Mr. Rubin Zhao Profile

Rubin Zhao

at Linyi People's Hospital

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Proceedings Article | 16 February 2022 Paper

Deep learning radiomics model based on contrast-enhanced T1-weighted image multi-plane reconstruction for prediction of glioma grading

Jialin Ding, Rubin Zhao, Qingtao Qiu, Jinhu Chen, Jinghao Duan, Xiujuan Cao, Yong Yin

Proceedings Volume 12083, 120831H (2022) https://doi.org/10.1117/12.2623421

KEYWORDS: Lawrencium, Tumors, Magnetism, Cancer, Feature selection, Scanners, Medical imaging, Magnetic resonance imaging, Image segmentation, Convolution

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The survival time of high-grade and low-grade gliomas is different, so it is very important to accurately identify the grade of gliomas and develop personalized treatment plans for patients. We aim to non-invasively differentiate glioma grades based on the deep learning radiology (DLR) model of multiplanar reconstruction of contrast-enhanced T1-weighted (CE-T1W) images. First, we included 122 and 52 patients with gliomas diagnosed by pathology in the two institutions, respectively, and made sure that these cases had MPR axial, coronal, and sagittal CE-T1W images. Then, we extract the radiomics features from CE-T1W images and the deep learning features of the VGG6 pre-training model respectively. Spearman and recursive feature elimination (RFE) feature selection methods are used to select important features, and support vector machine (SVM) and logical regression (LR) modeling are used to distinguish high-grade and low-grade gliomas. Finally, the area under receiver operating curve (AUC), sensitivity, specificity, and accuracy were evaluated in an independent test set. In SVM and LR models that use radiomics features, the result of the MPR three-phase merge model is better than that of the MPR single-phase model. In LR, the merged model of deep learning and radiomics is better than the model of using both alone. Therefore, the CE-T1W image MPR three-phase merge feature is superior to the singlephase feature in differentiating high-grade and low-grade gliomas. The model combined with radiomics features and deep learning features can improve the prediction accuracy of glioma grading.

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