Keywords: Breast, Cancer, breast cancer; DCE MRI; Non-mass enhancement; shrinkage pattern
Motivation: Currently, there are no standardized methods or imaging biomarkers available in clinical practice to accurately predict pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in patients with non-mass enhancement breast cancer.
Goal(s): Utilize longitudinal dynamic contrast-enhanced (DCE) MRI radiological features alongside clinical and pathological data to predict pCR in breast cancer patients.
Approach: We retrospectively collected longitudinal MRI and clinical data from 303 patients undergoing NAC and utilized machine learning approaches for pCR prediction.
Results: Our results showed significant associations between multiple radiological and clinical characteristics and pCR. The XGBoost machine learning models effectively predicted pCR in breast cancer.
Impact: We proposed a novel model based on longitudinal MRI data to predict pCR to NAC in breast cancer, including both mass and non-mass enhancement lesions, to inform personalized clinical treatment plans.
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