Radiomics-based MRI for predicting Erythropoietin-producing hepatocellular receptor A2 expression and tumor grade in brain diffuse gliomas.
Purpose: EphA2 is a key factor underlying invasive propensity of gliomas, and is associated with poor prognosis of tumors. We aimed to develop a radiomics-based imaging index for predicting EphA2 expression in diffuse gliomas, and further estimating its value for grading of tumors. Methods: A total...
| Published in: | Neuroradiology Vol. 64; no. 2; pp. 323 - 332 |
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| Main Authors: | , , , , , , , , , , |
| Format: | pictorial research tables/charts Journal Article |
| Published: |
Springer Nature
Feb2022
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=154884803&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 154884803 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00283940 NYZ jtl: Neuroradiology issn: 00283940 maglogo: N pubinfo: dt: Feb2022 vid: 64 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 154884803 151813568 154884803 154884803 10.1007/s00234-021-02780-1 154884803 ppf: 323 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Radiomics-based MRI for predicting Erythropoietin-producing hepatocellular receptor A2 expression and tumor grade in brain diffuse gliomas. aug: au: Liu, Xiaoxue Li, Jianrui Liao, Xiang Luo, Zhongqiang Xu, Qiang Pan, Hao Zhou, Qing Tao, Yan Shi, Feng Lu, Guangming Zhang, Zhiqiang affil: Department of Diagnostic Radiology, Affiliated Jinling Hospital, Medical School of Nanjing University, 305#, Eastern Zhongshan Rd, 210002, Nanjing, China sug: subj: Magnetic Resonance Imaging Erythropoietin Carcinoma, Hepatocellular Diagnosis Receptors, Cell Surface Brain Neoplasms Classification Glioma Classification Predictive Value of Tests Human Immunohistochemistry Staining and Labeling Imaging, Three-Dimensional Contrast Media Prediction Models Machine Learning Spearman's Rank Correlation Coefficient Logistic Regression Descriptive Statistics ab: Purpose: EphA2 is a key factor underlying invasive propensity of gliomas, and is associated with poor prognosis of tumors. We aimed to develop a radiomics-based imaging index for predicting EphA2 expression in diffuse gliomas, and further estimating its value for grading of tumors. Methods: A total of 182 patients with diffuse gliomas were included. All subjects underwent pre-operative MRI and post-operative pathological diagnosis. EphA2 expression of tumors was scored on pathological sections with immunohistochemical staining using monoclonal EphA2 antibody. MRI radiomics features were extracted from three-dimensional contrast-enhanced T1-weighted imaging and diffusion kurtosis imaging. Predictive models were constructed using machine learning–based radiomics features selection and three classifiers for predicting EphA2 expression and tumor grade. Features of best EphA2 expression model were subsequently used to construct another model of tumor grading. For each model, 146 cases (80%) were randomly picked as training and the rest 36 (20%) were testing cohorts. EphA2 expression was further correlated to the radiomics features in both grade models using Spearman's correlation. Results: Logistic regression model presented highest performance for predicting EphA2 expression (AUC: 0.836/0.724 in training/validation set). Tumor gradings model guided by features from EphA2 expression model demonstrated comparable performance (AUC: 0.930/0.983) to that constructed directly using imaging radiomics features (AUC: 0.960/0.977). Two radiomics features which included in both LR-grade models showed strong correlation (P < 0.05) with EphA2 expression. Conclusion: The expression of EphA2 in gliomas could be predicted by radiomics features extracted from diffusion kurtosis MRI, which could also be used to assist tumor grading. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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