A Combined Approach Using T2*-Weighted Dynamic Susceptibility Contrast MRI Perfusion Parameters and Radiomics to...

Differentiating tumor progression from radionecrosis in patients with treated brain glioma represents a significant clinical challenge due to overlapping imaging features. This study aimed to develop and evaluate a machine learning model that integrates radiomics features and T2*-weighted Dynamic Susceptibility Contrast MRI perfusion (DSC MRI) parameters to improve diagnostic accuracy in distinguishing these entities. A retrospective cohort of 46 patients (25 with confirmed radionecrosis, 21 with glioma progression) was analyzed. From lesion segmentation on DSC MRI, 851 radiomics features were extracted using PyRadiomics, alongside seven perfusion parameters (e.g., relative cerebral blood volume, time to peak) obtained from time-intensity curves (TICs). These features were combined into a single dataset and 14 classification algorithms were evaluated with GroupKFold cross-validation (k = 4). The top-performing model was selected based on predictive area under the curve (AUC) yield. The Logistic Regression classifier achieved the highest performance, with an AUC of 0.88, followed by multilayer perceptron and AdaBoost with AUC values of 0.85 and 0.79, respectively. The precision values were 72%, 74%, and 78% for the three models, respectively, while the accuracy was 63%, 70%, and 71%. Key predictive variables included radiomics features like wavelet-HHH_firstorder_Mean and mean normalized TIC values. Our combined approach integrating radiomics and DSC MRI parameters shows strong potential for distinguishing radionecrosis from glioma progression. However, further validation with larger cohorts is essential to confirm the generalizability of this approach.