{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T13:12:34Z","timestamp":1787317954522,"version":"build-2736575974"},"reference-count":31,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T00:00:00Z","timestamp":1765152000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>\n                      Granulomatous lobular mastitis (GLM) frequently mimics ductal carcinoma\n                      <jats:italic>in situ<\/jats:italic>\n                      (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.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fonc.2025.1668908","type":"journal-article","created":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T05:12:37Z","timestamp":1765170757000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Development and validation of a nomogram for differentiating granulomatous lobular mastitis from ductal carcinoma in 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