Differentiation of recurrent glioblastoma from radiation necrosis using diffusion radiomics with machine learning model development and external validation

Abstract

The purpose of this study was to establish a high-performing radiomics strategy with machine learning from conventional and diffusion MRI to differentiate recurrent glioblastoma (GBM) from radiation necrosis (RN) after concurrent chemoradiotherapy (CCRT) or radiotherapy. Eighty-six patients with GBM were enrolled in the training set after they underwent CCRT or radiotherapy and presented with new or enlarging contrast enhancement within the radiation field on follow-up MRI. A diagnosis was established either pathologically or clinicoradiologically (63 recurrent GBM and 23 RN). Another 41 patients (23 recurrent GBM and 18 RN) from a different institution were enrolled in the test set. Conventional MRI sequences (T2-weighted and postcontrast T1-weighted images) and ADC were analyzed to extract 263 radiomic features. After feature selection, various machine learning models with oversampling methods were trained with combinations of MRI sequences and subsequently validated in the test set. In the independent test set, the model using ADC sequence showed the best diagnostic performance, with an AUC, accuracy, sensitivity, specificity of 0.80, 78%, 66.7%, and 87%, respectively. In conclusion, the radiomics models models using other MRI sequences showed AUCs ranging from 0.65 to 0.66 in the test set. The diffusion radiomics may be helpful in differentiating recurrent GBM from RN..

Figure 1

Heatmap depicting the diagnostic performance (AUCs) of combinations of feature selection methods, classifiers, and combination of sequences in the training set. AUC area under the curve, KNN k-nearest neighbors, MI mutual information, LASSO least absolute shrinkage and selection operator, SMOTE synthetic minority over-sampling technique, SVM support vector machine, T1C postcontrast T1WI, T2 T2WI. The best performing model in each combination of MRI sequence and mask are marked in asterisks (*).

Figure 2

The radiomics pipeline of our study. KNN k-nearest neighbors, MI mutual information, LASSO least absolute shrinkage and selection operator, SVM support vector machine, T1C postcontrast T1WI, T2 T2WI.