Objective: With 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.
Methods: In 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.
Results: A 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%.
Conclusions: The T2-FLAIR-mismatch sign is an insensitive but highly specific marker of IDH mutation but not 1p/19q codeletion in diffuse LGGs, although there may be significant exceptions. These findings support the utility of T2-FLAIR mismatch as an imaging-based biomarker for positive selection of patients with IDH-mutant gliomas.
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http://dx.doi.org/10.3171/2019.9.FOCUS19660 | DOI Listing |
Neuroradiology
January 2025
Department of Molecular Imaging and Diagnosis, Graduate School of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan.
Background And Purpose: The cortical high-flow sign has been more commonly reported in oligodendroglioma, IDH-mutant and 1p/19q-codeleted (ODG IDHm-codel) compared to diffuse glioma with IDH-wildtype or astrocytoma, IDH-mutant. Besides tumor types, higher grades of glioma might also contribute to the cortical high flow. Therefore, we investigated whether the histological cortical vascular density or CNS WHO grade was associated with the cortical high-flow sign in patients with ODG IDHm-codel.
View Article and Find Full Text PDFJ Neurooncol
January 2025
Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No.119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, China.
Purpose: To investigate the prognostic significance of contrast enhancement (CE) in grade 2 oligodendroglioma (ODG) and explore its clinical implications.
Methods: Patients diagnosed with isocitrate dehydrogenase (IDH)-mutant, 1p/19q co-deleted ODG between 2009 and 2016 were retrospectively enrolled from a single institution. The presence of CE was identified on preoperative MRIs, and clinical, radiologic, and histopathological data that was extracted.
Ther Clin Risk Manag
January 2025
Department of Oncology, Gaoxin Branch of the First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, People's Republic of China.
Background: The relationship between molecular phenotype and prognosis in high-grade gliomas (WHO III and IV, HGG) treated with radiotherapy and chemotherapy is not fully understood and needs further exploration.
Methods: The HGG patients following surgery and treatment with radiotherapy and chemotherapy. Univariate and multivariate Cox analyses were used to assess the independent prognostic factors.
J Neuropathol Exp Neurol
January 2025
Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL, United States.
Neurooncol Adv
December 2024
Division for Medical Image Computing (MIC), German Cancer Research Center (DKFZ), Heidelberg, Germany.
Background: This study aimed to explore the potential of the Advanced Data Analytics (ADA) package of GPT-4 to autonomously develop machine learning models (MLMs) for predicting glioma molecular types using radiomics from MRI.
Methods: Radiomic features were extracted from preoperative MRI of = 615 newly diagnosed glioma patients to predict glioma molecular types (IDH-wildtype vs IDH-mutant 1p19q-codeleted vs IDH-mutant 1p19q-non-codeleted) with a multiclass ML approach. Specifically, ADA was used to autonomously develop an ML pipeline and benchmark performance against an established handcrafted model using various MRI normalization methods (N4, Zscore, and WhiteStripe).
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