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Hovhannisyan M, Zemankova P, Nehasil P, Matejkova K, Borecka M, Cerna M, Dolezalova T, Dvorakova L, Foretova L, Horackova K, Jelinkova S, Just P, Kalousova M, Kral J, Machackova E, Nemcova B, Safarikova M, Springer D, Stastna B, Tavandzis S, Vocka M, Zima T, Soukupova J, Kleiblova P, Ernst C, Kleibl Z, Janatova M. Population-specific validation and comparison of the performance of 77- and 313-variant polygenic risk scores for breast cancer risk prediction. Cancer 2024; 130:2978-2987. [PMID: 38718029 DOI: 10.1002/cncr.35337] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 03/22/2024] [Accepted: 04/03/2024] [Indexed: 08/10/2024]
Abstract
BACKGROUND The polygenic risk score (PRS) allows the quantification of the polygenic effect of many low-penetrance alleles on the risk of breast cancer (BC). This study aimed to evaluate the performance of two sets comprising 77 or 313 low-penetrance loci (PRS77 and PRS313) in patients with BC in the Czech population. METHODS In a retrospective case-control study, variants were genotyped from both the PRS77 and PRS313 sets in 1329 patients with BC and 1324 noncancer controls, all women without germline pathogenic variants in BC predisposition genes. Odds ratios (ORs) were calculated according to the categorical PRS in individual deciles. Weighted Cox regression analysis was used to estimate the hazard ratio (HR) per standard deviation (SD) increase in PRS. RESULTS The distributions of standardized PRSs in patients and controls were significantly different (p < 2.2 × 10-16) with both sets. PRS313 outperformed PRS77 in categorical and continuous PRS analyses. For patients in the highest 2.5% of PRS313, the risk reached an OR of 3.05 (95% CI, 1.66-5.89; p = 1.76 × 10-4). The continuous risk was estimated as an HRper SD of 1.64 (95% CI, 1.49-1.81; p < 2.0 × 10-16), which resulted in an absolute risk of 21.03% at age 80 years for individuals in the 95th percentile of PRS313. Discordant categorization into PRS deciles was observed in 248 individuals (9.3%). CONCLUSIONS Both PRS77 and PRS313 are able to stratify individuals according to their BC risk in the Czech population. PRS313 shows better discriminatory ability. The results support the potential clinical utility of using PRS313 in individualized BC risk prediction.
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Affiliation(s)
- Milena Hovhannisyan
- Institute of Medical Biochemistry and Laboratory Diagnostics, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
| | - Petra Zemankova
- Institute of Medical Biochemistry and Laboratory Diagnostics, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
- Institute of Pathological Physiology, First Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Petr Nehasil
- Institute of Medical Biochemistry and Laboratory Diagnostics, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
- Institute of Pathological Physiology, First Faculty of Medicine, Charles University, Prague, Czech Republic
- Department of Paediatrics and Inherited Metabolic Disorders, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
| | - Katerina Matejkova
- Institute of Medical Biochemistry and Laboratory Diagnostics, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
- Department of Genetics and Microbiology, Faculty of Science, Charles University in Prague, Prague, Czech Republic
| | - Marianna Borecka
- Institute of Medical Biochemistry and Laboratory Diagnostics, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
| | - Marta Cerna
- Institute of Medical Biochemistry and Laboratory Diagnostics, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
| | - Tatana Dolezalova
- Institute of Medical Biochemistry and Laboratory Diagnostics, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
| | - Lenka Dvorakova
- Department of Paediatrics and Inherited Metabolic Disorders, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
| | - Lenka Foretova
- Department of Cancer Epidemiology and Genetics, Masaryk Memorial Cancer Institute, Brno, Czech Republic
| | - Klara Horackova
- Institute of Medical Biochemistry and Laboratory Diagnostics, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
| | - Sandra Jelinkova
- Institute of Medical Biochemistry and Laboratory Diagnostics, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
| | - Pavel Just
- Institute of Medical Biochemistry and Laboratory Diagnostics, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
| | - Marta Kalousova
- Institute of Medical Biochemistry and Laboratory Diagnostics, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
| | - Jan Kral
- Institute of Medical Biochemistry and Laboratory Diagnostics, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
- Centre for Medical Genetics and Reproductive Medicine, GENNET, Prague, Czech Republic
| | - Eva Machackova
- Department of Cancer Epidemiology and Genetics, Masaryk Memorial Cancer Institute, Brno, Czech Republic
| | - Barbora Nemcova
- Institute of Medical Biochemistry and Laboratory Diagnostics, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
| | - Marketa Safarikova
- Institute of Medical Biochemistry and Laboratory Diagnostics, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
| | - Drahomira Springer
- Institute of Medical Biochemistry and Laboratory Diagnostics, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
| | - Barbora Stastna
- Institute of Medical Biochemistry and Laboratory Diagnostics, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
- Department of Biochemistry, Faculty of Science, Charles University, Prague, Czech Republic
| | - Spiros Tavandzis
- Department of Medical Genetics, AGEL Research and Training Institute, AGEL Laboratories, Novy Jicin, Czech Republic
| | - Michal Vocka
- Department of Oncology, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
| | - Tomas Zima
- Institute of Medical Biochemistry and Laboratory Diagnostics, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
| | - Jana Soukupova
- Institute of Medical Biochemistry and Laboratory Diagnostics, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
| | - Petra Kleiblova
- Institute of Medical Biochemistry and Laboratory Diagnostics, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
- Institute of Biology and Medical Genetics, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
| | - Corinna Ernst
- Centre for Familial Breast and Ovarian Cancer, Center for Integrated Oncology, Medical Faculty, University of Cologne and University Hospital Cologne, Cologne, Germany
| | - Zdenek Kleibl
- Institute of Medical Biochemistry and Laboratory Diagnostics, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
- Institute of Pathological Physiology, First Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Marketa Janatova
- Institute of Medical Biochemistry and Laboratory Diagnostics, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
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Zaki-Metias KM, Wang H, Tawil TF, Miles EB, Deptula L, Agrawal P, Davis KM, Spalluto LB, Seely JM, Yong-Hing CJ. Breast Cancer Screening in the Intermediate-Risk Population: Falling Through the Cracks? Can Assoc Radiol J 2024; 75:593-600. [PMID: 38420877 DOI: 10.1177/08465371241234544] [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] [Indexed: 03/02/2024] Open
Abstract
Breast cancer screening guidelines vary for women at intermediate risk (15%-20% lifetime risk) for developing breast cancer across jurisdictions. Currently available risk assessment models have differing strengths and weaknesses, creating difficulty and ambiguity in selecting the most appropriate model to utilize. Clarifying which model to utilize in individual circumstances may help determine the best screening guidelines to use for each individual.
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Affiliation(s)
- Kaitlin M Zaki-Metias
- Department of Radiology, Trinity Health Oakland Hospital/Wayne State University School of Medicine, Pontiac, MI, USA
| | - Huijuan Wang
- Department of Radiology, Trinity Health Oakland Hospital/Wayne State University School of Medicine, Pontiac, MI, USA
| | - Tima F Tawil
- Department of Radiology, Trinity Health Oakland Hospital/Wayne State University School of Medicine, Pontiac, MI, USA
| | - Eda B Miles
- Department of Internal Medicine, Arnot Ogden Medical Center, Elmira, NY, USA
| | - Lisa Deptula
- Ross University School of Medicine, Bridgetown, Barbados
| | - Pooja Agrawal
- Department of Radiology, Baylor College of Medicine, Houston, TX, USA
- Department of Internal Medicine, HCA Houston Healthcare Kingwood, Houston, TX, USA
| | - Katie M Davis
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lucy B Spalluto
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Ingram Cancer Center, Nashville, TN, USA
- Veterans Health Administration, Tennessee Valley Healthcare System Geriatric Research, Education and Clinical Center (GRECC), Nashville, TN, USA
| | - Jean M Seely
- Department of Medical Imaging, The Ottawa Hospital, University of Ottawa, Ottawa, ON, Canada
| | - Charlotte J Yong-Hing
- Diagnostic Imaging, BC Cancer Vancouver, Vancouver, BC, Canada
- Department of Radiology, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
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3
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Hung C, Chu W, Huang W, Lee P, Kuo W, Hou C, Yang C, Yang A, Wu W, Kuo M, Wu M, Chen W. Safety assessment of a proprietary fermented soybean solution, Symbiota®, as an ingredient for use in foods and dietary supplements: Non-clinical studies and a randomized trial. Food Sci Nutr 2024; 12:2346-2363. [PMID: 38628176 PMCID: PMC11016383 DOI: 10.1002/fsn3.3921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 12/12/2023] [Accepted: 12/14/2023] [Indexed: 04/20/2024] Open
Abstract
A safety evaluation was performed of Symbiota®, which is made by a proprietary anaerobic fermentation process of soybean with multistrains of probiotics and a yeast. The battery of genotoxicity studies showed that Symbiota® has no genotoxic effects. Safety and tolerability were further assessed by acute or repeated dose 28- and 90-day rodent studies, and no alterations in clinical observations, ophthalmological examination, blood chemistry, urinalysis, or hematology were observed between the control group and the different dosing groups (1.5, 5, and 15 mL/kg/day). There were no adverse effects on specific tissues or organs in terms of weight and histopathology. Importantly, the Symbiota® treatment did not perturb hormones and other endocrine-related endpoints. Of note, the No-Observed-Adverse-Effect-Level was determined to be 15 mL/kg/day in rats. Moreover, a randomized, double-blind, placebo-controlled clinical trial was recently conducted with healthy volunteers who consumed 8 mL/day of placebo or Symbiota® for 8 weeks. Only mild adverse events were reported in both groups, and the blood chemistry and blood cell profiles were also similar between the two groups. In summary, this study concluded that the oral consumption of Symbiota® at 8 mL/day by the general population does not pose any human health concerns.
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Affiliation(s)
| | | | | | | | | | | | - Chia‐Chun Yang
- Microbio Co., LtdTaipeiTaiwan
- Microbio (Shanghai) Biotech CompanyShanghaiChina
| | | | - Wei‐Kai Wu
- Department of Medical ResearchNational Taiwan University HospitalTaipeiTaiwan
- Department of Internal MedicineNational Taiwan University HospitalTaipeiTaiwan
| | | | - Ming‐Shiang Wu
- Department of Internal MedicineNational Taiwan University HospitalTaipeiTaiwan
- Department of Internal MedicineNational Taiwan University College of MedicineTaipeiTaiwan
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4
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Gard CC, Tice JA, Miglioretti DL, Sprague BL, Bissell MC, Henderson LM, Kerlikowske K. Extending the Breast Cancer Surveillance Consortium Model of Invasive Breast Cancer. J Clin Oncol 2024; 42:779-789. [PMID: 37976443 PMCID: PMC10906584 DOI: 10.1200/jco.22.02470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Revised: 08/08/2023] [Accepted: 09/18/2023] [Indexed: 11/19/2023] Open
Abstract
PURPOSE We extended the Breast Cancer Surveillance Consortium (BCSC) version 2 (v2) model of invasive breast cancer risk to include BMI, extended family history of breast cancer, and age at first live birth (version 3 [v3]) to better inform appropriate breast cancer prevention therapies and risk-based screening. METHODS We used Cox proportional hazards regression to estimate the age- and race- and ethnicity-specific relative hazards for family history of breast cancer, breast density, history of benign breast biopsy, BMI, and age at first live birth for invasive breast cancer in the BCSC cohort. We evaluated calibration using the ratio of expected-to-observed (E/O) invasive breast cancers in the cohort and discrimination using the area under the receiver operating characteristic curve (AUROC). RESULTS We analyzed data from 1,455,493 women age 35-79 years without a history of breast cancer. During a mean follow-up of 7.3 years, 30,266 women were diagnosed with invasive breast cancer. The BCSC v3 model had an E/O of 1.03 (95% CI, 1.01 to 1.04) and an AUROC of 0.646 for 5-year risk. Compared with the v2 model, discrimination of the v3 model improved most in Asian, White, and Black women. Among women with a BMI of 30.0-34.9 kg/m2, the true-positive rate in women with an estimated 5-year risk of 3% or higher increased from 10.0% (v2) to 19.8% (v3) and the improvement was greater among women with a BMI of ≥35 kg/m2 (7.6%-19.8%). CONCLUSION The BCSC v3 model updates an already well-calibrated and validated breast cancer risk assessment tool to include additional important risk factors. The inclusion of BMI was associated with the largest improvement in estimated risk for individual women.
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Affiliation(s)
- Charlotte C. Gard
- Department of Economics, Applied Statistics, and International Business, New Mexico State University, Las Cruces, NM
| | - Jeffrey A. Tice
- Division of General Internal Medicine, Department of Medicine, University of California, San Francisco, San Francisco, CA
| | - Diana L. Miglioretti
- University of California, Davis, Davis, CA
- Kaiser Permanente Washington Health Research Institute, Seattle, WA
| | - Brian L. Sprague
- Department of Surgery, University of Vermont Cancer Center, Burlington, VT
- Department of Radiology, University of Vermont Cancer Center, Burlington, VT
| | | | | | - Karla Kerlikowske
- General Internal Medicine Section, Department of Veteran Affairs, University of California, San Francisco, San Francisco, CA
- Departments of Medicine and Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA
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5
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Stan DL, Kim JO, Schaid DJ, Carlson EE, Kim CA, Sinnwell JP, Couch FJ, Vachon CM, Cooke AL, Goldenberg BA, Pruthi S. Breast Cancer Polygenic-Risk Score Influence on Risk-Reducing Endocrine Therapy Use: Genetic Risk Estimate (GENRE) Trial 1-Year and 2-Year Follow-Up. Cancer Prev Res (Phila) 2024; 17:77-84. [PMID: 38154464 DOI: 10.1158/1940-6207.capr-23-0256] [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: 07/13/2023] [Revised: 10/26/2023] [Accepted: 12/21/2023] [Indexed: 12/30/2023]
Abstract
Refinement of breast cancer risk estimates with a polygenic-risk score (PRS) may improve uptake of risk-reducing endocrine therapy (ET). A previous clinical trial assessed the influence of adding a PRS to traditional risk estimates on ET use. We stratified participants according to PRS-refined breast cancer risk and evaluated ET use and ET-related quality of life (QOL) at 1-year (previously reported) and 2-year follow-ups. Of 151 participants, 58 (38.4%) initiated ET, and 22 (14.6%) discontinued ET by 2 years; 42 (27.8%) and 36 (23.8%) participants were using ET at 1- and 2-year follow-ups, respectively. At the 2-year follow-up, 39% of participants with a lifetime breast cancer risk of 40.1% to 100.0%, 18% with a 20.1% to 40.0% risk, and 16% with a 0.0% to 20.0% risk were taking ET (overall P = 0.01). Moreover, 40% of participants whose breast cancer risk increased by 10% or greater with addition of the PRS to a traditional breast cancer-risk model were taking ET versus 0% whose risk decreased by 10% or greater (P = 0.004). QOL was similar for participants taking or not taking ET at 1- and 2-year follow-ups, although most who discontinued ET did so because of adverse effects. However, these QOL results may have been skewed by the long interval between QOL surveys and lack of baseline QOL data. PRS-informed breast cancer prevention counseling has a lasting, but waning, effect over time. Additional follow-up studies are needed to address the effect of PRS on ET adherence, ET-related QOL, supplemental breast cancer screening, and other risk-reducing behaviors. PREVENTION RELEVANCE Risk-reducing medications for breast cancer are considerably underused. Informing women at risk with precise and individualized risk assessment tools may substantially affect the incidence of breast cancer. In our study, a risk assessment tool (IBIS-polygenic-risk score) yielded promising results, with 39% of women at highest risk starting preventive medication.
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Affiliation(s)
- Daniela L Stan
- Breast Diagnostic Clinic, Mayo Clinic, Rochester, Minnesota
- Mayo Clinic Cancer Center, Mayo Clinic, Rochester, Minnesota
| | - Julian O Kim
- Department of Radiation Oncology, Max Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Daniel J Schaid
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota
| | - Erin E Carlson
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota
| | - Christina A Kim
- Department of Medical Oncology and Hematology, Max Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Jason P Sinnwell
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota
| | - Fergus J Couch
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
- Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, Minnesota
| | - Celine M Vachon
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota
| | - Andrew L Cooke
- Department of Radiation Oncology, Max Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Benjamin A Goldenberg
- Department of Medical Oncology and Hematology, Max Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Sandhya Pruthi
- Breast Diagnostic Clinic, Mayo Clinic, Rochester, Minnesota
- Mayo Clinic Cancer Center, Mayo Clinic, Rochester, Minnesota
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Enogieru IE, Comstock CE, Grimm LJ. Breast Cancer Screening and Treatment Clinical Trials Updated for 2023. JOURNAL OF BREAST IMAGING 2024; 6:14-22. [PMID: 38243862 DOI: 10.1093/jbi/wbad089] [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: 07/05/2023] [Indexed: 01/22/2024]
Abstract
There are many active or recently completed breast cancer screening and treatment trials in 2023 that have the potential to fundamentally change the way breast radiologists practice medicine. Breast cancer screening trials may provide evidence to support supplemental screening beyond mammography to include US, contrast-enhanced mammography, and breast MRI. Furthermore, there are multiple efforts to support risk-adaptive screening strategies that would personalize screening modalities, frequencies, and ages of initiation. For breast cancer treatment, aims to reduce overtreatment may provide nonsurgical treatment options for women with low-risk breast cancer. Breast radiologists must be familiar with the study designs, major inclusion and exclusion criteria, and principal endpoints in order to determine when and how the study results should influence clinical care. As multidisciplinary team members, breast radiologists will have major roles in the success or failure of these trials as they transition from research to actual clinical practice.
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Affiliation(s)
- Imarhia E Enogieru
- Department of Radiology, Duke University Medical Center, Durham, NC, USA
| | | | - Lars J Grimm
- Department of Radiology, Duke University Medical Center, Durham, NC, USA
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Kamil D, Wojcik KM, Smith L, Zhang J, Wilson OWA, Butera G, Jayasekera J. A Scoping Review of Personalized, Interactive, Web-Based Clinical Decision Tools Available for Breast Cancer Prevention and Screening in the United States. MDM Policy Pract 2024; 9:23814683241236511. [PMID: 38500600 PMCID: PMC10946080 DOI: 10.1177/23814683241236511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 02/04/2024] [Indexed: 03/20/2024] Open
Abstract
Introduction. Personalized web-based clinical decision tools for breast cancer prevention and screening could address knowledge gaps, enhance patient autonomy in shared decision-making, and promote equitable care. The purpose of this review was to present evidence on the availability, usability, feasibility, acceptability, quality, and uptake of breast cancer prevention and screening tools to support their integration into clinical care. Methods. We used the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews Checklist to conduct this review. We searched 6 databases to identify literature on the development, validation, usability, feasibility, acceptability testing, and uptake of the tools into practice settings. Quality assessment for each tool was conducted using the International Patient Decision Aid Standard instrument, with quality scores ranging from 0 to 63 (lowest-highest). Results. We identified 10 tools for breast cancer prevention and 9 tools for screening. The tools included individual (e.g., age), clinical (e.g., genomic risk factors), and health behavior (e.g., alcohol use) characteristics. Fourteen tools included race/ethnicity, but no tool incorporated contextual factors (e.g., insurance, access) associated with breast cancer. All tools were internally or externally validated. Six tools had undergone usability testing in samples including White (median, 71%; range, 9%-96%), insured (99%; 97%-100%) women, with college education or higher (60%; 27%-100%). All of the tools were developed and tested in academic settings. Seven (37%) tools showed potential evidence of uptake in clinical practice. The tools had an average quality assessment score of 21 (range, 9-39). Conclusions. There is limited evidence on testing and uptake of breast cancer prevention and screening tools in diverse clinical settings. The development, testing, and integration of tools in academic and nonacademic settings could potentially improve uptake and equitable access to these tools. Highlights There were 19 personalized, interactive, Web-based decision tools for breast cancer prevention and screening.Breast cancer outcomes were personalized based on individual clinical characteristics (e.g., age, medical history), genomic risk factors (e.g., BRCA1/2), race and ethnicity, and health behaviors (e.g., smoking). The tools did not include contextual factors (e.g., insurance status, access to screening facilities) that could potentially contribute to breast cancer outcomes.Validation, usability, acceptability, and feasibility testing were conducted mostly among White and/or insured patients with some college education (or higher) in academic settings. There was limited evidence on testing and uptake of the tools in nonacademic clinical settings.
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Affiliation(s)
- Dalya Kamil
- Health Equity and Decision Sciences Research Laboratory, Division of Intramural Research, National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, USA
| | - Kaitlyn M. Wojcik
- Health Equity and Decision Sciences Research Laboratory, Division of Intramural Research, National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, USA
| | - Laney Smith
- Frederick P. Whiddon College of Medicine, Mobile, AL, USA
| | | | - Oliver W. A. Wilson
- Health Equity and Decision Sciences Research Laboratory, Division of Intramural Research, National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, USA
| | - Gisela Butera
- Office of Research Services, National Institutes of Health Library, Bethesda, MD, USA
| | - Jinani Jayasekera
- Health Equity and Decision Sciences Research Laboratory, Division of Intramural Research, National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, USA
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Berg WA, Seitzman RL, Pushkin J. Implementing the National Dense Breast Reporting Standard, Expanding Supplemental Screening Using Current Guidelines, and the Proposed Find It Early Act. JOURNAL OF BREAST IMAGING 2023; 5:712-723. [PMID: 38141231 DOI: 10.1093/jbi/wbad034] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Indexed: 12/25/2023]
Abstract
Thirty-eight states and the District of Columbia (DC) have dense breast notification laws that mandate varying levels of patient notification about breast density after a mammogram, and these cover over 90% of American women. On March 10, 2023, the Food and Drug Administration issued a final rule amending regulations under the Mammography Quality Standards Act for a national dense breast reporting standard for both patient results letters and mammogram reports. Effective September 10, 2024, letters will be required to tell a woman her breasts are "dense" or "not dense," that dense tissue makes it harder to find cancers on a mammogram, and that it increases the risk of developing cancer. Women with dense breasts will also be told that other imaging tests in addition to a mammogram may help find cancers. The specific density category can be added (eg, if mandated by a state "inform" law). Reports to providers must include the Breast Imaging Reporting and Data System density category. Implementing appropriate supplemental screening should be based on patient risk for missed breast cancer on mammography; such assessment should include consideration of breast density and other risk factors. This article discusses strategies for implementation. Currently 21 states and DC have varying insurance laws for supplemental breast imaging; in addition, Oklahoma requires coverage for diagnostic breast imaging. A federal insurance bill, the Find It Early Act, has been introduced that would ensure no-cost screening and diagnostic imaging for women with dense breasts or at increased risk and close loopholes in state laws.
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Affiliation(s)
- Wendie A Berg
- University of Pittsburgh School of Medicine, Magee-Womens Hospital of UPMC, Department of Radiology, Pittsburgh, PA, USA
| | - Robin L Seitzman
- Seitzman Epidemiology, LLC, San Diego, CA, USA
- DenseBreast-info, Inc, Deer Park, NY, USA
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Mbuya-Bienge C, Pashayan N, Kazemali CD, Lapointe J, Simard J, Nabi H. A Systematic Review and Critical Assessment of Breast Cancer Risk Prediction Tools Incorporating a Polygenic Risk Score for the General Population. Cancers (Basel) 2023; 15:5380. [PMID: 38001640 PMCID: PMC10670420 DOI: 10.3390/cancers15225380] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 10/26/2023] [Accepted: 11/03/2023] [Indexed: 11/26/2023] Open
Abstract
Single nucleotide polymorphisms (SNPs) in the form of a polygenic risk score (PRS) have emerged as a promising factor that could improve the predictive performance of breast cancer (BC) risk prediction tools. This study aims to appraise and critically assess the current evidence on these tools. Studies were identified using Medline, EMBASE and the Cochrane Library up to November 2022 and were included if they described the development and/ or validation of a BC risk prediction model using a PRS for women of the general population and if they reported a measure of predictive performance. We identified 37 articles, of which 29 combined genetic and non-genetic risk factors using seven different risk prediction tools. Most models (55.0%) were developed on populations from European ancestry and performed better than those developed on populations from other ancestry groups. Regardless of the number of SNPs in each PRS, models combining a PRS with genetic and non-genetic risk factors generally had better discriminatory accuracy (AUC from 0.52 to 0.77) than those using a PRS alone (AUC from 0.48 to 0.68). The overall risk of bias was considered low in most studies. BC risk prediction tools combining a PRS with genetic and non-genetic risk factors provided better discriminative accuracy than either used alone. Further studies are needed to cross-compare their clinical utility and readiness for implementation in public health practices.
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Affiliation(s)
- Cynthia Mbuya-Bienge
- Department of Social and Preventive Medicine, Faculty of Medicine, Université Laval, Quebec City, QC G1V 0A6, Canada; (C.M.-B.); (C.D.K.)
- Oncology Division, CHU de Québec-Université Laval Research Center, Quebec City, QC G1S 4L8, Canada;
| | - Nora Pashayan
- Department of Applied Health Research, University College London, London WC1E 6BT, UK;
| | - Cornelia D. Kazemali
- Department of Social and Preventive Medicine, Faculty of Medicine, Université Laval, Quebec City, QC G1V 0A6, Canada; (C.M.-B.); (C.D.K.)
- Oncology Division, CHU de Québec-Université Laval Research Center, Quebec City, QC G1S 4L8, Canada;
| | - Julie Lapointe
- Oncology Division, CHU de Québec-Université Laval Research Center, Quebec City, QC G1S 4L8, Canada;
| | - Jacques Simard
- Endocrinology and Nephology Division, CHU de Québec-Université Laval Research Center, Quebec City, QC G1V 4G2, Canada;
- Department of Molecular Medicine, Faculty of Medicine, Université Laval, Quebec City, QC G1V 0A6, Canada
| | - Hermann Nabi
- Department of Social and Preventive Medicine, Faculty of Medicine, Université Laval, Quebec City, QC G1V 0A6, Canada; (C.M.-B.); (C.D.K.)
- Oncology Division, CHU de Québec-Université Laval Research Center, Quebec City, QC G1S 4L8, Canada;
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10
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Heine J, Fowler EEE, Weinfurtner RJ, Hume E, Tworoger SS. Breast density analysis of digital breast tomosynthesis. Sci Rep 2023; 13:18760. [PMID: 37907569 PMCID: PMC10618274 DOI: 10.1038/s41598-023-45402-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 10/19/2023] [Indexed: 11/02/2023] Open
Abstract
Mammography shifted to digital breast tomosynthesis (DBT) in the US. An automated percentage of breast density (PD) technique designed for two-dimensional (2D) applications was evaluated with DBT using several breast cancer risk prediction measures: normalized-volumetric; dense volume; applied to the volume slices and averaged (slice-mean); and applied to synthetic 2D images. Volumetric measures were derived theoretically. PD was modeled as a function of compressed breast thickness (CBT). The mean and standard deviation of the pixel values were investigated. A matched case-control (CC) study (n = 426 pairs) was evaluated. Odd ratios (ORs) were estimated with 95% confidence intervals. ORs were significant for PD: identical for volumetric and slice-mean measures [OR = 1.43 (1.18, 1.72)] and [OR = 1.44 (1.18, 1.75)] for synthetic images. A 2nd degree polynomial (concave-down) was used to model PD as a function of CBT: location of the maximum PD value was similar across CCs, occurring at 0.41 × CBT, and PD was significant [OR = 1.47 (1.21, 1.78)]. The means from the volume and synthetic images were also significant [ORs ~ 1.31 (1.09, 1.57)]. An alternative standardized 2D synthetic image was constructed, where each pixel value represents the percentage of breast density above its location. Several measures were significant and an alternative method for constructing a standardized 2D synthetic image was produced.
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Affiliation(s)
- John Heine
- Cancer Epidemiology Department, Moffitt Cancer Center and Research Institute, 12902 Bruce B. Downs Blvd, Tampa, FL, 33612, USA.
| | - Erin E E Fowler
- Cancer Epidemiology Department, Moffitt Cancer Center and Research Institute, 12902 Bruce B. Downs Blvd, Tampa, FL, 33612, USA
| | - R Jared Weinfurtner
- Diagnostic Imaging and Interventional Radiology, Moffitt Cancer Center and Research Institute, 12902 Bruce B. Downs Blvd, Tampa, FL, 33612, USA
| | - Emma Hume
- Cancer Epidemiology Department, Moffitt Cancer Center and Research Institute, 12902 Bruce B. Downs Blvd, Tampa, FL, 33612, USA
| | - Shelley S Tworoger
- Cancer Epidemiology Department, Moffitt Cancer Center and Research Institute, 12902 Bruce B. Downs Blvd, Tampa, FL, 33612, USA
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11
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Shim S, Unkelbach J, Landsmann A, Boss A. Quantitative Study on the Breast Density and the Volume of the Mammary Gland According to the Patient's Age and Breast Quadrant. Diagnostics (Basel) 2023; 13:3343. [PMID: 37958239 PMCID: PMC10648521 DOI: 10.3390/diagnostics13213343] [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: 07/31/2023] [Revised: 09/29/2023] [Accepted: 10/13/2023] [Indexed: 11/15/2023] Open
Abstract
OBJECTIVES Breast density is considered an independent risk factor for the development of breast cancer. This study aimed to quantitatively assess the percent breast density (PBD) and the mammary glands volume (MGV) according to the patient's age and breast quadrant. We propose a regression model to estimate PBD and MGV as a function of the patient's age. METHODS The breast composition in 1027 spiral breast CT (BCT) datasets without soft tissue masses, calcifications, or implants from 517 women (57 ± 8 years) were segmented. The breast tissue volume (BTV), MGV, and PBD of the breasts were measured in the entire breast and each of the four quadrants. The three breast composition features were analyzed in the seven age groups, from 40 to 74 years in 5-year intervals. A logarithmic model was fitted to the BTV, and a multiplicative inverse model to the MGV and PBD as a function of age was established using a least-squares method. RESULTS The BTV increased from 545 ± 345 to 676 ± 412 cm3, and the MGV and PBD decreased from 111 ± 164 to 57 ± 43 cm3 and from 21 ± 21 to 11 ± 9%, respectively, from the youngest to the oldest group (p < 0.05). The average PBD over all ages were 14 ± 13%. The regression models could predict the BTV, MGV, and PBD based on the patient's age with residual standard errors of 386 cm3, 67 cm3, and 13%, respectively. The reduction in MGV and PBD in each quadrant followed the ones in the entire breast. CONCLUSIONS The PBD and MGV computed from BCT examinations provide important information for breast cancer risk assessment in women. The study quantified the breast mammary gland reduction and density decrease over the entire breast. It established mathematical models to estimate the breast composition features-BTV, MGV, and PBD, as a function of the patient's age.
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Affiliation(s)
- Sojin Shim
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Raemistrasse 100, 8091 Zurich, Switzerland; (A.L.); (A.B.)
| | - Jan Unkelbach
- Department of Radiation Oncology, University Hospital Zurich, 8091 Zurich, Switzerland;
| | - Anna Landsmann
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Raemistrasse 100, 8091 Zurich, Switzerland; (A.L.); (A.B.)
| | - Andreas Boss
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Raemistrasse 100, 8091 Zurich, Switzerland; (A.L.); (A.B.)
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12
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Koch S, Schmidtke J, Krawczak M, Caliebe A. Clinical utility of polygenic risk scores: a critical 2023 appraisal. J Community Genet 2023; 14:471-487. [PMID: 37133683 PMCID: PMC10576695 DOI: 10.1007/s12687-023-00645-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 03/31/2023] [Indexed: 05/04/2023] Open
Abstract
Since their first appearance in the context of schizophrenia and bipolar disorder in 2009, polygenic risk scores (PRSs) have been described for a large number of common complex diseases. However, the clinical utility of PRSs in disease risk assessment or therapeutic decision making is likely limited because PRSs usually only account for the heritable component of a trait and ignore the etiological role of environment and lifestyle. We surveyed the current state of PRSs for various diseases, including breast cancer, diabetes, prostate cancer, coronary artery disease, and Parkinson disease, with an extra focus upon the potential improvement of clinical scores by their combination with PRSs. We observed that the diagnostic and prognostic performance of PRSs alone is consistently low, as expected. Moreover, combining a PRS with a clinical score at best led to moderate improvement of the power of either risk marker. Despite the large number of PRSs reported in the scientific literature, prospective studies of their clinical utility, particularly of the PRS-associated improvement of standard screening or therapeutic procedures, are still rare. In conclusion, the benefit to individual patients or the health care system in general of PRS-based extensions of existing diagnostic or treatment regimens is still difficult to judge.
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Affiliation(s)
- Sebastian Koch
- Institut für Medizinische Informatik und Statistik, Christian-Albrechts-Universität zu Kiel, Universitätsklinikum Schleswig-Holstein Campus Kiel, Kiel, Germany
| | - Jörg Schmidtke
- Amedes MVZ Wagnerstibbe, Hannover, Germany
- Institut für Humangenetik, Medizinische Hochschule Hannover, Hannover, Germany
| | - Michael Krawczak
- Institut für Medizinische Informatik und Statistik, Christian-Albrechts-Universität zu Kiel, Universitätsklinikum Schleswig-Holstein Campus Kiel, Kiel, Germany
| | - Amke Caliebe
- Institut für Medizinische Informatik und Statistik, Christian-Albrechts-Universität zu Kiel, Universitätsklinikum Schleswig-Holstein Campus Kiel, Kiel, Germany.
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13
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Sprague BL, Ichikawa L, Eavey J, Lowry KP, Rauscher G, O’Meara ES, Miglioretti DL, Chen S, Lee JM, Stout NK, Mandelblatt JS, Alsheik N, Herschorn SD, Perry H, Weaver DL, Kerlikowske K. Breast cancer risk characteristics of women undergoing whole-breast ultrasound screening versus mammography alone. Cancer 2023; 129:2456-2468. [PMID: 37303202 PMCID: PMC10506533 DOI: 10.1002/cncr.34768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 02/06/2023] [Accepted: 02/24/2023] [Indexed: 06/13/2023]
Abstract
BACKGROUND There are no consensus guidelines for supplemental breast cancer screening with whole-breast ultrasound. However, criteria for women at high risk of mammography screening failures (interval invasive cancer or advanced cancer) have been identified. Mammography screening failure risk was evaluated among women undergoing supplemental ultrasound screening in clinical practice compared with women undergoing mammography alone. METHODS A total of 38,166 screening ultrasounds and 825,360 screening mammograms without supplemental screening were identified during 2014-2020 within three Breast Cancer Surveillance Consortium (BCSC) registries. Risk of interval invasive cancer and advanced cancer were determined using BCSC prediction models. High interval invasive breast cancer risk was defined as heterogeneously dense breasts and BCSC 5-year breast cancer risk ≥2.5% or extremely dense breasts and BCSC 5-year breast cancer risk ≥1.67%. Intermediate/high advanced cancer risk was defined as BCSC 6-year advanced breast cancer risk ≥0.38%. RESULTS A total of 95.3% of 38,166 ultrasounds were among women with heterogeneously or extremely dense breasts, compared with 41.8% of 825,360 screening mammograms without supplemental screening (p < .0001). Among women with dense breasts, high interval invasive breast cancer risk was prevalent in 23.7% of screening ultrasounds compared with 18.5% of screening mammograms without supplemental imaging (adjusted odds ratio, 1.35; 95% CI, 1.30-1.39); intermediate/high advanced cancer risk was prevalent in 32.0% of screening ultrasounds versus 30.5% of screening mammograms without supplemental screening (adjusted odds ratio, 0.91; 95% CI, 0.89-0.94). CONCLUSIONS Ultrasound screening was highly targeted to women with dense breasts, but only a modest proportion were at high mammography screening failure risk. A clinically significant proportion of women undergoing mammography screening alone were at high mammography screening failure risk.
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Affiliation(s)
- Brian L. Sprague
- Office of Health Promotion Research, Department of Surgery, University of Vermont Larner College of Medicine, Burlington, VT
- Department of Radiology, University of Vermont Larner College of Medicine, Burlington, VT
- University of Vermont Cancer Center, University of Vermont Larner College of Medicine, Burlington, VT
| | - Laura Ichikawa
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente WA, Seattle, Washington
| | - Joanna Eavey
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente WA, Seattle, Washington
| | - Kathryn P. Lowry
- Department of Radiology, University of Washington and Seattle Cancer Care Alliance, Seattle, WA
| | - Garth Rauscher
- Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago, Chicago, IL
| | - Ellen S. O’Meara
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente WA, Seattle, Washington
| | - Diana L. Miglioretti
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente WA, Seattle, Washington
- Division of Biostatistics, Department of Public Health Sciences, University of California Davis, Davis, CA
| | - Shuai Chen
- Division of Biostatistics, Department of Public Health Sciences, University of California Davis, Davis, CA
| | - Janie M. Lee
- Department of Radiology, University of Washington and Seattle Cancer Care Alliance, Seattle, WA
| | - Natasha K. Stout
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA
| | - Jeanne S. Mandelblatt
- Department of Oncology, Georgetown University Medical Center, Georgetown University, Washington, DC, USA
| | - Nila Alsheik
- Advocate Caldwell Breast Center, Advocate Lutheran General Hospital, 1700 Luther Lane, Park Ridge, IL
| | - Sally D. Herschorn
- Department of Radiology, University of Vermont Larner College of Medicine, Burlington, VT
- University of Vermont Cancer Center, University of Vermont Larner College of Medicine, Burlington, VT
| | - Hannah Perry
- Department of Radiology, University of Vermont Larner College of Medicine, Burlington, VT
- University of Vermont Cancer Center, University of Vermont Larner College of Medicine, Burlington, VT
| | - Donald L. Weaver
- University of Vermont Cancer Center, University of Vermont Larner College of Medicine, Burlington, VT
- Department of Pathology & Laboratory Medicine, University of Vermont Larner College of Medicine, Burlington, VT
| | - Karla Kerlikowske
- Departments of Medicine and Epidemiology and Biostatistics, University of California, San Francisco, CA
- General Internal Medicine Section, Department of Veterans Affairs, University of California, San Francisco, CA
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14
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Nguyen AA, McCarthy AM, Kontos D. Combining Molecular and Radiomic Features for Risk Assessment in Breast Cancer. Annu Rev Biomed Data Sci 2023; 6:299-311. [PMID: 37159874 DOI: 10.1146/annurev-biodatasci-020722-092748] [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: 05/11/2023]
Abstract
Breast cancer risk is highly variable within the population and current research is leading the shift toward personalized medicine. By accurately assessing an individual woman's risk, we can reduce the risk of over/undertreatment by preventing unnecessary procedures or by elevating screening procedures. Breast density measured from conventional mammography has been established as one of the most dominant risk factors for breast cancer; however, it is currently limited by its ability to characterize more complex breast parenchymal patterns that have been shown to provide additional information to strengthen cancer risk models. Molecular factors ranging from high penetrance, or high likelihood that a mutation will show signs and symptoms of the disease, to combinations of gene mutations with low penetrance have shown promise for augmenting risk assessment. Although imaging biomarkers and molecular biomarkers have both individually demonstrated improved performance in risk assessment, few studies have evaluated them together. This review aims to highlight the current state of the art in breast cancer risk assessment using imaging and genetic biomarkers.
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Affiliation(s)
- Alex A Nguyen
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Anne Marie McCarthy
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Despina Kontos
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA;
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15
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Stiller S, Drukewitz S, Lehmann K, Hentschel J, Strehlow V. Clinical Impact of Polygenic Risk Score for Breast Cancer Risk Prediction in 382 Individuals with Hereditary Breast and Ovarian Cancer Syndrome. Cancers (Basel) 2023; 15:3938. [PMID: 37568754 PMCID: PMC10417109 DOI: 10.3390/cancers15153938] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 07/21/2023] [Accepted: 07/28/2023] [Indexed: 08/13/2023] Open
Abstract
Single nucleotide polymorphisms are currently not considered in breast cancer (BC) risk predictions used in daily practice of genetic counselling and clinical management of familial BC in Germany. This study aimed to assess the clinical value of incorporating a 313-variant-based polygenic risk score (PRS) into BC risk calculations in a cohort of German women with suspected hereditary breast and ovarian cancer syndrome (HBOC). Data from 382 individuals seeking counselling for HBOC were analysed. Risk calculations were performed using the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm with and without the inclusion of the PRS. Changes in risk predictions and their impact on clinical management were evaluated. The PRS led to changes in risk stratification based on 10-year risk calculations in 13.6% of individuals. Furthermore, the inclusion of the PRS in BC risk predictions resulted in clinically significant changes in 12.0% of cases, impacting the prevention recommendations established by the German Consortium for Hereditary Breast and Ovarian Cancer. These findings support the implementation of the PRS in genetic counselling for personalized BC risk assessment.
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Affiliation(s)
- Sarah Stiller
- Institute of Human Genetics, University of Leipzig Medical Center, 04103 Leipzig, Germany
| | - Stephan Drukewitz
- Institute of Human Genetics, University of Leipzig Medical Center, 04103 Leipzig, Germany
- Core Unit for Molecular Tumor Diagnostics (CMTD), National Center for Tumor Diseases (NCT), Partner Site Dresden, 01307 Dresden, Germany
| | - Kathleen Lehmann
- Institute of Human Genetics, University of Leipzig Medical Center, 04103 Leipzig, Germany
| | - Julia Hentschel
- Institute of Human Genetics, University of Leipzig Medical Center, 04103 Leipzig, Germany
| | - Vincent Strehlow
- Institute of Human Genetics, University of Leipzig Medical Center, 04103 Leipzig, Germany
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16
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Vachon CM, Scott CG, Norman AD, Khanani SA, Jensen MR, Hruska CB, Brandt KR, Winham SJ, Kerlikowske K. Impact of Artificial Intelligence System and Volumetric Density on Risk Prediction of Interval, Screen-Detected, and Advanced Breast Cancer. J Clin Oncol 2023; 41:3172-3183. [PMID: 37104728 PMCID: PMC10256336 DOI: 10.1200/jco.22.01153] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 12/13/2022] [Accepted: 02/24/2023] [Indexed: 04/29/2023] Open
Abstract
PURPOSE Artificial intelligence (AI) algorithms improve breast cancer detection on mammography, but their contribution to long-term risk prediction for advanced and interval cancers is unknown. METHODS We identified 2,412 women with invasive breast cancer and 4,995 controls matched on age, race, and date of mammogram, from two US mammography cohorts, who had two-dimensional full-field digital mammograms performed 2-5.5 years before cancer diagnosis. We assessed Breast Imaging Reporting and Data System density, an AI malignancy score (1-10), and volumetric density measures. We used conditional logistic regression to estimate odds ratios (ORs), 95% CIs, adjusted for age and BMI, and C-statistics (AUC) to describe the association of AI score with invasive cancer and its contribution to models with breast density measures. Likelihood ratio tests (LRTs) and bootstrapping methods were used to compare model performance. RESULTS On mammograms between 2-5.5 years prior to cancer, a one unit increase in AI score was associated with 20% greater odds of invasive breast cancer (OR, 1.20; 95% CI, 1.17 to 1.22; AUC, 0.63; 95% CI, 0.62 to 0.64) and was similarly predictive of interval (OR, 1.20; 95% CI, 1.13 to 1.27; AUC, 0.63) and advanced cancers (OR, 1.23; 95% CI, 1.16 to 1.31; AUC, 0.64) and in dense (OR, 1.18; 95% CI, 1.15 to 1.22; AUC, 0.66) breasts. AI score improved prediction of all cancer types in models with density measures (PLRT values < .001); discrimination improved for advanced cancer (ie, AUC for dense volume increased from 0.624 to 0.679, Δ AUC 0.065, P = .01) but did not reach statistical significance for interval cancer. CONCLUSION AI imaging algorithms coupled with breast density independently contribute to long-term risk prediction of invasive breast cancers, in particular, advanced cancer.
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Affiliation(s)
- Celine M. Vachon
- Division of Epidemiology, Department Quantitative Sciences, Mayo Clinic, Rochester, MN
| | - Christopher G. Scott
- Division of Clinical Trials and Biostatistics, Department Quantitative Sciences, Mayo Clinic, Rochester, MN
| | - Aaron D. Norman
- Division of Epidemiology, Department Quantitative Sciences, Mayo Clinic, Rochester, MN
| | - Sadia A. Khanani
- Division of Breast Imaging, Department of Radiology, Mayo Clinic, Rochester, MN
| | - Matthew R. Jensen
- Division of Clinical Trials and Biostatistics, Department Quantitative Sciences, Mayo Clinic, Rochester, MN
| | - Carrie B. Hruska
- Division of Breast Imaging, Department of Radiology, Mayo Clinic, Rochester, MN
| | - Kathleen R. Brandt
- Division of Breast Imaging, Department of Radiology, Mayo Clinic, Rochester, MN
| | - Stacey J. Winham
- Division of Computational Biology, Department Quantitative Sciences, Mayo Clinic, Rochester, MN
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17
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Hartkopf AD, Fehm TN, Welslau M, Müller V, Schütz F, Fasching PA, Janni W, Witzel I, Thomssen C, Beierlein M, Belleville E, Untch M, Thill M, Tesch H, Ditsch N, Lux MP, Aktas B, Banys-Paluchowski M, Kolberg-Liedtke C, Wöckel A, Kolberg HC, Harbeck N, Stickeler E, Bartsch R, Schneeweiss A, Ettl J, Würstlein R, Krug D, Taran FA, Lüftner D. Update Breast Cancer 2023 Part 1 - Early Stage Breast Cancer. Geburtshilfe Frauenheilkd 2023; 83:653-663. [PMID: 37916183 PMCID: PMC10617391 DOI: 10.1055/a-2074-0551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 04/13/2023] [Indexed: 11/03/2023] Open
Abstract
With abemaciclib (monarchE study) and olaparib (OlympiA study) gaining approval in the adjuvant treatment setting, a significant change in the standard of care for patients with early stage breast cancer has been established for some time now. Accordingly, some diverse developments are slowly being transferred from the metastatic to the adjuvant treatment setting. Recently, there have also been positive reports of the NATALEE study. Other clinical studies are currently investigating substances that are already established in the metastatic setting. These include, for example, the DESTINY Breast05 study with trastuzumab deruxtecan and the SASCIA study with sacituzumab govitecan. In this review paper, we summarize and place in context the latest developments over the past months.
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Affiliation(s)
- Andreas D. Hartkopf
- Department of Gynecology and Obstetrics, Ulm University Hospital, Ulm, Germany
| | - Tanja N. Fehm
- Department of Gynecology and Obstetrics, University Hospital Düsseldorf, Düsseldorf, Germany
| | | | - Volkmar Müller
- Department of Gynecology, Hamburg-Eppendorf University Medical Center, Hamburg, Germany
| | - Florian Schütz
- Gynäkologie und Geburtshilfe, Diakonissen-Stiftungs-Krankenhaus Speyer, Speyer, Germany
| | - Peter A. Fasching
- Erlangen University Hospital, Department of Gynecology and Obstetrics; Comprehensive Cancer Center Erlangen EMN, Friedrich-Alexander University Erlangen-Nuremberg,
Erlangen, Germany
| | - Wolfgang Janni
- Department of Gynecology and Obstetrics, Ulm University Hospital, Ulm, Germany
| | - Isabell Witzel
- Klinik für Gynäkologie, Universitätsspital Zürich, Zürich, Switzerland
| | - Christoph Thomssen
- Department of Gynaecology, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany
| | - Milena Beierlein
- Erlangen University Hospital, Department of Gynecology and Obstetrics; Comprehensive Cancer Center Erlangen EMN, Friedrich-Alexander University Erlangen-Nuremberg,
Erlangen, Germany
| | | | - Michael Untch
- Clinic for Gynecology and Obstetrics, Breast Cancer Center, Gynecologic Oncology Center, Helios Klinikum Berlin Buch, Berlin, Germany
| | - Marc Thill
- Department of Gynecology and Gynecological Oncology, Agaplesion Markus Krankenhaus, Frankfurt am Main, Germany
| | - Hans Tesch
- Oncology Practice at Bethanien Hospital, Frankfurt am Main, Germany
| | - Nina Ditsch
- Department of Gynecology and Obstetrics, University Hospital Augsburg, Augsburg, Germany
| | - Michael P. Lux
- Klinik für Gynäkologie und Geburtshilfe, Frauenklinik St. Louise, Paderborn, St. Josefs-Krankenhaus, Salzkotten, St. Vincenz Krankenhaus GmbH, Paderborn, Germany
| | - Bahriye Aktas
- Department of Gynecology, University of Leipzig Medical Center, Leipzig, Germany
| | - Maggie Banys-Paluchowski
- Department of Gynecology and Obstetrics, University Hospital Schleswig-Holstein, Campus Lübeck, Lübeck, Germany
| | | | - Achim Wöckel
- Department of Gynecology and Obstetrics, University Hospital Würzburg, Würzburg, Germany
| | | | - Nadia Harbeck
- Breast Center, Department of Gynecology and Obstetrics and CCC Munich LMU, LMU University Hospital, München, Germany
| | - Elmar Stickeler
- Department of Obstetrics and Gynecology, Center for Integrated Oncology (CIO Aachen, Bonn, Cologne, Düsseldorf), University Hospital of RWTH Aachen, Aachen, Germany
| | - Rupert Bartsch
- Department of Medicine I, Division of Oncology, Medical University of Vienna, Vienna, Austria
| | - Andreas Schneeweiss
- National Center for Tumor Diseases, University Hospital and German Cancer Research Center, Heidelberg, Germany
| | - Johannes Ettl
- Klinik für Frauenheilkunde und Gynäkologie, Klinikum Kempten, Klinikverbund Allgäu, Kempten, Germany
| | - Rachel Würstlein
- Breast Center, Department of Gynecology and Obstetrics and CCC Munich LMU, LMU University Hospital, München, Germany
| | - David Krug
- Klinik für Strahlentherapie, Universitätsklinkum Schleswig-Holstein, Campus Kiel, Kiel, Germany
| | - Florin-Andrei Taran
- Department of Gynecology and Obstetrics, University Hospital Freiburg, Freiburg, Germany
| | - Diana Lüftner
- Medical University of Brandenburg Theodor-Fontane, Immanuel Hospital Märkische Schweiz, Buckow, Germany
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18
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Abstract
Breast cancer is the most common cancer in women globally with enormous associated morbidity, mortality and economic impact. Prevention of breast cancer is a global public health imperative. To date, most of our global efforts have been directed at expanding population breast cancer screening programs for early cancer detection and not at breast cancer prevention efforts. It is imperative that we change the paradigm. As with other diseases, prevention of breast cancer starts with identification of individuals at high risk, and for breast cancer this requires improved identification of individuals who carry a hereditary cancer mutation associated with an elevated risk of breast cancer, and identification of others who are at high risk due to non-genetic, established modifiable and non-modifiable factors. This article will review basic breast cancer genetics and the most common hereditary breast cancer mutations associated with increased risk. We will also discuss the other non-genetic modifiable and non-modifiable breast cancer risk factors, available risk assessment models and an approach to incorporating screening for genetic mutation carriers and identifying high-risk women in clinical practice. A discussion of guidelines for enhanced screening, chemoprevention and surgical management of high-risk women is beyond the scope of this review.
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Affiliation(s)
- L Larkin
- MS.Medicine, Cincinnati, OH, USA
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19
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Allman R, Mu Y, Dite GS, Spaeth E, Hopper JL, Rosner BA. Validation of a breast cancer risk prediction model based on the key risk factors: family history, mammographic density and polygenic risk. Breast Cancer Res Treat 2023; 198:335-347. [PMID: 36749458 PMCID: PMC10020257 DOI: 10.1007/s10549-022-06834-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 12/02/2022] [Indexed: 02/08/2023]
Abstract
PURPOSE We compared a simple breast cancer risk prediction model, BRISK (which includes mammographic density, polygenic risk and clinical factors), against a similar model with more risk factors (simplified Rosner) and against two commonly used clinical models (Gail and IBIS). METHODS Using nested case-control data from the Nurses' Health Study, we compared the models' association, discrimination and calibration. Classification performance was compared between Gail and BRISK for 5-year risks and between IBIS and BRISK for remaining lifetime risk. RESULTS The odds ratio per standard deviation was 1.43 (95% CI 1.32, 1.55) for BRISK 5-year risk, 1.07 (95% CI 0.99, 1.14) for Gail 5-year risk, 1.72 (95% CI 1.59, 1.87) for simplified Rosner 10-year risk, 1.51 (95% CI 1.41, 1.62) for BRISK remaining lifetime risk and 1.26 (95% CI 1.16, 1.36) for IBIS remaining lifetime risk. The area under the receiver operating characteristic curve (AUC) was improved for BRISK over Gail for 5-year risk (AUC = 0.636 versus 0.511, P < 0.0001) and for BRISK over IBIS for remaining lifetime risk (AUC = 0.647 versus 0.571, P < 0.0001). BRISK was well calibrated for the estimation of both 5-year risk (expected/observed [E/O] = 1.03; 95% CI 0.73, 1.46) and remaining lifetime risk (E/O = 1.01; 95% CI 0.86, 1.17). The Gail 5-year risk (E/O = 0.85; 95% CI 0.58, 1.24) and IBIS remaining lifetime risk (E/O = 0.73; 95% CI 0.60, 0.87) were not well calibrated, with both under-estimating risk. BRISK improves classification of risk compared to Gail 5-year risk (NRI = 0.31; standard error [SE] = 0.031) and IBIS remaining lifetime risk (NRI = 0.287; SE = 0.035). CONCLUSION BRISK performs better than two commonly used clinical risk models and no worse compared to a similar model with more risk factors.
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Affiliation(s)
- Richard Allman
- Genetic Technologies Limited, 60-66 Hanover St, Fitzroy, VIC, 3065, Australia.
| | - Yi Mu
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Gillian S Dite
- Genetic Technologies Limited, 60-66 Hanover St, Fitzroy, VIC, 3065, Australia
| | | | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia
| | - Bernard A Rosner
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
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Seitzman RL, Pushkin J, Berg WA. Effect of an Educational Intervention on Women's Health Care Provider Knowledge Gaps About Breast Cancer Risk Model Use and High-risk Screening Recommendations. JOURNAL OF BREAST IMAGING 2023; 5:30-39. [PMID: 38416962 DOI: 10.1093/jbi/wbac072] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Indexed: 03/01/2024]
Abstract
OBJECTIVE To assess effectiveness of a web-based educational intervention on women's health care provider knowledge of breast cancer risk models and high-risk screening recommendations. METHODS A web-based pre- and post-test study including 177 U.S.-based women's health care providers was conducted in 2019. Knowledge gaps were defined as fewer than 75% of respondents answering correctly. Pre- and post-test knowledge differences (McNemar test) and associations of baseline characteristics with pre-test knowledge gaps (logistic regression) were evaluated. RESULTS Respondents included 131/177 (74.0%) physicians; 127/177 (71.8%) practiced obstetrics/gynecology. Pre-test, 118/177 (66.7%) knew the Gail model predicts lifetime invasive breast cancer risk; this knowledge gap persisted post-test [(121/177, 68.4%); P = 0.77]. Just 39.0% (69/177) knew the Gail model identifies women eligible for risk-reducing medications; this knowledge gap resolved. Only 48.6% (86/177) knew the Gail model should not be used to identify women meeting high-risk MRI screening guidelines; this deficiency decreased to 66.1% (117/177) post-test (P = 0.001). Pre-test, 47.5% (84/177) knew the Tyrer-Cuzick model is used to identify women meeting high-risk screening MRI criteria, 42.9% (76/177) to predict BRCA1/2 pathogenic mutation risk, and 26.0% (46/177) to predict lifetime invasive breast cancer risk. These knowledge gaps persisted but improved. For a high-risk 30-year-old, 67.8% (120/177) and 54.2% (96/177) pre-test knew screening MRI and mammography/tomosynthesis are recommended, respectively; 19.2% (34/177) knew both are recommended; and 53% (94/177) knew US is not recommended. These knowledge gaps resolved or reduced. CONCLUSION Web-based education can reduce important provider knowledge gaps about breast cancer risk models and high-risk screening recommendations.
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Affiliation(s)
| | | | - Wendie A Berg
- DenseBreast-info, Inc, Deer Park, NY, USA
- University of Pittsburgh School of Medicine, Magee-Womens Hospital of UPMC, Department of Radiology, Pittsburgh, PA, USA
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21
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Ohbe H, Hachiya T, Yamaji T, Nakano S, Miyamoto Y, Sutoh Y, Otsuka-Yamasaki Y, Shimizu A, Yasunaga H, Sawada N, Inoue M, Tsugane S, Iwasaki M. Development and validation of genome-wide polygenic risk scores for predicting breast cancer incidence in Japanese females: a population-based case-cohort study. Breast Cancer Res Treat 2023; 197:661-671. [PMID: 36538246 DOI: 10.1007/s10549-022-06843-6] [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: 09/28/2022] [Accepted: 12/08/2022] [Indexed: 12/24/2022]
Abstract
PURPOSE This study aimed to develop an ancestry-specific polygenic risk scores (PRSs) for the prediction of breast cancer events in Japanese females and validate it in a longitudinal cohort study. METHODS Using publicly available summary statistics of female breast cancer genome-wide association study (GWAS) of Japanese and European ancestries, we, respectively, developed 31 candidate genome-wide PRSs using pruning and thresholding (P + T) and LDpred methods with varying parameters. Among the candidate PRS models, the best model was selected using a case-cohort dataset (63 breast cancer cases and 2213 sub-cohorts of Japanese females during a median follow-up of 11.9 years) according to the maximal predictive ability by Harrell's C-statistics. The best-performing PRS for each derivation GWAS was evaluated in another independent case-cohort dataset (260 breast cancer cases and 7845 sub-cohorts of Japanese females during a median follow-up of 16.9 years). RESULTS For the best PRS model involving 46,861 single nucleotide polymorphisms (SNPs; P + T method with PT = 0.05 and R2 = 0.2) derived from Japanese-ancestry GWAS, the Harrell's C-statistic was 0.598 ± 0.018 in the evaluation dataset. The age-adjusted hazard ratio for breast cancer in females with the highest PRS quintile compared with those in the lowest PRS quintile was 2.47 (95% confidence intervals, 1.64-3.70). The PRS constructed using Japanese-ancestry GWAS demonstrated better predictive performance for breast cancer in Japanese females than that using European-ancestry GWAS (Harrell's C-statistics 0.598 versus 0.586). CONCLUSION This study developed a breast cancer PRS for Japanese females and demonstrated the usefulness of the PRS for breast cancer risk stratification.
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Affiliation(s)
- Hiroyuki Ohbe
- Department of Clinical Epidemiology and Health Economics, School of Public Health, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Tsuyoshi Hachiya
- Division of Biomedical Information Analysis, Iwate Tohoku Medical Megabank Organization, Iwate Medical University, 1-1-1 Idaidori, Yahaba, Shiwa, Iwate, 028-3694, Japan.
| | - Taiki Yamaji
- Division of Epidemiology, National Cancer Center Institute for Cancer Control, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
| | - Shiori Nakano
- Division of Epidemiology, National Cancer Center Institute for Cancer Control, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Yoshihisa Miyamoto
- Division of Cohort Research, National Cancer Center Institute for Cancer Control, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Yoichi Sutoh
- Division of Biomedical Information Analysis, Iwate Tohoku Medical Megabank Organization, Iwate Medical University, 1-1-1 Idaidori, Yahaba, Shiwa, Iwate, 028-3694, Japan
| | - Yayoi Otsuka-Yamasaki
- Division of Biomedical Information Analysis, Iwate Tohoku Medical Megabank Organization, Iwate Medical University, 1-1-1 Idaidori, Yahaba, Shiwa, Iwate, 028-3694, Japan
| | - Atsushi Shimizu
- Division of Biomedical Information Analysis, Iwate Tohoku Medical Megabank Organization, Iwate Medical University, 1-1-1 Idaidori, Yahaba, Shiwa, Iwate, 028-3694, Japan
| | - Hideo Yasunaga
- Department of Clinical Epidemiology and Health Economics, School of Public Health, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Norie Sawada
- Division of Cohort Research, National Cancer Center Institute for Cancer Control, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Manami Inoue
- Division of Cohort Research, National Cancer Center Institute for Cancer Control, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.,Division of Prevention, National Cancer Center Institute for Cancer Control, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Shoichiro Tsugane
- Division of Cohort Research, National Cancer Center Institute for Cancer Control, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.,National Institute of Health and Nutrition, National Institutes of Biomedical Innovation, Health and Nutrition, Tokyo, 162-8636, Japan
| | - Motoki Iwasaki
- Division of Epidemiology, National Cancer Center Institute for Cancer Control, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.,Division of Cohort Research, National Cancer Center Institute for Cancer Control, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
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22
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Edmonds CE, O'Brien SR, Conant EF. Mammographic Breast Density: Current Assessment Methods, Clinical Implications, and Future Directions. Semin Ultrasound CT MR 2023; 44:35-45. [PMID: 36792272 DOI: 10.1053/j.sult.2022.11.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Mammographic breast density is widely accepted as an independent risk factor for the development of breast cancer. In addition, because dense breast tissue may mask breast malignancies, breast density is inversely related to the sensitivity of screening mammography. Given the risks associated with breast density, as well as ongoing efforts to stratify individual risk and personalize breast cancer screening and prevention, numerous studies have sought to better understand the factors that impact breast density, and to develop and implement reproducible, quantitative methods to assess mammographic density. Breast density assessments have been incorporated into risk assessment models to improve risk stratification. Recently, novel techniques for analyzing mammographic parenchymal complexity, or texture, have been explored as potential means of refining mammographic tissue-based risk assessment beyond breast density.
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Affiliation(s)
- Christine E Edmonds
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA.
| | - Sophia R O'Brien
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA
| | - Emily F Conant
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA
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23
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Choridah L, Icanervilia AV, Rengganis AA, At Thobari J, Postma MJ, D I van Asselt A. Comparing the performance of three modalities of breast cancer screening within a combined programme targeting at-risk women in Indonesia: An implementation study. Glob Public Health 2023; 18:2284370. [PMID: 38015726 DOI: 10.1080/17441692.2023.2284370] [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: 12/30/2022] [Accepted: 11/10/2023] [Indexed: 11/30/2023]
Abstract
ABSTRACTAlthough mammography is the gold standard for breast cancer screening, the World Health Organization recommends clinical breast examination (CBE) as the preferred early detection method in countries with limited resources. However, its effectiveness as a 'stand-alone' screening modality compared with other techniques remains unclear. Therefore, we evaluated a risk-based opportunistic breast cancer screening programme using three modalities. Between June and December 2018, we conducted a cross-sectional study in Yogyakarta, Indonesia, of women aged >40 years with at least one risk factor for breast cancer. Subjects underwent CBE, mammography, and ultrasonography. We calculated the proportion of breast lesions detected through each modality and compared their mass size. A total of 503 eligible subjects were screened. Five cases of potential malignant lesions were detected; pathological tests conducted for 4 of them confirmed breast cancer diagnoses. A combined assessment of mammography and ultrasonography examinations revealed 343 breast lesions (68.2%), whereas CBE screening detected only 76 breast lesions (15.1%). The mean lesion sizes detected by mammography or ultrasonography, but not through CBE, were significantly smaller (p-values of 0.037 and 0.007 for mammography and ultrasonography, respectively). In conclusion, mammography and ultrasonography produced higher detection rates for benign and malignant breast lesions compared with CBE.
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Affiliation(s)
- Lina Choridah
- Department of Radiology, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - Ajeng Viska Icanervilia
- Department of Radiology, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia
- Clinical Epidemiology and Biostatistics Unit (CEBU), Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia
- Department of Health Sciences, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
| | - Anggraeni Ayu Rengganis
- Department of Radiology, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia
- Clinical Epidemiology and Biostatistics Unit (CEBU), Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - Jarir At Thobari
- Clinical Epidemiology and Biostatistics Unit (CEBU), Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia
- Department of Pharmacology and Therapy, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - Maarten J Postma
- Department of Health Sciences, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
| | - Antoinette D I van Asselt
- Department of Health Sciences, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
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24
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Jiao Y, Truong T, Eon-Marchais S, Mebirouk N, Caputo SM, Dondon MG, Karimi M, Le Gal D, Beauvallet J, Le Floch É, Dandine-Roulland C, Bacq-Daian D, Olaso R, Albuisson J, Audebert-Bellanger S, Berthet P, Bonadona V, Buecher B, Caron O, Cavaillé M, Chiesa J, Colas C, Collonge-Rame MA, Coupier I, Delnatte C, De Pauw A, Dreyfus H, Fert-Ferrer S, Gauthier-Villars M, Gesta P, Giraud S, Gladieff L, Golmard L, Lasset C, Lejeune-Dumoulin S, Léoné M, Limacher JM, Lortholary A, Luporsi É, Mari V, Maugard CM, Mortemousque I, Mouret-Fourme E, Nambot S, Noguès C, Popovici C, Prieur F, Pujol P, Sevenet N, Sobol H, Toulas C, Uhrhammer N, Vaur D, Venat L, Boland-Augé A, Guénel P, Deleuze JF, Stoppa-Lyonnet D, Andrieu N, Lesueur F. Association and performance of polygenic risk scores for breast cancer among French women presenting or not a familial predisposition to the disease. Eur J Cancer 2023; 179:76-86. [PMID: 36509001 DOI: 10.1016/j.ejca.2022.11.007] [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: 08/24/2022] [Revised: 10/26/2022] [Accepted: 11/06/2022] [Indexed: 11/15/2022]
Abstract
BACKGROUND Three partially overlapping breast cancer polygenic risk scores (PRS) comprising 77, 179 and 313 SNPs have been proposed for European-ancestry women by the Breast Cancer Association Consortium (BCAC) for improving risk prediction in the general population. However, the effect of these SNPs may vary from one country to another and within a country because of other factors. OBJECTIVE To assess their associated risk and predictive performance in French women from (1) the CECILE population-based case-control study, (2) BRCA1 or BRCA2 (BRCA1/2) pathogenic variant (PV) carriers from the GEMO study, and (3) familial breast cancer cases with no BRCA1/2 PV and unrelated controls from the GENESIS study. RESULTS All three PRS were associated with breast cancer in all studies, with odds ratios per standard deviation varying from 1.7 to 2.0 in CECILE and GENESIS, and hazard ratios varying from 1.1 to 1.4 in GEMO. The predictive performance of PRS313 in CECILE was similar to that reported in BCAC but lower than that in GENESIS (area under the receiver operating characteristic curve (AUC) = 0.67 and 0.75, respectively). PRS were less performant in BRCA2 and BRCA1 PV carriers (AUC = 0.58 and 0.54 respectively). CONCLUSION Our results are in line with previous validation studies in the general population and in BRCA1/2 PV carriers. Additionally, we showed that PRS may be of clinical utility for women with a strong family history of breast cancer and no BRCA1/2 PV, and for those carrying a predicted PV in a moderate-risk gene like ATM, CHEK2 or PALB2.
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Affiliation(s)
- Yue Jiao
- INSERM, U900, Paris, France; Institut Curie, Paris, France; Mines ParisTech, Fontainebleau, France; PSL Research University, Paris, France
| | - Thérèse Truong
- Université Paris-Saclay, UVSQ, INSERM, U1018, Gustave Roussy, CESP, Team Exposome and Heredity, Villejuif, France
| | - Séverine Eon-Marchais
- INSERM, U900, Paris, France; Institut Curie, Paris, France; Mines ParisTech, Fontainebleau, France; PSL Research University, Paris, France
| | - Noura Mebirouk
- INSERM, U900, Paris, France; Institut Curie, Paris, France; Mines ParisTech, Fontainebleau, France; PSL Research University, Paris, France
| | - Sandrine M Caputo
- PSL Research University, Paris, France; Department of Genetics, Institut Curie, Paris, France
| | - Marie-Gabrielle Dondon
- INSERM, U900, Paris, France; Institut Curie, Paris, France; Mines ParisTech, Fontainebleau, France; PSL Research University, Paris, France
| | - Mojgan Karimi
- Université Paris-Saclay, UVSQ, INSERM, U1018, Gustave Roussy, CESP, Team Exposome and Heredity, Villejuif, France
| | - Dorothée Le Gal
- INSERM, U900, Paris, France; Institut Curie, Paris, France; Mines ParisTech, Fontainebleau, France; PSL Research University, Paris, France
| | - Juana Beauvallet
- INSERM, U900, Paris, France; Institut Curie, Paris, France; Mines ParisTech, Fontainebleau, France; PSL Research University, Paris, France
| | - Édith Le Floch
- Centre National de Recherche en Génomique Humaine, Institut de Biologie François Jacob, CEA, Université Paris-Saclay, Evry, France
| | - Claire Dandine-Roulland
- Centre National de Recherche en Génomique Humaine, Institut de Biologie François Jacob, CEA, Université Paris-Saclay, Evry, France
| | - Delphine Bacq-Daian
- Centre National de Recherche en Génomique Humaine, Institut de Biologie François Jacob, CEA, Université Paris-Saclay, Evry, France
| | - Robert Olaso
- Centre National de Recherche en Génomique Humaine, Institut de Biologie François Jacob, CEA, Université Paris-Saclay, Evry, France
| | - Juliette Albuisson
- Centre de Lutte contre le Cancer Georges François Leclerc, Dijon, France
| | | | - Pascaline Berthet
- Département de Biopathologie, Centre François Baclesse, Caen, France; INSERM, U1245, Rouen, France
| | - Valérie Bonadona
- Université Claude Bernard Lyon 1, Villeurbanne, France; CNRS UMR 5558, Centre Léon Bérard, Unité de Prévention et épidémiologie Génétique, Lyon, France
| | - Bruno Buecher
- PSL Research University, Paris, France; Department of Genetics, Institut Curie, Paris, France
| | - Olivier Caron
- Gustave Roussy, Département de Médecine Oncologique, Villejuif, France
| | - Mathias Cavaillé
- Université Clermont Auvergne, UMR INSERM, U1240, Clermont Ferrand, France; Département d'Oncogénétique, Centre Jean Perrin, Clermont Ferrand, France
| | - Jean Chiesa
- UF de Génétique Médicale et Cytogénétique, CHRU Caremeau, Nîmes, France
| | - Chrystelle Colas
- PSL Research University, Paris, France; Department of Genetics, Institut Curie, Paris, France; INSERM, U830, Paris, France
| | - Marie-Agnès Collonge-Rame
- Service Génétique et Biologie du Développement - Histologie, CHU Hôpital Saint-Jacques, Besançon, France
| | - Isabelle Coupier
- Hôpital Arnaud de Villeneuve, CHU Montpellier, Service de Génétique Médicale et Oncogénétique, Montpellier, France; INSERM, U896, CRCM Val d'Aurelle, Montpellier, France
| | - Capucine Delnatte
- Institut de Cancérologie de l'Ouest, Unité d'Oncogénétique, Saint Herblain, France
| | - Antoine De Pauw
- PSL Research University, Paris, France; Department of Genetics, Institut Curie, Paris, France
| | - Hélène Dreyfus
- Clinique Sainte Catherine, Avignon, CHU de Grenoble, Grenoble, France; Hôpital Couple-Enfant, Département de Génétique, Grenoble, France
| | | | - Marion Gauthier-Villars
- PSL Research University, Paris, France; Department of Genetics, Institut Curie, Paris, France
| | - Paul Gesta
- CH Georges Renon, Service d'Oncogénétique Régional Poitou-Charentes, Niort, France
| | - Sophie Giraud
- Hospices Civils de Lyon, Service de Génétique, Groupement Hospitalier Est, Bron, France
| | - Laurence Gladieff
- Institut Claudius Regaud - IUCT-Oncopole, Service d'Oncologie Médicale, Toulouse, France
| | - Lisa Golmard
- PSL Research University, Paris, France; Department of Genetics, Institut Curie, Paris, France
| | - Christine Lasset
- Université Claude Bernard Lyon 1, Villeurbanne, France; CNRS UMR 5558, Centre Léon Bérard, Unité de Prévention et épidémiologie Génétique, Lyon, France
| | | | - Mélanie Léoné
- Hospices Civils de Lyon, Service de Génétique, Groupement Hospitalier Est, Bron, France
| | | | - Alain Lortholary
- Service d'Oncologie Médicale, Centre Catherine de Sienne, Nantes, France; Hôpital Privé du Confluent, Nantes, France
| | - Élisabeth Luporsi
- Service de Génétique UF4128 CHR Metz-Thionville, Hôpital de Mercy, Metz, France
| | - Véronique Mari
- Unité d'Oncogénétique, Centre Antoine Lacassagne, Nice, France
| | - Christine M Maugard
- Génétique Oncologique Moléculaire, UF1422, Département d'Oncobiologie, LBBM, Hôpitaux Universitaires de Strasbourg, Strasbourg, France; UF6948 Génétique Oncologique Clinique, évaluation Familiale et Suivi, Strasbourg, France
| | | | | | - Sophie Nambot
- Centre de Lutte contre le Cancer Georges François Leclerc, Dijon, France; Institut GIMI, CHU de Dijon, Hôpital d'Enfants, France; Oncogénétique, Dijon, France
| | - Catherine Noguès
- Département d'Anticipation et de Suivi des Cancers, Oncogénétique Clinique, Institut Paoli-Calmettes, Marseille, France; Aix Marseille Université, INSERM, IRD, SESSTIM, Marseille, France
| | - Cornel Popovici
- Département d'Anticipation et de Suivi des Cancers, Oncogénétique Clinique, Institut Paoli-Calmettes, Marseille, France
| | - Fabienne Prieur
- CHU de Saint-Etienne; Hôpital Nord, Service de Génétique, Saint-Etienne, France
| | - Pascal Pujol
- Hôpital Arnaud de Villeneuve, CHU Montpellier, Service de Génétique Médicale et Oncogénétique, Montpellier, France; INSERM, U896, CRCM Val d'Aurelle, Montpellier, France
| | | | - Hagay Sobol
- Département d'Anticipation et de Suivi des Cancers, Oncogénétique Clinique, Institut Paoli-Calmettes, Marseille, France
| | - Christine Toulas
- Institut Claudius Regaud - IUCT-Oncopole, Service d'Oncologie Médicale, Toulouse, France
| | - Nancy Uhrhammer
- Centre Jean Perrin, LBM OncoGenAuvergne, Clermont Ferrand, France
| | - Dominique Vaur
- Département de Biopathologie, Centre François Baclesse, Caen, France; INSERM, U1245, Rouen, France
| | - Laurence Venat
- Hôpital Universitaire Dupuytren, Service d'Oncologie Médicale, Limoges, France
| | - Anne Boland-Augé
- Centre National de Recherche en Génomique Humaine, Institut de Biologie François Jacob, CEA, Université Paris-Saclay, Evry, France
| | - Pascal Guénel
- Université Paris-Saclay, UVSQ, INSERM, U1018, Gustave Roussy, CESP, Team Exposome and Heredity, Villejuif, France
| | - Jean-François Deleuze
- Centre National de Recherche en Génomique Humaine, Institut de Biologie François Jacob, CEA, Université Paris-Saclay, Evry, France
| | - Dominique Stoppa-Lyonnet
- Department of Genetics, Institut Curie, Paris, France; Département d'Oncogénétique, Centre Jean Perrin, Clermont Ferrand, France; Université Paris-Cité, Paris, France
| | - Nadine Andrieu
- INSERM, U900, Paris, France; Institut Curie, Paris, France; Mines ParisTech, Fontainebleau, France; PSL Research University, Paris, France
| | - Fabienne Lesueur
- INSERM, U900, Paris, France; Institut Curie, Paris, France; Mines ParisTech, Fontainebleau, France; PSL Research University, Paris, France.
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Fehm TN, Welslau M, Müller V, Lüftner D, Schütz F, Fasching PA, Janni W, Thomssen C, Witzel I, Belleville E, Untch M, Thill M, Tesch H, Ditsch N, Lux MP, Aktas B, Banys-Paluchowski M, Schneeweiss A, Kolberg-Liedtke C, Hartkopf AD, Wöckel A, Kolberg HC, Harbeck N, Stickeler E. Update Breast Cancer 2022 Part 3 - Early-Stage Breast Cancer. Geburtshilfe Frauenheilkd 2022; 82:912-921. [PMID: 36110894 PMCID: PMC9470293 DOI: 10.1055/a-1912-7105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 07/31/2022] [Indexed: 11/01/2022] Open
Abstract
This review summarizes recent developments in the prevention and treatment of patients with early-stage breast cancer. The individual disease risk for different molecular subtypes was investigated in a large epidemiological study. With regard to treatment, new data are available from long-term follow-up of the Aphinity study, as well as new data on neoadjuvant therapy with atezolizumab in HER2-positive patients. Biomarkers, such as residual cancer burden, were investigated in the context of pembrolizumab therapy. A Genomic Grade Index study in elderly patients is one of a group of studies investigating the use of modern multigene tests to identify patients with an excellent prognosis in whom chemotherapy may be avoided. These and other aspects of the latest developments in the diagnosis and treatment of breast cancer are described in this review.
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Affiliation(s)
- Tanja N. Fehm
- Department of Gynecology and Obstetrics, University Hospital Düsseldorf, Düsseldorf, Germany
| | | | - Volkmar Müller
- Department of Gynecology, Hamburg-Eppendorf University Medical Center, Hamburg, Germany
| | - Diana Lüftner
- Immanuel Hospital Märkische Schweiz & Medical University of Brandenburg Theodor-Fontane, Brandenburg, Buckow, Germany
| | - Florian Schütz
- Gynäkologie und Geburtshilfe, Diakonissen-Stiftungs-Krankenhaus Speyer, Speyer, Germany
| | - Peter A. Fasching
- Erlangen University Hospital, Department of Gynecology and Obstetrics, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen,
Germany,Correspondence/Korrespondenzadresse Peter A. Fasching, MD Erlangen University Hospital, Department of Gynecology and ObstetricsComprehensive Cancer
Center Erlangen EMNFriedrich Alexander University of Erlangen-NurembergUniversitätsstraße 21 – 2391054
ErlangenGermany
| | - Wolfgang Janni
- Department of Gynecology and Obstetrics, Ulm University Hospital, Ulm, Germany
| | - Christoph Thomssen
- Department of Gynaecology, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany
| | - Isabell Witzel
- Department of Gynecology, Hamburg-Eppendorf University Medical Center, Hamburg, Germany
| | | | - Michael Untch
- Clinic for Gynecology and Obstetrics, Breast Cancer Center, Gynecologic Oncology Center, Helios Klinikum Berlin Buch, Berlin, Germany
| | - Marc Thill
- Agaplesion Markus Krankenhaus, Department of Gynecology and Gynecological Oncology, Frankfurt am Main, Germany
| | - Hans Tesch
- Oncology Practice at Bethanien Hospital, Frankfurt am Main, Germany
| | - Nina Ditsch
- Department of Gynecology and Obstetrics, University Hospital Augsburg, Augsburg, Germany
| | - Michael P. Lux
- Klinik für Gynäkologie und Geburtshilfe, Frauenklinik St. Louise, Paderborn, St. Josefs-Krankenhaus, Salzkotten, St. Vincenz Krankenhaus GmbH, Germany
| | - Bahriye Aktas
- Department of Gynecology, University of Leipzig Medical Center, Leipzig, Germany
| | - Maggie Banys-Paluchowski
- Department of Gynecology and Obstetrics, University Hospital Schleswig-Holstein, Campus Lübeck, Lübeck, Germany
| | - Andreas Schneeweiss
- National Center for Tumor Diseases (NCT), Heidelberg University Hospital and German Cancer Research Center, Heidelberg, Germany
| | | | - Andreas D. Hartkopf
- Department of Gynecology and Obstetrics, Ulm University Hospital, Ulm, Germany
| | - Achim Wöckel
- Department of Gynecology and Obstetrics, University Hospital Würzburg, Würzburg, Germany
| | | | - Nadia Harbeck
- Breast Center, Department of Gynecology and Obstetrics and CCC Munich LMU, LMU University Hospital, Munich, Germany
| | - Elmar Stickeler
- Department of Gynecology and Obstetrics, RWTH University Hospital Aachen, Aachen, Germany
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Laza-Vásquez C, Martínez-Alonso M, Forné-Izquierdo C, Vilaplana-Mayoral J, Cruz-Esteve I, Sánchez-López I, Reñé-Reñé M, Cazorla-Sánchez C, Hernández-Andreu M, Galindo-Ortego G, Llorens-Gabandé M, Pons-Rodríguez A, Rué M. Feasibility and Acceptability of Personalized Breast Cancer Screening (DECIDO Study): A Single-Arm Proof-of-Concept Trial. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:10426. [PMID: 36012059 PMCID: PMC9407798 DOI: 10.3390/ijerph191610426] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Revised: 08/11/2022] [Accepted: 08/18/2022] [Indexed: 06/15/2023]
Abstract
The aim of this study was to assess the acceptability and feasibility of offering risk-based breast cancer screening and its integration into regular clinical practice. A single-arm proof-of-concept trial was conducted with a sample of 387 women aged 40-50 years residing in the city of Lleida (Spain). The study intervention consisted of breast cancer risk estimation, risk communication and screening recommendations, and a follow-up. A polygenic risk score with 83 single nucleotide polymorphisms was used to update the Breast Cancer Surveillance Consortium risk model and estimate the 5-year absolute risk of breast cancer. The women expressed a positive attitude towards varying the frequency of breast screening according to individual risk and, especially, more frequently inviting women at higher-than-average risk. A lower intensity screening for women at lower risk was not as welcome, although half of the participants would accept it. Knowledge of the benefits and harms of breast screening was low, especially with regard to false positives and overdiagnosis. The women expressed a high understanding of individual risk and screening recommendations. The participants' intention to participate in risk-based screening and satisfaction at 1-year were very high.
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Affiliation(s)
- Celmira Laza-Vásquez
- Department of Nursing and Physiotherapy and Health Care Research Group (GRECS), IRBLleida—Institut de Recerca Biomèdica de Lleida, University of Lleida, 25198 Lleida, Spain
| | - Montserrat Martínez-Alonso
- IRBLleida—Institut de Recerca Biomèdica de Lleida, Department of Basic Medical Sciences, University of Lleida, 25198 Lleida, Spain
| | - Carles Forné-Izquierdo
- Department of Basic Medical Sciences, University of Lleida, 25198 Lleida, Spain
- Heorfy Consulting, 25007 Lleida, Spain
| | - Jordi Vilaplana-Mayoral
- Department of Computing and Industrial Engineering, University of Lleida, 25001 Lleida, Spain
| | - Inés Cruz-Esteve
- Primer de Maig Basic Health Area, Catalan Institute of Health, 25003 Lleida, Spain
| | | | - Mercè Reñé-Reñé
- Department of Radiology, Arnau de Vilanova University Hospital, 25198 Lleida, Spain
| | | | | | | | | | - Anna Pons-Rodríguez
- Example Basic Health Area, Catalan Institute of Health, 25006 Lleida, Spain
- Health PhD Program, University of Lleida, 25198 Lleida, Spain
| | - Montserrat Rué
- IRBLleida—Institut de Recerca Biomèdica de Lleida, Department of Basic Medical Sciences, University of Lleida, 25198 Lleida, Spain
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Thanh Thi Ngoc Nguyen, Nguyen THN, Phan HN, Nguyen HT. Seven-Single Nucleotide Polymorphism Polygenic Risk Score for Breast Cancer Risk Prediction in a Vietnamese Population. CYTOL GENET+ 2022. [DOI: 10.3103/s0095452722040065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Mann RM, Athanasiou A, Baltzer PAT, Camps-Herrero J, Clauser P, Fallenberg EM, Forrai G, Fuchsjäger MH, Helbich TH, Killburn-Toppin F, Lesaru M, Panizza P, Pediconi F, Pijnappel RM, Pinker K, Sardanelli F, Sella T, Thomassin-Naggara I, Zackrisson S, Gilbert FJ, Kuhl CK. Breast cancer screening in women with extremely dense breasts recommendations of the European Society of Breast Imaging (EUSOBI). Eur Radiol 2022; 32:4036-4045. [PMID: 35258677 PMCID: PMC9122856 DOI: 10.1007/s00330-022-08617-6] [Citation(s) in RCA: 149] [Impact Index Per Article: 74.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 01/21/2022] [Accepted: 01/27/2022] [Indexed: 02/07/2023]
Abstract
Breast density is an independent risk factor for the development of breast cancer and also decreases the sensitivity of mammography for screening. Consequently, women with extremely dense breasts face an increased risk of late diagnosis of breast cancer. These women are, therefore, underserved with current mammographic screening programs. The results of recent studies reporting on contrast-enhanced breast MRI as a screening method in women with extremely dense breasts provide compelling evidence that this approach can enable an important reduction in breast cancer mortality for these women and is cost-effective. Because there is now a valid option to improve breast cancer screening, the European Society of Breast Imaging (EUSOBI) recommends that women should be informed about their breast density. EUSOBI thus calls on all providers of mammography screening to share density information with the women being screened. In light of the available evidence, in women aged 50 to 70 years with extremely dense breasts, the EUSOBI now recommends offering screening breast MRI every 2 to 4 years. The EUSOBI acknowledges that it may currently not be possible to offer breast MRI immediately and everywhere and underscores that quality assurance procedures need to be established, but urges radiological societies and policymakers to act on this now. Since the wishes and values of individual women differ, in screening the principles of shared decision-making should be embraced. In particular, women should be counselled on the benefits and risks of mammography and MRI-based screening, so that they are capable of making an informed choice about their preferred screening method. KEY POINTS: • The recommendations in Figure 1 summarize the key points of the manuscript.
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Affiliation(s)
- Ritse M Mann
- Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, Netherlands.
- The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, Netherlands.
| | - Alexandra Athanasiou
- Breast Imaging Department, MITERA Hospital, 6, Erithrou Stavrou Str. 151 23 Marousi, Athens, Greece
| | - Pascal A T Baltzer
- Department of Biomedical Imaging and Image-guided Therapy, Division of General and Pediatric Radiology, Research Group: Molecular and Gender Imaging, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Wien, Austria
| | - Julia Camps-Herrero
- Hospitales Ribera Salud, Avda.Cortes Valencianas, 58, 46015, Valencia, Spain
| | - Paola Clauser
- Department of Biomedical Imaging and Image-guided Therapy, Division of General and Pediatric Radiology, Research Group: Molecular and Gender Imaging, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Wien, Austria
| | - Eva M Fallenberg
- Department of Diagnostic and Interventional Radiology, School of Medicine &; Klinikum Rechts der Isar, Technical University of Munich, Munich (TUM), Ismaninger Str. 22, 81675, München, Germany
| | - Gabor Forrai
- Department of Radiology, Duna Medical Center, Budapest, Hungary
| | - Michael H Fuchsjäger
- Division of General Radiology, Department of Radiology, Medical University Graz, Auenbruggerplatz 9, 8036, Graz, Austria
| | - Thomas H Helbich
- Department of Biomedical Imaging and Image-guided Therapy, Division of General and Pediatric Radiology, Research Group: Molecular and Gender Imaging, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Wien, Austria
| | - Fleur Killburn-Toppin
- Department of Radiology, University of Cambridge, Cambridge Biomedical Campus, Hills road, Cambridge, CB20QQ, UK
| | - Mihai Lesaru
- Radiology and Imaging Laboratory, Carol Davila University, Bucharest, Romania
| | - Pietro Panizza
- Breast Imaging Unit, IRCCS Ospedale San Raffaele,, Via Olgettina 60, 20132, Milan, Italy
| | - Federica Pediconi
- Department of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, Viale Regina Elena, 324, 00161, Rome, Italy
| | - Ruud M Pijnappel
- Department of Imaging, University Medical Centre Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX, Utrecht, Netherlands
- Dutch Expert Centre for Screening (LRCB), Wijchenseweg 101, 6538 SW, Nijmegen, Netherlands
| | - Katja Pinker
- Department of Biomedical Imaging and Image-guided Therapy, Division of General and Pediatric Radiology, Research Group: Molecular and Gender Imaging, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Wien, Austria
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th Street, New York, NY, 10065, USA
| | - Francesco Sardanelli
- Unit of Radiology, IRCCS Policlinico San Donato, San Donato Milanese, Milan, Italy
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Morandi 30, 20097 San Donato Milanese, Milan, Italy
| | - Tamar Sella
- Department of Diagnostic Imaging, Hadassah Hebrew University Medical Center, Jerusalem, Israel
| | - Isabelle Thomassin-Naggara
- Department of Radiology, Sorbonne Université, APHP, Hôpital Tenon, 4, rue de la Chine, 75020, Paris, France
| | - Sophia Zackrisson
- Diagnostic Radiology, Department of Translational Medicine, Faculty of Medicine, Lund University, Skåne University Hospital Malmö, SE-205 02, Malmö, Sweden
| | - Fiona J Gilbert
- Department of Radiology, University of Cambridge, Cambridge Biomedical Campus, Hills road, Cambridge, CB20QQ, UK
| | - Christiane K Kuhl
- University Hospital of Aachen, Rheinisch-Westfälische Technische Hochschule, Pauwelsstraße30, 52074, Aachen, Germany
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Eroglu I, Sevilimedu V, Park A, King TA, Pilewskie ML. Accuracy of the Breast Cancer Surveillance Consortium Model Among Women with LCIS. Breast Cancer Res Treat 2022; 194:257-264. [PMID: 35595928 DOI: 10.1007/s10549-021-06499-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2021] [Accepted: 12/20/2021] [Indexed: 11/26/2022]
Abstract
PURPOSE The Breast Cancer Surveillance Consortium (BCSC) model predicts risk of invasive breast cancer risk based on age, race, family history, breast density, and history of benign breast disease, including lobular carcinoma in situ (LCIS). However, validation studies for this model included few women with LCIS. We sought to evaluate the accuracy of the BCSC model among this cohort. METHODS Women with LCIS diagnosed between 1983 and 2017 were identified from a prospectively maintained database. The BCSC score was calculated; those with prior breast cancer, unknown breast density, age < 35 years or > 74 years, or with history of chemoprevention use were excluded. The Kaplan-Meier method was used to estimate incidence rates. Time-dependent receiver operating characteristic (ROC) analysis was used to analyze the discriminative capacity of the model. RESULTS 1302 women with LCIS were included. At a median follow-up of 7 years, 152 women (12%) developed invasive cancer (6 with bilateral disease). Cumulative incidences of invasive breast cancer were 7.1% (95% CI 5.6-8.7) and 13.3% (95% CI 10.9-15.6), respectively, and the median BCSC risk scores were 4.9 and 10.4, respectively, at 5 and 10 years. The median 10-year BCSC score was significantly lower than the 10-year Tyrer-Cuzick score (10.4 vs 20.8, p < 0.001). The ROC curve scores (AUC) for BCSC at 5 and 10 years were 0.59 (95% CI 0.52-0.66) and 0.58 (95% CI 0.52-0.64), respectively. CONCLUSION The BCSC model has moderate accuracy in predicting invasive breast cancer risk among women with LCIS with fair discrimination for risk prediction between individuals.
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Affiliation(s)
- Idil Eroglu
- Weill Cornell Medical College, New York, NY, USA
- Breast Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, 300 East 66th Street, New York, NY, 10065, USA
| | - Varadan Sevilimedu
- Biostatistics Service, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Anna Park
- Breast Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, 300 East 66th Street, New York, NY, 10065, USA
| | - Tari A King
- Division of Breast Surgery, Department of Surgery, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Breast Oncology Program, Dana-Farber Cancer Institute/Brigham and Women's Cancer Center, Boston, MA, USA
| | - Melissa L Pilewskie
- Breast Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, 300 East 66th Street, New York, NY, 10065, USA.
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Mital S, Nguyen HV. Cost-effectiveness of using artificial intelligence versus polygenic risk score to guide breast cancer screening. BMC Cancer 2022; 22:501. [PMID: 35524200 PMCID: PMC9074290 DOI: 10.1186/s12885-022-09613-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Accepted: 04/26/2022] [Indexed: 11/10/2022] Open
Abstract
Background Current guidelines for mammography screening for breast cancer vary across agencies, especially for women aged 40–49. Using artificial Intelligence (AI) to read mammography images has been shown to predict breast cancer risk with higher accuracy than alternative approaches including polygenic risk scores (PRS), raising the question whether AI-based screening is more cost-effective than screening based on PRS or existing guidelines. This study provides the first evidence to shed light on this important question. Methods This study is a model-based economic evaluation. We used a hybrid decision tree/microsimulation model to compare the cost-effectiveness of eight strategies of mammography screening for women aged 40–49 (screening beyond age 50 follows existing guidelines). Six of these strategies were defined by combinations of risk prediction approaches (AI, PRS or family history) and screening frequency for low-risk women (no screening or biennial screening). The other two strategies involved annual screening for all women and no screening, respectively. Data used to populate the model were sourced from the published literature. Results Risk prediction using AI followed by no screening for low-risk women is the most cost-effective strategy. It dominates (i.e., costs more and generates fewer quality adjusted life years (QALYs)) strategies for risk prediction using PRS followed by no screening or biennial screening for low-risk women, risk prediction using AI or family history followed by biennial screening for low-risk women, and annual screening for all women. It also extendedly dominates (i.e., achieves higher QALYs at a lower incremental cost per QALY) the strategy for risk prediction using family history followed by no screening for low-risk women. Meanwhile, it is cost-effective versus no screening, with an incremental cost-effectiveness ratio of $23,755 per QALY gained. Conclusions Risk prediction using AI followed by no breast cancer screening for low-risk women is the most cost-effective strategy. This finding can be explained by AI’s ability to identify high-risk women more accurately than PRS and family history (which reduces the possibility of delayed breast cancer diagnosis) and fewer false-positive diagnoses from not screening low-risk women. Supplementary Information The online version contains supplementary material available at 10.1186/s12885-022-09613-1.
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Affiliation(s)
- Shweta Mital
- School of Pharmacy, Memorial University of Newfoundland, 300 Prince Philip Drive, St. John's, NL A1B 3V6, Canada
| | - Hai V Nguyen
- School of Pharmacy, Memorial University of Newfoundland, 300 Prince Philip Drive, St. John's, NL A1B 3V6, Canada.
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Evans DGR, van Veen EM, Harkness EF, Brentnall AR, Astley SM, Byers H, Woodward ER, Sampson S, Southworth J, Howell SJ, Maxwell AJ, Newman WG, Cuzick J, Howell A. Breast cancer risk stratification in women of screening age: Incremental effects of adding mammographic density, polygenic risk, and a gene panel. Genet Med 2022; 24:1485-1494. [PMID: 35426792 DOI: 10.1016/j.gim.2022.03.009] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 03/14/2022] [Accepted: 03/15/2022] [Indexed: 12/17/2022] Open
Abstract
PURPOSE There is great promise in breast cancer risk stratification to target screening and prevention. It is unclear whether adding gene panels to other risk tools improves breast cancer risk stratification and adds discriminatory benefit on a population basis. METHODS In total, 10,025 of 57,902 women aged 46 to 73 years in the Predicting Risk of Cancer at Screening study provided DNA samples. A case-control study was used to evaluate breast cancer risk assessment using polygenic risk scores (PRSs), cancer gene panel (n = 33), mammographic density (density residual [DR]), and risk factors collected using a self-completed 2-page questionnaire (Tyrer-Cuzick [TC] model version 8). In total, 525 cases and 1410 controls underwent gene panel testing and PRS calculation (18, 143, and/or 313 single-nucleotide polymorphisms [SNPs]). RESULTS Actionable pathogenic variants (PGVs) in BRCA1/2 were found in 1.7% of cases and 0.55% of controls, and overall PGVs were found in 6.1% of cases and 1.3% of controls. A combined assessment of TC8-DR-SNP313 and gene panel provided the best risk stratification with 26.1% of controls and 9.7% of cases identified at <1.4% 10-year risk and 9.01% of controls and 23.3% of cases at ≥8% 10-year risk. Because actionable PGVs were uncommon, discrimination was identical with/without gene panel (with/without: area under the curve = 0.67, 95% CI = 0.64-0.70). Only 7 of 17 PGVs in cases resulted in actionable risk category change. Extended case (n = 644)-control (n = 1779) series with TC8-DR-SNP143 identified 18.9% of controls and only 6.4% of stage 2+ cases at <1.4% 10-year risk and 20.7% of controls and 47.9% of stage 2+ cases at ≥5% 10-year risk. CONCLUSION Further studies and economic analysis will determine whether adding panels to PRS is a cost-effective strategy for risk stratification.
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Affiliation(s)
- D Gareth R Evans
- Division of Evolution, Infection and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom; Prevention Breast Cancer Unit and Nightingale Breast Screening Centre, Manchester University NHS Foundation Trust (South), Manchester, United Kingdom; The Christie NHS Foundation Trust, Manchester, United Kingdom; Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust (Central), Manchester, United Kingdom; Manchester Breast Centre, Manchester Cancer Research Centre, The University of Manchester, Manchester, United Kingdom; Cancer Prevention Early Detection Theme, NIHR Manchester Biomedical Research Centre, The Christie NHS Foundation Trust, Manchester, United Kingdom.
| | - Elke M van Veen
- Division of Evolution, Infection and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom
| | - Elaine F Harkness
- Prevention Breast Cancer Unit and Nightingale Breast Screening Centre, Manchester University NHS Foundation Trust (South), Manchester, United Kingdom; Cancer Prevention Early Detection Theme, NIHR Manchester Biomedical Research Centre, The Christie NHS Foundation Trust, Manchester, United Kingdom; Division of Informatics, Imaging and Data Sciences, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom; Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom
| | - Adam R Brentnall
- Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Charterhouse Square, Barts and The London, Queen Mary University of London, London, United Kingdom
| | - Susan M Astley
- Prevention Breast Cancer Unit and Nightingale Breast Screening Centre, Manchester University NHS Foundation Trust (South), Manchester, United Kingdom; Manchester Breast Centre, Manchester Cancer Research Centre, The University of Manchester, Manchester, United Kingdom; Cancer Prevention Early Detection Theme, NIHR Manchester Biomedical Research Centre, The Christie NHS Foundation Trust, Manchester, United Kingdom; Division of Informatics, Imaging and Data Sciences, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom; Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom
| | - Helen Byers
- Division of Evolution, Infection and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom; Cancer Prevention Early Detection Theme, NIHR Manchester Biomedical Research Centre, The Christie NHS Foundation Trust, Manchester, United Kingdom
| | - Emma R Woodward
- Division of Evolution, Infection and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom; Cancer Prevention Early Detection Theme, NIHR Manchester Biomedical Research Centre, The Christie NHS Foundation Trust, Manchester, United Kingdom
| | - Sarah Sampson
- Prevention Breast Cancer Unit and Nightingale Breast Screening Centre, Manchester University NHS Foundation Trust (South), Manchester, United Kingdom
| | - Jake Southworth
- Prevention Breast Cancer Unit and Nightingale Breast Screening Centre, Manchester University NHS Foundation Trust (South), Manchester, United Kingdom
| | - Sacha J Howell
- Prevention Breast Cancer Unit and Nightingale Breast Screening Centre, Manchester University NHS Foundation Trust (South), Manchester, United Kingdom; The Christie NHS Foundation Trust, Manchester, United Kingdom; Manchester Breast Centre, Manchester Cancer Research Centre, The University of Manchester, Manchester, United Kingdom; Cancer Prevention Early Detection Theme, NIHR Manchester Biomedical Research Centre, The Christie NHS Foundation Trust, Manchester, United Kingdom
| | - Anthony J Maxwell
- Prevention Breast Cancer Unit and Nightingale Breast Screening Centre, Manchester University NHS Foundation Trust (South), Manchester, United Kingdom; Manchester Breast Centre, Manchester Cancer Research Centre, The University of Manchester, Manchester, United Kingdom; Cancer Prevention Early Detection Theme, NIHR Manchester Biomedical Research Centre, The Christie NHS Foundation Trust, Manchester, United Kingdom; Division of Informatics, Imaging and Data Sciences, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom; Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom
| | - William G Newman
- Division of Evolution, Infection and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom; Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust (Central), Manchester, United Kingdom; Manchester Breast Centre, Manchester Cancer Research Centre, The University of Manchester, Manchester, United Kingdom
| | - Jack Cuzick
- Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Charterhouse Square, Barts and The London, Queen Mary University of London, London, United Kingdom
| | - Anthony Howell
- Prevention Breast Cancer Unit and Nightingale Breast Screening Centre, Manchester University NHS Foundation Trust (South), Manchester, United Kingdom; The Christie NHS Foundation Trust, Manchester, United Kingdom; Manchester Breast Centre, Manchester Cancer Research Centre, The University of Manchester, Manchester, United Kingdom; Cancer Prevention Early Detection Theme, NIHR Manchester Biomedical Research Centre, The Christie NHS Foundation Trust, Manchester, United Kingdom
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Clift AK, Dodwell D, Lord S, Petrou S, Brady SM, Collins GS, Hippisley-Cox J. The current status of risk-stratified breast screening. Br J Cancer 2022; 126:533-550. [PMID: 34703006 PMCID: PMC8854575 DOI: 10.1038/s41416-021-01550-3] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 08/25/2021] [Accepted: 09/14/2021] [Indexed: 12/23/2022] Open
Abstract
Apart from high-risk scenarios such as the presence of highly penetrant genetic mutations, breast screening typically comprises mammography or tomosynthesis strategies defined by age. However, age-based screening ignores the range of breast cancer risks that individual women may possess and is antithetical to the ambitions of personalised early detection. Whilst screening mammography reduces breast cancer mortality, this is at the risk of potentially significant harms including overdiagnosis with overtreatment, and psychological morbidity associated with false positives. In risk-stratified screening, individualised risk assessment may inform screening intensity/interval, starting age, imaging modality used, or even decisions not to screen. However, clear evidence for its benefits and harms needs to be established. In this scoping review, the authors summarise the established and emerging evidence regarding several critical dependencies for successful risk-stratified breast screening: risk prediction model performance, epidemiological studies, retrospective clinical evaluations, health economic evaluations and qualitative research on feasibility and acceptability. Family history, breast density or reproductive factors are not on their own suitable for precisely estimating risk and risk prediction models increasingly incorporate combinations of demographic, clinical, genetic and imaging-related parameters. Clinical evaluations of risk-stratified screening are currently limited. Epidemiological evidence is sparse, and randomised trials only began in recent years.
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Affiliation(s)
- Ash Kieran Clift
- Cancer Research UK Oxford Centre, Department of Oncology, University of Oxford, Oxford, UK.
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
| | - David Dodwell
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Simon Lord
- Department of Oncology, University of Oxford, Oxford, UK
| | - Stavros Petrou
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | | | - Gary S Collins
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology & Musculoskeletal Sciences, University of Oxford, Oxford, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - Julia Hippisley-Cox
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
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Carle C, Velentzis LS, Nickson C. BreastScreen Australia national data by factors of interest for risk-based screening: routinely reported data and opportunities for enhancement. Aust N Z J Public Health 2022; 46:230-236. [PMID: 35112749 DOI: 10.1111/1753-6405.13203] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 10/01/2021] [Accepted: 11/01/2021] [Indexed: 11/28/2022] Open
Abstract
OBJECTIVE There is growing interest in more risk-based approaches to breast cancer screening in Australia. This would require more detailed reporting of BreastScreen data for factors of interest in the assessment and monitoring of risk-based screening. This review assesses the current and potential availability and reporting of BreastScreen data for this purpose. METHODS We systematically searched governmental BreastScreen reports and peer-reviewed literature to assess current and potential availability of outcomes for predetermined factors including breast cancer risk factors and factors important for implementing, monitoring or evaluating risk-based screening. Outcomes evaluated were BreastScreen Performance Indicators routinely included in BreastScreen Australia monitoring reports, and key tumour characteristics. RESULTS All outcomes were reported annually by age group, except for tumour hormone receptor status, nodal involvement and grade. Screening participation was reported nationally for many factors important for risk-based screening; other reporting was ad hoc or unavailable. CONCLUSIONS There is potential to build on BreastScreen's existing high-quality national data collection and reporting systems to inform and support risk-based breast screening. Implications for public health: Enhanced BreastScreen data collection and reporting would improve the evidence base and support evaluation of risk-based screening and improve the detail available for benchmarking any future changes to the program.
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Affiliation(s)
- Chelsea Carle
- The Daffodil Centre, The University of Sydney, a joint venture with Cancer Council NSW
| | - Louiza S Velentzis
- The Daffodil Centre, The University of Sydney, a joint venture with Cancer Council NSW.,Melbourne School of Population and Global Health, The University of Melbourne, Victoria
| | - Carolyn Nickson
- The Daffodil Centre, The University of Sydney, a joint venture with Cancer Council NSW.,Melbourne School of Population and Global Health, The University of Melbourne, Victoria
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Hurson AN, Pal Choudhury P, Gao C, Hüsing A, Eriksson M, Shi M, Jones ME, Evans DGR, Milne RL, Gaudet MM, Vachon CM, Chasman DI, Easton DF, Schmidt MK, Kraft P, Garcia-Closas M, Chatterjee N. Prospective evaluation of a breast-cancer risk model integrating classical risk factors and polygenic risk in 15 cohorts from six countries. Int J Epidemiol 2022; 50:1897-1911. [PMID: 34999890 PMCID: PMC8743128 DOI: 10.1093/ije/dyab036] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Accepted: 02/19/2021] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Rigorous evaluation of the calibration and discrimination of breast-cancer risk-prediction models in prospective cohorts is critical for applications under clinical guidelines. We comprehensively evaluated an integrated model incorporating classical risk factors and a 313-variant polygenic risk score (PRS) to predict breast-cancer risk. METHODS Fifteen prospective cohorts from six countries with 239 340 women (7646 incident breast-cancer cases) of European ancestry aged 19-75 years were included. Calibration of 5-year risk was assessed by comparing expected and observed proportions of cases overall and within risk categories. Risk stratification for women of European ancestry aged 50-70 years in those countries was evaluated by the proportion of women and future cases crossing clinically relevant risk thresholds. RESULTS Among women <50 years old, the median (range) expected-to-observed ratio for the integrated model across 15 cohorts was 0.9 (0.7-1.0) overall and 0.9 (0.7-1.4) at the highest-risk decile; among women ≥50 years old, these were 1.0 (0.7-1.3) and 1.2 (0.7-1.6), respectively. The proportion of women identified above a 3% 5-year risk threshold (used for recommending risk-reducing medications in the USA) ranged from 7.0% in Germany (∼841 000 of 12 million) to 17.7% in the USA (∼5.3 of 30 million). At this threshold, 14.7% of US women were reclassified by adding the PRS to classical risk factors, with identification of 12.2% of additional future cases. CONCLUSION Integrating a 313-variant PRS with classical risk factors can improve the identification of European-ancestry women at elevated risk who could benefit from targeted risk-reducing strategies under current clinical guidelines.
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Affiliation(s)
- Amber N Hurson
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Parichoy Pal Choudhury
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Chi Gao
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Anika Hüsing
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Mikael Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Karolinska Univ Hospital, Stockholm, Sweden
| | - Min Shi
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC, USA
| | - Michael E Jones
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK
| | - D Gareth R Evans
- Division of Evolution and Genomic Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
- Manchester Centre for Genomic Medicine, St Mary’s Hospital, Manchester NIHR Biomedical Research Centre, Manchester University Hospitals NHS, Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
| | - Roger L Milne
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Mia M Gaudet
- Behavioral and Epidemiology Research Group, American Cancer Society, Atlanta, GA, USA
| | - Celine M Vachon
- Department of Health Sciences Research, Division of Epidemiology, Mayo Clinic, Rochester, MN, USA
| | - Daniel I Chasman
- Division of Preventive Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Douglas F Easton
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - 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
| | - Peter Kraft
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | | | - Nilanjan Chatterjee
- Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
- Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
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35
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Olsen M, Fischer K, Bossuyt PM, Goetghebeur E. Evaluating the prognostic performance of a polygenic risk score for breast cancer risk stratification. BMC Cancer 2021; 21:1351. [PMID: 34930164 PMCID: PMC8691010 DOI: 10.1186/s12885-021-08937-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Accepted: 10/29/2021] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Polygenic risk scores (PRS) could potentially improve breast cancer screening recommendations. Before a PRS can be considered for implementation, it needs rigorous evaluation, using performance measures that can inform about its future clinical value. OBJECTIVES To evaluate the prognostic performance of a regression model with a previously developed, prevalence-based PRS and age as predictors for breast cancer incidence in women from the Estonian biobank (EstBB) cohort; to compare it to the performance of a model including age only. METHODS We analyzed data on 30,312 women from the EstBB cohort. They entered the cohort between 2002 and 2011, were between 20 and 89 years, without a history of breast cancer, and with full 5-year follow-up by 2015. We examined PRS and other potential risk factors as possible predictors in Cox regression models for breast cancer incidence. With 10-fold cross-validation we estimated 3- and 5-year breast cancer incidence predicted by age alone and by PRS plus age, fitting models on 90% of the data. Calibration, discrimination, and reclassification were calculated on the left-out folds to express prognostic performance. RESULTS A total of 101 (3.33‰) and 185 (6.1‰) incident breast cancers were observed within 3 and 5 years, respectively. For women in a defined screening age of 50-62 years, the ratio of observed vs PRS-age modelled 3-year incidence was 0.86 for women in the 75-85% PRS-group, 1.34 for the 85-95% PRS-group, and 1.41 for the top 5% PRS-group. For 5-year incidence, this was respectively 0.94, 1.15, and 1.08. Yet the number of breast cancer events was relatively low in each PRS-subgroup. For all women, the model's AUC was 0.720 (95% CI: 0.675-0.765) for 3-year and 0.704 (95% CI: 0.670-0.737) for 5-year follow-up, respectively, just 0.022 and 0.023 higher than for the model with age alone. Using a 1% risk prediction threshold, the 3-year NRI for the PRS-age model was 0.09, and 0.05 for 5 years. CONCLUSION The model including PRS had modest incremental performance over one based on age only. A larger, independent study is needed to assess whether and how the PRS can meaningfully contribute to age, for developing more efficient screening strategies.
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Affiliation(s)
- Maria Olsen
- Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Amsterdam University Medical Centers, Amsterdam Public Health Research Institute, Meibergdreef 9, 1105, AZ, Amsterdam, The Netherlands.
| | - Krista Fischer
- Institute of Mathematics and Statistics, University of Tartu, Narva mnt 18, 51009, Tartu, Estonia.,Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Patrick M Bossuyt
- Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Amsterdam University Medical Centers, Amsterdam Public Health Research Institute, Meibergdreef 9, 1105, AZ, Amsterdam, The Netherlands
| | - Els Goetghebeur
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Institute for Continuing Education Center for Statistics, Campus Sterre, S9, Krijgslaan 281, 9000, Ghent, Belgium
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36
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Breast cancer risk reduction: who, why, and what? Best Pract Res Clin Obstet Gynaecol 2021; 83:36-45. [PMID: 34991977 DOI: 10.1016/j.bpobgyn.2021.11.012] [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: 11/02/2021] [Revised: 11/25/2021] [Accepted: 11/30/2021] [Indexed: 11/20/2022]
Abstract
Women at increased risk of breast cancer have options to mitigate that risk. Understanding factors that increase risk and utilizing tools for quantitative estimates are important to be able to adequately counsel and target strategies for patients. On the basis of these estimates, patients may be able to engage in risk reduction interventions and increased screening, including chemoprevention or surgical risk reduction. Multiple organizations have published guidelines supporting risk assessment, genetic assessment, increased screening, and prevention measures for these women.
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37
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Wang Y, Zhu M, Ma H, Shen H. Polygenic risk scores: the future of cancer risk prediction, screening, and precision prevention. MEDICAL REVIEW (BERLIN, GERMANY) 2021; 1:129-149. [PMID: 37724297 PMCID: PMC10471106 DOI: 10.1515/mr-2021-0025] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 12/13/2021] [Indexed: 09/20/2023]
Abstract
Genome-wide association studies (GWASs) have shown that the genetic architecture of cancers are highly polygenic and enabled researchers to identify genetic risk loci for cancers. The genetic variants associated with a cancer can be combined into a polygenic risk score (PRS), which captures part of an individual's genetic susceptibility to cancer. Recently, PRSs have been widely used in cancer risk prediction and are shown to be capable of identifying groups of individuals who could benefit from the knowledge of their probabilistic susceptibility to cancer, which leads to an increased interest in understanding the potential utility of PRSs that might further refine the assessment and management of cancer risk. In this context, we provide an overview of the major discoveries from cancer GWASs. We then review the methodologies used for PRS construction, and describe steps for the development and evaluation of risk prediction models that include PRS and/or conventional risk factors. Potential utility of PRSs in cancer risk prediction, screening, and precision prevention are illustrated. Challenges and practical considerations relevant to the implementation of PRSs in health care settings are discussed.
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Affiliation(s)
- Yuzhuo Wang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Meng Zhu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Hongxia Ma
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, Jiangsu, China
- Research Units of Cohort Study on Cardiovascular Diseases and Cancers, Chinese Academy of Medical Sciences, Beijing, China
| | - Hongbing Shen
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, Jiangsu, China
- Research Units of Cohort Study on Cardiovascular Diseases and Cancers, Chinese Academy of Medical Sciences, Beijing, China
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Esserman L, Eklund M, Veer LV, Shieh Y, Tice J, Ziv E, Blanco A, Kaplan C, Hiatt R, Fiscalini AS, Yau C, Scheuner M, Naeim A, Wenger N, Lee V, Heditsian D, Brain S, Parker BA, LaCroix AZ, Madlensky L, Hogarth M, Borowsky A, Anton-Culver H, Kaster A, Olopade OI, Sheth D, Garcia A, Lancaster R, Plaza M. The WISDOM study: a new approach to screening can and should be tested. Breast Cancer Res Treat 2021; 189:593-598. [PMID: 34529196 DOI: 10.1007/s10549-021-06346-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Accepted: 07/28/2021] [Indexed: 11/25/2022]
Affiliation(s)
- Laura Esserman
- University of California, San Francisco, CA, 94158, USA.
| | | | | | - Yiwey Shieh
- University of California, San Francisco, CA, 94158, USA
| | - Jeffrey Tice
- University of California, San Francisco, CA, 94158, USA
| | - Elad Ziv
- University of California, San Francisco, CA, 94158, USA
| | - Amie Blanco
- University of California, San Francisco, CA, 94158, USA
| | - Celia Kaplan
- University of California, San Francisco, CA, 94158, USA
| | - Robert Hiatt
- University of California, San Francisco, CA, 94158, USA
| | | | - Christina Yau
- University of California, San Francisco, CA, 94158, USA
| | | | - Arash Naeim
- University of California, Los Angeles, CA, 90095, USA
| | - Neil Wenger
- University of California, Los Angeles, CA, 90095, USA
| | - Vivian Lee
- University of California, San Francisco, CA, 94158, USA
| | | | - Susie Brain
- University of California, San Francisco, CA, 94158, USA
| | | | | | | | | | | | | | | | | | - Deepa Sheth
- University of Chicago, Chicago, IL, 60637, USA
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Gilbert FJ, Hickman SE, Baxter GC, Allajbeu I, James J, Caraco C, Vinnicombe S. Opportunities in cancer imaging: risk-adapted breast imaging in screening. Clin Radiol 2021; 76:763-773. [PMID: 33820637 DOI: 10.1016/j.crad.2021.02.013] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 02/19/2021] [Indexed: 12/17/2022]
Abstract
In the UK, women between 50-70 years are invited for 3-yearly mammography screening irrespective of their likelihood of developing breast cancer. The only risk adaption is for women with >30% lifetime risk who are offered annual magnetic resonance imaging (MRI) and mammography, and annual mammography for some moderate-risk women. Using questionnaires, breast density, and polygenic risk scores, it is possible to stratify the population into the lowest 20% risk, who will develop <4% of cancers and the top 4%, who will develop 18% of cancers. Mammography is a good screening test but has low sensitivity of 60% in the 9% of women with the highest category of breast density (BIRADS D) who have a 2.5- to fourfold breast cancer risk. There is evidence that adding ultrasound to the screening mammogram can increase the cancer detection rate and reduce advanced stage interval and next round cancers. Similarly, alternative tests such as contrast-enhanced mammography (CESM) or abbreviated MRI (ABB-MRI) are much more effective in detecting cancer in women with dense breasts. Scintimammography has been shown to be a viable alternative for dense breasts or for follow-up in those with a personal history of breast cancer and scarring as result of treatment. For supplemental screening to be worthwhile in these women, new technologies need to reduce the number of stage II cancers and be cost effective when tested in large scale trials. This article reviews the evidence for supplemental imaging and examines whether a risk-stratified approach is feasible.
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Affiliation(s)
- F J Gilbert
- Department of Radiology, University of Cambridge School of Clinical Medicine, Box 218, Level 5, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK; Department of Radiology, Addenbrookes Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
| | - S E Hickman
- Department of Radiology, University of Cambridge School of Clinical Medicine, Box 218, Level 5, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
| | - G C Baxter
- Department of Radiology, University of Cambridge School of Clinical Medicine, Box 218, Level 5, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
| | - I Allajbeu
- Department of Radiology, University of Cambridge School of Clinical Medicine, Box 218, Level 5, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK; Department of Radiology, Addenbrookes Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - J James
- Nottingham Breast Institute, City Hospital, Nottingham, UK
| | - C Caraco
- Department of Radiology, University of Cambridge School of Clinical Medicine, Box 218, Level 5, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
| | - S Vinnicombe
- Thirlestaine Breast Centre, Cheltenham, UK; Ninewells Hospital and Medical School, University of Dundee, UK
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Keane H, Huilgol YS, Shieh Y, Tice JA, Belkora J, Sepucha K, Shibley WP, Wang T, Che M, Goodman D, Ozanne E, Fiscalini AS, Esserman LJ. Development and pilot of an online, personalized risk assessment tool for a breast cancer precision medicine trial. NPJ Breast Cancer 2021; 7:78. [PMID: 34140528 PMCID: PMC8211836 DOI: 10.1038/s41523-021-00288-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Accepted: 05/27/2021] [Indexed: 12/24/2022] Open
Abstract
Breast cancer risk reduction has been validated by large-scale clinical trials, but uptake remains low. A risk communication tool could provide personalized risk-reduction information for high-risk women. A low-literacy-friendly, visual, and personalized tool was designed as part of the Women Informed to Screen Depending On Measures of risk (WISDOM) study. The tool integrates genetic, polygenic, and lifestyle factors, and quantifies the risk-reduction from undertaking medication and lifestyle interventions. The development and design process utilized feedback from clinicians, decision-making scientists, software engineers, and patient advocates. We piloted the tool with 17 study participants, collecting quantitative and qualitative feedback. Overall, participants felt they better understood their personalized breast cancer risk, were motivated to reduce their risk, and considered lifestyle interventions. The tool will be used to evaluate whether risk-based screening leads to more informed decisions and higher uptake of risk-reduction interventions among those most likely to benefit.
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Affiliation(s)
- Holly Keane
- Department of Surgery, University of California, San Francisco, San Francisco, CA, USA
- Peter MacCallum Cancer Centre, Melbourne, Australia
| | - Yash S Huilgol
- School of Medicine, University of California, San Francisco, San Francisco, CA, USA
- Joint Medical Program, School of Public Health, University of California, Berkeley, Berkeley, CA, USA
| | - Yiwey Shieh
- School of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Jeffrey A Tice
- School of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Jeff Belkora
- Institute for Health Policy Studies, University of California, San Francisco, San Francisco, CA, USA
| | - Karen Sepucha
- Health Decision Sciences Center, Massachusetts General Hospital, Boston, MA, USA
| | - W Patrick Shibley
- School of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Tianyi Wang
- Department of Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Mandy Che
- Department of Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Deborah Goodman
- Department of Epidemiology, University of California, Irvine, Irvine, CA, USA
| | - Elissa Ozanne
- Department of Population Health Sciences, University of Utah School of Medicine, Salt Lake City, UT, USA
| | | | - Laura J Esserman
- Department of Surgery, University of California, San Francisco, San Francisco, CA, USA.
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Houghton SC, Hankinson SE. Cancer Progress and Priorities: Breast Cancer. Cancer Epidemiol Biomarkers Prev 2021; 30:822-844. [PMID: 33947744 PMCID: PMC8104131 DOI: 10.1158/1055-9965.epi-20-1193] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 10/13/2020] [Accepted: 02/19/2021] [Indexed: 12/24/2022] Open
Affiliation(s)
- Serena C Houghton
- Department of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst, Massachusetts.
| | - Susan E Hankinson
- Department of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst, Massachusetts
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42
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Dontchos BN, Yala A, Barzilay R, Xiang J, Lehman CD. External Validation of a Deep Learning Model for Predicting Mammographic Breast Density in Routine Clinical Practice. Acad Radiol 2021; 28:475-480. [PMID: 32089465 DOI: 10.1016/j.acra.2019.12.012] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Revised: 12/11/2019] [Accepted: 12/12/2019] [Indexed: 11/29/2022]
Abstract
RATIONALE AND OBJECTIVES Federal legislation requires patient notification of dense mammographic breast tissue because increased density is a marker of breast cancer risk and can limit the sensitivity of mammography. As previously described, we clinically implemented our deep learning model at the academic breast imaging practice where the model was developed with high clinical acceptance. Our objective was to externally validate our deep learning model on radiologist breast density assessments in a community breast imaging practice. MATERIALS AND METHODS Our deep learning model was implemented at a dedicated breast imaging practice staffed by both academic and community breast imaging radiologists in October 2018. Deep learning model assessment of mammographic breast density was presented to the radiologist during routine clinical practice at the time of mammogram interpretation. We identified 2174 consecutive screening mammograms after implementation of the deep learning model. Radiologist agreement with the model's assessment was measured and compared across radiologist groups. RESULTS Both academic and community radiologists had high clinical acceptance of the deep learning model's density prediction, with 94.9% (academic) and 90.7% (community) acceptance for dense versus nondense categories (p < 0.001). The proportion of mammograms assessed as dense by all radiologists decreased from 47.0% before deep learning model implementation to 41.0% after deep learning model implementation (p < 0.001). CONCLUSION Our deep learning model had a high clinical acceptance rate among both academic and community radiologists and reduced the proportion of mammograms assessed as dense. This is an important step to validating our deep learning model prior to potential widespread implementation.
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Affiliation(s)
- Brian N Dontchos
- Massachusetts General Hospital, 55 Fruit Street, WAC-240, Boston, MA 02114.
| | - Adam Yala
- Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Regina Barzilay
- Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Justin Xiang
- Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Constance D Lehman
- Massachusetts General Hospital, 55 Fruit Street, WAC-240, Boston, MA 02114
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Kim G, Bahl M. Assessing Risk of Breast Cancer: A Review of Risk Prediction Models. JOURNAL OF BREAST IMAGING 2021; 3:144-155. [PMID: 33778488 DOI: 10.1093/jbi/wbab001] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Indexed: 12/17/2022]
Abstract
Accurate and individualized breast cancer risk assessment can be used to guide personalized screening and prevention recommendations. Existing risk prediction models use genetic and nongenetic risk factors to provide an estimate of a woman's breast cancer risk and/or the likelihood that she has a BRCA1 or BRCA2 mutation. Each model is best suited for specific clinical scenarios and may have limited applicability in certain types of patients. For example, the Breast Cancer Risk Assessment Tool, which identifies women who would benefit from chemoprevention, is readily accessible and user-friendly but cannot be used in women under 35 years of age or those with prior breast cancer or lobular carcinoma in situ. Emerging research on deep learning-based artificial intelligence (AI) models suggests that mammographic images contain risk indicators that could be used to strengthen existing risk prediction models. This article reviews breast cancer risk factors, describes the appropriate use, strengths, and limitations of each risk prediction model, and discusses the emerging role of AI for risk assessment.
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Affiliation(s)
- Geunwon Kim
- Beth Israel Deaconess Medical Center, Department of Radiology, Boston, MA, USA
| | - Manisha Bahl
- Massachusetts General Hospital, Department of Radiology, Boston, MA, USA
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44
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Zeinomar N, Chung WK. Cases in Precision Medicine: The Role of Polygenic Risk Scores in Breast Cancer Risk Assessment. Ann Intern Med 2021; 174:408-412. [PMID: 33253037 PMCID: PMC7965355 DOI: 10.7326/m20-5874] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Polygenic risk scores (PRSs) have been consistently associated with elevated breast cancer risk in cohort studies and are associated with risk in both women with and those without a family history of breast cancer. However, before clinical implementation, several issues must be addressed, including understanding the potential clinical utility and optimal method to communicate personalized screening recommendations that incorporate the PRS. Several trials are under way to answer some of these questions and facilitate clinical implementation. Because these PRSs have been developed in women of European ancestry, it is important to understand the limitations of their predictive ability in other ancestral groups. Finally, the value of the PRS will lie in considering it along with other clinical, familial, and rare genetic factors that are currently used in personalized risk assessment of breast cancer.
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Affiliation(s)
- Nur Zeinomar
- Mailman School of Public Health, Columbia University, New York, New York (N.Z.)
| | - Wendy K Chung
- Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, Columbia University, New York, New York (W.K.C.)
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Associations of one-carbon metabolism-related gene polymorphisms with breast cancer risk are modulated by diet, being higher when adherence to the Mediterranean dietary pattern is low. Breast Cancer Res Treat 2021; 187:793-804. [PMID: 33599865 DOI: 10.1007/s10549-021-06108-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 01/19/2021] [Indexed: 12/15/2022]
Abstract
PURPOSE Breast cancer is more likely attributed to a combination of genetic variations and lifestyle factors. Both one-carbon metabolism and diet-related factors could interfere with the carcinogenesis of breast cancer (BC), but whether diet consumed underlie a specific metabolism pathway could influence the impact of genetic variants on breast cancer risk remains equivocal. METHODS A case-control study of the Chinese female population (818 cases, 935 controls). 13 SNPs in eight one-carbon metabolism-related genes (MTHFD1, TYMS, MTRR, MAT2B, CDO1, FOLR1, UNG2, ADA) were performed. Diet was assessed by a validated food-frequency questionnaire. We examined the associations of the adherence to the Mediterranean dietary pattern (MDP) and single-nucleotide polymorphisms (SNPs) of one-carbon metabolism with breast cancer risk. We constructed an aggregate polygenic risk score (PRS) to test the additive effects of genetic variants and analyzed the gene-diet interactions. RESULTS High adherence (highest quartile) to the MDP decreased the risk of breast cancer among post- but not premenopausal women, respectively (OR = 0.54, 95% CI = 0.38 to 0.78 and 0.90, 0.53 to 1.53). Neither of the polymorphisms or haplotypes was associated with breast cancer risk, irrespective of menopause. However, a high PRS (highest quartile) was associated with more than a doubling risk in both post- and premenopausal women, respectively (OR = 1.95, 95% CI = 1.32 to 2.87 and 2.09, 1.54 to 2.85). We found a gene-diet interaction with adherence to the MDP for aggregate PRS (P-interaction = 0.000) among postmenopausal women. When adherence to the MDP was low (< median), carries with high PRS (highest quartile) had higher BC risk (OR = 2.80, 95% CI = 1.55 to 5.07) than low PRS (lowest quartile), while adherence to the MDP was high (≥ median), the association disappeared (OR = 1.57, 95% CI = 0.92 to 2.66). CONCLUSION High adherence to the MDP may counteract the genetic predisposition associated with one-carbon metabolism on breast cancer risk in postmenopausal women.
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Buist DSM. Factors to Consider in Developing Breast Cancer Risk Models to Implement into Clinical Care. CURR EPIDEMIOL REP 2021; 7:113-116. [PMID: 33552842 DOI: 10.1007/s40471-020-00230-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Purpose of the review This article outlines considerations for individuals interested in developing and implementing breast cancer risk models and has relevance for individuals developing risk-models with the goal of implementing them into health systems. Recent findings There has been increased focus on developing risk models for clinical use-often with less attention model implementation. Epidemiologists developing risk-models must think through model outcomes including stakeholder needs, time horizons, terminology and reference groups and clarity on what actionable steps are for health systems, providers and patients following its implementation. Summary Model performance needs to be evaluated relative to complexity of the model to be implemented-not just from the risk-prediction perspective, but also from the burden on patients, providers and systems for the amount and frequency of required data collection and with clear actionable steps to be taken with the information collected.
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Affiliation(s)
- Diana S M Buist
- Kaiser Permanente Washington Health Research Institute, Seattle WA
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Shieh Y, Fejerman L, Lott PC, Marker K, Sawyer SD, Hu D, Huntsman S, Torres J, Echeverry M, Bohórquez ME, Martínez-Chéquer JC, Polanco-Echeverry G, Estrada-Flórez AP, Haiman CA, John EM, Kushi LH, Torres-Mejía G, Vidaurre T, Weitzel JN, Zambrano SC, Carvajal-Carmona LG, Ziv E, Neuhausen SL. A Polygenic Risk Score for Breast Cancer in US Latinas and Latin American Women. J Natl Cancer Inst 2021; 112:590-598. [PMID: 31553449 PMCID: PMC7301155 DOI: 10.1093/jnci/djz174] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Revised: 07/23/2019] [Accepted: 09/04/2019] [Indexed: 01/19/2023] Open
Abstract
Background More than 180 single nucleotide polymorphisms (SNPs) associated with breast cancer susceptibility have been identified; these SNPs can be combined into polygenic risk scores (PRS) to predict breast cancer risk. Because most SNPs were identified in predominantly European populations, little is known about the performance of PRS in non-Europeans. We tested the performance of a 180-SNP PRS in Latinas, a large ethnic group with variable levels of Indigenous American, European, and African ancestry. Methods We conducted a pooled case-control analysis of US Latinas and Latin American women (4658 cases and 7622 controls). We constructed a 180-SNP PRS consisting of SNPs associated with breast cancer risk (P < 5 × 10–8). We evaluated the association between the PRS and breast cancer risk using multivariable logistic regression, and assessed discrimination using an area under the receiver operating characteristic curve. We also assessed PRS performance across quartiles of Indigenous American genetic ancestry. All statistical tests were two-sided. Results Of 180 SNPs tested, 142 showed directionally consistent associations compared with European populations, and 39 were nominally statistically significant (P < .05). The PRS was associated with breast cancer risk, with an odds ratio per SD increment of 1.58 (95% confidence interval [CI = 1.52 to 1.64) and an area under the receiver operating characteristic curve of 0.63 (95% CI = 0.62 to 0.64). The discrimination of the PRS was similar between the top and bottom quartiles of Indigenous American ancestry. Conclusions The 180-SNP PRS predicts breast cancer risk in Latinas, with similar performance as reported for Europeans. The performance of the PRS did not vary substantially according to Indigenous American ancestry.
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Affiliation(s)
- Yiwey Shieh
- Division of General Internal Medicine, Department of Medicine, University of California, San Francisco, San Francisco, CA
| | - Laura Fejerman
- Division of General Internal Medicine, Department of Medicine, University of California, San Francisco, San Francisco, CA
| | - Paul C Lott
- UC Davis Genome Center, University of California, Davis, Davis, CA
| | - Katie Marker
- School of Public Health, University of California, Berkeley; Berkeley, CA
| | | | - Donglei Hu
- Division of General Internal Medicine, Department of Medicine, University of California, San Francisco, San Francisco, CA
| | - Scott Huntsman
- Division of General Internal Medicine, Department of Medicine, University of California, San Francisco, San Francisco, CA
| | - Javier Torres
- Unidad de Investigación en Enfermedades Infecciosas, Instituto Mexicano del Seguro Social, Mexico City, Mexico
| | - Magdalena Echeverry
- Grupo de Citogenética, Filogenia y Evolución de Poblaciones, Facultades de Ciencias y Facultad de Ciencias de la Salud, Universidad del Tolima, Ibagué, Colombia
| | - Mabel E Bohórquez
- Grupo de Citogenética, Filogenia y Evolución de Poblaciones, Facultades de Ciencias y Facultad de Ciencias de la Salud, Universidad del Tolima, Ibagué, Colombia
| | | | | | - Ana P Estrada-Flórez
- UC Davis Genome Center, University of California, Davis, Davis, CA.,Grupo de Citogenética, Filogenia y Evolución de Poblaciones, Facultades de Ciencias y Facultad de Ciencias de la Salud, Universidad del Tolima, Ibagué, Colombia
| | | | - Christopher A Haiman
- Department of Preventive Medicine, Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA
| | - Esther M John
- Department of Medicine, Stanford University School of Medicine, Stanford, CA.,Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA
| | - Lawrence H Kushi
- UC Davis Genome Center, University of California, Davis, Davis, CA.,Division of Research, Kaiser Permanente Northern California, Oakland, CA
| | | | | | - Jeffrey N Weitzel
- Division of Clinical Genetics, City of Hope National Medical Center, Duarte, CA
| | | | - Luis G Carvajal-Carmona
- Department of Biochemistry and Molecular Medicine, School of Medicine, University of California, Davis, Sacramento, CA.,Population Science and Health Disparities Program, University of California Davis Comprehensive Cancer Center, Sacramento, CA
| | - Elad Ziv
- Division of General Internal Medicine, Department of Medicine, University of California, San Francisco, San Francisco, CA
| | - Susan L Neuhausen
- Department of Population Sciences, Beckman Research Institute of City of Hope, Duarte, CA
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Pons-Rodriguez A, Forné Izquierdo C, Vilaplana-Mayoral J, Cruz-Esteve I, Sánchez-López I, Reñé-Reñé M, Cazorla C, Hernández-Andreu M, Galindo-Ortego G, Llorens Gabandé M, Laza-Vásquez C, Balaguer-Llaquet P, Martínez-Alonso M, Rué M. Feasibility and acceptability of personalised breast cancer screening (DECIDO study): protocol of a single-arm proof-of-concept trial. BMJ Open 2020; 10:e044597. [PMID: 33361170 PMCID: PMC7759966 DOI: 10.1136/bmjopen-2020-044597] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
INTRODUCTION Personalised cancer screening aims to improve benefits, reduce harms and being more cost-effective than age-based screening. The objective of the DECIDO study is to assess the acceptability and feasibility of offering risk-based personalised breast cancer screening and its integration in regular clinical practice in a National Health System setting. METHODS AND ANALYSIS The study is designed as a single-arm proof-of-concept trial. The study sample will include 385 women aged 40-50 years resident in a primary care health area in Spain. The study intervention consists of (1) a baseline visit; (2) breast cancer risk estimation; (3) a second visit for risk communication and screening recommendations based on breast cancer risk and (4) a follow-up to obtain the study outcomes.A polygenic risk score (PRS) will be constructed as a composite likelihood ratio of 83 single nucleotide polymorphisms. The Breast Cancer Surveillance Consortium risk model, including age, race/ethnicity, family history of breast cancer, benign breast disease and breast density will be used to estimate a preliminary 5-year absolute risk of breast cancer. A Bayesian approach will be used to update this risk with the PRS value.The primary outcome measures will be attitude towards, intention to participate in and satisfaction with personalised breast cancer screening. Secondary outcomes will include the proportions of women who accept to participate and who complete the different phases of the study. The exact binomial and the Student's t-test will be used to obtain 95% CIs. ETHICS AND DISSEMINATION The study protocol was approved by the Drug Research Ethics Committee of the University Hospital Arnau de Vilanova. The trial will be conducted in compliance with this study protocol, the Declaration of Helsinki and Good Clinical Practice.The results will be published in peer-reviewed scientific journals and disseminated in scientific conferences and media. TRIAL REGISTRATION NUMBER NCT03791008.
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Affiliation(s)
- Anna Pons-Rodriguez
- Eixample Basic Health Area, Catalan Institute of Health, Lleida, Spain
- Health PhD Program, University of Lleida, Lleida, Spain
| | - Carles Forné Izquierdo
- Basic Medical Sciences, University of Lleida, Lleida, Spain
- Research Group on Statistics and Economic Evaluation in Health (GRAEES), University of Lleida, Lleida, Spain
| | | | - Inés Cruz-Esteve
- Primer de Maig Basic Health Area, Catalan Institute of Health, Lleida, Spain
| | | | - Mercè Reñé-Reñé
- Radiology Department, Arnau de Vilanova University Hospital, Lleida, Spain
| | - Cristina Cazorla
- Primer de Maig Basic Health Area, Catalan Institute of Health, Lleida, Spain
| | | | | | | | | | | | - Montserrat Martínez-Alonso
- Basic Medical Sciences, University of Lleida, Lleida, Spain
- Research Group on Statistics and Economic Evaluation in Health (GRAEES), University of Lleida, Lleida, Spain
- IRBLleida, Lleida, Spain
| | - Montserrat Rué
- Basic Medical Sciences, University of Lleida, Lleida, Spain
- Research Group on Statistics and Economic Evaluation in Health (GRAEES), University of Lleida, Lleida, Spain
- IRBLleida, Lleida, Spain
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Long-Term Evaluation of Women Referred to a Breast Cancer Family History Clinic (Manchester UK 1987-2020). Cancers (Basel) 2020; 12:cancers12123697. [PMID: 33317064 PMCID: PMC7763143 DOI: 10.3390/cancers12123697] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2020] [Revised: 12/02/2020] [Accepted: 12/05/2020] [Indexed: 12/20/2022] Open
Abstract
Simple Summary This study reports the management of women at high risk for breast cancer over a 33 years period. The aim was to summarize the numbers seen and to report the results of our studies on gene testing, the outcomes of screening and the success of preventive methods including lifestyle change, chemoprevention and risk-reducing mastectomy. We also discuss how the clinical Family History Service may be improved in the future. Abstract Clinics for women concerned about their family history of breast cancer are widely established. A Family History Clinic was set-up in Manchester, UK, in 1987 in a Breast Unit serving a population of 1.8 million. In this review, we report the outcome of risk assessment, screening and prevention strategies in the clinic and propose future approaches. Between 1987–2020, 14,311 women were referred, of whom 6.4% were from known gene families, 38.2% were at high risk (≥30% lifetime risk), 37.7% at moderate risk (17–29%), and 17.7% at an average/population risk who were discharged. A total of 4168 (29.1%) women were eligible for genetic testing and 736 carried pathogenic variants, predominantly in BRCA1 and BRCA2 but also other genes (5.1% of direct referrals). All women at high or moderate risk were offered annual mammographic screening between ages 30 and 40 years old: 646 cancers were detected in women at high and moderate risk (5.5%) with a detection rate of 5 per 1000 screens. Incident breast cancers were largely of good prognosis and resulted in a predicted survival advantage. All high/moderate-risk women were offered lifestyle prevention advice and 14–27% entered various lifestyle studies. From 1992–2003, women were offered entry into IBIS-I (tamoxifen) and IBIS-II (anastrozole) trials (12.5% of invitees joined). The NICE guidelines ratified the use of tamoxifen and raloxifene (2013) and subsequently anastrozole (2017) for prevention; 10.8% women took up the offer of such treatment between 2013–2020. Since 1994, 7164 eligible women at ≥25% lifetime risk of breast cancer were offered a discussion of risk-reducing breast surgery and 451 (6.2%) had surgery. New approaches in all aspects of the service are needed to build on these results.
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Rosner B, Tamimi RM, Kraft P, Gao C, Mu Y, Scott C, Winham SJ, Vachon CM, Colditz GA. Simplified Breast Risk Tool Integrating Questionnaire Risk Factors, Mammographic Density, and Polygenic Risk Score: Development and Validation. Cancer Epidemiol Biomarkers Prev 2020; 30:600-607. [PMID: 33277321 DOI: 10.1158/1055-9965.epi-20-0900] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Revised: 09/01/2020] [Accepted: 12/01/2020] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Clinical use of breast cancer risk prediction requires simplified models. We evaluate a simplified version of the validated Rosner-Colditz model and add percent mammographic density (MD) and polygenic risk score (PRS), to assess performance from ages 45-74. We validate using the Mayo Mammography Health Study (MMHS). METHODS We derived the model in the Nurses' Health Study (NHS) based on: MD, 77 SNP PRS and a questionnaire score (QS; lifestyle and reproductive factors). A total of 2,799 invasive breast cancer cases were diagnosed from 1990-2000. MD (using Cumulus software) and PRS were assessed in a nested case-control study. We assess model performance using this case-control dataset and evaluate 10-year absolute breast cancer risk. The prospective MMHS validation dataset includes 21.8% of women age <50, and 434 incident cases identified over 10 years of follow-up. RESULTS In the NHS, MD has the highest odds ratio (OR) for 10-year risk prediction: ORper SD = 1.48 [95% confidence interval (CI): 1.31-1.68], followed by PRS, ORper SD = 1.37 (95% CI: 1.21-1.55) and QS, ORper SD = 1.25 (95% CI: 1.11-1.41). In MMHS, the AUC adjusted for age + MD + QS 0.650; for age + MD + QS + PRS 0.687, and the NRI was 6% in cases and 16% in controls. CONCLUSION A simplified assessment of QS, MD, and PRS performs consistently to discriminate those at high 10-year breast cancer risk. IMPACT This simplified model provides accurate estimation of 10-year risk of invasive breast cancer that can be used in a clinical setting to identify women who may benefit from chemopreventive intervention.See related commentary by Tehranifar et al., p. 587.
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Affiliation(s)
- Bernard Rosner
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Rulla M Tamimi
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
- Division of Epidemiology, Population Health Sciences Department, Weill Cornell Medicine, New York, New York
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Peter Kraft
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Program in Genetic Epidemiology and Statistical Genetics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Chi Gao
- Program in Genetic Epidemiology and Statistical Genetics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Yi Mu
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Christopher Scott
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota
| | - Stacey J Winham
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota
| | - Celine M Vachon
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota
| | - Graham A Colditz
- Alvin J. Siteman Cancer Center and Department of Surgery, Division of Public Health Sciences, School of Medicine, Washington University in St. Louis, St. Louis, Missouri
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