Evidence map›Paper›PMID 42467150›Full record

ArticleAnnals of surgical oncology2026

Preoperative Overestimation of Disease Extent and Discordance-Driven Mastectomy: A Retrospective Review of 245 Cases.

Hadas Kadar Sfarad, Cheng-Bang Chen, Yujie Wang, Mehmet Akcin, Heidi Santa Cruz, Karishma Chapalamadugu, Emily McFarlane, Eli Avisar

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Article in Annals of surgical oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

8 authors.

Hadas Kadar SfaradDivision of Surgical Oncology, Dewitt Daughtry Department of Surgery, University of Miami Miller School of Medicine, Miami, FL, USA. hadaskadar@gmail.com.ORCID http://orcid.org/0009-0002-9993-4657
Cheng-Bang ChenDepartment of Industrial and Systems Engineering, University of Miami, Coral Gables, FL, USA.
Yujie WangDepartment of Industrial and Systems Engineering, University of Miami, Coral Gables, FL, USA.
Mehmet AkcinDepartment of Industrial and Systems Engineering, University of Miami, Coral Gables, FL, USA.
Heidi Santa CruzMiller School of Medicine, University of Miami, Miami, FL, USA.
Karishma ChapalamaduguMiller School of Medicine, University of Miami, Miami, FL, USA.
Emily McFarlaneMiller School of Medicine, University of Miami, Miami, FL, USA.
Eli AvisarDivision of Surgical Oncology, Dewitt Daughtry Department of Surgery, University of Miami Miller School of Medicine, Miami, FL, USA. EAvisar@med.miami.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPreoperative mammography and magnetic resonance imaging (MRI) guide surgical planning in breast cancer but may misestimate disease extent. We quantified imaging-pathology discordance among patients undergoing mastectomy and estimated the discordance-driven mastectomy rate (DDMR), defined as imaging-extensive disease with localized final pathology, as a measure of discordance. PATIENTS AND

methodsWe retrospectively reviewed 245 consecutive mastectomy cases (2018-2019) at a tertiary academic center; 236 had usable postoperative pathology and were analyzed. Preoperative imaging (mammography and/or MRI) was classified as localized versus extensive using predefined extent-indicating features and/or maximum size > 5 cm. Final pathology was similarly classified using size and multifocality/multicentricity as the reference standard. Concordance categories were concordant-localized, concordant-extensive, imaging overestimation, and imaging underestimation. The primary outcome was any discordance; the key clinical outcome was DDMR. Decision drivers were abstracted from the medical record when available.

resultsOverall discordance occurred in 119/236 (50.4%) cases and was predominantly imaging due to overestimation (113/236, 47.9%; 95% CI 41.4-54.5); underestimation was uncommon (6/236, 2.5%). DDMR events occurred only when at least one modality had extent-indicating features and were common with mammography-only (44/53, 83.0%), MRI-only (30/43, 69.8%), or both (39/62, 62.9%). DDMR rates were similar across mastectomy subtypes and by neoadjuvant chemotherapy receipt. Among DDMR cases, surgeon-only requests were most frequent (71/113, 62.8%) versus patient-only requests (31/113, 27.4%; p < 0.001). Agreement between imaging and pathology was low (MRI κ = 0.241; mammography κ = 0.123).

conclusionsAmong a cohort of patients undergoing mastectomy, imaging-pathology discordance was common and predominantly reflected overestimation. These findings highlight the importance of cautious interpretation of imaging-defined extent within clinical decision-making.

Indexed as

Breast NeoplasmsMagnetic Resonance ImagingMammographyMastectomyAdultAgedFemaleFollow-Up StudiesHumansMiddle AgedPreoperative CarePrognosisRetrospective StudiesBreast cancerImaging–pathology discordanceMagnetic resonance imagingMammographyMastectomySurgical decision-making

Identifiers

PMID42467150
PMCPMC13630832

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.