MRI-based radiomics model for distinguishing endometrial carcinoma from benign mimics: A multicenter study. Issue 146 (January 2022)

• Record Type: Journal Article
• Authors: Chen, Xiaojun; Wang, Xue; Gan, Meng; Li, Lan; Chen, Fangfang; Pan, Jiangfeng; Hou, Zujun; Yan, Zhihan; Wang, Cong
• Is Part Of: European journal of radiology. Issue 146(2022)
• Publication Date: 2022-01
• Subjects: Magnetic resonance imaging -- Radiomics -- Endometrial carcinoma -- Multicenter study; Medical radiology -- Periodicals; Radiology -- Periodicals; Radiologie médicale -- Périodiques; Medical radiology; Periodicals; 616.075705
• DOI: 10.1016/j.ejrad.2021.110072
• Languages: English
• ISSNs: 0720-048X

Abstract

Highlights: The proposed radiomics model achieved high diagnostic accuracy in distinguishing endometrial carcinoma from benign mimics. Features from the ADC map are most effective in the diagnosis. Features from different MRI sequences can be integrated in the proposed model. The proposed model has the potential to assist gynecologists in treatment decision-making. Abstract: Purpose: To develop and validate an MRI-based radiomics model for preoperatively distinguishing endometrial carcinoma (EC) with benign mimics in a multicenter setting. Methods: Preoperative MRI scans of EC patients were retrospectively reviewed and divided into training set (158 patients from device 1 in center A), test set #1 (78 patients from device 2 in center A) and test set #2 (109 patients from device 3 in center B). Two radiologists performed manual delineation of tumor segmentation on the map of apparent diffusion coefficient (ADC), diffusion-weighted imaging (DWI) and T2-weighted imaging (T2WI). The features were extracted and firstly selected using Chi-square test, followed by refining using binary least absolute shrinkage and selection operator (LASSO) regression. The support vector machine (SVM) was employed to build the radiomics model, which is tuned in the training set using 10-fold cross-validation, and then assessed on the test set. Performance of the model was determined by area under the receiver-operating characteristic curve (AUC), accuracy, sensitivity, specificity and F1-score. Results: Five most informative features are selected from the extracted 3142 features. The SVM with linear kernel was employed to build the radiomics model. The AUCs of the model were 0.989 (95% confidence interval [CI]: 0.968–0.997) for the training set, 0.999 (95% CI: 0.991–1.000) for test set #1, 0.961 (95% CI: 0.902–0.983) for test set #2. Accuracies of the model were 0.937 for the training set (precision: 0.919, recall: 0.963, F1-score: 0.940), 0.974 for test set #1 (precision: 0.949, recall: 1.000, F1-score: 0.974) and 0.908 for test set #2 (precision: 0.899, recall: 0.954, F1-score: 0.925). These results confirmed the efficacy of this model in predicting EC in different centers. Conclusion: The MRI-based radiomics model showed promising potential for distinguishing EC with benign mimics and might be useful for surgical management of EC.