ArticleFrontiers in immunology2025
Machine learning-based integration of DCE-MRI radiomics for STAT3 expression prediction and survival stratification in breast cancer.
Article in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Radiomics and Deep Learning: Bridging Breast Cancer Imaging Phenotypes and Genomic Heterogeneity.Breast cancer (Dove Medical Press) · 2026Review
- Developing a carotid ultrasound radiomics-semantic fusion model to identify aortic dissection: a two-center retrospective study.Frontiers in medicine · 2026Article
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5 authors.
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Abstract
Objective: 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). Methods: 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. Results: Low STAT3 expression was significantly associated with poorer OS (hazard ratio [HR] = 1.927, Conclusion: 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.
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