ORIGINAL RESEARCH article
Front. Oncol.
Sec. Cancer Imaging and Image-directed Interventions
Development and Validation of a Nomogram for Differentiating Granulomatous Lobular Mastitis from Ductal Carcinoma In Situ
Provisionally accepted
- Outpatient Department of Quanzhou First Hospital, Quanzhou, China
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Background: Granulomatous lobular mastitis (GLM) frequently mimics ductal carcinoma in situ (DCIS) in clinical presentation and imaging characteristics, leading to misdiagnosis and unnecessary aggressive interventions. This study aimed to develop and validate a practical nomogram for differentiating GLM from DCIS. Methods: We conducted a retrospective study at Quanzhou First Hospital from January 2020 to April 2025, including 290 patients with histopathologically confirmed GLM (n=128) or DCIS (n=162). Patients were randomly divided into training (n=203) and validation (n=87) sets. Clinical, laboratory, and ultrasound features were analyzed using univariate and multivariate logistic regression to identify independent predictors. A nomogram was constructed and evaluated using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis. Results: Six independent predictors were incorporated into the final nomogram: age, lesion size, margin characteristics, microcalcifications, posterior acoustic enhancement, and peri-lesional flow. The nomogram demonstrated excellent discriminative performance with areas under the ROC curve of 0.95 (95% CI: 0.92-0.98) in the training set and 0.93 (95% CI: 0.88-0.98) in the validation set. At optimal thresholds, the model achieved sensitivity of 92% and specificity of 89% in the training set, and 89% and 79% respectively in the validation set. Calibration plots confirmed high predictive accuracy, and decision curve analysis demonstrated substantial clinical benefit across clinically relevant threshold probabilities. Conclusions: This novel nomogram represents a diagnostic tool specifically designed for GLM versus DCIS differentiation. Its reliance on widely available clinical and ultrasound parameters makes it particularly valuable for resource-limited settings, potentially reducing unnecessary biopsies and associated patient morbidity.
Keywords: Granulomatous, Mastitis, Carcinoma, Intraductal, Noninfiltrating, Nomograms, Ultrasonography, Mammary, Predictive learning models
Received: 24 Jul 2025; Accepted: 24 Nov 2025.
Copyright: © 2025 Li, su, zhang and liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: zhonghua liu
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