Hepatic Alveolar Echinococcosis: Predictive Biological Activity Based on Radiomics of MRI.
Background. To evaluate the role of radiomics based on magnetic resonance imaging (MRI) in the biological activity of hepatic alveolar echinococcosis (HAE). Methods. In this study, 90 active and 46 inactive cases of HAE patients were analyzed retrospectively. All the subjects underwent MRI and posit...
| Publicado en: | BioMed Research International pp. 1 - 15 |
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| Autores principales: | , , , , , , |
| Formato: | diagnostic images equations & formulas pictorial research Journal Article |
| Publicado: |
Wiley-Blackwell
4/9/2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=149733446&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 149733446 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 4/9/2021 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 149733446 149733446 149733446 10.1155/2021/6681092 149733446 ppf: 1 ppct: 14 formats: fmt: @attributes: type: P tig: atl: Hepatic Alveolar Echinococcosis: Predictive Biological Activity Based on Radiomics of MRI. aug: au: Ren, Bo Wang, Jian Miao, Zhoulin Xia, Yuwei Liu, Wenya Zhang, Tieliang Aikebaier, Aierken affil: Department of Imaging Center, The First Affiliated Hospital of Xinjiang Medical University, Li Yu Shan Road, No. 137 Urumqi City 830054, China sug: subj: Echinococcosis, Hepatic Physiopathology Echinococcosis, Hepatic Prognosis Magnetic Resonance Imaging Human Retrospective Design Tomography, Emission-Computed Regression Models, Theoretical ab: Background. To evaluate the role of radiomics based on magnetic resonance imaging (MRI) in the biological activity of hepatic alveolar echinococcosis (HAE). Methods. In 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. Results. The 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). Conclusions. We 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. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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