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 (n = 33) and low (n = 2). Recurring objectives were overall survival (n = 18) and isocitrate dehydrogenase mutation (IDH; n = 12) prediction. Progression-free survival was examined in 7 studies. In 4 studies (glioblastoma n = 2, grade 2/3 glioma n = 1, grade 3 glioma n = 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; I2 = 53%), ependymal invasion (hazard ratio = 1.73; 95% CI, 1.45-2.05; I2 = 0%), and enhancing tumor crossing the midline (hazard ratio = 2.08; 95% CI, 1.35-3.18; I2 = 52%). IDH 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 (I2 = 100%). Combined input models using VASARI plus clinical and/or radiomics features outperformed single data-type models in all relevant studies (n = 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 IDH was limited due to a high study heterogeneity. CONCLUSIONS: Some VASARI features perform well in predicting overall survival and IDH mutation status, but combined models outperform single features. More studies with less heterogeneity are needed to increase the evidence level.

Brain Imaging Amsterdam Neuroscience Amsterdam the Netherlands

From the Radiology and Nuclear Medicine Department Amsterdam UMC location Vrije Universiteit Amsterdam Amsterdam the Netherlands

Imaging and Biomarkers Cancer Center Amsterdam Amsterdam the Netherlands

Institute of Radiopharmaceutical Cancer Research Helmholtz Zentrum Dresden Rossendorf Dresden Germany

Queen Square Institute of Neurology and Center for Medical Image Computing University College London London United Kingdom

The 2nd Faculty of Medicine Department of Pathophysiology Charles University Prague Czech Republic

Zobrazit více v PubMed

Ellingson BM, Bendszus M, Boxerman J, et al. . Consensus recommendations for a standardized brain tumor imaging protocol in clinical trials. Neuro Oncol 2015;17:1188–98 10.1093/neuonc/nov095 PubMed DOI PMC

Wen PY, van den Bent M, Youssef G, et al. . RANO 2.0: update to the Response Assessment in Neuro-Oncology criteria for high- and low-grade gliomas in adults. J Clin Oncol 2023;41:5187–99 10.1200/JCO.23.01059 PubMed DOI PMC

The Cancer Imaging Archive. VASARI Research Project. https://wiki.cancerimagingarchive.net/display/Public/VASARI+Research+Project. Accessed September 26, 2023

Mazurowski MA, Desjardins A, Malof JM. Imaging descriptors improve the predictive power of survival models for glioblastoma patients. Neuro Oncol 2013;15:1389–94 10.1093/neuonc/nos335 PubMed DOI PMC

Gutman DA, Cooper LA, Hwang SN, et al. . MR imaging predictors of molecular profile and survival: multi-institutional study of the TCGA glioblastoma data set. Radiology 2013;267:560–69 10.1148/radiol.13120118 PubMed DOI PMC

Agarwal A, Kumar S, Narang J, et al. . Morphologic MRI features, diffusion tensor imaging and radiation dosimetric analysis to differentiate pseudo-progression from early tumor progression. J Neurooncol 2013;112:413–20 10.1007/s11060-013-1070-1 PubMed DOI

Jain R, Poisson LM, Gutman D, et al. . Outcome prediction in patients with glioblastoma by using imaging, clinical, and genomic biomarkers: focus on the nonenhancing component of the tumor. Radiology 2014;272:484–93 10.1148/radiol.14131691 PubMed DOI PMC

Colen RR, Vangel M, Wang J, et al. ; TCGA Glioma Phenotype Research Group. Imaging genomic mapping of an invasive MRI phenotype predicts patient outcome and metabolic dysfunction: a TCGA glioma phenotype research group project. BMC Med Genomics 2014;7:30 10.1186/1755-8794-7-30 PubMed DOI PMC

Nicolasjilwan M, Hu Y, Yan C, et al. ; TCGA Glioma Phenotype Research Group. Addition of MR imaging features and genetic biomarkers strengthens glioblastoma survival prediction in TCGA patients. J Neuroradiol 2015;42:212–21 10.1016/j.neurad.2014.02.006 PubMed DOI PMC

Wangaryattawanich P, Hatami M, Wang J, et al. . Multicenter imaging outcomes study of The Cancer Genome Atlas glioblastoma patient cohort: imaging predictors of overall and progression-free survival. Neuro Oncol 2015;17:1525–37 10.1093/neuonc/nov117 PubMed DOI PMC

Rao A, Rao G, Gutman DA, et al. ; TCGA Glioma Phenotype Research Group. A combinatorial radiographic phenotype may stratify patient survival and be associated with invasion and proliferation characteristics in glioblastoma. J Neurosurg 2016;124:1008–17 10.3171/2015.4.JNS142732 PubMed DOI PMC

Yu J, Wang M, Song J, et al. . Potential utility of Visually AcceSAble Rembrandt Images assessment in brain astrocytoma grading. J Comput Assist Tomogr 2016;40:301–06 10.1097/RCT.0000000000000352 PubMed DOI

Zhou H, Vallières M, Bai HX, et al. . MRI features predict survival and molecular markers in diffuse lower-grade gliomas. Neuro Oncol 2017;19:862–70 10.1093/neuonc/now256 PubMed DOI PMC

Lasocki A, Tsui A, Gaillard F, et al. . Reliability of noncontrast-enhancing tumor as a biomarker of IDH1 mutation status in glioblastoma. J Clin Neurosci 2017;39:170–75 10.1016/j.jocn.2017.01.007 PubMed DOI

Park M, Lee SK, Chang JH, et al. . Elderly patients with newly diagnosed glioblastoma: can preoperative imaging descriptors improve the predictive power of a survival model? J Neurooncol 2017;134:423–31 10.1007/s11060-017-2544-3 PubMed DOI

Ersoy TF, Keil VC, Hadizadeh DR, et al. . New prognostic factor telomerase reverse transcriptase promotor mutation presents without MR imaging biomarkers in primary glioblastoma. Neuroradiology 2017;59:1223–31 10.1007/s00234-017-1920-1 PubMed DOI

Park YW, Han K, Ahn SS, et al. . Prediction of IDH1-mutation and 1p/19q-codeletion status using preoperative MR imaging phenotypes in lower grade gliomas. AJNR Am J Neuroradiol 2018;39:37–42 10.3174/ajnr.A5421 PubMed DOI PMC

Peeken JC, Hesse J, Haller B, et al. . Semantic imaging features predict disease progression and survival in glioblastoma multiforme patients. Strahlenther Onkol 2018;194:580–90 10.1007/s00066-018-1276-4 PubMed DOI

Su CQ, Lu SS, Han QY, et al. . Intergrating conventional MRI, texture analysis of dynamic contrast-enhanced MRI, and susceptibility weighted imaging for glioma grading. Acta Radiol 2019;60:777–87 10.1177/0284185118801127 PubMed DOI

Su CQ, Lu SS, Zhou MD, et al. . Combined texture analysis of diffusion-weighted imaging with conventional MRI for non-invasive assessment of IDH1 mutation in anaplastic gliomas. Clin Radiol 2019;74:154–60 10.1016/j.crad.2018.10.002 PubMed DOI

Peeken JC, Goldberg T, Pyka T, et al. . Combining multimodal imaging and treatment features improves machine learning-based prognostic assessment in patients with glioblastoma multiforme. Cancer Med 2019;8:128–36 10.1002/cam4.1908 PubMed DOI PMC

Lee M, Han K, Ahn SS, et al. . The added prognostic value of radiological phenotype combined with clinical features and molecular subtype in anaplastic gliomas. J Neurooncol 2019;142:129–38 10.1007/s11060-018-03072-0 PubMed DOI

Ivanidze J, Lum M, Pisapia D, et al. . MRI features associated with TERT promoter mutation status in glioblastoma. J Neuroimaging 2019;29:357–63 10.1111/jon.12596 PubMed DOI

Chen X, Fang M, Dong D, et al. . Development and validation of a MRI-based radiomics prognostic classifier in patients with primary glioblastoma multiforme. Acad Radiol 2019;26:1292–300 10.1016/j.acra.2018.12.016 PubMed DOI

Hyare H, Rice L, Thust S, et al. . Modelling MR and clinical features in grade II/III astrocytomas to predict IDH mutation status. Eur J Radiol 2019;114:120–27 10.1016/j.ejrad.2019.03.003 PubMed DOI

Park CJ, Han K, Shin H, et al. . MR image phenotypes may add prognostic value to clinical features in IDH wild-type lower-grade gliomas. Eur Radiol 2020;30:3035–45 10.1007/s00330-020-06683-2 PubMed DOI

Lu Y, Patel M, Natarajan K, et al. . Machine learning-based radiomic, clinical and semantic feature analysis for predicting overall survival and MGMT promoter methylation status in patients with glioblastoma. Magn Reson Imaging 2020;74:161–70 10.1016/j.mri.2020.09.017 PubMed DOI

Cao M, Suo S, Zhang X, et al. . Qualitative and quantitative MRI analysis in IDH1 genotype prediction of lower-grade gliomas: a machine learning approach. Biomed Res Int 2021;2021:1235314 10.1155/2021/1235314 PubMed DOI PMC

Verduin M, Primakov S, Compter I, et al. . Prognostic and predictive value of integrated qualitative and quantitative magnetic resonance imaging analysis in glioblastoma. Cancers (Basel) 2021;13:722 10.3390/cancers13040722 PubMed DOI PMC

Ahn SS, An C, Park YW, et al. . Identification of magnetic resonance imaging features for the prediction of molecular profiles of newly diagnosed glioblastoma. J Neurooncol 2021;154:83–92 10.1007/s11060-021-03801-y PubMed DOI

Wang J, Yi X, Fu Y, et al. . Preoperative magnetic resonance imaging radiomics for predicting early recurrence of glioblastoma. Front Oncol 2021;11:769188 10.3389/fonc.2021.769188 PubMed DOI PMC

Sun C, Fan L, Wang W, et al. . Radiomics and qualitative features from multiparametric MRI predict molecular subtypes in patients with lower-grade glioma. Front Oncol 2021;11:756828 10.3389/fonc.2021.756828 PubMed DOI PMC

Ge X, Wang M, Ma H, et al. . Investigated diagnostic value of synthetic relaxometry, three-dimensional pseudo-continuous arterial spin labelling and diffusion-weighted imaging in the grading of glioma. Magn Reson Imaging 2022;86:20–27 10.1016/j.mri.2021.11.006 PubMed DOI

Ruan Z, Mei N, Lu Y, et al. . A comparative and summative study of radiomics-based overall survival prediction in glioblastoma patients. J Comput Assist Tomogr 2022;46:470–79 10.1097/RCT.0000000000001300 PubMed DOI

Sacli-Bilmez B, Firat Z, Topcuoglu OM, et al. . Identifying overall survival in 98 glioblastomas using VASARI features at 3T. Clin Imaging 2023;93:86–92 10.1016/j.clinimag.2022.10.011 PubMed DOI

Sahu A, Patnam NG, Goda JS, et al. . Multiparametric magnetic resonance imaging correlates of isocitrate dehydrogenase mutation in WHO high-grade astrocytomas. J Pers Med 2022;13:72 10.3390/jpm13010072 PubMed DOI PMC

Gemini L, Tortora M, Giordano P, et al. . Vasari scoring system in discerning between different degrees of glioma and IDH status prediction: a possible machine learning application? J Imaging 2023;9:75 10.3390/jimaging9040075 PubMed DOI PMC

You W, Mao Y, Jiao X, et al. . The combination of radiomics features and VASARI standard to predict glioma grade. Front Oncol 2023;13:1083216 10.3389/fonc.2023.1083216 PubMed DOI PMC

Andersen BM, Miranda C, Hatzoglou V, et al. . Leptomeningeal metastases in glioma: the Memorial Sloan Kettering Cancer Center experience. Neurology 2019;92:e2483–91 10.1212/WNL.0000000000007529 PubMed DOI PMC

Page MJ, McKenzie JE, Bossuyt PM, et al. . The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Int J Surg 2021;88:105906 10.1016/j.ijsu.2021.105906 PubMed DOI

Salameh J-P, Bossuyt PM, McGrath TA, et al. . Preferred reporting items for systematic review and meta-analysis of diagnostic test accuracy studies (PRISMA-DTA): explanation, elaboration, and checklist. BMJ 2020;370:m2632 10.1136/bmj.m2632 PubMed DOI

Whiting PF, Rutjes AW, Westwood ME, et al. ; QUADAS-2 Group. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 2011;155:529–36 10.7326/0003-4819-155-8-201110180-00009 PubMed DOI

Cortes C, Mohri M. Confidence Intervals for the Area Under the ROC Curve. In: Saul L, Weiss Y, Bottou L, eds. Advances in Neural Information Processing Systems.Vol 17. MIT Press; 2004

Higgins J, Thomas J, Miranda JC, et al. . Cochrane Handbook for Systematic Reviews of Interventions. https://training.cochrane.org/handbook#how-to-access. Accessed December 28, 2023

Thomas RP, Xu LW, Lober RM, et al. . The incidence and significance of multiple lesions in glioblastoma. J Neurooncol 2013;112:91–97 10.1007/s11060-012-1030-1 PubMed DOI

Lim DA, Cha S, Mayo MC, et al. . Relationship of glioblastoma multiforme to neural stem cell regions predicts invasive and multifocal tumor phenotype. Neuro Oncol 2007;9:424–29 10.1215/15228517-2007-023 PubMed DOI PMC

Kurokawa R, Kurokawa M, Baba A, et al. . Major changes in 2021 World Health Organization Classification of Central Nervous System Tumors. Radiographics 2022;42:1474–93 10.1148/rg.210236 PubMed DOI