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 of...

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Bibliographic Details
Published in:Neuroradiology 2022-02, Vol.64 (2), p.323-331
Main Authors: Liu, Xiaoxue, Li, Jianrui, Liao, Xiang, Luo, Zhongqiang, Xu, Qiang, Pan, Hao, Zhou, Qing, Tao, Yan, Shi, Feng, Lu, Guangming, Zhang, Zhiqiang
Format: Article
Language:English
Subjects:
Antibodies
Brain
Brain Neoplasms - diagnostic imaging
Brain tumors
Carcinoma, Hepatocellular
Correlation
EphA2 protein
Erythropoietin
Feature extraction
Functional Neuroradiology
Glioma
Glioma - diagnostic imaging
Humans
Imaging
Kurtosis
Learning algorithms
Liver Neoplasms
Machine learning
Magnetic Resonance Imaging
Medical imaging
Medicine
Medicine & Public Health
Neuroimaging
Neurology
Neuroradiology
Neurosciences
Neurosurgery
Performance prediction
Prediction models
Radiology
Radiomics
Receptors, Erythropoietin
Regression models
Retrospective Studies
Training
Tumors
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Summary: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  
ISSN:0028-3940
1432-1920
DOI:10.1007/s00234-021-02780-1