Diagnosis of Breast Cancer Using Radiomics Models Built Based on Dynamic Contrast Enhanced MRI Combined With Mammography

Frontiers in Oncology

Min-Ying Su¹  Ritesh Parajuli²  Rita S. Mehta²  Yang Zhang³  Freddie J. Combs³  Jeon-Hor Chen⁴  Zhongwei Chen⁵  You-Fan Zhao⁵  Meihao Wang⁵  Jiejie Zhou⁵  Kyoung Eun Lee⁶ 

[1] Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan;Department of Medicine, University of California, Irvine, Irvine, CA, United States;Department of Radiological Sciences, University of California, Irvine, Irvine, CA, United States;Department of Radiology, E-Da Hospital and I-Shou University, Kaohsiung, Taiwan;Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China;Department of Radiology, Inje University Seoul Paik Hospital, Inje University, Seoul, South Korea;

关键词: breast neoplasms;    diagnosis;    radiomics;    machine learning;    magnetic resonance imaging;    mammography;   

DOI  :  10.3389/fonc.2021.774248

来源: DOAJ

【 摘 要 】

ObjectiveTo build radiomics models using features extracted from DCE-MRI and mammography for diagnosis of breast cancer.Materials and Methods266 patients receiving MRI and mammography, who had well-enhanced lesions on MRI and histologically confirmed diagnosis were analyzed. Training dataset had 146 malignant and 56 benign, and testing dataset had 48 malignant and 18 benign lesions. Fuzzy-C-means clustering algorithm was used to segment the enhanced lesion on subtraction MRI maps. Two radiologists manually outlined the corresponding lesion on mammography by consensus, with the guidance of MRI maximum intensity projection. Features were extracted using PyRadiomics from three DCE-MRI parametric maps, and from the lesion and a 2-cm bandshell margin on mammography. The support vector machine (SVM) was applied for feature selection and model building, using 5 datasets: DCE-MRI, mammography lesion-ROI, mammography margin-ROI, mammography lesion+margin, and all combined.ResultsIn the training dataset evaluated using 10-fold cross-validation, the diagnostic accuracy of the individual model was 83.2% for DCE-MRI, 75.7% for mammography lesion, 64.4% for mammography margin, and 77.2% for lesion+margin. When all features were combined, the accuracy was improved to 89.6%. By adding mammography features to MRI, the specificity was significantly improved from 69.6% (39/56) to 82.1% (46/56), p<0.01. When the developed models were applied to the independent testing dataset, the accuracy was 78.8% for DCE-MRI and 83.3% for combined MRI+Mammography.ConclusionThe radiomics model built from the combined MRI and mammography has the potential to provide a machine learning-based diagnostic tool and decrease the false positive diagnosis of contrast-enhanced benign lesions on MRI.

【 授权许可】

Unknown