Keywords: Breast, Breast, Luminal breast cancer, Radiomics, Magnetic resonance imaging (MRI), Tumor heterogeneity, Interpretability
Motivation: There is an urgent clinical need to develop predictive biomarkers that can help identify proper candidates for neoadjuvant chemotherapy (NAC) in luminal breast cancer.
Goal(s): To develop tailored prediction model for response to NAC, identify stable predictive features shared between Eastern and Western populations and reveal their biological interpretability.
Approach: Multiscale radiomic features, multiple feature selection methods and classifiers, bioinformatics analysis, three independent cohorts.
Results: The combination of "high-frequency features-XGBoost" demonstrated the best predictive performance for NAC response. Four multiscale radiomic features were identified as stable and discriminative predictive features between Eastern and Western populations, which associated with the immune-related and PPAR pathways.
Impact: We proposed a more rigorous approach to ensure the robustness of radiomic features and explored the stable predictive features and their biological significance across different populations of luminal breast cancer. It will capture the interest of radiologist and clinicians.
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