Multiparametric MRI radiomics for the differentiation of brain glial cell hyperplasia from low-grade glioma

Abstract

Background: Differentiating between low-grade glioma and brain glial cell hyperplasia is crucial for the customized clinical treatment of patients.

Objective: Based on multiparametric MRI imaging and clinical risk factors, a radiomics-clinical model and nomogram were constructed for the distinction of brain glial cell hyperplasia from low-grade glioma.

Methods: Patients with brain glial cell hyperplasia and low-grade glioma who underwent surgery at the First Affiliated Hospital of Soochow University from March 2016 to March 2022 were retrospectively included. In this study, A total of 41 patients of brain glial cell hyperplasia and 87 patients of low-grade glioma were divided into training group and validation group randomly at a ratio of 7:3. Radiomics features were extracted from T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), contrast-enhanced T1-weighted imaging (T1-enhanced). Then, LASSO, SVM, and RF models were created in order to choose a model with a greater level of efficiency for calculating each patient's Rad-score (radiomics score). The independent risk factors were identified via univariate and multivariate logistic regression analysis to filter the Rad-score and clinical risk variables in turn. A radiomics-clinical model was next built of which effectiveness was assessed.

Results: Brain glial cell hyperplasia and low-grade gliomas from the 128 cases were randomly divided into 10 groups, of which 7 served as training group and 3 as validation group. The mass effect and Rad-score were two independent risk variables used in the construction of the radiomics-clinical model, and their respective AUCs for the training group and validation group were 0.847 and 0.858. The diagnostic accuracy, sensitivity, and specificity of the validation group were 0.821, 0.750, and 0.852 respectively.

Conclusion: Combining with radiomics constructed by multiparametric MRI images and clinical features, the radiomics-clinical model and nomogram that were developed to distinguish between brain glial cell hyperplasia and low-grade glioma had a good performance.

Keywords: Brain glial cell hyperplasia; Low-grade glioma; Multiparametric MRI images; Radiomics.

Fig. 1

Shows the inclusion and exclusion criteria for 41 patients with glial cell hyperplasia and 87 patients with low-grade glioma

Fig. 2

(a-d) The red areas showed the tumor level of T1WI, T2WI, DWI, T1-enhanced, (e) the generated tumor 3D-VOI

Fig. 3

The ROC curve of the three radiomics model

Fig. 4

The 24 best feature information and the corresponding feature weight

Fig. 5

(a) is nomogram constructed based on combined model, (b) and (c) are the ROC curves of the training cohort and the validation cohort of the combined model, (d) and (e) are the calibration curves, (f) is the decision curve to differentiate glial cell hyperplasia from low-grade gliomas