast Cancer Screening Technical Office, Private Foundation Asil Hospital, 08402 Granollers, Spain;
| | - Maria Sala
- Epidemiology and Evaluation Department, IMIM (Hospital del Mar Medical Research Institute), 08003 Barcelona, Spain; (J.L.); (M.P.); (M.S.)
- Research Network on Health Services in Chronic Diseases (REDISSEC), 08003 Barcelona, Spain
| | - Xavier Castells
- Epidemiology and Evaluation Department, IMIM (Hospital del Mar Medical Research Institute), 08003 Barcelona, Spain; (J.L.); (M.P.); (M.S.)
- Research Network on Health Services in Chronic Diseases (REDISSEC), 08003 Barcelona, Spain
- Correspondence: (M.R.); (X.C.)
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Tice JA, Gard CC, Miglioretti DL, Sprague BL, Tosteson ANA, Joe BN, Ho TQH, Kerlikowske K. Comparing Mammographic Density Assessed by Digital Breast Tomosynthesis or Digital Mammography: The Breast Cancer Surveillance Consortium. Radiology 2022; 302:286-292. [PMID: 34812671 PMCID: PMC8805687 DOI: 10.1148/radiol.2021204579] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 08/13/2021] [Accepted: 09/10/2021] [Indexed: 02/03/2023]
Abstract
Background Consistency in reporting Breast Imaging Reporting and Data System (BI-RADS) breast density on mammograms is important because breast density is used for breast cancer risk assessment and is reported directly to women and clinicians to inform decisions about supplemental screening. Purpose To assess the consistency of BI-RADS density reporting between digital breast tomosynthesis (DBT) and digital mammography (DM) and evaluate density as a breast cancer risk factor when assessed using DM versus DBT. Materials and Methods The Breast Cancer Surveillance Consortium is a prospective cohort study of women undergoing mammography with DM or DBT. This secondary analysis included women aged 40-79 years who underwent at least two screening mammography examinations less than 36 months apart. Percentage agreement and κ statistic were estimated for pairs of BI-RADS density assessments. Cox proportional hazards regression was used to calculate hazard ratios (HRs) of breast density as a risk factor for invasive breast cancer. Results A total of 403 326 pairs of mammograms from 342 149 women were evaluated. There were no significant differences in breast density assessment in pairs consisting of one DM and one DBT examination (57 516 of 74 729 [77%]; κ = 0.64), two DM examinations (238 678 of 301 743 [79%]; κ = 0.67), and two DBT examinations (20 763 of 26 854 [77%]; κ = 0.65). Results were similar when restricting the analyses to pairs read by the same radiologist. The breast cancer HRs for breast density were similar for DM and DBT (P = .45 for interaction). The HRs for density acquired using DM and DBT, respectively, were 0.55 (95% CI: 0.49, 0.63) and 0.37 (95% CI: 0.21, 0.66) for almost entirely fat, 1.47 (95% CI: 1.37, 1.58) and 1.36 (95% CI: 1.02, 1.82) for heterogeneously dense, and 1.72 (95% CI: 1.54, 1.93) and 2.05 (95% CI: 1.25, 3.36) for extremely dense breasts. Conclusion Radiologist reporting of Breast Imaging Reporting and Data System density obtained with digital breast tomosynthesis did not differ from that obtained with digital mammography. © RSNA, 2021 Online supplemental material is available for this article.
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Affiliation(s)
- Jeffrey A. Tice
- From the Division of General Internal Medicine, Department of
Medicine (J.A.T.), and Department of Radiology and Biomedical Imaging (B.N.J.),
University of California, San Francisco, 1545 Divisadero St, Suite 309, San
Francisco, CA 94143-0320; General Internal Medicine Section, Department of
Veterans Affairs and Departments of Medicine and Epidemiology and Biostatistics,
San Francisco, Calif (K.K.); Department of Economics, Applied Statistics, and
International Business, New Mexico State University, Las Cruces, NM (C.C.G.);
Department of Public Health Sciences, University of California, Davis, School of
Medicine, Davis, Calif (D.L.M., T.Q.H.H.); Kaiser Permanente Washington Health
Research Institute, Seattle, Wash (D.L.M.); Department of Surgery, University of
Vermont, Burlington, Vt (B.L.S.); The Dartmouth Institute for Health Policy and
Clinical Practice, Geisel School of Medicine at Dartmouth, Lebanon, NH
(A.N.A.T.); and Department of Training and Scientific Research, University
Medical Center, Ho Chi Minh City, Vietnam (T.Q.H.H.)
| | - Charlotte C. Gard
- From the Division of General Internal Medicine, Department of
Medicine (J.A.T.), and Department of Radiology and Biomedical Imaging (B.N.J.),
University of California, San Francisco, 1545 Divisadero St, Suite 309, San
Francisco, CA 94143-0320; General Internal Medicine Section, Department of
Veterans Affairs and Departments of Medicine and Epidemiology and Biostatistics,
San Francisco, Calif (K.K.); Department of Economics, Applied Statistics, and
International Business, New Mexico State University, Las Cruces, NM (C.C.G.);
Department of Public Health Sciences, University of California, Davis, School of
Medicine, Davis, Calif (D.L.M., T.Q.H.H.); Kaiser Permanente Washington Health
Research Institute, Seattle, Wash (D.L.M.); Department of Surgery, University of
Vermont, Burlington, Vt (B.L.S.); The Dartmouth Institute for Health Policy and
Clinical Practice, Geisel School of Medicine at Dartmouth, Lebanon, NH
(A.N.A.T.); and Department of Training and Scientific Research, University
Medical Center, Ho Chi Minh City, Vietnam (T.Q.H.H.)
| | - Diana L. Miglioretti
- From the Division of General Internal Medicine, Department of
Medicine (J.A.T.), and Department of Radiology and Biomedical Imaging (B.N.J.),
University of California, San Francisco, 1545 Divisadero St, Suite 309, San
Francisco, CA 94143-0320; General Internal Medicine Section, Department of
Veterans Affairs and Departments of Medicine and Epidemiology and Biostatistics,
San Francisco, Calif (K.K.); Department of Economics, Applied Statistics, and
International Business, New Mexico State University, Las Cruces, NM (C.C.G.);
Department of Public Health Sciences, University of California, Davis, School of
Medicine, Davis, Calif (D.L.M., T.Q.H.H.); Kaiser Permanente Washington Health
Research Institute, Seattle, Wash (D.L.M.); Department of Surgery, University of
Vermont, Burlington, Vt (B.L.S.); The Dartmouth Institute for Health Policy and
Clinical Practice, Geisel School of Medicine at Dartmouth, Lebanon, NH
(A.N.A.T.); and Department of Training and Scientific Research, University
Medical Center, Ho Chi Minh City, Vietnam (T.Q.H.H.)
| | - Brian L. Sprague
- From the Division of General Internal Medicine, Department of
Medicine (J.A.T.), and Department of Radiology and Biomedical Imaging (B.N.J.),
University of California, San Francisco, 1545 Divisadero St, Suite 309, San
Francisco, CA 94143-0320; General Internal Medicine Section, Department of
Veterans Affairs and Departments of Medicine and Epidemiology and Biostatistics,
San Francisco, Calif (K.K.); Department of Economics, Applied Statistics, and
International Business, New Mexico State University, Las Cruces, NM (C.C.G.);
Department of Public Health Sciences, University of California, Davis, School of
Medicine, Davis, Calif (D.L.M., T.Q.H.H.); Kaiser Permanente Washington Health
Research Institute, Seattle, Wash (D.L.M.); Department of Surgery, University of
Vermont, Burlington, Vt (B.L.S.); The Dartmouth Institute for Health Policy and
Clinical Practice, Geisel School of Medicine at Dartmouth, Lebanon, NH
(A.N.A.T.); and Department of Training and Scientific Research, University
Medical Center, Ho Chi Minh City, Vietnam (T.Q.H.H.)
| | - Anna N. A. Tosteson
- From the Division of General Internal Medicine, Department of
Medicine (J.A.T.), and Department of Radiology and Biomedical Imaging (B.N.J.),
University of California, San Francisco, 1545 Divisadero St, Suite 309, San
Francisco, CA 94143-0320; General Internal Medicine Section, Department of
Veterans Affairs and Departments of Medicine and Epidemiology and Biostatistics,
San Francisco, Calif (K.K.); Department of Economics, Applied Statistics, and
International Business, New Mexico State University, Las Cruces, NM (C.C.G.);
Department of Public Health Sciences, University of California, Davis, School of
Medicine, Davis, Calif (D.L.M., T.Q.H.H.); Kaiser Permanente Washington Health
Research Institute, Seattle, Wash (D.L.M.); Department of Surgery, University of
Vermont, Burlington, Vt (B.L.S.); The Dartmouth Institute for Health Policy and
Clinical Practice, Geisel School of Medicine at Dartmouth, Lebanon, NH
(A.N.A.T.); and Department of Training and Scientific Research, University
Medical Center, Ho Chi Minh City, Vietnam (T.Q.H.H.)
| | - Bonnie N. Joe
- From the Division of General Internal Medicine, Department of
Medicine (J.A.T.), and Department of Radiology and Biomedical Imaging (B.N.J.),
University of California, San Francisco, 1545 Divisadero St, Suite 309, San
Francisco, CA 94143-0320; General Internal Medicine Section, Department of
Veterans Affairs and Departments of Medicine and Epidemiology and Biostatistics,
San Francisco, Calif (K.K.); Department of Economics, Applied Statistics, and
International Business, New Mexico State University, Las Cruces, NM (C.C.G.);
Department of Public Health Sciences, University of California, Davis, School of
Medicine, Davis, Calif (D.L.M., T.Q.H.H.); Kaiser Permanente Washington Health
Research Institute, Seattle, Wash (D.L.M.); Department of Surgery, University of
Vermont, Burlington, Vt (B.L.S.); The Dartmouth Institute for Health Policy and
Clinical Practice, Geisel School of Medicine at Dartmouth, Lebanon, NH
(A.N.A.T.); and Department of Training and Scientific Research, University
Medical Center, Ho Chi Minh City, Vietnam (T.Q.H.H.)
| | - Thao-Quyen H. Ho
- From the Division of General Internal Medicine, Department of
Medicine (J.A.T.), and Department of Radiology and Biomedical Imaging (B.N.J.),
University of California, San Francisco, 1545 Divisadero St, Suite 309, San
Francisco, CA 94143-0320; General Internal Medicine Section, Department of
Veterans Affairs and Departments of Medicine and Epidemiology and Biostatistics,
San Francisco, Calif (K.K.); Department of Economics, Applied Statistics, and
International Business, New Mexico State University, Las Cruces, NM (C.C.G.);
Department of Public Health Sciences, University of California, Davis, School of
Medicine, Davis, Calif (D.L.M., T.Q.H.H.); Kaiser Permanente Washington Health
Research Institute, Seattle, Wash (D.L.M.); Department of Surgery, University of
Vermont, Burlington, Vt (B.L.S.); The Dartmouth Institute for Health Policy and
Clinical Practice, Geisel School of Medicine at Dartmouth, Lebanon, NH
(A.N.A.T.); and Department of Training and Scientific Research, University
Medical Center, Ho Chi Minh City, Vietnam (T.Q.H.H.)
| | - Karla Kerlikowske
- From the Division of General Internal Medicine, Department of
Medicine (J.A.T.), and Department of Radiology and Biomedical Imaging (B.N.J.),
University of California, San Francisco, 1545 Divisadero St, Suite 309, San
Francisco, CA 94143-0320; General Internal Medicine Section, Department of
Veterans Affairs and Departments of Medicine and Epidemiology and Biostatistics,
San Francisco, Calif (K.K.); Department of Economics, Applied Statistics, and
International Business, New Mexico State University, Las Cruces, NM (C.C.G.);
Department of Public Health Sciences, University of California, Davis, School of
Medicine, Davis, Calif (D.L.M., T.Q.H.H.); Kaiser Permanente Washington Health
Research Institute, Seattle, Wash (D.L.M.); Department of Surgery, University of
Vermont, Burlington, Vt (B.L.S.); The Dartmouth Institute for Health Policy and
Clinical Practice, Geisel School of Medicine at Dartmouth, Lebanon, NH
(A.N.A.T.); and Department of Training and Scientific Research, University
Medical Center, Ho Chi Minh City, Vietnam (T.Q.H.H.)
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Houssami N, Kerlikowske K. AI as a new paradigm for risk-based screening for breast cancer. Nat Med 2022; 28:29-30. [PMID: 35027759 DOI: 10.1038/s41591-021-01649-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Nehmat Houssami
- The Daffodil Centre, University of Sydney-a joint venture with Cancer Council NSW, Sydney, NSW, Australia.
| | - Karla Kerlikowske
- Departments of Medicine and Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
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Abstract
In screening for breast cancer (BC), mammographic breast density (MBD) is a powerful risk factor that increases breast carcinogenesis and synergistically reduces the sensitivity of mammography. It also reduces specificity of lesion identification, leading to recalls, additional testing, and delayed and later-stage diagnoses, which result in increased health care costs. These findings provide the foundation for dense breast notification laws and lead to the increase in patient and provider interest in MBD. However, unlike other risk factors for BC, MBD is dynamic through a woman’s lifetime and is modifiable. Although MBD is known to change as a result of factors such as reproductive history and hormonal status, few conclusions have been reached for lifestyle factors such as alcohol, diet, physical activity, smoking, body mass index (BMI), and some commonly used medications. Our review examines the emerging evidence for the association of modifiable factors on MBD and the influence of MBD on BC risk. There are clear associations between alcohol use and menopausal hormone therapy and increased MBD. Physical activity and the Mediterranean diet lower the risk of BC without significant effect on MBD. Although high BMI and smoking are known risk factors for BC, they have been found to decrease MBD. The influence of several other factors, including caffeine intake, nonhormonal medications, and vitamins, on MBD is unclear. We recommend counseling patients on these modifiable risk factors and using this knowledge to help with informed decision making for tailored BC prevention strategies.
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Affiliation(s)
- Sara P Lester
- Corresponding author: Sara P. Lester, MD, Division of General Internal Medicine, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA.
| | - Aparna S Kaur
- Division of General Internal Medicine, Mayo Clinic, Rochester, MN, USA
| | - Suneela Vegunta
- Division of Women’s Health Internal Medicine, Mayo Clinic, Scottsdale, AZ, USA
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Shamsi U, Afzal S, Shamsi A, Azam I, Callen D. Factors associated with mammographic breast density among women in Karachi Pakistan. BMC Womens Health 2021; 21:438. [PMID: 34972514 PMCID: PMC8720218 DOI: 10.1186/s12905-021-01538-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Accepted: 11/10/2021] [Indexed: 12/31/2022] Open
Abstract
Background There are no studies done to evaluate the distribution of mammographic breast density and factors associated with it among Pakistani women. Methods Participants included 477 women, who had received either diagnostic or screening mammography at two hospitals in Karachi Pakistan. Mammographic breast density was assessed using the Breast Imaging Reporting and Data System. In person interviews were conducted using a detailed questionnaire, to assess risk factors of interest, and venous blood was collected to measure serum vitamin D level at the end of the interview. To determine the association of potential factors with mammographic breast density, multivariable polytomous logistic regression was used. Results High-density mammographic breast density (heterogeneously and dense categories) was high and found in 62.4% of women. There was a significant association of both heterogeneously dense and dense breasts with women of a younger age group < 45 years (OR 2.68, 95% CI 1.60–4.49) and (OR 4.83, 95% CI 2.54–9.16) respectively. Women with heterogeneously dense and dense breasts versus fatty and fibroglandular breasts had a higher history of benign breast disease (OR 1.90, 95% CI 1.14–3.17) and (OR 3.61, 95% CI 1.90–6.86) respectively. There was an inverse relationship between breast density and body mass index. Women with dense breasts and heterogeneously dense breasts had lower body mass index (OR 0.94 95% CI 0.90–0.99) and (OR 0.81, 95% CI 0.76–0.87) respectively. There was no association of mammographic breast density with serum vitamin D levels, diet, and breast cancer. Conclusions The findings of a positive association of higher mammographic density with younger age and benign breast disease and a negative association between body mass index and breast density are important findings that need to be considered in developing screening guidelines for the Pakistani population.
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Affiliation(s)
- Uzma Shamsi
- School of Medicine, University of Adelaide, Adelaide, Australia.
| | - Shaista Afzal
- Department of Radiology, Aga Khan University, Karachi, Pakistan
| | - Azra Shamsi
- Department of Gynecology and Obstetrics, Combined Military Hospital, Karachi, Pakistan
| | - Iqbal Azam
- Department of Community Health Sciences, Aga Khan University, Karachi, Pakistan
| | - David Callen
- School of Medicine, University of Adelaide, Adelaide, Australia
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Mammography screening and mortality by risk status in the California teachers study. BMC Cancer 2021; 21:1341. [PMID: 34922473 PMCID: PMC8684058 DOI: 10.1186/s12885-021-09071-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 11/25/2021] [Indexed: 01/05/2023] Open
Abstract
Background The debate continues among medical professionals regarding the frequency, starting age, and stopping age for mammography screening. Some experts suggest tailoring recommendations based on individuals’ personal breast cancer risk. Previous studies have not compared the impact of annual versus biennial mammography stratified by age group and risk category. The purpose of this study was to examine the relationship between mammography frequency and mortality by age group and risk category in the California Teachers Study. Methods Using data from study questionnaires from 93,438 women between the ages of 40 and 85 and linkages to the California Cancer Registry and other indices, overall and breast cancer-specific mortality by mammography frequency were estimated using multivariable Cox proportional hazards models, stratified by age group and risk category at baseline as determined by the Gail breast cancer risk model. Results During the follow-up period of 20 years, overall mortality risk was lower in women who had annual or biennial mammography compared to less frequent or no mammography in all age groups. Annual mammography was associated with lower overall mortality risk compared to biennial mammography among women age 50–85. This difference was especially apparent in women age 60–74, regardless of estimated Gail risk category at baseline. Breast cancer-specific mortality was lower among women who had annual mammography compared to biennial or less frequent mammography among women age 60–74, regardless of their baseline risk. Conclusions Our findings suggest that at least biennial mammography is beneficial to most women age 40–85 and that annual mammography is more beneficial than biennial mammography to most women age 50–85 in terms of overall mortality. Supplementary Information The online version contains supplementary material available at 10.1186/s12885-021-09071-1.
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