Radiomics and machine learning analysis to differentiate low grade glioma and encephalitis on non-contrast MRI

Accurate differentiation between low-grade gliomas and encephalitis remains challenging due to overlapping MRI features. This study investigated whether MRI-based radiomics derived from routine non-contrast MRI could improve the differentiation of these entities in diagnostically challenging cases presenting in emergency settings. This retrospective single-center study included 41 patients with ambiguous MRI findings who underwent 3T MRI within 48 h of symptom onset.

Radiomic features extracted from T2-FLAIR, DWI, and T1-TSE images, together with laboratory data available at presentation, were analyzed. Random Forest classifiers were developed using selected radiomic features, and model performance was evaluated using nested cross-validation, independent validation, receiver operating characteristic (ROC) analysis, and DeLong’s test. The cohort included 23 patients with histologically proven low-grade gliomas and 18 with confirmed encephalitis and was divided into training (n = 31) and validation (n = 10) sets.

FLAIR-based models showed the best discriminatory performance, achieving an AUC of 0.82 (95% CI: 0.54–1.00) for both the radiomics-only and radiomics-clinical models outperforming T1-weighted (AUC 0.52 for radiomics-only and 0.60 for radiomics-clinical model) and DWI models (AUC 0.72 for both radiomics-only and radiomics-clinical approaches). The combined-sequence radiomics-clinical model achieved an AUC of 0.80 (95% CI: 0.41–1.00), with 100% sensitivity, 80% specificity, and 90% accuracy. Comparison of the ROC curves using DeLong’s test did not demonstrate statistically significant differences between the radiomics-only and radiomics-clinical models for any MRI sequence.

This proof-of-concept study suggests that radiomic features extracted from routine non-contrast MRI may help differentiate low-grade gliomas from encephalitis in diagnostically challenging cases; however, these preliminary findings warrant validation in larger multicenter cohorts before clinical implementation. This research is co-funded by the Ministry of University and Research within the Complementary National Plan PNL-I.1 “Research initiatives for innovative technologies and pathways in the health and welfare sector”-“DARE - Digital Lifelong Prevention” (project code: PNC0000002 - CUP: B53C22006470001). These authors contributed equally: Eliseo Picchi and Carlo Di Di Donna.

Diagnostic Imaging Unit, Department of Biomedicine and Prevention, University of Rome Tor Vergata, Via Montpellier 1, 00133, Rome, Italy Eliseo Picchi, Carlo Di Donna, Alessia Amatruda, Valerio Da Ros & Francesco Garaci Division of Radiology, Istituto Dermopatico dell’Immacolata IRCCS, Rome, Italy Diagnostic Imaging Unit, University Hospital of Rome Tor Vergata, Viale Oxford 81, 00133, Rome, Italy Epilepsy Centre, Neurology Unit, Policlinico Tor Vergata, University of Rome Tor Vergata, Viale Oxford 81, 00133, Rome, Italy Memory Clinic and Neurodegenerative Dementia Research Unit, Policlinico Tor Vergata, University of Rome Tor Vergata, Viale Oxford 81, 00133, Rome, Italy Neuroradiology Unit, Department of Biomedicine and Prevention, University of Rome Tor Vergata, Via Montpellier 1, 00133, Rome, Italy Francesco Garaci & Francesca Di Giuliano The authors declare no competing interests. This retrospective study was approved by the Institutional Review Board (N.113.20) and conducted in accordance with the Declaration of Helsinki and its subsequent amendments. Written informed consent was obtained from all patients or their legal representatives prior to the MRI examination.

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