This analysis demonstrates improved predictive accuracy of pathologic complete response in breast cancer, highlighting the potential of hyper-fused radiomics.
Motivation: Predicting pathologic complete response (pCR) to neoadjuvant therapy (NAT) in breast cancer (BC) is essential for personalized treatment planning. Goal(s): This study aimed to enhance predictive accuracy of MRI-based radiomics by developing a hyper-fused radiomic model. Approach: Hyper-fused radiomic features were extracted by combining voxel-wise data from multiple MRI parametric volumes, transformed into spherical coordinates for more comprehensive analysis. Results: The hyper-fused model demonstrated higher predictive performance compared to conventional methods, with greater AUCs and robust associations with pCR, validated in an external test set. Impact: This research highlights hyper-fused radiomics as a promising tool in precision oncology, potentially replacing contrast-based imaging for patients with contraindications and advancing predictive accuracy in therapy response.
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Cui et al. (2025) studied this question.
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