Mammography Lesion ROI Drawing Guided by Breast MRI MIP to Extract Features from Corresponding Lesions to Build Radiomics Diagnostic Models
Yan-Lin Liu1, Zhongwei Chen2, Youfan Zhao2, Yang Zhang1,3, Jiejie Zhou2, Jeon-Hor Chen1, Ke Nie3, Meihao Wang2, and Min-Ying Su1
1Department of Radiological Sciences, University of California, Irvine, CA, United States, 2Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China, 3Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ, United States
268 patients with DCE-MRI and mammography were analyzed to evaluate the diagnostic performance of radiomics models. The dataset was split to 202 (146 malignant 56 benign) for training, and 66 (48 malignant 18 benign) for testing. The MIP of MR contrast enhancement maps was generated to simulate the CC and MLO view as guidance for manual ROI drawing on mammography. The models were built using features extracted by PyRadiomics. Combined MRI and mammography features can reach 89.6% accuracy in training and 83.3% in testing datasets, and the addition of mammography can improve specificity while maintaining high sensitivity of MRI.
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