Pathologic findings in reduction mammoplasty specimens: a surrogate for the population prevalence of breast cancer and high-risk lesions
Name
Barzilay_Pathologic findings.pdf
Description
Accepted version
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568.59 KB
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Checksum (MD5)
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Author(s)
Acevedo, Francisco
Armengol, V Diego
Deng, Zhengyi
Tang, Rong
Coopey, Suzanne B
Braun, Danielle
Yala, Adam
Barzilay, Regina
Li, Clara
Colwell, Amy
Date Issued
September 2018
Journal
Breast Cancer Research and Treatment
Publisher
Springer Nature
Version
Author's final manuscript
Abstract
© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Purpose: Mammoplasty removes random samples of breast tissue from asymptomatic women providing a unique method for evaluating background prevalence of breast pathology in normal population. Our goal was to identify the rate of atypical breast lesions and cancers in women of various ages in the largest mammoplasty cohort reported to date. Methods: We analyzed pathologic reports from patients undergoing bilateral mammoplasty, using natural language processing algorithm, verified by human review. Patients with a prior history of breast cancer or atypia were excluded. Results: A total of 4775 patients were deemed eligible. Median age was 40 (range 13–86) and was higher in patients with any incidental finding compared to patients with normal reports (52 vs. 39 years, p = 0.0001). Pathological findings were detected in 7.06% (337) of procedures. Benign high-risk lesions were found in 299 patients (6.26%). Invasive carcinoma and ductal carcinoma in situ were detected in 15 (0.31%) and 23 (0.48%) patients, respectively. The rate of atypias and cancers increased with age. Conclusion: The overall rate of abnormal findings in asymptomatic patients undergoing mammoplasty was 7.06%, increasing with age. As these results are based on random sample of breast tissue, they likely underestimate the prevalence of abnormal findings in asymptomatic women.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1007/S10549-018-4962-0
