Deciphering Breast Cancer Complexity: A Study on the Predictive Power of MRI Texture Analysis for Tumor...

Objectives: This study aimed to examine the association between MRI radiomics features of malignant breast masses and their histopathological, molecular characteristics, and response to neoadjuvant treatments. Materials and methods: A retrospective cohort of 70 breast cancer patients was analyzed. Texture analysis was performed on preoperative MRI scans, extracting features such as entropy, contrast, and homogeneity. These features were analyzed against histopathological and clinical targets (e.g., lymph node metastasis) and molecular profiles. Statistical analyses and machine learning algorithms, including logistic regression and support vector machines, were employed to evaluate the predictive power of MRI texture features for molecular subtypes and the association of radiomics markers with neoadjuvant treatments. Results: The findings revealed significant associations between MRI texture features and the histopathological and molecular characteristics of breast tumors. Certain texture parameters correlated with aggressive phenotypes and poor chemotherapy response. Despite the limited dataset, machine learning models performed well in classifying tumors and predicting outcomes, highlighting the potential of MRI texture analysis in clinical decision‑making. Conclusion: MRI texture analysis emerges as a non‑invasive tool for personalized breast cancer management. The significant associations between MRI texture features and critical tumor characteristics suggest that these features could serve as biomarkers for predicting tumor behavior and treatment efficacy. Further large‑scale research is needed to integrate MRI texture analysis into clinical practice and improve patient outcomes.