The feasibility of MRI texture analysis in distinguishing glioblastoma, anaplastic astrocytoma and anaplastic oligodendroglioma.

BACKGROUND

The aim of this study was to investigate whether texture analysis-based machine learning could be utilized in presurgical differentiation of high-grade gliomas in adults.

METHODS

This is a single-center retrospective study involving 150 patients diagnosed with glioblastoma (GBM) (n=50), anaplastic astrocytoma (AA) (n=50) or anaplastic oligodendroglioma (AO) (n=50). The training group and validation group were randomly divided with a 4:1 ratio. Forty texture features were extracted from contrast-enhanced T1-weighted images using LIFEx software. Two feature-selection methods were separately introduced to select optimal features, including distance correlation (DC) and least absolute shrinkage and selection operator (LASSO). Optimal features selected were fed into linear discriminant analysis (LDA) classifier and support vector machine (SVM) classifier to establish multiple classification models. The performance was evaluated by using the accuracy, Kappa value and area under receiver operating characteristic curve (AUC) of each model.

RESULTS

The overall diagnostic accuracies of LDA-based models were 76.0% (DC + LDA) and 74.3% (LASSO + LDA) in the validation group, while for SVM-based models were 58.0% (DC + SVM) and 63.3% (LASSO + SVM). The combination of DC and LDA reach the highest diagnostic accuracy, AUC of GBM, AA and AO were 0.999, 0.834 and 0.865 separately, indicating that this model could distinguish GBM from AA and AO commendably, whereas the differentiation between AA and AO was relatively difficult.

CONCLUSIONS

This study indicated that MRI texture analysis combined with LDA algorithm has the potential to be utilized in distinguishing the subtypes of high-grade gliomas.