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Ten years of VASARI glioma features: Systematic review and meta-analysis of their impact and performance

Azizova, A.; Prysiazhniuk, Y.; Wamelink, I. J. H. G.; Petr, J.; Barkhof, F.; Keil, V. C.

Abstract

Background: Visually Accessible Rembrandt (Repository for Molecular Brain Neoplasia Data) Images (VASARI) features, a controlled vocabulary aimed at establishing reproducible terminology for glioma reporting, have been in use for a decade, but there is no systematic evaluation of their performance.

Purpose: To conduct a systematic review and meta-analysis of the performance of the VASARI set for glioma assessment.

Materials and methods: MEDLINE and Web of Science were systematically searched until July 7, 2023. Original articles using VASARI to predict diagnosis, progression, and survival in patients with glioma were included. A modified Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was used for quality assessment. Meta-analysis was performed using a random effects model and forest-plot visualizations if five or more comparable studies with low or medium risk-of-bias QUADAS-2 were available.

Results: Thirty-five studies (4,348 patients; 34 retrospective, 1 prospective) were included. The overall risk-of-bias score was medium (n=33) and low (n=2). The most frequent objectives were overall survival (OS) (n=18) and isocitrate dehydrogenase (IDH) mutation (n=12) prediction. IDH mutation-predicting models combining different VASARI features rendered a pooled AUC-ROC of 0.82 (95% CI 0.76, 0.88) with considerable heterogeneity (I² = 100%). The highest pooled hazard ratios (HR) for single features predicting OS involved features 9 (multifocality; HR 1.80; 95%: CI 1.21, 2.67; I² = 53% ), 19 (ependymal invasion; HR 1.73; 95% CI: 1.45, 2.05; I² = 0%), and 23 (enhancing tumor crossing midline; HR 2.08; 95% CI: 1.35, 3.18; I² = 52%). Combined models including various inputs such as VASARI, clinical, pathological, and/or radiomics features outperformed single data type models in all relevant studies (n=17).

Conclusion: VASARI features perform well in predicting OS and IDH mutation status, and improve when combined models are used. The discriminatory power of single VASARI features is, however, variable.

Involved research facilities

  • PET-Center

Permalink: https://www.hzdr.de/publications/Publ-37364


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