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ORIGINAL ARTICLE
Year : 2022  |  Volume : 9  |  Issue : 2  |  Page : 37-46

Magnetic resonance imaging-based radiomics analysis for the assessment of hepatic alveolar echinococcosis biological activity: A preliminary study


1 Department of Radiology, Qingdao Municipal Hospital, Qingdao, China
2 First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
3 HuiYing Medical Technology Co., Ltd., Urumqi, China
4 Imaging Center, First Affiliated Hospital of Xinjiang Medical University, Urumqi, China

Correspondence Address:
Wenya Liu
Imaging Center, First Affiliated Hospital of Xinjiang Medical University, Urumqi Xinjiang
China
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/RID.RID_21_22

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OBJECTIVE: The objective of this study was to develop and evaluate predictive models based on a combination of T2-weighted images (T2WI) and different machine learning algorithms, and to explore the value of hepatic alveolar echinococcosis (HAE) activity assessment by magnetic resonance imaging (MRI) radiomics. MATERIALS AND METHODS: This retrospective study included 136 patients diagnosed with HAE at the First Affiliated Hospital of Xinjiang Medical University between 2012 and 2020. All subjects underwent MRI and positron emission tomography–computed tomography (PET-CT) before surgery. Taking the PET-CT examination results as the reference standard, patients were divided into active (90 cases) and inactive groups (46 cases). The volume of interest of the lesion was manually delineated on T2WI, and quantitative radiomics features were extracted. Synthetic Minority Oversampling Technology was used to balance the number of patients in the categories. To control for redundancy, the least absolute shrinkage and selection operator was used for feature screening after normalization, and ten optimal features were obtained based on correlation coefficient screening. Three machine learning classifiers were trained using five-fold cross-validation and their performance was compared to establish an optimal HAE activity assessment model. The performance of the classifier was evaluated by area under the receiver operating characteristics curve (AUC), sensitivity, specificity, and accuracy (ACC). The ten optimal features selected from each fold were combined using three machine learning algorithms: logistic regression, multilayer perceptron (MLP), and support vector machine, to establish an HAE activity prediction model. RESULTS: The three machine learning classifiers all showed good prediction performance with a mean AUC on the test set of more than 0.80, and the MLP showing the best performance (AUC = 0.830 ± 0.053, ACC = 0.817, sensitivity = 0.822, and specificity = 0.811). CONCLUSION: HAE activity can be accurately evaluated by a radiomics method using a combination of quantitative T2WI features and machine learning.


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