Molecular classification of patients with grade II/III glioma using quantitative MRI characteristics

Yolcu

et al. 2019

Neurosurgical Focus

OBJECTIVEWith the revised WHO 2016 classification of brain tumors, there has been increasing interest in imaging biomarkers to predict molecular status and improve the yield of genetic testing for diffuse low-grade gliomas (LGGs). The T2-FLAIR–mismatch sign has been suggested to be a highly specific radiographic marker of isocitrate dehydrogenase (IDH) gene mutation and 1p/19q codeletion status in diffuse LGGs. The presence of T2-FLAIR mismatch indicates a T2-hyperintense lesion that is hypointense on FLAIR with the exception of a hyperintense rim.METHODSIn accordance with PRISMA guidelines, we performed a systematic review of the Ovid Medline, Embase, Scopus, and Cochrane databases for reports of studies evaluating the diagnostic performance of T2-FLAIR mismatch in predicting the IDH and 1p/19q codeletion status in diffuse LGGs. Results were combined into a 2 × 2 format, and the following diagnostic performance parameters were calculated: sensitivity, specificity, positive predictive value, negative predictive value, and positive (LR+) and negative (LR−) likelihood ratios. In addition, we utilized Bayes theorem to calculate posttest probabilities as a function of known pretest probabilities from previous genome-wide association studies and the calculated LRs. Calculations were performed for 1) IDH mutation with 1p/19q codeletion (IDHmut-Codel), 2) IDH mutation without 1p/19q codeletion (IDHmut-Noncodel), 3) IDH mutation overall, and 4) 1p/19q codeletion overall. The QUADAS-2 (revised Quality Assessment of Diagnostic Accuracy Studies) tool was utilized for critical appraisal of included studies.RESULTSA total of 4 studies were included, with inclusion of 2 separate cohorts from a study reporting testing and validation (n = 746). From pooled analysis of all cohorts, the following values were obtained for each molecular profile—IDHmut-Codel: sensitivity 30%, specificity 73%, LR+ 1.1, LR− 1.0; IDHmut-Noncodel: sensitivity 33.7%, specificity 98.5%, LR+ 22.5, LR− 0.7; IDH: sensitivity 32%, specificity 100%, LR+ 32.1, LR− 0.7; 1p/19q codeletion: sensitivity 0%, specificity 54%, LR+ 0.01, LR− 1.9. Bayes theorem was used to calculate the following posttest probabilities after a positive and negative result, respectively—IDHmut-Codel: 32.2% and 29.4%; IDHmut-Noncodel: 95% and 40%; IDH: 99.2% and 73.5%; 1p/19q codeletion: 0.4% and 35.1%.CONCLUSIONSThe T2-FLAIR–mismatch sign was an insensitive but highly specific marker of IDH mutation and IDHmut-Noncodel profile, although significant exceptions may exist to this finding. Tumors with a positive sign may still be IDHwt or 1p/19q codeleted. These findings support the utility of T2-FLAIR mismatch as an imaging-based biomarker for positive selection of patients with IDH-mutant gliomas.

Section: Discussionmentioning

confidence: 92%

The T2-FLAIR–mismatch sign as an imaging biomarker for IDH and 1p/19q status in diffuse low-grade gliomas: a systematic review with a Bayesian approach to evaluation of diagnostic test performance

Yolcu

et al. 2019

Neurosurgical Focus

“…TA using B 0 and fractional anisotropy maps has also shown a high accuracy of 95% in predicting IDH status. 50 Bahrami et al 51 reported greater FLAIR tissue heterogeneity and lower edge contrast in IDH wild-type compared with IDH mutants. Jakola et al 53 also reported greater accuracy for predicting IDH mutation using 3D-FLAIR.…”

Section: Glioma Radiogenomicsmentioning

confidence: 99%

Texture Analysis in Cerebral Gliomas: A Review of the Literature

Soni

Priya

Bathla

2019

AJNR Am J Neuroradiol

Texture analysis is a continuously evolving, noninvasive radiomics technique to quantify macroscopic tissue heterogeneity indirectly linked to microscopic tissue heterogeneity beyond human visual perception. In recent years, systemic oncologic applications of texture analysis have been increasingly explored. Here we discuss the basic concepts and methodologies of texture analysis, along with a review of various MR imaging texture analysis applications in glioma imaging. We also discuss MR imaging texture analysis limitations and the technical challenges that impede its widespread clinical implementation. With continued advancement in computational processing, MR imaging texture analysis could potentially develop into a valuable clinical tool in routine oncologic imaging. ABBREVIATIONS:AUC ϭ area under the curve; CE ϭ contrast-enhanced; GLCM ϭ gray-level co-occurrence matrix; GLRLM ϭ gray-level run-length matrix; HGG ϭ high-grade glioma; IDH ϭ isocitrate dehydrogenase; IDM ϭ inverse difference moment; LGG ϭ low-grade glioma; MRTA ϭ MR imaging texture analysis; PCNSLϭ primary central nervous system lymphoma; PCA ϭ principal component analysis; SVM ϭ support vector machine; TA ϭ texture analysis Indicates open access to non-subscribers at www.ajnr.org Indicates article with supplemental on-line tables.

“…And the texture analysis based on T2 FLAIR images has been demonstrated to be helpful and accurate in predicting IDH mutation [30,31]. T2 FLAIR features also showed distinct characteristics between IDH wildtype and mutant tumors,1p/19q co-deleted and 1p/19q intact tumors, MGMT methylated and unmethylated tumors respectively, which could useful in molecular classification of patients with grade II/III glioma [32]. In addition, T2 FLAIR is increasingly involved in the texture analysis for assessment of therapeutic response and survival outcomes.…”

Section: Introductionmentioning

confidence: 97%

High-order radiomics features based on T2 FLAIR MRI predict multiple glioma immunohistochemical features: A more precise and personalized gliomas management

Liu²,

Qin³

et al. 2020

PLoS ONE

Objective To investigate the performance of high-order radiomics features and models based on T2weighted fluid-attenuated inversion recovery (T2 FLAIR) in predicting the immunohistochemical biomarkers of glioma, in order to execute a non-invasive, more precise and personalized glioma disease management. Methods 51 pathologically confirmed gliomas patients committed in our hospital from March 2015 to June 2018 were retrospective analysis, and Ki-67, vimentin, S-100 and CD34 immunohistochemical data were collected. The volumes of interest (VOIs) were manually sketched and the radiomics features were extracted. Feature reduction was performed by ANOVA+ Mann-Whiney, spearman correlation analysis, least absolute shrinkage and selection operator (LASSO) and Gradient descent algorithm (GBDT). SMOTE technique was used to solve the data bias between two groups. Comprehensive binary logistic regression models were established. Area under the ROC curves (AUC), sensitivity, specificity and accuracy were used to evaluate the predict performance of models. Models reliability were decided according to the standard net benefit of the decision curves. Results Four clusters of significant features were screened out and four predicting models were constructed. AUC of Ki-67, S-100, vimentin and CD34 models were 0.