P Herent1, B Schmauch1, P Jehanno2, O Dehaene3, C Saillard1, C Balleyguier4, J Arfi-Rouche4, S Jégou1. 1. Owkin Inc, Research and Development Laboratory, 75003 Paris, France. 2. Owkin Inc, Research and Development Laboratory, 75003 Paris, France. Electronic address: paul.jehanno@owkin.com. 3. École Centrale d'Electronique (ECE), 75015 Paris, France. 4. Radiology Department, Institut Gustave-Roussy, 94805 Villejuif, France.
Abstract
PURPOSE: The purpose of this study was to assess the potential of a deep learning model to discriminate between benign and malignant breast lesions using magnetic resonance imaging (MRI) and characterize different histological subtypes of breast lesions. MATERIALS AND METHODS: We developed a deep learning model that simultaneously learns to detect lesions and characterize them. We created a lesion-characterization model based on a single two-dimensional T1-weighted fat suppressed MR image obtained after intravenous administration of a gadolinium chelate selected by radiologists. The data included 335 MR images from 335 patients, representing 17 different histological subtypes of breast lesions grouped into four categories (mammary gland, benign lesions, invasive ductal carcinoma and other malignant lesions). Algorithm performance was evaluated on an independent test set of 168 MR images using weighted sums of the area under the curve (AUC) scores. RESULTS: We obtained a cross-validation score of 0.817 weighted average receiver operating characteristic (ROC)-AUC on the training set computed as the mean of three-shuffle three-fold cross-validation. Our model reached a weighted mean AUC of 0.816 on the independent challenge test set. CONCLUSION: This study shows good performance of a supervised-attention model with deep learning for breast MRI. This method should be validated on a larger and independent cohort.
PURPOSE: The purpose of this study was to assess the potential of a deep learning model to discriminate between benign and malignant breast lesions using magnetic resonance imaging (MRI) and characterize different histological subtypes of breast lesions. MATERIALS AND METHODS: We developed a deep learning model that simultaneously learns to detect lesions and characterize them. We created a lesion-characterization model based on a single two-dimensional T1-weighted fat suppressed MR image obtained after intravenous administration of a gadolinium chelate selected by radiologists. The data included 335 MR images from 335 patients, representing 17 different histological subtypes of breast lesions grouped into four categories (mammary gland, benign lesions, invasive ductal carcinoma and other malignant lesions). Algorithm performance was evaluated on an independent test set of 168 MR images using weighted sums of the area under the curve (AUC) scores. RESULTS: We obtained a cross-validation score of 0.817 weighted average receiver operating characteristic (ROC)-AUC on the training set computed as the mean of three-shuffle three-fold cross-validation. Our model reached a weighted mean AUC of 0.816 on the independent challenge test set. CONCLUSION: This study shows good performance of a supervised-attention model with deep learning for breast MRI. This method should be validated on a larger and independent cohort.
Authors: Ravi Aggarwal; Viknesh Sounderajah; Guy Martin; Daniel S W Ting; Alan Karthikesalingam; Dominic King; Hutan Ashrafian; Ara Darzi Journal: NPJ Digit Med Date: 2021-04-07