Faculty Bibliography

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.

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

PURPOSE/OBJECTIVE:There is variability in survival within IDH mutant gliomas determined by chromosomal events. Copy number variation (CNV) abundance associated with survival in low-grade and IDH mutant astrocytoma has been reported. Our purpose was to correlate the extent of genome-wide CNV abundance in IDH mutant astrocytomas with MRI features. METHODS:Presurgical MRI and CNV plots derived from Illumina 850k EPIC DNA methylation arrays of 18 cases of WHO grade II-IV IDH mutant astrocytomas were reviewed. IDH mutant astrocytomas were divided into CNV stable group (CNV-S) with ≤ 3 chromosomal gains or losses and lack of focal gene amplifications and CNV unstable group (CNV-U) with > 3 large chromosomal gains/losses and/or focal amplifications. The associations between MR features, relative cerebral blood volume (rCBV), CNV abundance, and time to progression were assessed. Tumor rCBV estimates were obtained using DSC T2* perfusion analysis. RESULTS:There were nine (50%) CNV-S and nine (50%) CNV-U IDH mutant astrocytomas. CNV-U tumors showed larger mean tumor size (P = 0.004) and maximum diameter on FLAIR (P = 0.004) and also demonstrated significantly higher median rCBV than CNV-S tumors (2.62 vs 0.78, P = 0.019). CNV-U tumors tended to have shorter time to progression although without statistical significance (P = 0.393). CONCLUSIONS:Larger size/diameter and higher rCBVs were seen associated CNV-U astrocytomas, suggesting a correlation of aggressive imaging phenotype with unstable and aggressive genotype in IDH mutant astrocytomas.

BACKGROUND AND PURPOSE/OBJECTIVE:Diffuse lower-grade gliomas are classified into prognostically meaningful molecular subtypes. We aimed to determine the impact of surgical resection on overall survival in lower-grade glioma molecular subtypes. MATERIALS AND METHODS/METHODS:) and 1p/19q status, after adjustment for age, sex, World Health Organization grade, chemotherapy administration, and radiation therapy administration. RESULTS:-wild-type lower-grade gliomas, pre-/postsurgical glioma volume and percentage of resection were not associated with overall survival. CONCLUSIONS:-mutant 1p/19q-noncodeleted astrocytomas.

BACKGROUND AND PURPOSE/OBJECTIVE:-mutant lower grade gliomas based on simple neuroimaging metrics. MATERIALS AND METHODS/METHODS:-mutant lower grade gliomas. RESULTS:= 21); and 2) a logistic regression model based on texture, patient age, T2* susceptibility, primary lobe, and hydrocephalus. Independent validation of the classification algorithm rendered codeletion prediction accuracies of 81.1% and 79.2% in 2 independent readers. The metrics used in the algorithm were associated with moderate-substantial interreader agreement (κ = 0.56-0.79). CONCLUSIONS:-mutant lower grade gliomas.

PURPOSE/OBJECTIVE:Isocitrate dehydrogenase (IDH) mutant gliomas are a distinct glioma molecular subtype for which no effective molecularly-directed therapy exists. Low-grade gliomas, which are 80-90% IDH mutant, have high RNA levels of the cell surface Notch ligand DLL3. We sought to determine DLL3 expression by immunohistochemistry in glioma molecular subtypes and the potential efficacy of an anti-DLL3 antibody drug conjugate (ADC), rovalpituzumab tesirine (Rova-T), in IDH mutant glioma. EXPERIMENTAL DESIGN/METHODS:We evaluated DLL3 expression by RNA using TCGA data and by immunohistochemistry in a discovery set of 63 gliomas and 20 non-tumor brain tissues and a validation set of 62 known IDH wildtype and mutant gliomas using a monoclonal anti-DLL3 antibody. Genotype was determined using a DNA methylation array classifier or by sequencing. The effect of Rova-T on patient-derived endogenous IDH mutant glioma tumorspheres was determined by cell viability assay. RESULTS:Compared to IDH wildtype glioblastoma, IDH mutant gliomas have significantly higher DLL3 RNA (P<1x10-15) and protein by immunohistochemistry (P=0.0014 and P<4.3x10-6 in the discovery and validation set, respectively). DLL3 immunostaining was intense and homogeneous in IDH mutant gliomas, retained in all recurrent tumors, and detected in only 1 of 20 non-tumor brains. Patient-derived IDH mutant glioma tumorspheres overexpressed DLL3 and were potently sensitive to Rova-T in an antigen-dependent manner. CONCLUSIONS:DLL3 is selectively and homogeneously expressed in IDH mutant gliomas and can be targeted with Rova-T in patient-derived IDH mutant glioma tumorspheres. Our findings are potentially immediately translatable and have implications for therapeutic strategies that exploit cell surface tumor-associated antigens.

The T2-FLAIR mismatch sign, in which a low-grade glioma is hyperintense on T2-weighted MR and centrally hypointense on T2-weighted FLAIR MR, has been reported as 100% specific for IDH-mutant astrocytomas in several series. We report several cases of "false positive" T2-FLAIR mismatch sign occurring outside the context of IDH-mutant astrocytomas, predominantly in children or young adults with pediatric-type gliomas. These results suggest caution in the interpretation of the T2-FLAIR mismatch sign in the pediatric glioma population.

A higher degree of angiogenesis is associated with shortened survival in glioblastoma. Feasible morphometric parameters for analyzing vascular networks in brain tumors in clinical practice are lacking. We investigated whether the macrovascular network classified by the number of vessel-like structures (nVS) visible on three-dimensional T1-weighted contrast⁻enhanced (3D-T1CE) magnetic resonance imaging (MRI) could improve survival prediction models for newly diagnosed glioblastoma based on clinical and other imaging features. Ninety-seven consecutive patients (62 men; mean age, 58 ± 15 years) with histologically proven glioblastoma underwent 1.5T-MRI, including anatomical, diffusion-weighted, dynamic susceptibility contrast perfusion, and 3D-T1CE sequences after 0.1 mmol/kg gadobutrol. We assessed nVS related to the tumor on 1-mm isovoxel 3D-T1CE images, and relative cerebral blood volume, relative cerebral flow volume (rCBF), delay mean time, and apparent diffusion coefficient in volumes of interest for contrast-enhancing lesion (CEL), non-CEL, and contralateral normal-appearing white matter. We also assessed Visually Accessible Rembrandt Images scoring system features. We used ROC curves to determine the cutoff for nVS and univariate and multivariate cox proportional hazards regression for overall survival. Prognostic factors were evaluated by Kaplan-Meier survival and ROC analyses. Lesions with nVS > 5 were classified as having highly developed macrovascular network; 58 (60.4%) tumors had highly developed macrovascular network. Patients with highly developed macrovascular network were older, had higher volume_CEL, increased rCBF_CEL, and poor survival; nVS correlated negatively with survival (r = -0.286; p = 0.008). On multivariate analysis, standard treatment, age at diagnosis, and macrovascular network best predicted survival at 1 year (AUC 0.901, 83.3% sensitivity, 93.3% specificity, 96.2% PPV, 73.7% NPV). Contrast-enhanced MRI macrovascular network improves survival prediction in newly diagnosed glioblastoma.

OBJECTIVE:Machine learning has recently gained considerable attention because of promising results for a wide range of radiology applications. Here we review recent work using machine learning in brain tumor imaging, specifically segmentation and MRI radiomics of gliomas. CONCLUSION/CONCLUSIONS:We discuss available resources, state-of-the-art segmentation methods, and machine learning radiomics for glioma. We highlight the challenges of these techniques as well as the future potential in clinical diagnostics, prognostics, and decision making.