Multi-modal magnetic resonance imaging-based grading analysis for gliomas by integrating radiomics and deep features

To investigate the feasibility of integrating global radiomics and local deep features based on multi-modal magnetic resonance imaging (MRI) for developing a noninvasive glioma grading model.

In this study, 567 patients [211 patients with glioblastomas (GBMs) and 356 patients with low-grade gliomas (LGGs)] between May 2006 and September 2018, were enrolled and divided into training (n=186), validation (n=47), and testing cohorts (n=334), respectively. All patients underwent postcontrast enhanced T1-weighted and T2 fluid-attenuated inversion recovery MRI scanning. Radiomics and deep features (trained by 8,510 3D patches) were extracted to quantify the global and local information of gliomas, respectively. A kernel fusion-based support vector machine (SVM) classifier was used to integrate these multi-modal features for grading gliomas. The performance of the grading model was assessed using the area under receiver operating curve (AUC), sensitivity, specificity, Delong test, and t-test.

The AUC, sensitivity, and specificity of the model based on combination of radiomics and deep features were 0.94 [95% confidence interval (CI): 0.85, 0.99], 86% (95% CI: 64%, 97%), and 92% (95% CI: 75%, 99%), respectively, for the validation cohort; and 0.88 (95% CI: 0.84, 0.91), 88% (95% CI: 80%, 93%), and 81% (95% CI: 76%, 86%), respectively, for the independent testing cohort from a local hospital. The developed model outperformed the models based only on either radiomics or deep features (Delong test, both of P<0.001), and was also comparable to the clinical radiologists.

This study demonstrated the feasibility of integrating multi-modal MRI radiomics and deep features to develop a promising noninvasive grading model for gliomas.