Evidence map›Paper›PMID 42902029›Full record

ArticleBreast cancer research and treatment2026

A breast clinical scoring system to identify women with Idiopathic granulomatous Mastitis.

Shyamin Mehra, Jison Hong, Christine Yeh, Jared Su, Kimberly Stone, Irene Wapnir, Jacqueline Tsai

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Article in Breast cancer research and treatment, 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

7 authors.

Shyamin MehraStanford University School of Medicine, 300 Pasteur Dr, 3rd floor, Suite #H3625, Stanford, CA, 94305, USA.
Jison HongStanford University School of Medicine, 300 Pasteur Dr, 3rd floor, Suite #H3625, Stanford, CA, 94305, USA.
Christine YehStanford University School of Medicine, 300 Pasteur Dr, 3rd floor, Suite #H3625, Stanford, CA, 94305, USA.
Jared SuStanford University School of Medicine, 300 Pasteur Dr, 3rd floor, Suite #H3625, Stanford, CA, 94305, USA.
Kimberly StoneStanford University School of Medicine, 300 Pasteur Dr, 3rd floor, Suite #H3625, Stanford, CA, 94305, USA.
Irene WapnirStanford University School of Medicine, 300 Pasteur Dr, 3rd floor, Suite #H3625, Stanford, CA, 94305, USA.
Jacqueline TsaiStanford University School of Medicine, 300 Pasteur Dr, 3rd floor, Suite #H3625, Stanford, CA, 94305, USA. jatsai@stanford.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeIdiopathic granulomatous mastitis (IGM) is a benign inflammatory breast condition that can resemble mastitis or malignancy clinically and radiographically. We aimed to develop a quantitative scoring system, based off the Komen Inflammatory Breast Cancer (IBC) scoring system [1], evaluating clinical and radiologic features to distinguish IGM from benign and malignant breast conditions.

methodsWe conducted a retrospective chart review of biopsy-proven IGM patients diagnosed from 2014 to 2025 via institutional electronic medical records. Clinical symptom onset, features, and diagnostic imaging were analyzed. A continuous scoring scale (1-50) was created, encompassing three categories: symptom onset, clinical features, and radiographic findings. Scores of IGM patients were compared with those of patients diagnosed with IBC, mastitis (lactational and non-lactational), and locally advanced breast cancer (LABC). Welch's two-sample t-test assessed statistical differences, and Youden index identified the optimal score threshold for predicting IGM.

resultsAmong 148 IGM patients, predominantly Hispanic (56.8%) and Asian (26.7%), the mean age was 37.2 years, older than mastitis patients and younger than IBC patients. Key clinical features included pain or painful breast mass (100%), fistula or draining wound (77.7%), erythema (94.6%), and phlegmon (87.8%). Imaging revealed mass with collection in 84.6% and multiple masses/collections in 38.9%. The mean IGM score (35.5) was significantly higher than scores for IBC, mastitis, and LABC groups (14.4, 18, and 15.2, respectively; p ≤ 0.05). A score ≥ 24.5 predicted IGM with 93% sensitivity and 95% specificity.

conclusionsThis novel scoring system provides an objective framework to differentiate IGM from similar breast conditions, facilitating early diagnosis and guiding appropriate management. CLINICAL TRIAL NUMBER: not applicable.

Indexed as

Breast NeoplasmsGranulomatous MastitisAdultDiagnosis, DifferentialFemaleHumansInflammatory Breast NeoplasmsMiddle AgedRetrospective StudiesBreast imagingClinical scoring systemDiagnostic accuracyIdiopathic granulomatous mastitisInflammatory breast cancerLocally advanced breast cancerMastitis

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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.