Classification of MR-Detected Additional Lesions in Patients With Breast Cancer Using a Combination of Radiomics Analysis and Machine Learning - Lee, Hyo-jae; Nguyen, Anh-Tien; Ki, So Yeon; Lee, Jong Eun; Do, Luu-Ngoc; Park, Min Ho; Lee, Ji Shin; Kim, Hye Jung; Park, Ilwoo; Lim, Hyo Soon (Frontiers in Oncology, 2021)
Objective: To investigate the feasibility of using radiomics analysis (RA) with machine learning algorithms based on breast magnetic resonance (MR) images for discriminating malignant from benign MR-detected additional lesions in patients with primary breast cancer. Materials and methods: 174 MR-detected additional lesions (benign, n = 86; malignancy, n = 88) from 158 patients with ipsilateral primary breast cancer from a tertiary medical center were included in this retrospective study. The entire data was randomly split to training (80%) and independent test set (20%). In addition, 25 patients (benign, n = 21; malignancy, n = 15) from another tertiary medical center were included for external test. Radiomics features that were extracted from three regions-of-interest (ROIs; intratumor, peritumor, combined) using fat-saturated T1-weighted images obtained by subtracting pre- from post-contrast images (SUB) and T2-weighted image (T2) were utilized to train support vector machine for the binary classification. A decision tree method was utilized to build a classifier model using clinical imaging interpretation (CII) features assessed by radiologists. Area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, and specificity were used to compare the diagnostic performance. Results: The RA models trained using radiomics features from the intratumor-ROI showed comparable performance to the CII model (accuracy, AUROC; 73.3%, 69.6% for SUB RA model; 70.0%, 75.1% for T2 RA model; 73.3%, 72.0% for CII model). The diagnostic performance increased when the radiomics and CII features were combined to build a fusion model. The fusion model that combines the CII features and radiomics features from multiparametric MRI data demonstrated the highest performance with an accuracy of 86.7% and an AUROC of 91.1%. The external test showed a similar pattern where the fusion models demonstrated higher levels of performance compared to RA- or CII-only models. The accuracy and AUROC of the SUB+T2 RA+CII model in the external test were 80.6% and 91.4%, respectively. Conclusion: Our study demonstrated the feasibility of using RA with machine learning approach based on multiparametric MRI for quantitatively characterizing MR-detected additional lesions. The fusion model demonstrated an improved diagnostic performance over the models trained with either RA or CII alone.