Faculty Bibliography

Recent advances in the understanding of the genetic makeup of gliomas have led to a paradigm shift in the diagnosis and classification of these tumors. Driven by these changes, the World Health Organization (WHO) introduced an update to its classification system of central nervous system (CNS) tumors in 2016. The updated glioma classification system incorporates molecular markers into tumor subgrouping, which has been shown to better correlate with tumor biology and behavior as well as patient prognosis than the previous purely histology-based classification system. Familiarity with this new classification scheme, the individual molecular markers, and corresponding imaging findings is critical for the radiologists who play an important role in diagnostic and surveillance imaging of patients with CNS tumors. The goals of this article are to review these updates to the WHO classification of CNS tumors with a focus on adult gliomas, provide an overview of key genomic markers of gliomas, and review imaging features pertaining to various genomic subgroups of adult gliomas.

BACKGROUND AND PURPOSE/OBJECTIVE:Recent studies have highlighted the importance of isocitrate dehydrogenase (IDH) mutational status in stratifying biologically distinct subgroups of gliomas. This study aimed to evaluate whether MRI-based radiomic features could improve the accuracy of survival predictions for lower grade gliomas over clinical and IDH status. MATERIALS AND METHODS/METHODS:Radiomic features (n = 250) were extracted from preoperative MRI data of 296 lower grade glioma patients from databases at our institutional (n = 205) and The Cancer Genome Atlas (TCGA)/The Cancer Imaging Archive (TCIA) (n = 91) datasets. For predicting overall survival, random survival forest models were trained with radiomic features; non-imaging prognostic factors including age, resection extent, WHO grade, and IDH status on the institutional dataset, and validated on the TCGA/TCIA dataset. The performance of the random survival forest (RSF) model and incremental value of radiomic features were assessed by time-dependent receiver operating characteristics. RESULTS:The radiomics RSF model identified 71 radiomic features to predict overall survival, which were successfully validated on TCGA/TCIA dataset (iAUC, 0.620; 95% CI, 0.501-0.756). Relative to the RSF model from the non-imaging prognostic parameters, the addition of radiomic features significantly improved the overall survival prediction accuracy of the random survival forest model (iAUC, 0.627 vs. 0.709; difference, 0.097; 95% CI, 0.003-0.209). CONCLUSION/CONCLUSIONS:Radiomic phenotyping with machine learning can improve survival prediction over clinical profile and genomic data for lower grade gliomas. KEY POINTS/CONCLUSIONS:• Radiomics analysis with machine learning can improve survival prediction over the non-imaging factors (clinical and molecular profiles) for lower grade gliomas, across different institutions.

The T2-FLAIR mismatch sign is an easily detectable imaging sign on routine clinical MRI studies that suggests diagnosis of IDH-mutant 1p/19q non-codeleted gliomas. Multiple independent studies show that the T2-FLAIR mismatch sign has near-perfect specificity, but low sensitivity, for diagnosing IDH-mutant astrocytomas. Thus, the T2-FLAIR mismatch sign represents a non-invasive radiogenomic diagnostic finding with potential clinical impact. Recently, false positive cases have been reported, many related to variable application of the sign's imaging criteria, differences in image acquisition as well as to differences in the included patient populations. Here we summarize the imaging criteria for the T2-FLAIR mismatch sign, review similarities and differences between the multiple validation studies, outline strategies to optimize its clinical use, and discuss potential opportunities to refine imaging criteria in order to maximize its impact in glioma diagnostics.

PURPOSE/OBJECTIVE:To identify significant prognostic magnetic resonance imaging (MRI) features and their prognostic value when added to clinical features in patients with isocitrate dehydrogenase wild-type (IDHwt) lower-grade gliomas. MATERIALS AND METHODS/METHODS:Preoperative MR images of 158 patients (discovery set = 112, external validation set = 46) with IDHwt lower-grade gliomas (WHO grade II or III) were retrospectively analyzed using the Visually Accessible Rembrandt Images feature set. Radiologic risk scores (RRSs) for overall survival were derived from the least absolute shrinkage and selection operator and elastic net. Multivariable Cox regression analysis, including age, Karnofsky Performance score, extent of resection, WHO grade, and RRS, was performed. The added prognostic value of RRS was calculated by comparing the integrated area under the receiver operating characteristic curve (iAUC) between models with and without RRS. RESULTS:The presence of cysts, pial invasion, and cortical involvement were favorable prognostic factors, while ependymal extension, multifocal or multicentric distribution, nonlobar location, proportion of necrosis > 33%, satellites, and eloquent cortex involvement were significantly associated with worse prognosis. RRS independently predicted survival and significantly enhanced model performance for survival prediction when integrated to clinical features (iAUC increased to 0.773-0.777 from 0.737), which was successfully validated on the validation set (iAUC increased to 0.805-0.830 from 0.735). CONCLUSION/CONCLUSIONS:MRI features associated with prognosis in patients with IDHwt lower-grade gliomas were identified. RRSs derived from MRI features independently predicted survival and significantly improved performance of survival prediction models when integrated into clinical features. KEY POINTS/CONCLUSIONS:• Comprehensive analysis of MRI features conveys prognostic information in patients with isocitrate dehydrogenase wild-type lower-grade gliomas. • Presence of cysts, pial invasion, and cortical involvement of the tumor were favorable prognostic factors. • Radiological phenotypes derived from MRI independently predict survival and have the potential to improve survival prediction when added to clinical features.

INTRODUCTION/BACKGROUND:The glial-lymphatic pathway is a fluid-clearance pathway consisting of a para-arterial route for the flow of cerebrospinal fluid along perivascular spaces and subsequently toward the brain interstitium. In this case series we aim to investigate an empirical demonstration of glymphatic clearance of extravasated iodine following perforation incurred during endovascular therapy on serial CT. METHODS AND RESULTS/RESULTS:Six consecutive cases of endovascular perforation during thrombectomy performed between 2005 and 2018 were retrospectively collected by searching our internal database of total 446 thrombectomies. Two cases were excluded because care was withdrawn shortly following the procedure and no follow-up imaging was available. One case was excluded because a ventricular drain was placed. Three cases were hence included in this analysis. All three cases demonstrated progressive absorption of contrast by the brain parenchyma with eventual contrast disappearance. CONCLUSION/CONCLUSIONS:We described a likely in vivo CT correlate of the glymphatic system in a cohort of patients who sustained intraprocedural extravasation during thrombectomy for acute ischemic stroke.

BACKGROUND: The purpose of this study was (i) to assess the reproducibility of the previously described T2-FLAIR mismatch sign as a highly specific MR imaging marker in non-enhancing IDH-mutant, 1p/19q noncodeleted lower-grade gliomas (LGG) of the WHO grades II or III, and (ii) its association with the uptake of the radiolabeled amino acid O-(2-[¹⁸F]-fluoroethyl)-L-tyrosine (FET) in PET to further metabolically characterize that sign, which is currently poorly understood.
METHOD(S): Consecutive MRI and dynamic FET PET scans (n=134) from newly diagnosed and neuropathologically confirmed IDH-mutant LGG (n=65) and IDH-wildtype gliomas as control group (n=69) were evaluated by two independent raters to assess presence/absence of the T2-FLAIR mismatch sign as well as FET uptake. Interrater agreement was assessed using Cohen's kappa (kappa), as well as diagnostic performance (i.e., positive/negative predictive value; PPV, NPV) of the T2-FLAIR mismatch sign to identify IDH-mutant astrocytomas.
RESULT(S): In the LGG group, 13 patients (20%) had a T2-FLAIR mismatch sign, which could be identified with a substantial interrater agreement (kappa=0.75). In contrast, that sign was absent in IDH-wildtype gliomas. All 13 cases that were positive for the T2/FLAIR mismatch sign were IDH-mutant, 1p/19q non-codeleted tumors (PPV=100%, NPV=57%). Interestingly, compared to IDH-mutant gliomas without the T2-FLAIR mismatch sign, the sign was significantly (P=0.027; 10 of 13 patients) associated with a negative FET PET scan (i.e., 5 tumors with indifferent FET uptake comparable to the background activity, or FET uptake below background activity (photopenic defect) in 5 tumors).
CONCLUSION(S): With a robust interrater agreement, our findings are in line with previously reported findings regarding the T2-FLAIR mismatch sign. Additionally, the T2-FLAIR mismatch sign seems to be significantly related with a lack of increased FET uptake in PET, which may help to further characterize patients with that sign. Notwithstanding, the clinical relevance of this imaging constellation warrants further investigation

Background Fluorescence in situ hybridization (FISH) is a standard method for 1p/19q codeletion testing in diffuse gliomas but occasionally renders erroneous results. Purpose To determine whether MRI/CT analysis identifies isocitrate dehydrogenase (IDH)-mutant gliomas misassigned to 1p/19q codeletion status with FISH. Materials and Methods Data in patients with IDH-mutant lower-grade gliomas (World Health Organization grade II/III) and 1p/19q codeletion status determined with FISH that were accrued from January 1, 2010 to October 1, 2017, were included in this retrospective study. Two neuroradiologist readers analyzed the pre-resection MRI findings (and CT findings, when available) to predict 1p/19q status (codeleted or noncodeleted) and provided a prediction confidence score (1 = low, 2 = moderate, 3 = high). Percentage concordance between the consensus neuroradiologist 1p/19q prediction and the FISH result was calculated. For gliomas where (a) consensus neuroradiologist 1p/19q prediction differed from the FISH result and (b) consensus neuroradiologist confidence score was 2 or greater, further 1p/19q testing was performed with chromosomal microarray analysis (CMA). Nine control specimens were randomly chosen from the remaining study sample for CMA. Percentage concordance between FISH and CMA among the CMA-tested cases was calculated. Results A total of 112 patients (median age, 38 years [interquartile range, 31-51 years]; 57 men) were evaluated (112 gliomas). Percentage concordance between the consensus neuroradiologist 1p/19q prediction and the FISH result was 84.8% (95 of 112; 95% confidence interval: 76.8%, 90.9%). Among the 17 neuroradiologist-FISH discordances, there were nine gliomas associated with a consensus neuroradiologist confidence score of 2 or greater. In six (66.7%) of these nine gliomas, the 1p/19q codeletion status as determined with CMA disagreed with the FISH result and agreed with the consensus neuroradiologist prediction. For the nine control specimens, there was 100% agreement between CMA and FISH for 1p/19q determination. Conclusion MRI and CT analysis can identify diffuse gliomas misassigned to 1p/19q codeletion status with fluorescence in situ hybridization (FISH). Further molecular testing should be considered for gliomas with discordant neuroimaging and FISH results. © RSNA, 2019 Online supplemental material is available for this article.