Keywords: Diagnosis/Prediction, Breast, Pathological Complete Response, Neoadjuvant Chemotherapy, Breast Cancer, Multiparametric MRI, Multimodal Radiomic Analysis, Radiomic Analysis
Motivation: Identifying optimal responders to neoadjuvant chemotherapy (NAC) in breast cancer before treatment is challenging but crucial for optimizing therapeutic strategies.
Goal(s): Our goal was to evaluate if combining pretreatment DCE-MRI and ADC maps, with or without clinical factors, can predict treatment response in breast cancer.
Approach: This retrospective study employed a rigorous feature selection process to identify key clinical and radiomic features from semi-automatically segmented tumor regions on pretreatment MR images, followed by multivariate logistic regression to assess models' predicitve performance.
Results: DCE-MRI and ADC independently provided valuable insights, with their combination enhancing predictive performance. Integrating clinical features further improved model performance.
Impact: This study demonstrates the potential of combining pretreatment DCE-MRI, ADC maps, and clinical factors in predicting NAC repsponse in breast cancer, offering a non-invasive approach to guide personalized treatment strategies, ultimately improving patient outcomes and reducing unnecessary interventions for non-responders.
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