Ten Years of VASARI Glioma Features: Systematic Review and Meta-Analysis of Their Impact and Performance.

Background: Visually Accessible Rembrandt (Repository for Molecular Brain Neoplasia Data) Images (VASARI) features, a vocabulary to establish reproducible terminology for glioma reporting, have been applied for a decade, but a systematic performance evaluation is lacking.

Purpose: Our aim was to conduct a systematic review and meta-analysis of the performance of the VASARI features set for glioma assessment.

Data Sources: MEDLINE, Web of Science, EMBASE, and the Cochrane Library were systematically searched until September 26, 2023.

Study Selection: Original articles predicting diagnosis, progression, and survival in patients with glioma were included.

Data Analysis: The modified Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was applied to evaluate the risk-of-bias. The meta-analysis used a random effects model and forest plot visualizations, if ≥5 comparable studies with a low or medium risk of bias were provided.

Data Synthesis: Thirty-five studies (3304 patients) were included. Risk-of-bias scores were medium ( = 33) and low ( = 2). Recurring objectives were overall survival ( = 18) and isocitrate dehydrogenase mutation (;  = 12) prediction. Progression-free survival was examined in 7 studies. In 4 studies (glioblastoma  = 2, grade 2/3 glioma  = 1, grade 3 glioma  = 1), a significant association was found between progression-free survival and single VASARI features. The single features predicting overall survival with the highest pooled hazard ratios were multifocality (hazard ratio = 1.80; 95%-CI, 1.21-2.67; I = 53%), ependymal invasion (hazard ratio = 1.73; 95% CI, 1.45-2.05; I = 0%), and enhancing tumor crossing the midline (hazard ratio = 2.08; 95% CI, 1.35-3.18; I = 52%). mutation-predicting models combining VASARI features rendered a pooled area under the receiver operating characteristic curve of 0.82 (95% CI, 0.76-0.88) at considerable heterogeneity (I = 100%). Combined input models using VASARI plus clinical and/or radiomics features outperformed single data-type models in all relevant studies ( = 17).

Limitations: Studies were heterogeneously designed and often with a small sample size. Several studies used The Cancer Imaging Archive database, with likely overlapping cohorts. The meta-analysis for was limited due to a high study heterogeneity.

Conclusions: Some VASARI features perform well in predicting overall survival and mutation status, but combined models outperform single features. More studies with less heterogeneity are needed to increase the evidence level.