Objective: To explore the application value of the radiomics method based on enhanced T1WI in glioma grading.
Materials and methods: A retrospective analysis was performed using data of 114 patients with glioma, which was confirmed using surgery and pathological tests, at our hospital between January 2017 and November 2020. The patients were randomly divided into the training and test groups in a ratio of 7 : 3. The Analysis Kit (AK) software was used for radiomic analysis, and a total of 461 tumor texture features were extracted. Spearman correlation analysis and the least absolute shrinkage and selection (LASSO) algorithm were employed to perform feature dimensionality reduction on the training group. A radiomics model was then constructed for glioma grading, and the validation group was used for verification.
Results: The area under the ROC curve (AUC) of the proposed model was calculated to identify its performance in the training group, which was 0.95 (95% CI = 0.905-0.994), accuracy was 84.8%, sensitivity was 100%, and specificity was 77.8%. The AUC of the validation group was 0.952 (95% CI = 0.871-1.000), accuracy was 93.9%, sensitivity was 90.0%, and specificity was 95.6%.
Conclusions: The radiomics model based on enhanced T1WI improved the accuracy of glioma grading and better assisted clinical decision-making.
Copyright © 2022 Hongzhang Zhou et al.