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Mao X, He W, Eriksson M, Lindström LS, Holowko N, Bajalica-Lagercrantz S, Hammarström M, Grassmann F, Humphreys K, Easton D, Hall P, Czene K. Prediction of breast cancer risk for sisters of women attending screening. J Natl Cancer Inst 2023; 115:1310-1317. [PMID: 37243694 PMCID: PMC10637039 DOI: 10.1093/jnci/djad101] [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: 01/09/2023] [Revised: 04/17/2023] [Accepted: 05/23/2023] [Indexed: 05/29/2023] Open
Abstract
BACKGROUND Risk assessment is important for breast cancer prevention and early detection. We aimed to examine whether common risk factors, mammographic features, and breast cancer risk prediction scores of a woman were associated with breast cancer risk for her sisters. METHODS We included 53 051 women from the Karolinska Mammography Project for Risk Prediction of Breast Cancer (KARMA) study. Established risk factors were derived using self-reported questionnaires, mammograms, and single nucleotide polymorphism genotyping. Using the Swedish Multi-Generation Register, we identified 32 198 sisters of the KARMA women (including 5352 KARMA participants and 26 846 nonparticipants). Cox models were used to estimate the hazard ratios of breast cancer for both women and their sisters, respectively. RESULTS A higher breast cancer polygenic risk score, a history of benign breast disease, and higher breast density in women were associated with an increased risk of breast cancer for both women and their sisters. No statistically significant association was observed between breast microcalcifications and masses in women and breast cancer risk for their sisters. Furthermore, higher breast cancer risk scores in women were associated with an increased risk of breast cancer for their sisters. Specifically, the hazard ratios for breast cancer per 1 standard deviation increase in age-adjusted KARMA, Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA), and Tyrer-Cuzick risk scores were 1.16 (95% confidence interval [CI] = 1.07 to 1.27), 1.23 (95% CI = 1.12 to 1.35), and 1.21 (95% CI = 1.11 to 1.32), respectively. CONCLUSION A woman's breast cancer risk factors are associated with her sister's breast cancer risk. However, the clinical utility of these findings requires further investigation.
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Affiliation(s)
- Xinhe Mao
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Wei He
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Chronic Disease Research Institute, The Children’s Hospital, and National Clinical Research Center for Child Health, School of Public Health, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
- Department of Nutrition and Food Hygiene, School of Public Health, Zhejiang University, Hangzhou, Zhejiang, China
| | - Mikael Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Linda S Lindström
- Department of Oncology-Pathology, Karolinska Institutet and Hereditary Cancer Unit, Karolinska University Hospital, Stockholm, Sweden
| | - Natalie Holowko
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Medicine Solna, Clinical Epidemiology Division, Karolinska Institutet, Stockholm, Sweden
| | - Svetlana Bajalica-Lagercrantz
- Department of Oncology-Pathology, Karolinska Institutet and Hereditary Cancer Unit, Karolinska University Hospital, Stockholm, Sweden
| | - Mattias Hammarström
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Felix Grassmann
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Institute for Clinical Research and Systems Medicine, Health and Medical University, Potsdam, Germany
| | - Keith Humphreys
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Douglas Easton
- Department of Oncology, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Oncology, Södersjukhuset, Stockholm, Sweden
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
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Drukker K, Giger ML, Joe BN, Kerlikowske K, Greenwood H, Drukteinis JS, Niell B, Fan B, Malkov S, Avila J, Kazemi L, Shepherd J. Combined Benefit of Quantitative Three-Compartment Breast Image Analysis and Mammography Radiomics in the Classification of Breast Masses in a Clinical Data Set. Radiology 2018; 290:621-628. [PMID: 30526359 DOI: 10.1148/radiol.2018180608] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Purpose To investigate the combination of mammography radiomics and quantitative three-compartment breast (3CB) image analysis of dual-energy mammography to limit unnecessary benign breast biopsies. Materials and Methods For this prospective study, dual-energy craniocaudal and mediolateral oblique mammograms were obtained immediately before biopsy in 109 women (mean age, 51 years; range, 31-85 years) with Breast Imaging Reporting and Data System category 4 or 5 breast masses (35 invasive cancers, 74 benign) from 2013 through 2017. The three quantitative compartments of water, lipid, and protein thickness at each pixel were calculated from the attenuation at high and low energy by using a within-image phantom. Masses were automatically segmented and features were extracted from the low-energy mammograms and the quantitative compartment images. Tenfold cross-validations using a linear discriminant classifier with predefined feature signatures helped differentiate between malignant and benign masses by means of (a) water-lipid-protein composition images alone, (b) mammography radiomics alone, and (c) a combined image analysis of both. Positive predictive value of biopsy performed (PPV3) at maximum sensitivity was the primary performance metric, and results were compared with those for conventional diagnostic digital mammography. Results The PPV3 for conventional diagnostic digital mammography in our data set was 32.1% (35 of 109; 95% confidence interval [CI]: 23.9%, 41.3%), with a sensitivity of 100%. In comparison, combined mammography radiomics plus quantitative 3CB image analysis had PPV3 of 49% (34 of 70; 95% CI: 36.5%, 58.9%; P < .001), with a sensitivity of 97% (34 of 35; 95% CI: 90.3%, 100%; P < .001) and 35.8% (39 of 109) fewer total biopsies (P < .001). Conclusion Quantitative three-compartment breast image analysis of breast masses combined with mammography radiomics has the potential to reduce unnecessary breast biopsies. © RSNA, 2018 Online supplemental material is available for this article.
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Affiliation(s)
- Karen Drukker
- From the Department of Radiology, University of Chicago, 5481 S Maryland Ave, MC2026, Chicago, IL 60637 (K.D., M.L.G.); Department of Radiology and Biomedical Imaging (B.N.J., H.G., B.F., S.M., J.A., L.K., J.S.) and Department of Medicine and Epidemiology (K.K.), University of California, San Francisco, San Francisco, Calif; and Department of Diagnostic Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Fla (J.S.D., B.N.)
| | - Maryellen L Giger
- From the Department of Radiology, University of Chicago, 5481 S Maryland Ave, MC2026, Chicago, IL 60637 (K.D., M.L.G.); Department of Radiology and Biomedical Imaging (B.N.J., H.G., B.F., S.M., J.A., L.K., J.S.) and Department of Medicine and Epidemiology (K.K.), University of California, San Francisco, San Francisco, Calif; and Department of Diagnostic Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Fla (J.S.D., B.N.)
| | - Bonnie N Joe
- From the Department of Radiology, University of Chicago, 5481 S Maryland Ave, MC2026, Chicago, IL 60637 (K.D., M.L.G.); Department of Radiology and Biomedical Imaging (B.N.J., H.G., B.F., S.M., J.A., L.K., J.S.) and Department of Medicine and Epidemiology (K.K.), University of California, San Francisco, San Francisco, Calif; and Department of Diagnostic Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Fla (J.S.D., B.N.)
| | - Karla Kerlikowske
- From the Department of Radiology, University of Chicago, 5481 S Maryland Ave, MC2026, Chicago, IL 60637 (K.D., M.L.G.); Department of Radiology and Biomedical Imaging (B.N.J., H.G., B.F., S.M., J.A., L.K., J.S.) and Department of Medicine and Epidemiology (K.K.), University of California, San Francisco, San Francisco, Calif; and Department of Diagnostic Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Fla (J.S.D., B.N.)
| | - Heather Greenwood
- From the Department of Radiology, University of Chicago, 5481 S Maryland Ave, MC2026, Chicago, IL 60637 (K.D., M.L.G.); Department of Radiology and Biomedical Imaging (B.N.J., H.G., B.F., S.M., J.A., L.K., J.S.) and Department of Medicine and Epidemiology (K.K.), University of California, San Francisco, San Francisco, Calif; and Department of Diagnostic Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Fla (J.S.D., B.N.)
| | - Jennifer S Drukteinis
- From the Department of Radiology, University of Chicago, 5481 S Maryland Ave, MC2026, Chicago, IL 60637 (K.D., M.L.G.); Department of Radiology and Biomedical Imaging (B.N.J., H.G., B.F., S.M., J.A., L.K., J.S.) and Department of Medicine and Epidemiology (K.K.), University of California, San Francisco, San Francisco, Calif; and Department of Diagnostic Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Fla (J.S.D., B.N.)
| | - Bethany Niell
- From the Department of Radiology, University of Chicago, 5481 S Maryland Ave, MC2026, Chicago, IL 60637 (K.D., M.L.G.); Department of Radiology and Biomedical Imaging (B.N.J., H.G., B.F., S.M., J.A., L.K., J.S.) and Department of Medicine and Epidemiology (K.K.), University of California, San Francisco, San Francisco, Calif; and Department of Diagnostic Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Fla (J.S.D., B.N.)
| | - Bo Fan
- From the Department of Radiology, University of Chicago, 5481 S Maryland Ave, MC2026, Chicago, IL 60637 (K.D., M.L.G.); Department of Radiology and Biomedical Imaging (B.N.J., H.G., B.F., S.M., J.A., L.K., J.S.) and Department of Medicine and Epidemiology (K.K.), University of California, San Francisco, San Francisco, Calif; and Department of Diagnostic Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Fla (J.S.D., B.N.)
| | - Serghei Malkov
- From the Department of Radiology, University of Chicago, 5481 S Maryland Ave, MC2026, Chicago, IL 60637 (K.D., M.L.G.); Department of Radiology and Biomedical Imaging (B.N.J., H.G., B.F., S.M., J.A., L.K., J.S.) and Department of Medicine and Epidemiology (K.K.), University of California, San Francisco, San Francisco, Calif; and Department of Diagnostic Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Fla (J.S.D., B.N.)
| | - Jesus Avila
- From the Department of Radiology, University of Chicago, 5481 S Maryland Ave, MC2026, Chicago, IL 60637 (K.D., M.L.G.); Department of Radiology and Biomedical Imaging (B.N.J., H.G., B.F., S.M., J.A., L.K., J.S.) and Department of Medicine and Epidemiology (K.K.), University of California, San Francisco, San Francisco, Calif; and Department of Diagnostic Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Fla (J.S.D., B.N.)
| | - Leila Kazemi
- From the Department of Radiology, University of Chicago, 5481 S Maryland Ave, MC2026, Chicago, IL 60637 (K.D., M.L.G.); Department of Radiology and Biomedical Imaging (B.N.J., H.G., B.F., S.M., J.A., L.K., J.S.) and Department of Medicine and Epidemiology (K.K.), University of California, San Francisco, San Francisco, Calif; and Department of Diagnostic Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Fla (J.S.D., B.N.)
| | - John Shepherd
- From the Department of Radiology, University of Chicago, 5481 S Maryland Ave, MC2026, Chicago, IL 60637 (K.D., M.L.G.); Department of Radiology and Biomedical Imaging (B.N.J., H.G., B.F., S.M., J.A., L.K., J.S.) and Department of Medicine and Epidemiology (K.K.), University of California, San Francisco, San Francisco, Calif; and Department of Diagnostic Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Fla (J.S.D., B.N.)
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Ripping TM, Hubbard RA, Otten JDM, den Heeten GJ, Verbeek ALM, Broeders MJM. Towards personalized screening: Cumulative risk of breast cancer screening outcomes in women with and without a first-degree relative with a history of breast cancer. Int J Cancer 2015; 138:1619-25. [PMID: 26537645 DOI: 10.1002/ijc.29912] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2015] [Accepted: 10/29/2015] [Indexed: 12/29/2022]
Abstract
Several reviews have estimated the balance of benefits and harms of mammographic screening in the general population. The balance may, however, differ between individuals with and without family history. Therefore, our aim is to assess the cumulative risk of screening outcomes; screen-detected breast cancer, interval cancer, and false-positive results, in women screenees aged 50-75 and 40-75, with and without a first-degree relative with a history of breast cancer at the start of screening. Data on screening attendance, recall and breast cancer detection were collected for each woman living in Nijmegen (The Netherlands) since 1975. We used a discrete time survival model to calculate the cumulative probability of each major screening outcome over 19 screening rounds. Women with a family history of breast cancer had a higher risk of all screening outcomes. For women screened from age 50-75, the cumulative risk of screen-detected breast cancer, interval cancer and false-positive results were 9.0, 4.4 and 11.1% for women with a family history and 6.3, 2.7 and 7.3% for women without a family history, respectively. The results for women 40-75 followed the same pattern for women screened 50-75 for cancer outcomes, but were almost doubled for false-positive results. To conclude, women with a first-degree relative with a history of breast cancer are more likely to experience benefits and harms of screening than women without a family history. To complete the balance and provide risk-based screening recommendations, the breast cancer mortality reduction and overdiagnosis should be estimated for family history subgroups.
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Affiliation(s)
- Theodora Maria Ripping
- Radboud Institute for Health Sciences, Radboud University Medical Centre, Nijmegen, the Netherlands
| | - Rebecca A Hubbard
- Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Johannes D M Otten
- Radboud Institute for Health Sciences, Radboud University Medical Centre, Nijmegen, the Netherlands
| | - Gerard J den Heeten
- Dutch Reference Centre for Screening, Nijmegen, the Netherlands.,Department of Radiology, Academic Medical Centre, University of Amsterdam, Amsterdam, the Netherlands
| | - André L M Verbeek
- Radboud Institute for Health Sciences, Radboud University Medical Centre, Nijmegen, the Netherlands
| | - Mireille J M Broeders
- Radboud Institute for Health Sciences, Radboud University Medical Centre, Nijmegen, the Netherlands.,Dutch Reference Centre for Screening, Nijmegen, the Netherlands
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