Diagnosis of Benign and Malignant Breast Lesions on DCE‐MRI by Using Radiomics and Deep Learning With Consideration of Peritumor Tissue. Issue 3 (1st November 2019)

• Record Type: Journal Article
• Authors: Zhou, Jiejie; Zhang, Yang; Chang, Kai‐Ting; Lee, Kyoung Eun; Wang, Ouchen; Li, Jiance; Lin, Yezhi; Pan, Zhifang; Chang, Peter; Chow, Daniel; Wang, Meihao; Su, Min‐Ying
• Is Part Of: Journal of magnetic resonance imaging. Volume 51:Issue 3(2020)
• Page Start: 798
• Page End: 809
• Publication Date: 2019-11-01
• Subjects: breast cancer diagnosis -- DCE‐MRI -- deep learning -- peritumor tissue -- radiomics -- ResNet; Magnetic resonance imaging -- Periodicals; 616
• DOI: 10.1002/jmri.26981
• Languages: English
• ISSNs: 1053-1807

Abstract

Abstract : Background: Computer‐aided methods have been widely applied to diagnose lesions detected on breast MRI, but fully‐automatic diagnosis using deep learning is rarely reported. Purpose: To evaluate the diagnostic accuracy of mass lesions using region of interest (ROI)‐based, radiomics and deep‐learning methods, by taking peritumor tissues into consideration. Study Type: Retrospective. Population: In all, 133 patients with histologically confirmed 91 malignant and 62 benign mass lesions for training (74 patients with 48 malignant and 26 benign lesions for testing). Field Strength/Sequence: 3T, using the volume imaging for breast assessment (VIBRANT) dynamic contrast‐enhanced (DCE) sequence. Assessment: 3D tumor segmentation was done automatically by using fuzzy‐C‐means algorithm with connected‐component labeling. A total of 99 texture and histogram parameters were calculated for each case, and 15 were selected using random forest to build a radiomics model. Deep learning was implemented using ResNet50, evaluated with 10‐fold crossvalidation. The tumor alone, smallest bounding box, and 1.2, 1.5, 2.0 times enlarged boxes were used as inputs. Statistical Tests: The malignancy probability was calculated using each model, and the threshold of 0.5 was used to make a diagnosis. Results: In the training dataset, the diagnostic accuracy was 76% using three ROI‐based parameters, 84% using the radiomics model, and 86% using ROI + radiomics model. In deep learning using the per‐slice basis, the area under the receiver operating characteristic (ROC) was comparable for tumor alone, smallest and 1.2 times box (AUC = 0.97‐0.99), which were significantly higher than 1.5 and 2.0 times box (AUC = 0.86 and 0.71, respectively). For per‐lesion diagnosis, the highest accuracy of 91% was achieved when using the smallest bounding box, and that decreased to 84% for tumor alone and 1.2 times box, and further to 73% for 1.5 times box and 69% for 2.0 times box. In the independent testing dataset, the per‐lesion diagnostic accuracy was also the highest when using the smallest bounding box, 89%. Data Conclusion: Deep learning using ResNet50 achieved a high diagnostic accuracy. Using the smallest bounding box containing proximal peritumor tissue as input had higher accuracy compared to using tumor alone or larger boxes. Level of Evidence: 3 Technical Efficacy: Stage 2