{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T16:39:13Z","timestamp":1777480753809,"version":"3.51.4"},"reference-count":55,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,6,25]],"date-time":"2025-06-25T00:00:00Z","timestamp":1750809600000},"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. Immunol."],"abstract":"<jats:sec><jats:title>Objective<\/jats:title><jats:p>To explore the association between signal transducer and activator of transcription 3 (STAT3) expression, tumor immune microenvironment, and overall survival (OS) in breast cancer, and to develop a non-invasive radiomics model for early risk stratification using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Data from 1,008 patients with breast cancer in The Cancer Genome Atlas were analyzed to evaluate the prognostic significance of STAT3 expression using Kaplan-Meier survival analysis and Cox regression models. Functional enrichment and immune cell infiltration analyses were performed to assess tumor immune microenvironment characteristics. Additionally, DCE-MRI data from 101 patients in The Cancer Imaging Archive were used to extract radiomic features from early- and delayed-phase images. A STAT3 predictive model was developed using six machine learning algorithms. Model performance was assessed using receiver operating characteristic (ROC) and related diagnostic statistical indicators.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Low STAT3 expression was significantly associated with poorer OS (hazard ratio [HR] = 1.927, <jats:italic>p<\/jats:italic> &amp;lt; 0.001). GSEA revealed that high STAT3 expression enhanced epithelial apoptosis and TNF-\u03b1\/NF\u03baB signaling while suppressing pro-tumorigenic pathways, which was associated with an immunosuppressive microenvironment, whereas low STAT3 correlated with T-cell exhaustion. DIA confirmed elevated STAT3 in tumor versus normal tissue (<jats:italic>p<\/jats:italic> &amp;lt; 0.05). The logistic regression-derived radiomics model for STAT3 expression prediction exhibited consistent discriminative performance, with area under curve (AUC) values of 0.861 (95% CI: 0.749 - 0.947) in the development cohort and 0.742 (95% CI: 0.588 - 0.884) in the validation cohort. High radiomics-derived scores were positively correlated with elevated STAT3 expression, longer OS (<jats:italic>p<\/jats:italic> = 0.034), and immune-related gene signatures indicative of a heightened immune response.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Radiomics analysis of DCE-MRI images in this study offered a non-invasive method for predicting STAT3 expression and characterization of the tumor immune microenvironment. This approach can offer valuable insights into breast cancer prognosis and support the development of personalized therapies.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fimmu.2025.1619186","type":"journal-article","created":{"date-parts":[[2025,6,25]],"date-time":"2025-06-25T04:10:35Z","timestamp":1750824635000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Machine learning-based integration of DCE-MRI radiomics for STAT3 expression prediction and survival stratification in breast 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