Keywords: Diagnosis/Prediction, Breast, dynamic contrast enhanced magnetic resonance imaging, diffusion weighted imaging, apparent diffusion coefficient, Deep learning
Motivation: No mature neural network model based on multiparametric MRI to predict benign and malignant breast lesions accurately.
Goal(s): To develop a deep learning (DL) model based on multiparametric MRI for distinguishing benign and malignant breast lesions
Approach: A DL model based on the dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) and diffusion weighted imaging (DWI) with apparent diffusion coefficient (ADC) map.
Results: The DL combined model based on DCE-MRI and ADC achieved the highest diagnostic efficiency with an area under the curve (AUC) of 0.889.
Impact: The DL model based on multiparametric MRI achieved high accuracy for distinguishing benign and malignant breast lesions and showed the potential for future application as a new tool for clinical diagnosis.
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