Keywords: Machine Learning/Artificial Intelligence, Breast
To predict ductal carcinoma in situ with microinvasion (DCISMI) based on clinicopathologic, conventional breast magnetic resonance imaging (MRI), and dynamic contrast enhanced MRI (DCE-MRI) radiomics signatures in women with biopsy-confirmed ductal carcinoma in situ (DCIS) to choose high-risk women who may benefit from sentinel lymph node biopsy at initial surgery. The mixed model showed better AUC values than both clinicopathological and DCE-MRI radiomics models in both training/test sets with heterogeneous enhancement and radiomics scores as significant independent predict factors. The mixed model showed the greatest overall net benefit for upstaging and the second was the combine model.
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