[Predicting cerebral glioma enhancement pattern using a machine learning-based magnetic resonance imaging radiomics model].

Objective: To establish a machine learning radiomics model that can accurately predict MRI enhancement patterns of glioma based on T2 fluid attenuated inversion recovery (T2-FLAIR) images for optimizing the workflow of magnetic resonance imaging (MRI) examinations of glioma patients.
Methods: We retrospectively collected preoperative MR T2-FLAIR images from 385 patients with pathologically confirmed glioma, who were divided into enhancing and non-enhancing groups according to the enhancement pattern. Predictive radiomics models were established using Gaussian Process, Linear Regression, Linear Regression-Least absolute shrinkage and selection operator, Support Vector Machine, Linear Discriminant Analysis or Naive Bayes as the classifiers in the training cohort ( n =201)and tested both in the internal ( n =85) and external validation cohorts ( n =99). The receiver-operating characteristic curve was used to assess the predictive performance of the models.
Results: The predictive model constructed based on 15 radiomics features using Gaussian Process as the classifier had the best predictive performance in both the training cohort and the internal validation cohort, with areas under the curve (AUC) of 0.88 (95% CI : 0.81-0.94) and 0.80 (95% CI : 0.71-0.88), respectively. In the external validation cohort, the model showed an AUC of 0.81 (95% CI : 0.71-0.90) with sensitivity, specificity, positive predictive value and negative predictive value of 0.98, 0.61, 0.76 and 0.96, respectively.
Conclusion: The T2-FLAIR-based machine learning radiomics model can accurately predict the enhancement pattern of gliomas on MRI.