Integrative Radiomics Clustering Analysis in Breast Cancer: Deciphering Heterogeneity and Prognostic Indicators through multiparametric MRI

Abstract

Background Breast cancer diagnosis and treatment have been revolutionized by advances in imaging techniques, particularly multiparametric Magnetic Resonance Imaging (mpMRI). This study aims to leverage mpMRI to enhance the understanding of breast cancer heterogeneity and improve diagnostic accuracy. Methods We conducted a comprehensive analysis of 194 breast cancer patients using mpMRI, which included T2-weighted imaging (T2WI), ZOOMit-Diffusion weighted imaging (ZOOMit-DWI), and Dynamic Contrast-Enhanced (DCE) MRI phase 2 and phase 7. Radiomics features were extracted using the open-source Python package 'pyradiomics'. Unsupervised analysis was performed using the MOVICS package, integrating various multi-omics clustering methods. The patients were clustered into different subtypes, and the associations between the subtypes and clinical prognostic indicators were investigated using univariate analyses. Results In total, 194 patients were included in the study with a mean age of 54.9 years and a predominance of Luminal B subtype (47.7%), followed by Luminal A (22.3%). Two mpMR-based radiomics subtypes were identified. Patient demographics showed no significant age difference between Subtype1 (average age 55.3 years, n = 109) and Subtype 2 (average age 54.4 years, n = 84) (p = 0.57). A significant disparity in molecular subtypes was observed between the groups, particularly in Luminal A subtype distribution (16.514% in Subtype 1 vs. 29.762% in Subtype 2, p = 0.03). Significant differences were also noted in hormone receptor status, with estrogen receptor (ER) (p = 0.01) and progesterone receptor (PR) (p = 0.04) differing notably between two subtypes. Group 1 presented a larger mean tumor size (p<0.01) and more varied histological grades (p<0.01). Lymph node metastasis (LNM) and edema showed significant differences (p<0.05). Conclusions Our study underscores the potential of mpMRI in enhancing breast cancer diagnostics. The radiomics-based cluster analysis offers a novel approach to categorizing breast cancer, providing insights into tumor heterogeneity and aiding in the development of personalized treatment strategies.