Glioma Grade Classification Using Machine Learning and MRI Radiomics: A Single-Center Prospective Study Comparing Original and Wavelet-Transformed Features From Anatomical, Diffusion-Weighted, and Post-Contrast Imaging.

Background and Aims: Accurate glioma grade classification is critical for prognostic assessment and clinical decision-making. This study aimed to evaluate the impact of wavelet-based radiomics feature analysis on the performance of machine learning (ML) models for glioma grade classification using diffusion-weighted imaging (DWI), structural MRI sequences, and contrast-enhanced T Methods: This prospective study included 92 patients with histopathologically confirmed gliomas. MRI examinations were performed using a 1.5 T scanner and included apparent diffusion coefficient (ADC) and exponential ADC (eADC) maps, T Results: The addition of wavelet-based radiomics features significantly improved classification performance across all ML models and MRI sequences. The highest performance was achieved by a fine-tuned Random Forest model trained with original and wavelet-transformed images' radiomics features extracted from eADC maps, with an average AUC of 0.96, accuracy of 0.97, sensitivity of 0.94, and specificity of 0.91. Conclusion: Adding wavelet-transformed radiomics feature to original image radiomics feature analysis of eADC maps combined with fine-tuned Random Forest classification provides an accurate, non-invasive, and efficient approach for glioma grade classification with strong potential for clinical application.