Radiomics and machine learning analysis to differentiate low grade glioma and encephalitis on non-contrast MRI
Abstract
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.
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BibTeX
@article{picchi2026radiomics,
title = {{Radiomics and machine learning analysis to differentiate low grade glioma and encephalitis on non-contrast MRI}},
author = {Eliseo Picchi and Carlo Di Donna and Armando Ugo Cavallo and Silvia Minosse and Alessandro Castelli and Chiara Giuseppina Bonomi and Francesca Izzi and Alessia Amatruda and Valerio Da Ros and Francesco Garaci and Francesca Di Giuliano},
journal = {Scientific Reports},
year = {2026},
month = oct,
doi = {10.1038/s41598-026-74097-z},
url = {https://www.nature.com/articles/s41598-026-74097-z},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Radiomics and machine learning analysis to differentiate low grade glioma and encephalitis on non-contrast MRI
AU - Eliseo Picchi
AU - Carlo Di Donna
AU - Armando Ugo Cavallo
AU - Silvia Minosse
AU - Alessandro Castelli
AU - Chiara Giuseppina Bonomi
AU - Francesca Izzi
AU - Alessia Amatruda
AU - Valerio Da Ros
AU - Francesco Garaci
AU - Francesca Di Giuliano
PY - 2026
DA - 2026/10/10
JO - Scientific Reports
DO - 10.1038/s41598-026-74097-z
UR - https://www.nature.com/articles/s41598-026-74097-z
AB - 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.
SN - 2045-2322
ER -