# Machine learning-based integration of DCE-MRI radiomics for STAT3 expression prediction and survival stratification in breast cancer

## TL;DR

This study uses MRI-based radiomics and machine learning to predict STAT3 expression and survival in breast cancer, linking it to immune environment features.

## Contribution

A non-invasive radiomics model for STAT3 expression prediction and immune microenvironment characterization in breast cancer using DCE-MRI.

## Key findings

- Low STAT3 expression correlates with poorer overall survival in breast cancer patients.
- A radiomics model achieved 0.861 AUC in predicting STAT3 expression from DCE-MRI data.
- High radiomics scores correlate with elevated STAT3, longer survival, and immune response signatures.

## Abstract

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).

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.

Low STAT3 expression was significantly associated with poorer OS (hazard ratio [HR] = 1.927, p < 0.001). GSEA revealed that high STAT3 expression enhanced epithelial apoptosis and TNF-α/NFκB 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 (p < 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 (p = 0.034), and immune-related gene signatures indicative of a heightened immune response.

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.