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Radiomics-based MRI for predicting Erythropoietin-producing hepatocellular receptor A2 expression and tumor grade in brain diffuse gliomas

  • Functional Neuroradiology
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Abstract

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.

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Abbreviations

EphA2:

Erythropoietin-producing hepatocellular receptor A2

WHO:

World Health Organization

3DT1-CE:

Three-dimensional T1-weighted with contrasted enhancement

DKI:

Diffusion kurtosis imaging

LR:

Logistic regression

SVM:

Support vector machine

lightGBM:

Light gradient boosting machine

ACC:

Accuracy

SEN:

Sensitivity

SPE:

Specificity

AUC:

Area under curve

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Funding

This study was funded by the National Key Technology (R&D) Program of the Ministry of Science and Technology (2018YFA0701703 to Z.Z.); National Natural Science Foundation of China (81530054 to G.L., 81830056 to F.S.); grants of the key talent project in Jiangsu province (ZDRCA2016093 to Z.Z.); Natural scientific foundation-social development (BE2016751 to Z.Z.); Post-doctoral grants of China (2016M603064 to Z.Z.) and Jiangsu Province (1501169B to Z.Z.); health system strengthening project with Science and Education of Jiangsu provincial Commission of Health and Family Planning (YXZXA2016007 to G.L.).

Author information

Authors and Affiliations

  1. Department of Diagnostic Radiology, Affiliated Jinling Hospital, Medical School of Nanjing University, 305#, Eastern Zhongshan Rd, Nanjing, 210002, China

    Xiaoxue Liu, Jianrui Li, Xiang Liao, Zhongqiang Luo, Qiang Xu, Guangming Lu & Zhiqiang Zhang

  2. Department of Neurosurgery, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, China

    Hao Pan

  3. Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd, Shanghai, 200232, China

    Qing Zhou, Yan Tao & Feng Shi

  4. State Key Laboratory of Analytical Chemistry for Life Science, Nanjing University, Nanjing, 210093, China

    Guangming Lu & Zhiqiang Zhang

Authors
  1. Xiaoxue Liu
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  2. Jianrui Li
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  3. Xiang Liao
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  11. Zhiqiang Zhang
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Corresponding author

Correspondence to Zhiqiang Zhang.

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Conflict of interest

Q.Z., Y.T., and F.S. are employees of Shanghai United Imaging Intelligence Co., Ltd. The company has no role in designing and performing the surveillances and analyzing and interpreting the data. All other authors report no conflicts of interest relevant to this article.

Ethics approval

All procedures performed in the studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

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Informed consent was obtained from all individual participants included in the study.

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Key Points

1. EphA2 plays an important role in the occurrence and development of glioma.

2. We developed a radiomics-based index of EphA2 expressing in diffuse glioma.

3. The prediction model showed high diagnostic performance in predicting glioma’s grade.

4. Associations were found between the top features in the model predicting EphA2 and that for tumor grading.

Supplementary Information

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Supplementary file1 (DOCX 19 KB)

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Liu, X., Li, J., Liao, X. et al. Radiomics-based MRI for predicting Erythropoietin-producing hepatocellular receptor A2 expression and tumor grade in brain diffuse gliomas. Neuroradiology 64, 323–331 (2022). https://doi.org/10.1007/s00234-021-02780-1

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  • DOI: https://doi.org/10.1007/s00234-021-02780-1

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