ArticleBioMed research international2021
Hepatic Alveolar Echinococcosis: Predictive Biological Activity Based on Radiomics of MRI.
Article in BioMed research international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Two-Stage Automatic Liver Classification System Based on Deep Learning Approach Using CT Images.Journal of imaging informatics in medicine · 2026Article
- The value of nomogram analysis in predicting pulmonary metastasis in hepatic alveolar echinococcosis.Scientific reports · 2025Article
- Spatiotemporal Transcriptomic Profiling Reveals the Dynamic Immunological Landscape of Alveolar Echinococcosis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- CT-based radiomics models using intralesional and different perilesional signatures in predicting the microvascular density of hepatic alveolar echinococcosis.BMC medical imaging · 2025Article
- Advances in Novel Diagnostic Techniques for Alveolar Echinococcosis.Diagnostics (Basel, Switzerland) · 2025Review
- Clinical value of the semi-quantitative parameters ofBMC medical imaging · 2024Article
- A deep learning radiomics model based on CT images for predicting the biological activity of hepatic cystic echinococcosis.Frontiers in physiology · 2024Article
- Progress and perspectives on BMP9-ID1 activation of HIF-1α and VEGFA to promote angiogenesis in hepatic alveolar echinococcosis.Frontiers in oncology · 2024Review
- Value of intralesional and perilesional radiomics for predicting the bioactivity of hepatic alveolar echinococcosis.Frontiers in oncology · 2024Article
- Feasibility analysis and study of an intrahepatic portal vein infection hepatic alveolar echinococcosis C57 mouse model.Frontiers in veterinary science · 2022Article
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7 authors.
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Abstract
backgroundTo evaluate the role of radiomics based on magnetic resonance imaging (MRI) in the biological activity of hepatic alveolar echinococcosis (HAE).
methodsIn this study, 90 active and 46 inactive cases of HAE patients were analyzed retrospectively. All the subjects underwent MRI and positron emission tomography computed tomography (PET-CT) before surgery. A total of 1409 three-dimensional radiomics features were extracted from the T2-weighted MR images (T2WI). The inactive group in the training cohort was balanced via the synthetic minority oversampling technique (SMOTE) method. The least absolute shrinkage and selection operator (LASSO) regression method was used for feature selection. The machine learning (ML) classifiers were logistic regression (LR), multilayer perceptron (MLP), and support vector machine (SVM). We used a fivefold cross-validation strategy in the training cohorts. The classification performance of the radiomics signature was evaluated using receiver operating characteristic curve (ROC) analysis in the training and test cohorts.
resultsThe radiomics features were significantly associated with the biological activity, and 10 features were selected to construct the radiomics model. The best performance of the radiomics model for the biological activity prediction was obtained by MLP (AUC = 0.830 ± 0.053; accuracy = 0.817; sensitivity = 0.822; specificity = 0.811).
conclusionsWe developed and validated a radiomics model as an adjunct tool to predict the HAE biological activity by combining T2WI images, which achieved results nearly equal to the PET-CT findings.
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