Diagnostic and Interventional Radiology
Neuroradiology - Original Article

Do the combination of multiparametric MRI-based radiomics and selected blood inflammatory markers predict the grade and proliferation in glioma patients?

1.

Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China

2.

Department of Radiology, Shanxi Provincial People’s Hospital, Taiyuan, China

3.

GE Healthcare China, Beijing, China

4.

Department of Neurosurgery, Shanxi Provincial People’s Hospital, Taiyuan, China

Diagn Interv Radiol 2021; 27: 440-449
DOI: 10.5152/dir.2021.20154
Read: 784 Downloads: 71 Published: 07 September 2020

PURPOSE 
We aimed to explore whether multiparametric magnetic resonance imaging (MRI)-based radiomics combined with selected blood inflammatory markers could effectively predict the grade and proliferation in glioma patients.

METHODS
This retrospective study included 152 patients histopathologically diagnosed with glioma. Stratified sampling was used to divide all patients into a training cohort (n=107) and a validation cohort (n=45) according to a ratio of 7:3, and five-fold repeat cross-validation was adopted in the training cohort. Multiparametric MRI and clinical parameters, including age, the neutrophil-lymphocyte ratio and red cell distribution width, were assessed. During image processing, image registration and gray normalization were conducted. A radiomics analysis was performed by extracting 1584 multiparametric MRI-based features, and the least absolute shrinkage and selection operator (LASSO) was applied to generate a radiomics signature for predicting grade and Ki-67 index in both training and validation cohorts. Statistical analysis included analysis of variance, Pearson correlation, intraclass correlation coefficient, multivariate logistic regression, Hosmer-Lemeshow test, and receiver operating characteristic (ROC) curve.

RESULTS
The radiomics signature demonstrated good performance in both the training and validation cohorts, with areas under the ROC curve (AUCs) of 0.92, 0.91, and 0.94 and 0.94, 0.75, and 0.82 for differentiating between low and high grade gliomas, grade III and grade IV gliomas, and low Ki-67 and high Ki-67, respectively, and was better than the clinical model; the AUCs of the combined model were 0.93, 0.91, and 0.95 and 0.94, 0.76, and 0.80, respectively.

CONCLUSION
Both the radiomics signature and combined model showed high diagnostic efficacy and outperformed the clinical model. The clinical factors did not provide additional improvement in the prediction of the grade and proliferation index in glioma patients, but the stability was improved.

You may cite this article as: Guo J, Ren J, Cheng R, He Y, Shen J. Do the combination of multiparametric MRI-based radiomics and selected blood inflammatory markers predict the grade and proliferation in glioma patients? Diagn Interv Radiol 2021; 27:440–449

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