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01 · ABSTRACT

Abstract

Background/Objectives. The performance of contralateral prophylactic mastectomy significantly reduces the risk of contralateral breast cancer but has also been linked to higher rates of unplanned future operations and psychological stress. The risks of undergoing contralateral prophylactic mastectomy after diagnosis of unilateral primary breast cancer should be weighed against the benefit of risk reduction, which varies widely based on the presence of pathogenic or likely pathogenic variants in high and moderate penetrance genes. In the absence of these mutations, contralateral breast cancer risk varies based on characteristics such as family history, primary tumor characteristics, precursor lesions and other risk factors. Physicians and patients can benefit from risk prediction tools that provide patients personalized risk estimates. Our goal was to develop such a decision support tool to help facilitate informed discussions with breast cancer patients and their clinicians, to make optimal surgical decisions regarding contralateral prophylactic mastectomy.

Methods. For carriers of BRCA1, BRCA2, CHEK2, ATM, PALB2, TP53 data was abstracted from published studies to estimate conditional age-specific contralateral breast cancer risk, which was then used to estimate future risk. For non-carriers, a previously developed and validated contralateral breast cancer risk model was adapted.

Results. We developed an easy-to-use web application that can estimate lifetime and age-conditional risks for both carriers of relatively common breast cancer-related high and moderate penetrance genes as well as for non-carriers.

Conclusions. Our developed tool immediately provides individualized contralateral breast cancer risk estimates and could be extremely helpful during surgical discussions, especially regarding whether or not to perform contralateral prophylactic mastectomy. The tool will be evaluated in a forthcoming randomized controlled trial.

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02 · PUBLICATION RECORD

Article details

JournalMedical Research Archives
IssueVol 13 No 7 (2025): Vol.13, Issue 7, July 2025
SectionResearch Articles
Published25 July 2025
DOI10.18103/mra.v13i7.6814
ISSN2375-1924
03 · RIGHTS & REUSE

Rights & reuse

This article is published under a Creative Commons Attribution License (CC BY 3.0) and may be shared or distributed by anyone as long as attribution is given to the journal.

Authors & affiliations

DB

Danielle Braun

Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA;  Department of Data Science, Dana-Farber Cancer Institute, Boston, MA

TH

Theodore Huang

Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA;  Department of Data Science, Dana-Farber Cancer Institute, Boston, MA;  Department of Biostatistics, Vertex Pharmaceuticals, Boston, MA

JS

Jill Stopfer

Division of Cancer Genetics and Prevention, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

JG

Judy Garber

 Division of Cancer Genetics and Prevention, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Breast Oncology Program, Dana-Farber Brigham Cancer Center, Boston, MA; Harvard Medical School, Boston, MA; Medical Oncology, Dana-Farber Cancer Institute, Boston, MA

AW

Anna Weiss

Harvard Medical School, Boston, MA; Division of Breast Surgery, Department of Surgery, Brigham and Women’s Hospital, Boston, MA; Breast Oncology Program, Dana-Farber Brigham Cancer Center, Boston, MA; Division of Surgical Oncology, Department of Surgery, University of Rochester, Rochester, NY

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