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Zhu J, Min N, Zhang Y, Wu H, Hong C, Geng R, Wei Y, Guan Q, Zheng Y, Li X. Contralateral prophylactic mastectomy for unilateral breast cancer in Chinese female population: a retrospective cohort study. Gland Surg 2023; 12:1668-1685. [PMID: 38229836 PMCID: PMC10788567 DOI: 10.21037/gs-23-384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 11/09/2023] [Indexed: 01/18/2024]
Abstract
Background Due to differences in socioeconomic and cultural backgrounds, the characteristics and prognosis of Asian female patients choosing contralateral prophylactic mastectomy (CPM) are likely to be different from Western patients. To fill the research gap of CPM in Asian populations, this study aims to explore the application trend, survival benefits, decision-making factors, and satisfaction of CPM based on the Chinese patients undergoing CPM. Methods The 0-III stage unilateral breast cancer (UBC) patients who received breast surgery in the Chinese PLA General Hospital from 2005 to 2017 were selected. The surgical procedures included simple mastectomy (SM), nipple-sparing mastectomy (NSM), breast conserving surgery (BCS), and CPM. Cox proportional regression analyses and Kaplan-Meier (KM) curve were performed to compare the overall survival (OS) and disease-free survival (DFS) rates between CPM group and unilateral mastectomy (UM) group. Proportional propensity score matching (PSM) with a 1:1 ratio was used to match the two groups and secondary survival analysis was performed. Logistic regression models were used to test predictive factors related to patients' CPM surgical decision-making. Results Four thousand two hundred and seventy-six patients were included in the study, with 73 patients receiving CPM, 3,567 receiving SM, 151 receiving NSM, and 485 receiving BCS. CPM surgery was first used in 2007, with a peak application rate of 3.02% in 2016. Three thousand seven hundred and ninety-one patients were included in the survival analysis, with a median follow-up time of 66.60 months. Compared to UM patients, neither the KM survival curve nor Cox regression hazard analyses of CPM showed better OS (P=0.963; P=0.834). After PSM, CPM also did not exhibit significant survival benefits in OS (P=0.335) and DFS (P=0.409). The logistic regression analyses showed that NSM surgery and lower tumor-node-metastasis (TNM) stage were independent factors to promote the CPM decision-making of patients. The CPM group showed high overall satisfaction (84.9%) and relatively low appearance satisfaction (69.9%). Conclusions CPM was practiced for the first time since 2007 in our hospital. CPM does not provide any OS and DFS benefits compared to UM and the appearance satisfaction procedure was relatively low. Therefore, clinicians should fully communicate with patients before surgery and be more cautious in giving CPM recommendations.
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Affiliation(s)
- Jingjin Zhu
- School of Medicine, Nankai University, Tianjin, China
- Department of General Surgery, The First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Ningning Min
- School of Medicine, Nankai University, Tianjin, China
- Department of General Surgery, The First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Yanjun Zhang
- Department of General Surgery, The First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Huan Wu
- Medical Big Data Research Center, Chinese PLA General Hospital, Beijing, China
| | - Chenyan Hong
- School of Medicine, Nankai University, Tianjin, China
- Department of General Surgery, The First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Rui Geng
- Department of General Surgery, The First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Yufan Wei
- School of Medicine, Nankai University, Tianjin, China
- Department of General Surgery, The First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Qingyu Guan
- School of Medicine, Nankai University, Tianjin, China
- Department of General Surgery, The First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Yiqiong Zheng
- Department of General Surgery, The First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Xiru Li
- School of Medicine, Nankai University, Tianjin, China
- Department of General Surgery, The First Medical Center of Chinese PLA General Hospital, Beijing, China
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Schmidt MK, Kelly JE, Brédart A, Cameron DA, de Boniface J, Easton DF, Offersen BV, Poulakaki F, Rubio IT, Sardanelli F, Schmutzler R, Spanic T, Weigelt B, Rutgers EJT. EBCC-13 manifesto: Balancing pros and cons for contralateral prophylactic mastectomy. Eur J Cancer 2023; 181:79-91. [PMID: 36641897 PMCID: PMC10326619 DOI: 10.1016/j.ejca.2022.11.036] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 11/17/2022] [Accepted: 11/26/2022] [Indexed: 12/15/2022]
Abstract
After a diagnosis of unilateral breast cancer, increasing numbers of patients are requesting contralateral prophylactic mastectomy (CPM), the surgical removal of the healthy breast after diagnosis of unilateral breast cancer. It is important for the community of breast cancer specialists to provide meaningful guidance to women considering CPM. This manifesto discusses the issues and challenges of CPM and provides recommendations to improve oncological, surgical, physical and psychological outcomes for women presenting with unilateral breast cancer: (1) Communicate best available risks in manageable timeframes to prioritise actions; better risk stratification and implementation of risk-assessment tools combining family history, genetic and genomic information, and treatment and prognosis of the first breast cancer are required; (2) Reserve CPM for specific situations; in women not at high risk of contralateral breast cancer (CBC), ipsilateral breast-conserving surgery is the recommended option; (3) Encourage patients at low or intermediate risk of CBC to delay decisions on CPM until treatment for the primary cancer is complete, to focus on treating the existing disease first; (4) Provide patients with personalised information about the risk:benefit balance of CPM in manageable timeframes; (5) Ensure patients have an informed understanding of the competing risks for CBC and that there is a realistic plan for the patient; (6) Ensure patients understand the short- and long-term physical effects of CPM; (7) In patients considering CPM, offer psychological and surgical counselling before surgery; anxiety alone is not an indication for CPM; (8) Eliminate inequality between countries in reimbursement strategies; CPM should be reimbursed if it is considered a reasonable option resulting from multidisciplinary tumour board assessment; (9) Treat breast cancer patients at specialist breast units providing the entire patient-centred pathway.
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Affiliation(s)
- Marjanka K Schmidt
- Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands; Department of Clinical Genetics, Leiden University Medical Center, Leiden, The Netherlands.
| | | | - Anne Brédart
- Institut Curie, Paris, France; Psychology Institute, Psychopathology and Health Process Laboratory UR4057, Paris City University, Paris, France
| | - David A Cameron
- Edinburgh University Cancer Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Jana de Boniface
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden; Department of Surgery, Breast Unit, Capio St. Göran's Hospital, Stockholm, Sweden
| | - Douglas F Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK; Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Birgitte V Offersen
- Department of Experimental Clinical Oncology, Aarhus University Hospital - Aarhus University, Aarhus N, Denmark
| | - Fiorita Poulakaki
- Breast Surgery Department, Athens Medical Center, Athens, Greece; Europa Donna - The European Breast Cancer Coalition, Milan, Italy
| | - Isabel T Rubio
- Breast Surgical Oncology, Clinica Universidad de Navarra, Madrid, Spain
| | - Francesco Sardanelli
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Milan, Italy; Unit of Radiology, IRCCS Policlinico San Donato, San Donato Milanese, Milan, Italy
| | - Rita Schmutzler
- Center for Hereditary Breast and Ovarian Cancer, Center for Integrated Oncology (CIO), University Hospital Cologne, Cologne, Germany
| | - Tanja Spanic
- Europa Donna - The European Breast Cancer Coalition, Milan, Italy; Europa Donna Slovenia, Ljubljana, Slovenia
| | - Britta Weigelt
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Emiel J T Rutgers
- Department of Surgery, The Netherlands Cancer Institute, Amsterdam, The Netherlands
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3
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Sun J, Chu F, Pan J, Zhang Y, Yao L, Chen J, Hu L, Zhang J, Xu Y, Wang X, Cao W, Xie Y. BRCA-CRisk: A Contralateral Breast Cancer Risk Prediction Model for BRCA Carriers. J Clin Oncol 2023; 41:991-999. [PMID: 36480783 DOI: 10.1200/jco.22.00833] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
PURPOSE The absolute cumulative risk of contralateral breast cancer (CBC) for patients with BRCA1/2 variants is unknown. The purpose of this study was to develop a CBC risk prediction model for assessing CBC risk for BRCA1/2 carriers. METHODS The primary cohort of 491 patients with BRCA1/2 variants was derived from a large series of unselected patients with breast cancer. A nomogram was established on the basis of the results of a multivariate Cox regression analysis from this cohort. This model, named BRCA-CRisk, was further validated by an independent cohort of 205 patients with BRCA1/2 variants. Discrimination and calibration of the model were assessed. RESULTS In the primary cohort of 491 patients, 66 developed contralateral breast cancer after a median follow-up of 7.0 years. Four variables were significantly associated with risk of CBC and were incorporated in the establishment of the BRCA-CRisk prediction model: younger age at first breast cancer (with continuous variable, P = .002), positive first-degree family history of breast and/or ovarian cancer (hazard ratio [HR], 1.89; 95% CI, 1.16 to 3.08; P = .011), variant located near the 3' region of BRCA (HR, 2.01; 95% CI, 1.23 to 3.30; P = .006), and endocrine therapy (HR, 0.54; 95% CI, 0.33 to 0.88; P = .013). The area under the time-dependent curves for the 5- and 10-year cumulative risks of CBC were 0.775 and 0.702, respectively. The model was well validated in the independent cohort of 205 BRCA1/2 carriers, with area under the curves of 0.750 and 0.691 for 5 and 10 years, respectively. CONCLUSION BRCA-CRisk model provides a useful tool for assessing the absolute cumulative risk of CBC for BRCA1/2 carriers and may help carriers and clinicians optimally select risk-reducing strategies on the basis of individual CBC risk.
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Affiliation(s)
- Jie Sun
- Familial & Hereditary Cancer Center, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Beijing, P. R. China
| | - Futao Chu
- Department of Breast Surgery, Peking University International Hospital, Beijing, P. R. China
| | - Jiani Pan
- Department of Breast Medical Oncology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, P. R. China.,Zhejiang Chinese Medical University, Hangzhou, P. R. China
| | - Yaxin Zhang
- Familial & Hereditary Cancer Center, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Beijing, P. R. China
| | - Lu Yao
- Familial & Hereditary Cancer Center, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Beijing, P. R. China
| | - Jiuan Chen
- Familial & Hereditary Cancer Center, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Beijing, P. R. China
| | - Li Hu
- Familial & Hereditary Cancer Center, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Beijing, P. R. China
| | - Juan Zhang
- Familial & Hereditary Cancer Center, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Beijing, P. R. China
| | - Ye Xu
- Familial & Hereditary Cancer Center, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Beijing, P. R. China
| | - Xiaojia Wang
- Department of Breast Medical Oncology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, P. R. China
| | - Wenming Cao
- Department of Breast Medical Oncology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, P. R. China
| | - Yuntao Xie
- Familial & Hereditary Cancer Center, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Beijing, P. R. China.,Department of Breast Surgery, Peking University International Hospital, Beijing, P. R. China
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4
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Coopey SB. Contralateral Prophylactic Mastectomy in Average Risk Women: Who Can Choose This Wisely? Ann Surg Oncol 2023; 30:4-5. [PMID: 36264517 DOI: 10.1245/s10434-022-12702-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 10/10/2022] [Indexed: 12/13/2022]
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5
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Lawson MB, Herschorn SD, Sprague BL, Buist DSM, Lee SJ, Newell MS, Lourenco AP, Lee JM. Imaging Surveillance Options for Individuals With a Personal History of Breast Cancer: AJR Expert Panel Narrative Review. AJR Am J Roentgenol 2022; 219:854-868. [PMID: 35544374 PMCID: PMC9691521 DOI: 10.2214/ajr.22.27635] [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: 12/14/2022]
Abstract
Annual surveillance mammography is recommended for breast cancer survivors on the basis of observational studies and meta-analyses showing reduced breast cancer mortality and improved quality of life. However, breast cancer survivors are at higher risk of subsequent breast cancer and have a fourfold increased risk of interval breast cancers compared with individuals without a personal history of breast cancer. Supplemental surveillance modalities offer increased cancer detection compared with mammography alone, but utilization is variable, and benefits must be balanced with possible harms of false-positive findings. In this review, we describe the current state of mammographic surveillance, summarize evidence for supplemental surveillance in breast cancer survivors, and explore a risk-based approach to selecting surveillance imaging strategies. Further research identifying predictors associated with increased risk of interval second breast cancers and development of validated risk prediction tools may help physicians and patients weigh the benefits and harms of surveillance breast imaging and decide on a personalized approach to surveillance for improved breast cancer outcomes.
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Affiliation(s)
- Marissa B Lawson
- Department of Radiology, University of Washington School of Medicine, Seattle Cancer Care Alliance, 825 Eastlake Ave E, LG-200, Seattle, WA 98040
| | - Sally D Herschorn
- Department of Radiology, University of Vermont Larner College of Medicine, University of Vermont Cancer Center, Burlington, VT
| | - Brian L Sprague
- Department of Surgery, University of Vermont Larner College of Medicine, Burlington, VT
| | - Diana S M Buist
- Kaiser Permanente Washington Health Research Institute, Seattle, WA
| | - Su-Ju Lee
- Department of Radiology, University of Cincinnati Medical Center, Cincinnati, OH
| | - Mary S Newell
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA
| | - Ana P Lourenco
- Department of Diagnostic Imaging, Alpert Medical School of Brown University, Providence, RI
| | - Janie M Lee
- Department of Radiology, University of Washington School of Medicine, Seattle Cancer Care Alliance, 825 Eastlake Ave E, LG-200, Seattle, WA 98040
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6
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Gail MH, Jatoi I. Tools for Contralateral Prophylactic Mastectomy Decision Making. J Clin Oncol 2022; 40:3653-3659. [PMID: 35759730 PMCID: PMC9622574 DOI: 10.1200/jco.21.02782] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 03/25/2022] [Accepted: 05/24/2022] [Indexed: 01/08/2023] Open
Abstract
PURPOSE Women with unilateral breast cancer are increasingly opting for the removal of not only the involved breast, but also for the removal of the opposite uninvolved breast (contralateral prophylactic mastectomy [CPM]), although the risk of contralateral breast cancer (CBC) has decreased in recent years. Models to predict the absolute risk of CBC can help a woman decide whether to undergo CPM. Our objective is to illustrate that a better decision can be made if the patient and doctor also have estimates of the absolute risks of regional and distant recurrences and mortality from non-breast cancer causes. MATERIALS AND METHODS We based our analyses on two published models for CBC and published information on the hazards of regional and distant recurrences and non-breast cancer mortality. Assuming that CPM eliminates CBC but has no effect on other events, we calculated how much CPM reduces a woman's CBC risk and total risk from all these events for 10 hypothetical women with various subtypes of breast cancer and risk factors. RESULTS The risk of CBC and total risk vary greatly, depending on the breast cancer subtype. In some cases, a decision for or against CPM can be based on CBC risk alone, but in others, additional consideration of total risk may cause a woman to decline CPM. CONCLUSION There is a potential to develop more informative tools for deciding on CPM. Realizing this potential will require more and better data to validate existing models of absolute CBC risk and to characterize the hazards of regional and distant recurrences and deaths from non-breast cancer causes for women with various subtypes of breast cancers and risk factors.
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Affiliation(s)
- Mitchell H. Gail
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD
| | - Ismail Jatoi
- Division of Surgical Oncology and Endocrine Surgery, University of Texas Health, San Antonio, TX
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7
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Giardiello D, Hooning MJ, Hauptmann M, Keeman R, Heemskerk-Gerritsen BAM, Becher H, Blomqvist C, Bojesen SE, Bolla MK, Camp NJ, Czene K, Devilee P, Eccles DM, Fasching PA, Figueroa JD, Flyger H, García-Closas M, Haiman CA, Hamann U, Hopper JL, Jakubowska A, Leeuwen FE, Lindblom A, Lubiński J, Margolin S, Martinez ME, Nevanlinna H, Nevelsteen I, Pelders S, Pharoah PDP, Siesling S, Southey MC, van der Hout AH, van Hest LP, Chang-Claude J, Hall P, Easton DF, Steyerberg EW, Schmidt MK. PredictCBC-2.0: a contralateral breast cancer risk prediction model developed and validated in ~ 200,000 patients. BREAST CANCER RESEARCH : BCR 2022; 24:69. [PMID: 36271417 PMCID: PMC9585761 DOI: 10.1186/s13058-022-01567-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 10/07/2022] [Indexed: 11/10/2022]
Abstract
BACKGROUND Prediction of contralateral breast cancer (CBC) risk is challenging due to moderate performances of the known risk factors. We aimed to improve our previous risk prediction model (PredictCBC) by updated follow-up and including additional risk factors. METHODS We included data from 207,510 invasive breast cancer patients participating in 23 studies. In total, 8225 CBC events occurred over a median follow-up of 10.2 years. In addition to the previously included risk factors, PredictCBC-2.0 included CHEK2 c.1100delC, a 313 variant polygenic risk score (PRS-313), body mass index (BMI), and parity. Fine and Gray regression was used to fit the model. Calibration and a time-dependent area under the curve (AUC) at 5 and 10 years were assessed to determine the performance of the models. Decision curve analysis was performed to evaluate the net benefit of PredictCBC-2.0 and previous PredictCBC models. RESULTS The discrimination of PredictCBC-2.0 at 10 years was higher than PredictCBC with an AUC of 0.65 (95% prediction intervals (PI) 0.56-0.74) versus 0.63 (95%PI 0.54-0.71). PredictCBC-2.0 was well calibrated with an observed/expected ratio at 10 years of 0.92 (95%PI 0.34-2.54). Decision curve analysis for contralateral preventive mastectomy (CPM) showed the potential clinical utility of PredictCBC-2.0 between thresholds of 4 and 12% 10-year CBC risk for BRCA1/2 mutation carriers and non-carriers. CONCLUSIONS Additional genetic information beyond BRCA1/2 germline mutations improved CBC risk prediction and might help tailor clinical decision-making toward CPM or alternative preventive strategies. Identifying patients who benefit from CPM, especially in the general breast cancer population, remains challenging.
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Affiliation(s)
- Daniele Giardiello
- Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.,Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands.,Institute of Biomedicine, EURAC Research Affiliated Institute of the University of Lübeck, Bolzano, Italy
| | - Maartje J Hooning
- Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Michael Hauptmann
- Brandenburg Medical School, Institute of Biostatistics and Registry Research, Neuruppin, Germany
| | - Renske Keeman
- Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | | | - Heiko Becher
- Institute of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Carl Blomqvist
- Department of Oncology, Helsinki University Hospital, University of Helsinki, Helsinki, Finland.,Department of Oncology, Örebro University Hospital, Örebro, Sweden
| | - Stig E Bojesen
- Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark.,Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark.,Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Manjeet K Bolla
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
| | - Nicola J Camp
- Department of Internal Medicine and Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Peter Devilee
- Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands.,Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Diana M Eccles
- Faculty of Medicine, University of Southampton, Southampton, UK
| | - Peter A Fasching
- Division of Hematology and Oncology, Department of Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, USA.,Department of Gynecology and Obstetrics, Comprehensive Cancer Center Erlangen-EMN, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg (FAU), Erlangen, Germany
| | - Jonine D Figueroa
- Usher Institute of Population Health Sciences and Informatics, The University of Edinburgh, Edinburgh, UK.,Cancer Research UK Edinburgh Centre, The University of Edinburgh, Edinburgh, UK.,Division of Cancer Epidemiology and Genetics, Department of Health and Human Services, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Henrik Flyger
- Department of Breast Surgery, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
| | - Montserrat García-Closas
- Division of Cancer Epidemiology and Genetics, Department of Health and Human Services, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Christopher A Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Ute Hamann
- Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - John L Hopper
- Melbourne School of Population and Global Health, Centre for Epidemiology and Biostatistics, The University of Melbourne, Melbourne, VIC, Australia
| | - Anna Jakubowska
- Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland.,Independent Laboratory of Molecular Biology and Genetic Diagnostics, Pomeranian Medical University, Szczecin, Poland
| | - Floor E Leeuwen
- Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Annika Lindblom
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.,Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden
| | - Jan Lubiński
- Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
| | - Sara Margolin
- Department of Oncology, Södersjukhuset, Stockholm, Sweden.,Department of Clinical Science and Education, Karolinska Institutet, Södersjukhuset, Stockholm, Sweden
| | - Maria Elena Martinez
- Moores Cancer Center, University of California San Diego, La Jolla, CA, USA.,Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA
| | - Heli Nevanlinna
- Department of Obstetrics and Gynecology, Helsinki University Hospital, University of Helsinki, Helsinki, Finland
| | - Ines Nevelsteen
- Department of Oncology, Leuven Multidisciplinary Breast Center, Leuven Cancer Institute, University Hospitals Leuven, Louven, Belgium
| | - Saskia Pelders
- Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Paul D P Pharoah
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK.,Department of Oncology, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
| | - Sabine Siesling
- Department of Research and Development, Netherlands Comprehensive Cancer Organisation (IKNL), Utrecht, The Netherlands.,Department of HealthTechnology and Services Research, Technical Medical Centre, University of Twente, Enschede, The Netherlands
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia.,Department of Clinical Pathology, The University of Melbourne, Melbourne, VIC, Australia.,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
| | - Annemieke H van der Hout
- Department of Genetics, University Medical Center Groningen, University Groningen, Groningen, The Netherlands
| | - Liselotte P van Hest
- Clinical Genetics, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.,Cancer Epidemiology Group, University Cancer Center Hamburg (UCCH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.,Department of Oncology, Södersjukhuset, Stockholm, Sweden
| | - Douglas F Easton
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK.,Department of Oncology, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
| | - Ewout W Steyerberg
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands.,Department of Public Health, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Marjanka K Schmidt
- Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands. .,Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Amsterdam, The Netherlands.
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8
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A Bayesian learning model to predict the risk for cannabis use disorder. Drug Alcohol Depend 2022; 236:109476. [PMID: 35588608 DOI: 10.1016/j.drugalcdep.2022.109476] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 04/19/2022] [Accepted: 04/23/2022] [Indexed: 01/31/2023]
Abstract
BACKGROUND The prevalence of cannabis use disorder (CUD) has been increasing recently and is expected to increase further due to the rising trend of cannabis legalization. To help stem this public health concern, a model is needed that predicts for an adolescent or young adult cannabis user their personalized risk of developing CUD in adulthood. However, there exists no such model that is built using nationally representative longitudinal data. METHODS We use a novel Bayesian learning approach and data from Add Health (n = 8712), a nationally representative longitudinal study, to build logistic regression models using four different regularization priors: lasso, ridge, horseshoe, and t. The models are compared by their prediction performance on unseen data via 5-fold-cross-validation (CV). We assess model discrimination using the area under the curve (AUC) and calibration by comparing the expected (E) and observed (O) number of CUD cases. We also externally validate the final model on independent test data from Add Health (n = 570). RESULTS Our final model is based on lasso prior and has seven predictors: biological sex; scores on personality traits of neuroticism, openness, and conscientiousness; and measures of adverse childhood experiences, delinquency, and peer cannabis use. It has good discrimination and calibration performance as reflected by its respective AUC and E/O of 0.69 and 0.95 based on 5-fold CV and 0.71 and 1.10 on validation data. CONCLUSION This externally validated model may help in identifying adolescent or young adult cannabis users at high risk of developing CUD in adulthood.
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CBCRisk-Black: a personalized contralateral breast cancer risk prediction model for black women. Breast Cancer Res Treat 2022; 194:179-186. [PMID: 35562619 DOI: 10.1007/s10549-022-06612-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2022] [Accepted: 04/18/2022] [Indexed: 11/02/2022]
Abstract
PURPOSE Black breast cancer (BC) survivors have a higher risk of developing contralateral breast cancer (CBC) than Whites. Existing CBC risk prediction tools are developed based on mostly White women. To address this racial disparity, it is crucial to develop tools tailored for Black women to help them inform about their actual risk of CBC. METHODS We propose an absolute risk prediction model, CBCRisk-Black, specifically for Black BC patients. It uses data on Black women from two sources: Breast Cancer Surveillance Consortium (BCSC) and Surveillance, Epidemiology, and End Results (SEER). First, a matched lasso logistic regression model for estimating relative risks (RR) is developed. Then, it is combined with relevant hazard rates and attributable risks to obtain absolute risks. Six-fold cross-validation is used to internally validate CBCRisk-Black. We also compare CBCRisk-Black with CBCRisk, an existing CBC risk prediction model. RESULTS The RR model uses data from BCSC on 744 Black women (186 cases). CBCRisk-Black has four risk factors (RR compared to baseline): breast density (2.13 for heterogeneous/extremely dense), family history of BC (2.28 for yes), first BC tumor size (2.14 for T3/T4, 1.56 for TIS), and age at first diagnosis of BC (1.41 for < 40). The area under the receiver operating characteristic curve (AUC) for 3- and 5-year predictions are 0.72 and 0.65 for CBCRisk-Black while those are 0.65 and 0.60 for CBCRisk. CONCLUSION CBCRisk-Black may serve as a useful tool to clinicians in counseling Black BC patients by providing a more accurate and personalized CBC risk estimate.
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10
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Domingo Bretón M, Allué Cabañuz M, Castán Villanueva N, Arribas Del Amo MD, Gil Romea I, Güemes Sánchez A. CBCRisk model to determine the risk of contralateral breast cancer in sporadic breast cancer. Cir Esp 2021; 99:724-729. [PMID: 34764058 DOI: 10.1016/j.cireng.2021.10.008] [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: 09/29/2020] [Accepted: 11/16/2020] [Indexed: 11/19/2022]
Abstract
INTRODUCTION The great majority of breast cancer (BC) cases are diagnosed in women who have no known family history of the disease and are not carriers of any risk mutation. During the past few decades an increase in the number of contralateral prophylactic mastectomy (CPM) has been produced in these patients. The CBCRisk model calculates the absolute risk of suffering from contralateral breast cancer (CBC); thus, it can be used to counselling patients with sporadic breast cancer. METHOD An observational, retrospective study including sporadic breast cancer patients treated with contralateral prophylactic mastectomy has been conducted between 2017 and 2019. A descriptive and comparative study with one variation of logistic regression has been carried out in order to identify predictive factors of occult tumors (OT) and medium/high risk damage (MHRD). Evaluation of the CBCRisk model published in 2017 and different limit values for the CPM recommendation. RESULTS 42 patients were selected. Incidence of MHRD and OT was lower than that described in the literatura (9.52%MHRD, 2.38%OT). None of the evaluated variables reached statistical significance for predicting injuries. The average value of CBCRisk 5 years ahead found in patients with pathological findings was 2.08 (DE 0.97), higher than the average value of the whole group (1.87 ± 0.91) and the subgroup without pathological findings (1.84 ± 0.91). Only values >3 for CBCRisk were considered statistically significant (P = .04) for the prediction of histological lesions. CONCLUSION Patients with sporadic breast cancer should be adequately informed about the estimated risks and benefits of undergoing a contralateral prophylactic mastectomy. The CBCRisk may be useful for the counseling of these patients, but it requires validation in larger and prospective cohorts.
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Affiliation(s)
- María Domingo Bretón
- Servicio de Cirugía General, Hospital Clínico Universitario Lozano Blesa, Zaragoza, Spain.
| | - Marta Allué Cabañuz
- Servicio de Cirugía General, Hospital Clínico Universitario Lozano Blesa, Zaragoza, Spain
| | | | | | - Ismael Gil Romea
- Servicio de Cirugía General, Unidad de Mama, Hospital Clínico Universitario Lozano Blesa, Zaragoza, Spain
| | - Antonio Güemes Sánchez
- Servicio de Cirugía General, Unidad de Mama, Hospital Clínico Universitario Lozano Blesa, Zaragoza, Spain
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11
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Giannakeas V, Lim DW, Narod SA. The risk of contralateral breast cancer: a SEER-based analysis. Br J Cancer 2021; 125:601-610. [PMID: 34040177 PMCID: PMC8368197 DOI: 10.1038/s41416-021-01417-7] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 03/26/2021] [Accepted: 04/22/2021] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND We sought to estimate the annual risk and 25-year cumulative risk of contralateral breast cancer among women with stage 0-III unilateral breast cancer. METHODS We identified 812,851 women with unilateral breast cancer diagnosed between 1990 and 2015 in the SEER database and followed them for contralateral breast cancer for up to 25 years. Women with a known bilateral mastectomy were excluded. We calculated the annual risk of contralateral breast cancer by age at diagnosis, by time since diagnosis and by current age. We compared risks by ductal carcinoma in situ (DCIS) versus invasive disease, by race and by oestrogen receptor (ER) status of the first cancer. RESULTS There were 25,958 cases of contralateral invasive breast cancer diagnosed (3.2% of all patients). The annual risk of contralateral breast cancer over the 25-year follow-up period was 0.37% and the 25-year actuarial risk of contralateral invasive breast cancer was 9.9%. The annual risk varied to a small degree by age of diagnosis, by time elapsed since diagnosis and by current age. The 25-year actuarial risk was similar for DCIS and invasive breast cancer patients (10.1 versus 9.9%). The 25-year actuarial risk was higher for black women (12.7%) than for white women (9.7%) and was lower for women with ER-positive breast cancer (9.5%) than for women with ER-negative breast cancer (11.2%). CONCLUSIONS Women with unilateral breast cancer experience an annual risk of contralateral breast cancer ~0.4% per year, which persists over the 25-year follow-up period.
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MESH Headings
- Adult
- Age Factors
- Aged
- Aged, 80 and over
- Breast Neoplasms/epidemiology
- Breast Neoplasms/metabolism
- Breast Neoplasms/pathology
- Carcinoma, Ductal, Breast/epidemiology
- Carcinoma, Ductal, Breast/metabolism
- Carcinoma, Ductal, Breast/pathology
- Female
- Humans
- Middle Aged
- Neoplasm Staging
- Neoplasms, Second Primary/epidemiology
- Neoplasms, Second Primary/metabolism
- Neoplasms, Second Primary/pathology
- Receptors, Estrogen/metabolism
- Risk Factors
- SEER Program
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Affiliation(s)
- Vasily Giannakeas
- Women's College Research Institute, Women's College Hospital, Toronto, ON, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- ICES, Toronto, ON, Canada
| | - David W Lim
- Women's College Research Institute, Women's College Hospital, Toronto, ON, Canada
| | - Steven A Narod
- Women's College Research Institute, Women's College Hospital, Toronto, ON, Canada.
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada.
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12
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Lim DW, Metcalfe KA, Narod SA. Bilateral Mastectomy in Women With Unilateral Breast Cancer: A Review. JAMA Surg 2021; 156:569-576. [PMID: 33566074 DOI: 10.1001/jamasurg.2020.6664] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Importance Rates of bilateral mastectomy continue to increase in average-risk women with unilateral in situ and invasive breast cancer. Contralateral prophylactic mastectomy rates increased from 5% to 12% of all operations for breast cancer in the US from 2004 to 2012. Among women having mastectomy, rates of contralateral prophylactic mastectomy have increased from less than 2% in 1998 to 30% in 2012. Observations The increased use of breast magnetic resonance imaging and genetic testing has marginally increased the number of candidates for bilateral mastectomy. Most bilateral mastectomies are performed on women who are at no special risk for contralateral cancer. The true risk of contralateral breast cancer is not associated with the decision for contralateral prophylactic mastectomy; rather, the clinical factors associated with the probability of distant recurrence are associated with bilateral mastectomy. Several changes in society and health care delivery appear to act concurrently and synergistically. First, the anxiety engendered by a fear of cancer recurrence is focused on the contralateral cancer because this is most easily conceptualized and provides a ready target that can be acted upon. Second, the modern woman with breast cancer is supported by the surgeon and the social community of breast cancer survivors. Surgeons want to respect patient autonomy, despite guidelines discouraging bilateral mastectomy, and most women have their expenses covered by a third-party payer. Satisfaction with the results is high, but the association with improved psychosocial well-being remains to be fully understood. Conclusions and Relevance Reducing the use of medically unnecessary contralateral prophylactic mastectomy in women with nonhereditary, unilateral breast cancer requires a social change that addresses patient-, physician-, cultural-, and systems-level enabling factors. Such a transformation begins with educating clinicians and patients. The concerns of women who want preventive contralateral mastectomy must be explored, and women need to be informed of the anticipated benefits (or lack thereof) and risks. Areas requiring further study are considered.
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Affiliation(s)
- David W Lim
- Women's College Research Institute, Women's College Hospital, Toronto, Ontario, Canada
| | - Kelly A Metcalfe
- Women's College Research Institute, Women's College Hospital, Toronto, Ontario, Canada.,Lawrence S. Bloomberg Faculty of Nursing, University of Toronto, Toronto, Ontario, Canada
| | - Steven A Narod
- Women's College Research Institute, Women's College Hospital, Toronto, Ontario, Canada.,Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.,Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
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13
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Scheepens JCC, Veer LV', Esserman L, Belkora J, Mukhtar RA. Contralateral prophylactic mastectomy: A narrative review of the evidence and acceptability. Breast 2021; 56:61-69. [PMID: 33621798 PMCID: PMC7907889 DOI: 10.1016/j.breast.2021.02.003] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 01/29/2021] [Accepted: 02/06/2021] [Indexed: 12/26/2022] Open
Abstract
The uptake of contralateral prophylactic mastectomy (CPM) has increased steadily over the last twenty years in women of all age groups and breast cancer stages. Since contralateral breast cancer is relatively rare and the breast cancer guidelines only recommend CPM in a small subset of patients with breast cancer, the drivers of this trend are unknown. This review aims to evaluate the evidence for and acceptability of CPM, data on patient rationales for choosing CPM, and some of the factors that might impact patient preferences. Based on the evidence, future recommendations will be provided. First, data on contralateral breast cancer risk and CPM rates and trends are addressed. After that, the evidence is structured around four main patient rationales for CPM formulated as questions that patients might ask their surgeon: Will CPM reduce mortality risk? Will CPM reduce the risk of contralateral breast cancer? Can I avoid future screening with CPM? Will I have better breast symmetry after CPM? Also, three different guidelines regarding CPM will be reviewed. Studies indicate a large gap between patient preferences for radical risk reduction with CPM and the current approaches recommended by important guidelines. We suggest a strategy including shared decision-making to enhance surgeons’ communication with patients about contralateral breast cancer and treatment options, to empower patients in order to optimize the use of CPM incorporating accurate risk assessment and individual patient preferences. Contralateral prophylactic mastectomy rates have increased over the last 20 years. Patients may want CPM to reduce risk of contralateral breast cancer and mortality. Patients do not always have the tools available to make a well-informed decision. Patient and surgeon’s shared decision-making could optimize the use of CPM.
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Affiliation(s)
- Josien C C Scheepens
- University of California, San Francisco, Department of Laboratory Medicine, 2340 Sutter St., Box 0808, San Francisco, CA, 94115, USA
| | - Laura van 't Veer
- University of California, San Francisco, Department of Laboratory Medicine, 2340 Sutter St., Box 0808, San Francisco, CA, 94115, USA
| | - Laura Esserman
- University of California, San Francisco, Department of Surgery, 1825 4th Street, 3rd Floor, Box 1710, San Francisco, CA, 94143-1710, USA
| | - Jeff Belkora
- University of California, San Francisco, Institute for Health Policy Studies and Department of Surgery, 3333 California Street, Suite 265, San Francisco, CA, 94118, USA
| | - Rita A Mukhtar
- University of California, San Francisco, Department of Surgery, 1825 4th Street, 3rd Floor, Box 1710, San Francisco, CA, 94143-1710, USA.
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14
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Domingo Bretón M, Allué Cabañuz M, Castán Villanueva N, Arribas Del Amo MD, Gil Romea I, Güemes Sánchez A. CBCRisk model to determine the risk of contralateral breast cancer in sporadic breast cancer. Cir Esp 2020; 99:S0009-739X(20)30381-X. [PMID: 33358405 DOI: 10.1016/j.ciresp.2020.11.007] [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: 09/29/2020] [Revised: 11/15/2020] [Accepted: 11/16/2020] [Indexed: 11/29/2022]
Abstract
INTRODUCTION The great majority of breast cancer (BC) cases are diagnosed in women who have no known family history of the disease and are not carriers of any risk mutation. During the past few decades an increase in the number of contralateral prophylactic mastectomy (CPM) has been produced in these patients. The CBCRisk model calculates the absolute risk of suffering from contralateral breast cancer (CBC); thus, it can be used to counselling patients with sporadic breast cancer. METHOD An observational, retrospective study including sporadic breast cancer patients treated with contralateral prophylactic mastectomy has been conducted between 2017 and 2019. A descriptive and comparative study with one variation of logistic regression has been carried out in order to identify predictive factors of occult tumors (OT) and medium/high risk damage (MHRD). Evaluation of the CBCRisk model published in 2017 and different limit values for the CPM recommendation. RESULTS 42 patients were selected. Incidence of MHRD and OT was lower than that described in the literatura (9.52% MHRD, 2.38% OT). None of the evaluated variables reached statistical significance for predicting injuries. The average value of CBCRisk 5 years ahead found in patients with pathological findings was 2.08 (SD 0.97), higher than the average value of the whole group (1.87 ± 0.91) and the subgroup without pathological findings (1.84 ± 0.91). Only values ≥ 3 for CBCRisk were considered statistically significant (p = 0.04) for the prediction of histological lesions. CONCLUSION Patients with sporadic breast cancer should be adequately informed about the estimated risks and benefits of undergoing a contralateral prophylactic mastectomy. The CBCRisk may be useful for the counseling of these patients, but it requires validation in larger and prospective cohorts.
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Affiliation(s)
- María Domingo Bretón
- Servicio de Cirugía General, Hospital Clínico Universitario Lozano Blesa, Zaragoza, España.
| | - Marta Allué Cabañuz
- Servicio de Cirugía General, Hospital Clínico Universitario Lozano Blesa, Zaragoza, España
| | | | | | - Ismael Gil Romea
- Servicio de Cirugía General, Unidad de Mama, Hospital Clínico Universitario Lozano Blesa, Zaragoza, España
| | - Antonio Güemes Sánchez
- Servicio de Cirugía General, Unidad de Mama, Hospital Clínico Universitario Lozano Blesa, Zaragoza, España
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15
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Giardiello D, Kramer I, Hooning MJ, Hauptmann M, Lips EH, Sawyer E, Thompson AM, de Munck L, Siesling S, Wesseling J, Steyerberg EW, Schmidt MK. Contralateral breast cancer risk in patients with ductal carcinoma in situ and invasive breast cancer. NPJ Breast Cancer 2020; 6:60. [PMID: 33298933 PMCID: PMC7609533 DOI: 10.1038/s41523-020-00202-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 10/01/2020] [Indexed: 12/11/2022] Open
Abstract
We aimed to assess contralateral breast cancer (CBC) risk in patients with ductal carcinoma in situ (DCIS) compared with invasive breast cancer (BC). Women diagnosed with DCIS (N = 28,003) or stage I-III BC (N = 275,836) between 1989 and 2017 were identified from the nationwide Netherlands Cancer Registry. Cumulative incidences were estimated, accounting for competing risks, and hazard ratios (HRs) for metachronous invasive CBC. To evaluate effects of adjuvant systemic therapy and screening, separate analyses were performed for stage I BC without adjuvant systemic therapy and by mode of first BC detection. Multivariable models including clinico-pathological and treatment data were created to assess CBC risk prediction performance in DCIS patients. The 10-year cumulative incidence of invasive CBC was 4.8% for DCIS patients (CBC = 1334). Invasive CBC risk was higher in DCIS patients compared with invasive BC overall (HR = 1.10, 95% confidence interval (CI) = 1.04-1.17), and lower compared with stage I BC without adjuvant systemic therapy (HR = 0.87; 95% CI = 0.82-0.92). In patients diagnosed ≥2011, the HR for invasive CBC was 1.38 (95% CI = 1.35-1.68) after screen-detected DCIS compared with screen-detected invasive BC, and was 2.14 (95% CI = 1.46-3.13) when not screen-detected. The C-index was 0.52 (95% CI = 0.50-0.54) for invasive CBC prediction in DCIS patients. In conclusion, CBC risks are low overall. DCIS patients had a slightly higher risk of invasive CBC compared with invasive BC, likely explained by the risk-reducing effect of (neo)adjuvant systemic therapy among BC patients. For support of clinical decision making more information is needed to differentiate CBC risks among DCIS patients.
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Affiliation(s)
- Daniele Giardiello
- Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
| | - Iris Kramer
- Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Maartje J Hooning
- Department of Medical Oncology-Cancer Epidemiology, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Michael Hauptmann
- Institute of Biostatistics and Registry Research, Brandenburg Medical School, Neuruppin, Germany
- Department of Epidemiology and Biostatistics, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Esther H Lips
- Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Elinor Sawyer
- School of Cancer & Pharmaceutical Sciences, Kings College London, London, UK
| | - Alastair M Thompson
- Department of Surgery, Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, USA
| | - Linda de Munck
- Department of Research and Development, Netherlands Comprehensive Cancer Organisation, Utrecht, the Netherlands
| | - Sabine Siesling
- Department of Research and Development, Netherlands Comprehensive Cancer Organisation, Utrecht, the Netherlands
- Department of Health Technology and Services Research, Technical Medical Centre, University of Twente, Enschede, the Netherlands
| | - Jelle Wesseling
- Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
- Department of Pathology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Ewout W Steyerberg
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
- Department of Public Health, Erasmus MC, Rotterdam, the Netherlands
| | - Marjanka K Schmidt
- Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, the Netherlands.
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16
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Rajapaksha RMDS, Hammonds R, Filbey F, Choudhary PK, Biswas S. A preliminary risk prediction model for cannabis use disorder. Prev Med Rep 2020; 20:101228. [PMID: 33204605 PMCID: PMC7649639 DOI: 10.1016/j.pmedr.2020.101228] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 09/27/2020] [Accepted: 10/13/2020] [Indexed: 12/19/2022] Open
Abstract
Substance use disorders are currently a major public health crisis in the US. The prevalence of cannabis use disorder is rising due to legalization of cannabis. This study built models to predict the risk of cannabis use disorder for a user. Risk factors include personality traits, impulsivity and initial smoking enjoyment.
The ongoing trend toward legalization of cannabis for medicinal/recreational purposes is expected to increase the prevalence of cannabis use disorder (CUD). Thus, it is imperative to be able to predict the quantitative risk of developing CUD for a cannabis user based on their personal risk factors. Yet no such model currently exists. In this study, we perform preliminary analysis toward building such a model. The data come from n = 94 regular cannabis users recruited from Albuquerque, New Mexico during 2007–2010. As the data are cross-sectional, we only consider risk factors that remain relatively stable over time. We apply statistical and machine learning classification techniques that allow n to be small relative to the number of predictors. We use predictive accuracy estimated using leave-one-out-cross-validation to evaluate model performance. The final model is a LASSO logistic regression model consisting of the following seven risk factors: age; level of enjoyment from initial cigarette smoking; total score on Impulsive Sensation-Seeking Scale questionnaire; score on cognitive instability factor of Barratt Impulsivity Scale questionnaire; and scores on neuroticism, openness, and conscientiousness personality traits of Neuroticism, Extraversion, and Openness inventory. This model has an overall accuracy of 0.66 and the area under its receiver operating characteristic curve is 0.65. In summary, a preliminary relative risk model for predicting the quantitative risk of CUD is developed. It can be employed to identify users at high risk of CUD who may be provided with early intervention.
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Affiliation(s)
| | - Ryan Hammonds
- School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, TX, USA
| | - Francesca Filbey
- School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, TX, USA
| | - Pankaj K Choudhary
- Department of Mathematical Sciences, University of Texas at Dallas, Richardson, TX, USA
| | - Swati Biswas
- Department of Mathematical Sciences, University of Texas at Dallas, Richardson, TX, USA
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17
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Borm KJ, Simonetto C, Kundrát P, Eidemüller M, Oechsner M, Düsberg M, Combs SE. Toxicity of internal mammary irradiation in breast cancer. Are concerns still justified in times of modern treatment techniques? Acta Oncol 2020; 59:1201-1209. [PMID: 32619381 DOI: 10.1080/0284186x.2020.1787509] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
BACKGROUND The purpose of this study was to estimate the additional risk of side effects attributed to internal mammary node irradiation (IMNI) as part of regional lymph node irradiation (RNI) in breast cancer patients and to compare it with estimated overall survival (OS) benefit from IMNI. MATERIAL AND METHODS Treatment plans (n = 80) with volumetric modulated arc therapy (VMAT) were calculated for 20 patients (4 plans per patient) with left-sided breast cancer from the prospective GATTUM trial in free breathing (FB) and in deep inspiration breath hold (DIBH). We assessed doses to organs at risk ((OARs) lung, contralateral breast and heart) during RNI with and without additional IMNI. Based on the OAR doses, the additional absolute risks of 10-year cardiac mortality, pneumonitis, and secondary lung and breast cancer were estimated using normal tissue complication probability (NTCP) and risk models assuming different age and risk levels. RESULTS IMNI notably increased the mean OAR doses. The mean heart dose increased upon IMNI by 0.2-3.4 Gy (median: 1.9 Gy) in FB and 0.0-1.5 Gy (median 0.4 Gy) in DIBH. However, the estimated absolute additional 10-year cardiac mortality caused by IMNI was <0.5% for all patients studied except 70-year-old high risk patients (0.2-2.4% in FB and 0.0-1.1% in DIBH). In comparison to this, the published oncological benefit of IMNI ranges between 3.3% and 4.7%. The estimated additional 10-year risk of secondary cancer of the lung or contralateral breast ranged from 0-1.5% and 0-2.8%, respectively, depending on age and risk levels. IMNI increased the pneumonitis risk in all groups (0-2.2%). CONCLUSION According to our analyses, the published oncological benefit of IMNI outweighs the estimated risk of cardiac mortality even in case of (e.g., cardiac) risk factors during VMAT. The estimated risk of secondary cancer or pneumonitis attributed to IMNI is low. DIBH reduces the estimated additional risk of IMNI even further and should be strongly considered especially in patients with a high baseline risk.
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Affiliation(s)
- Kai Joachim Borm
- Department of Radiation Oncology, Technical University of Munich (TUM), München, Germany
| | | | - Pavel Kundrát
- Institute of Radiation Medicine, Helmholtz Zentrum München, Neuherberg, Germany
- Department of Radiation Dosimetry, Nuclear Physics Institute of the Czech Academy of Sciences, Prague, Czech Republic
| | - Markus Eidemüller
- Institute of Radiation Medicine, Helmholtz Zentrum München, Neuherberg, Germany
| | - Markus Oechsner
- Department of Radiation Oncology, Technical University of Munich (TUM), München, Germany
| | - Mathias Düsberg
- Department of Radiation Oncology, Technical University of Munich (TUM), München, Germany
| | - Stephanie Elisabeth Combs
- Department of Radiation Oncology, Technical University of Munich (TUM), München, Germany
- Institute of Radiation Medicine, Helmholtz Zentrum München, Neuherberg, Germany
- Deutsches Konsortium für Translationale Krebsforschung, (DKTK)-Partner Site Munich, München, Germany
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18
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Giardiello D, Hauptmann M, Steyerberg EW, Adank MA, Akdeniz D, Blom JC, Blomqvist C, Bojesen SE, Bolla MK, Brinkhuis M, Chang-Claude J, Czene K, Devilee P, Dunning AM, Easton DF, Eccles DM, Fasching PA, Figueroa J, Flyger H, García-Closas M, Haeberle L, Haiman CA, Hall P, Hamann U, Hopper JL, Jager A, Jakubowska A, Jung A, Keeman R, Koppert LB, Kramer I, Lambrechts D, Le Marchand L, Lindblom A, Lubiński J, Manoochehri M, Mariani L, Nevanlinna H, Oldenburg HSA, Pelders S, Pharoah PDP, Shah M, Siesling S, Smit VTHBM, Southey MC, Tapper WJ, Tollenaar RAEM, van den Broek AJ, van Deurzen CHM, van Leeuwen FE, van Ongeval C, Van't Veer LJ, Wang Q, Wendt C, Westenend PJ, Hooning MJ, Schmidt MK. Prediction of contralateral breast cancer: external validation of risk calculators in 20 international cohorts. Breast Cancer Res Treat 2020; 181:423-434. [PMID: 32279280 PMCID: PMC8380991 DOI: 10.1007/s10549-020-05611-8] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Accepted: 03/21/2020] [Indexed: 12/18/2022]
Abstract
BACKGROUND Three tools are currently available to predict the risk of contralateral breast cancer (CBC). We aimed to compare the performance of the Manchester formula, CBCrisk, and PredictCBC in patients with invasive breast cancer (BC). METHODS We analyzed data of 132,756 patients (4682 CBC) from 20 international studies with a median follow-up of 8.8 years. Prediction performance included discrimination, quantified as a time-dependent Area-Under-the-Curve (AUC) at 5 and 10 years after diagnosis of primary BC, and calibration, quantified as the expected-observed (E/O) ratio at 5 and 10 years and the calibration slope. RESULTS The AUC at 10 years was: 0.58 (95% confidence intervals [CI] 0.57-0.59) for CBCrisk; 0.60 (95% CI 0.59-0.61) for the Manchester formula; 0.63 (95% CI 0.59-0.66) and 0.59 (95% CI 0.56-0.62) for PredictCBC-1A (for settings where BRCA1/2 mutation status is available) and PredictCBC-1B (for the general population), respectively. The E/O at 10 years: 0.82 (95% CI 0.51-1.32) for CBCrisk; 1.53 (95% CI 0.63-3.73) for the Manchester formula; 1.28 (95% CI 0.63-2.58) for PredictCBC-1A and 1.35 (95% CI 0.65-2.77) for PredictCBC-1B. The calibration slope was 1.26 (95% CI 1.01-1.50) for CBCrisk; 0.90 (95% CI 0.79-1.02) for PredictCBC-1A; 0.81 (95% CI 0.63-0.99) for PredictCBC-1B, and 0.39 (95% CI 0.34-0.43) for the Manchester formula. CONCLUSIONS Current CBC risk prediction tools provide only moderate discrimination and the Manchester formula was poorly calibrated. Better predictors and re-calibration are needed to improve CBC prediction and to identify low- and high-CBC risk patients for clinical decision-making.
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Affiliation(s)
- Daniele Giardiello
- Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Amsterdam, The Netherlands
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Michael Hauptmann
- Brandenburg Medical School, Institute of Biostatistics and Registry Research, Neuruppin, Germany
- Department of Epidemiology and Biostatistics, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Ewout W Steyerberg
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
- Department of Public Health, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Muriel A Adank
- Family Cancer Clinic, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Delal Akdeniz
- Department of Medical Oncology, Family Cancer Clinic, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Jannet C Blom
- Department of Medical Oncology, Family Cancer Clinic, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Carl Blomqvist
- Department of Oncology, Helsinki University Hospital, University of Helsinki, Helsinki, Finland
- Department of Oncology, Örebro University Hospital, Örebro, Sweden
| | - Stig E Bojesen
- Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Manjeet K Bolla
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Mariël Brinkhuis
- Laboratory for Pathology, East-Netherlands, Hengelo, The Netherlands
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- University Medical Center Hamburg-Eppendorf, Cancer Epidemiology, University Cancer Center Hamburg (UCCH), Hamburg, Germany
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Peter Devilee
- Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Alison M Dunning
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Douglas F Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Diana M Eccles
- Cancer Sciences Academic Unit, Faculty of Medicine, University of Southampton, Southampton, UK
| | - Peter A Fasching
- David Geffen School of Medicine, Department of Medicine Division of Hematology and Oncology, University of California At Los Angeles, Los Angeles, CA, USA
- University Hospital Erlangen, Department of Gynecology and Obstetrics, Comprehensive Cancer Center ER-EMN, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany
| | - Jonine Figueroa
- The University of Edinburgh Medical School, Usher Institute of Population Health Sciences and Informatics, Edinburgh, UK
- Cancer Research UK Edinburgh Centre, Edinburgh, UK
- Department of Health and Human Services, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Henrik Flyger
- Department of Breast Surgery, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
| | - Montserrat García-Closas
- Department of Health and Human Services, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
- Division of Genetics and Epidemiology, Institute of Cancer Research, London, UK
| | - Lothar Haeberle
- University Hospital Erlangen, Department of Gynecology and Obstetrics, Comprehensive Cancer Center ER-EMN, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany
| | - Christopher A Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Oncology, Södersjukhuset, Stockholm, Sweden
| | - Ute Hamann
- Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Agnes Jager
- Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Anna Jakubowska
- Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
- Independent Laboratory of Molecular Biology and Genetic Diagnostics, Pomeranian Medical University, Szczecin, Poland
| | - Audrey Jung
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Renske Keeman
- Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Linetta B Koppert
- Department of Surgical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Iris Kramer
- Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Diether Lambrechts
- VIB Center for Cancer Biology, Leuven, Belgium
- Laboratory for Translational Genetics, Department of Human Genetics, University of Leuven, Leuven, Belgium
| | - Loic Le Marchand
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Annika Lindblom
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden
| | - Jan Lubiński
- Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
| | - Mehdi Manoochehri
- Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Luigi Mariani
- Unit of Clinical Epidemiology and Trial Organization, Fondazione IRCCS Istituto Nazionale Dei Tumori, Milan, Italy
| | - Heli Nevanlinna
- Department of Obstetrics and Gynecology, Helsinki University Hospital, University of Helsinki, Helsinki, Finland
| | - Hester S A Oldenburg
- Department of Surgical Oncology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Saskia Pelders
- Department of Medical Oncology, Family Cancer Clinic, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Paul D P Pharoah
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Mitul Shah
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Sabine Siesling
- Department of Research, Netherlands Comprehensive Cancer Organisation, Utrecht, The Netherlands
| | - Vincent T H B M Smit
- Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
- Department of Clinical Pathology, The University of Melbourne, Melbourne, VIC, Australia
| | | | - Rob A E M Tollenaar
- Department of Surgery, Leiden University Medical Center, Leiden, The Netherlands
| | - Alexandra J van den Broek
- Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | | | - Flora E van Leeuwen
- Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Chantal van Ongeval
- Leuven Cancer Institute, Leuven Multidisciplinary Breast Center, Department of Oncology, University Hospitals Leuven, Leuven, Belgium
| | - Laura J Van't Veer
- Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Qin Wang
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Camilla Wendt
- Department of Clinical Science and Education, Karolinska Institutet, Södersjukhuset, Stockholm, Sweden
| | | | - Maartje J Hooning
- Department of Medical Oncology, Family Cancer Clinic, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Marjanka K Schmidt
- Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Amsterdam, The Netherlands.
- Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Amsterdam, The Netherlands.
- Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.
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19
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Giardiello D, Steyerberg EW, Hauptmann M, Adank MA, Akdeniz D, Blomqvist C, Bojesen SE, Bolla MK, Brinkhuis M, Chang-Claude J, Czene K, Devilee P, Dunning AM, Easton DF, Eccles DM, Fasching PA, Figueroa J, Flyger H, García-Closas M, Haeberle L, Haiman CA, Hall P, Hamann U, Hopper JL, Jager A, Jakubowska A, Jung A, Keeman R, Kramer I, Lambrechts D, Le Marchand L, Lindblom A, Lubiński J, Manoochehri M, Mariani L, Nevanlinna H, Oldenburg HSA, Pelders S, Pharoah PDP, Shah M, Siesling S, Smit VTHBM, Southey MC, Tapper WJ, Tollenaar RAEM, van den Broek AJ, van Deurzen CHM, van Leeuwen FE, van Ongeval C, Van't Veer LJ, Wang Q, Wendt C, Westenend PJ, Hooning MJ, Schmidt MK. Prediction and clinical utility of a contralateral breast cancer risk model. Breast Cancer Res 2019; 21:144. [PMID: 31847907 PMCID: PMC6918633 DOI: 10.1186/s13058-019-1221-1] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Accepted: 10/29/2019] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Breast cancer survivors are at risk for contralateral breast cancer (CBC), with the consequent burden of further treatment and potentially less favorable prognosis. We aimed to develop and validate a CBC risk prediction model and evaluate its applicability for clinical decision-making. METHODS We included data of 132,756 invasive non-metastatic breast cancer patients from 20 studies with 4682 CBC events and a median follow-up of 8.8 years. We developed a multivariable Fine and Gray prediction model (PredictCBC-1A) including patient, primary tumor, and treatment characteristics and BRCA1/2 germline mutation status, accounting for the competing risks of death and distant metastasis. We also developed a model without BRCA1/2 mutation status (PredictCBC-1B) since this information was available for only 6% of patients and is routinely unavailable in the general breast cancer population. Prediction performance was evaluated using calibration and discrimination, calculated by a time-dependent area under the curve (AUC) at 5 and 10 years after diagnosis of primary breast cancer, and an internal-external cross-validation procedure. Decision curve analysis was performed to evaluate the net benefit of the model to quantify clinical utility. RESULTS In the multivariable model, BRCA1/2 germline mutation status, family history, and systemic adjuvant treatment showed the strongest associations with CBC risk. The AUC of PredictCBC-1A was 0.63 (95% prediction interval (PI) at 5 years, 0.52-0.74; at 10 years, 0.53-0.72). Calibration-in-the-large was -0.13 (95% PI: -1.62-1.37), and the calibration slope was 0.90 (95% PI: 0.73-1.08). The AUC of Predict-1B at 10 years was 0.59 (95% PI: 0.52-0.66); calibration was slightly lower. Decision curve analysis for preventive contralateral mastectomy showed potential clinical utility of PredictCBC-1A between thresholds of 4-10% 10-year CBC risk for BRCA1/2 mutation carriers and non-carriers. CONCLUSIONS We developed a reasonably calibrated model to predict the risk of CBC in women of European-descent; however, prediction accuracy was moderate. Our model shows potential for improved risk counseling, but decision-making regarding contralateral preventive mastectomy, especially in the general breast cancer population where limited information of the mutation status in BRCA1/2 is available, remains challenging.
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Affiliation(s)
- Daniele Giardiello
- Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Ewout W Steyerberg
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
- Department of Public Health, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Michael Hauptmann
- Institute of Biometry and Registry Research, Brandenburg Medical School, Neuruppin, Germany
- Department of Epidemiology and Biostatistics, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Muriel A Adank
- The Netherlands Cancer Institute - Antoni van Leeuwenhoek hospital, Family Cancer Clinic, Amsterdam, The Netherlands
| | - Delal Akdeniz
- Department of Medical Oncology, Family Cancer Clinic, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Carl Blomqvist
- Department of Oncology, Helsinki University Hospital, University of Helsinki, Helsinki, Finland
- Department of Oncology, Örebro University Hospital, Örebro, Sweden
| | - Stig E Bojesen
- Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Manjeet K Bolla
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Mariël Brinkhuis
- East-Netherlands, Laboratory for Pathology, Hengelo, The Netherlands
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Cancer Epidemiology Group, University Cancer Center Hamburg (UCCH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
| | - Peter Devilee
- Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Alison M Dunning
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Douglas F Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Diana M Eccles
- Cancer Sciences Academic Unit, Faculty of Medicine, University of Southampton, Southampton, UK
| | - Peter A Fasching
- Department of Medicine Division of Hematology and Oncology, University of California at Los Angeles, David Geffen School of Medicine, Los Angeles, CA, USA
- Department of Gynecology and Obstetrics, Comprehensive Cancer Center ER-EMN, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany
| | - Jonine Figueroa
- Usher Institute of Population Health Sciences and Informatics, The University of Edinburgh Medical School, Edinburgh, UK
- Cancer Research UK Edinburgh Centre, Edinburgh, UK
- Department of Health and Human Services, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Henrik Flyger
- Department of Breast Surgery, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
| | - Montserrat García-Closas
- Department of Health and Human Services, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
- Division of Genetics and Epidemiology, Institute of Cancer Research, London, UK
| | - Lothar Haeberle
- Department of Gynecology and Obstetrics, Comprehensive Cancer Center ER-EMN, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany
| | - Christopher A Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
- Department of Oncology, Södersjukhuset, Stockholm, Sweden
| | - Ute Hamann
- Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Agnes Jager
- Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Anna Jakubowska
- Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
- Independent Laboratory of Molecular Biology and Genetic Diagnostics, Pomeranian Medical University, Szczecin, Poland
| | - Audrey Jung
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Renske Keeman
- Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Iris Kramer
- Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Diether Lambrechts
- VIB Center for Cancer Biology, VIB, Leuven, Belgium
- Laboratory for Translational Genetics, Department of Human Genetics, University of Leuven, Leuven, Belgium
| | - Loic Le Marchand
- University of Hawaii Cancer Center, Epidemiology Program, Honolulu, HI, USA
| | - Annika Lindblom
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden
| | - Jan Lubiński
- Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
| | - Mehdi Manoochehri
- Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Luigi Mariani
- Unit of Clinical Epidemiology and Trial Organization, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Heli Nevanlinna
- Department of Obstetrics and Gynecology, Helsinki University Hospital, University of Helsinki, Helsinki, Finland
| | - Hester S A Oldenburg
- Department of Surgical Oncology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Saskia Pelders
- Department of Medical Oncology, Family Cancer Clinic, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Paul D P Pharoah
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Mitul Shah
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Sabine Siesling
- Department of Research, Netherlands Comprehensive Cancer Organisation, Utrecht, The Netherlands
| | - Vincent T H B M Smit
- Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Department of Clinical Pathology, The University of Melbourne, Melbourne, Victoria, Australia
| | | | - Rob A E M Tollenaar
- Department of Surgery, Leiden University Medical Center, Leiden, The Netherlands
| | - Alexandra J van den Broek
- Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | | | - Flora E van Leeuwen
- Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Plesmanlaan 121, 1066, CX, Amsterdam, The Netherlands
| | - Chantal van Ongeval
- Leuven Multidisciplinary Breast Center, Department of Oncology, Leuven Cancer Institute, University Hospitals Leuven, Leuven, Belgium
| | - Laura J Van't Veer
- Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Qin Wang
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Camilla Wendt
- Department of Clinical Science and Education, Södersjukhuset, Karolinska Institutet, Stockholm, Sweden
| | | | - Maartje J Hooning
- Department of Medical Oncology, Family Cancer Clinic, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Marjanka K Schmidt
- Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands.
- Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Plesmanlaan 121, 1066, CX, Amsterdam, The Netherlands.
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