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Breast cancer, the most prevalent malignant tumor among women worldwide, exhibits substantial heterogeneity, which manifests as different molecular subtypes with different therapeutic and prognostic implications. Owing to existing studies focusing on either kinetic or spatial heterogeneity in isolation, this study proposed a Dual Heterogeneity Fusion Network (DHF-Net) that integrated both the kinetic and spatial heterogeneities from DCE-MRIs for diagnosing breast cancer molecular subtypes. Initially, a convex analysis of mixtures algorithm was employed to identify dynamic heterogeneity subregions by analyzing contrast enhancement patterns over time. Meanwhile, K-Means clustering was utilized for spatial analysis to delineate spatial heterogeneity subregions that reflected structural diversity within tumors. Then, the dynamic and spatial heterogeneity features obtained from a ResNet-based feature extractor were integrated using a dual-attention module that incorporated both cross- and self-attentions. Final molecular subtype diagnosis was performed by a Mixture of Experts (MoE) framework. Experimental results demonstrated the effectiveness of the DHF-Net on a publicly available TCIA dataset in two molecular subtype classification tasks.Clinical Relevance-This study preliminary exploits both the kinetic and spatial heterogeneity to predict breast cancer molecular subtypes, contributing to personalized treatment for breast cancer patients with different molecular subtypes.