A logistic regression model for prediction of glioma grading based on radiomics

Glioma is the most common intracranial primary tumor in central nervous system. Glioma grading possesses important guiding significance for the selection of clinical treatment and follow-up plan, and the assessment of prognosis. This study aims to explore the feasibility of logistic regression model based on radiomics to predict glioma grading.

Retrospective analysis was performed on 146 glioma patients with confirmed pathological diagnosis from January, 2012 to December, 2018. A total of 41 radiomics features were extracted from contrast-enhanced T₁-weighted imaging (T₁WI+C) lesion by manual segmentation. Least absolute shrinkage and selection operator (LASSO) was used to select the most-predictive radiomics features for pathological grading and to calculate radiomics score (Rad-score) of each patient. A logistic regression model was built to explore the correlation between giloma grading and Rad-score. Receiver operating characteristic (ROC) curve was performed to evaluate the model's predictive ability with area under the curve (AUC) for the evaluation index. Hosmer-Lemeshow test was used to measure the model's predictive accuracy.

A total of 5 imaging features selected by LASSO were used to establish a logistic regression model for predicting glioma grading. The model showed good discrimination with AUC value of 0.919. Hosmer-Lemeshow test showed no significant difference between the calibration curve and the ideal curve (P=0.808), indicating high predictive accuracy of the model.

The logistic regression model using radiomics exhibits a relatively high accuracy for predicting glioma grading, which may serve as a complementary tool for preoperative prediction of giloma grading.