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Abstract #4136

Multiparametric MRI Model with DCE-MRI and ADC map Enables Accurate Prediction of Benign and Malignant Breast Lesions

yanhong chen1, chen Luo2, Lijun Wang2, ran luo2, huanhuan liu2, and dengbin wang2
1Xinhua Hospital Affiliated to Shanghai Jiao Tong University School Of Medicine, Shanghai, China, 2Xinhua Hospital Affiliated to Shanghai Jiao Tong University School Of Medicine, shanghai, China

Synopsis

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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Keywords