A Clinical Decision Support Tool to Predict Contralateral Breast Cancer Risk for Women with Primary Breast Cancer

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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Publication Details

Journal
Medical Research Archives
Published
2025-01-01
DOI
https://doi.org/10.18103/mra.v13i7.6814
Citations
1
Primary Topic
AI in cancer detection
Type
article
Field-Weighted Citation Impact
2.48

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article

A Clinical Decision Support Tool to Predict Contralateral Breast Cancer Risk for Women with Primary Breast Cancer

Jill E. Stopfer, Theodore Huang, Danielle Braun, Anna Weiss et al.
1 citations
Medical Research Archives
AI in cancer detection
2.48
article

A Clinical Decision Support Tool to Predict Contralateral Breast Cancer Risk for Women with Primary Breast Cancer

Jill E. Stopfer, Theodore Huang, Danielle Braun, Anna Weiss, Judy Garber
article en
1 citations

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.

Medical Research ArchivesVol. 13(7)
Cancer Research And Biostatistics (US), Dana-Farber Cancer Institute (US), Dana-Farber/Harvard Cancer Center (US), Cancer Genetics (United States) (US), Dana-Farber Brigham Cancer Center (US)
Dana-Farber Cancer Institute, Dana-Farber/Harvard Cancer Center, National Institutes of Health, National Cancer Institute
Openalex Percentile: Top 9%
AI in cancer detection
2.48
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