Imaging Differentiation of Supratentorial Ependymoma and High-grade Glioma in Children using VASARI Features.

Background and purpose: Supratentorial ependymomas (sEPN) and supratentorial high-grade gliomas (sHGG) are rare pediatric tumors with overlapping imaging features, making preoperative differentiation challenging. Accurate distinction is crucial for determining the appropriate management, guiding surgical decisions. The Visually Accessible Rembrandt Images (VASARI) feature set is a standardized MRI-based system for describing imaging characteristics of gliomas. VASARI has proven accessible, reproducible, and clinically helpful in characterizing tumor morphology. We hypothesize that a combination of imaging features can distinguish between these two tumor types. We evaluated a pediatric cohort with sEPN and sHGG to identify distinguishing imaging features, considering demographic and imaging factors. This approach aims to enhance diagnostic accuracy and improve individualized treatment planning.

Materials and methods: This retrospective study enrolled patients < 21 years old, with a histologically or molecularly confirmed sEPN or sHGG between 2000 and 2023. We evaluated 36 imaging features (54 including subcategories), incorporating VASARI set and additional tumor characterization parameters. Univariate analysis assessed relationships between demographic and imaging features and tumor type, followed by multivariate logistic regression. Finally, generalized binomial regression with regularization and variable selection was used to construct simplified parsimonious models of key distinguishing features for clinical use.

Results: 45 patients were included, 26 sEPNs and 19 sHGGs. Sex distribution was similar between groups (61.5% female in sEPN and 78.9% in sHGG, p=0.18). By univariable analysis 16 imaging features differed significantly between tumors (p<0.05), including proportion of enhancing/non-enhancing components, calcifications, T1WI/FLAIR ratio, T2WI signal, calvarial remodeling, and involvement of specific brain regions. Multivariate analysis incorporating these features achieved 100% accuracy in differentiating the tumors (AUC=1). A smaller parsimonious model that combined presence of calcifications and non-enhancing margin definition, accurately distinguished the tumors (AUC=0.98). Alternatively, using enhancing and non-enhancing margin definitions also achieved high accuracy (AUC=0.95).

Conclusions: Although sEPN and sHGG share overlapping imaging characteristics, a combination of 16 routine MRI features can fully differentiate them. Smaller subsets of two features (calcifications with definition of non-enhancing margins or the definitions of both enhancing and non-enhancing margins), also provide high diagnostic accuracy. These feature combinations improve differentiation and may support more informed treatment decisions, potentially leading to better patient outcomes.

Abbreviations: sEPN = Supratentorial ependymomas; sHGG = supratentorial high-grade gliomas; VASARI = The Visually Accessible Rembrandt Images feature set.