Reliability of noncontrast-enhancing tumor as a biomarker of IDH1 mutation status in glioblastoma

Importance of Age and Noncontrast-Enhancing Tumor as Biomarkers for Isocitrate Dehydrogenase-Mutant Glioblastoma: A Multicenter Study

PURPOSE

This study aimed to investigate the most useful clinical and magnetic resonance imaging (MRI) parameters for differentiating isocitrate dehydrogenase (IDH)-mutant and -wildtype glioblastomas in the 2016 World Health Organization Classification of Tumors of the Central Nervous System.

METHODS

This multicenter study included 327 patients with IDH-mutant or IDH-wildtype glioblastoma in the 2016 World Health Organization classification who preoperatively underwent MRI. Isocitrate dehydrogenase mutation status was determined by immunohistochemistry, high-resolution melting analysis, and/or IDH1/2 sequencing. Three radiologists independently reviewed the tumor location, tumor contrast enhancement, noncontrast-enhancing tumor (nCET), and peritumoral edema. Two radiologists independently measured the maximum tumor size and mean and minimum apparent diffusion coefficients of the tumor. Univariate and multivariate logistic regression analyses with an odds ratio (OR) were performed.

RESULTS

The tumors were IDH-wildtype glioblastoma in 306 cases and IDH-mutant glioblastoma in 21. Interobserver agreement for both qualitative and quantitative evaluations was moderate to excellent. The univariate analyses revealed a significant difference in age, seizure, tumor contrast enhancement, and nCET (P < 0.05). The multivariate analysis revealed significant difference in age for all 3 readers (reader 1, odds ratio [OR] = 0.960, P = 0.012; reader 2, OR = 0.966, P = 0.048; reader 3, OR = 0.964, P = 0.026) and nCET for 2 readers (reader 1, OR = 3.082, P = 0.080; reader 2, OR = 4.500, P = 0.003; reader 3, OR = 3.078, P = 0.022).

CONCLUSIONS

Age and nCET are the most useful parameters among the clinical and MRI parameters for differentiating IDH-mutant and IDH-wildtype glioblastomas.

Diffusion Weighted Imaging in Neuro-Oncology: Diagnosis, Post-Treatment Changes, and Advanced Sequences-An Updated Review

DWI is an imaging technique commonly used for the assessment of acute ischemia, inflammatory disorders, and CNS neoplasia. It has several benefits since it is a quick, easily replicable sequence that is widely used on many standard scanners. In addition to its normal clinical purpose, DWI offers crucial functional and physiological information regarding brain neoplasia and the surrounding milieu. A narrative review of the literature was conducted based on the PubMed database with the purpose of investigating the potential role of DWI in the neuro-oncology field. A total of 179 articles were included in the study.

Multiparametric Magnetic Resonance Imaging Correlates of Isocitrate Dehydrogenase Mutation in WHO high-Grade Astrocytomas

Purpose and background: Isocitrate dehydrogenase (IDH) mutation and O-6 methyl guanine methyl transferase (MGMT) methylation are surrogate biomarkers of improved survival in gliomas. This study aims at studying the ability of semantic magnetic resonance imaging (MRI) features to predict the IDH mutation status confirmed by the gold standard molecular tests. Methods: The MRI of 148 patients were reviewed for various imaging parameters based on the Visually AcceSAble Rembrandt Images (VASARI) study. Their IDH status was determined using immunohistochemistry (IHC). Fisher’s exact or chi-square tests for univariate and logistic regression for multivariate analysis were used. Results: Parameters such as mild and patchy enhancement, minimal edema, necrosis < 25%, presence of cysts, and less rCBV (relative cerebral blood volume) correlated with IDH mutation. The median age of IDH-mutant and IDH-wild patients were 34 years (IQR: 29−43) and 52 years (IQR: 45−59), respectively. Mild to moderate enhancement was observed in 15/19 IDH-mutant patients (79%), while 99/129 IDH-wildtype (77%) had severe enhancement (p-value <0.001). The volume of edema with respect to tumor volume distinguished IDH-mutants from wild phenotypes (peritumoral edema volume < tumor volume was associated with higher IDH-mutant phenotypes; p-value < 0.025). IDH-mutant patients had a median rCBV value of 1.8 (IQR: 1.4−2.0), while for IDH-wild phenotypes, it was 2.6 (IQR: 1.9−3.5) {p-value = 0.001}. On multivariate analysis, a cut-off of 25% necrosis was able to differentiate IDH-mutant from IDH-wildtype (p-value < 0.001), and a cut-off rCBV of 2.0 could differentiate IDH-mutant from IDH-wild phenotypes (p-value < 0.007). Conclusion: Semantic imaging features could reliably predict the IDH mutation status in high-grade gliomas. Presurgical prediction of IDH mutation status could help the treating oncologist to tailor the adjuvant therapy or use novel IDH inhibitors.

Preoperative imaging features: Are they useful tools for predicting IDH1 mutation status in gliomas Grades II–IV?

Background:

It is already known that gliomas biomolecular parameters have a reliable prognostic value. However, an invasive procedure is required to determine them. Our aim was to better understand the clinical characteristics of gliomas Grades II–IV and to assess the usefulness of imaging features in magnetic resonance imaging (MRI) to predict the isocitrate dehydrogenase one (IDH1) mutation.

Methods:

Preoperative MRI characteristics were retrospectively reviewed and molecular diagnosis of gliomas was tested in adult patients between 2014 and 2021 in two institutions. We applied a biological criterion to divide the brain in cerebral compartments.

Results:

A total of 108 patients met the inclusion criteria. Contrast enhancement (CE) in MRI was significantly associated with wild-type IDH1 (IDH1-Wt) (P < 0.00002). Furthermore, the positive predictive value of CE for IDH1-Wt was of 87.1%. On the other hand, the negative predictive value of non-CE for mutated IDH1 (IDH1-Mut) was of 52.6%; 60.2% of gliomas were located in the neocortical and 24.1% in the allocortical/mesocortical telencephalon. Considering gliomas Grades II–III, 66.7% of IDH1-Mut and 28.6% of IDH1-Wt gliomas were located in the neocortex, without statistical significance.

Conclusion:

Our research revealed that CE is useful for predicting IDH1-Wt in gliomas. On the contrary, nonCE is not useful for predicting IDH1-Mut gliomas. Thus, the traditional concept of associating non-CE MRI with a low-grade glioma should be reviewed, as it can lead to an underestimation of the potential aggressiveness of the tumor. If this association was validated with the future prospective studies, a noninvasive tool would be available for predicting gliomas IDH1 mutation status.

Preoperative and Noninvasive Prediction of Gliomas Histopathological Grades and IDH Molecular Types Using Multiple MRI Characteristics

Background and Purpose

Gliomas are one of the most common tumors in the central nervous system. This study aimed to explore the correlation between MRI morphological characteristics, apparent diffusion coefficient (ADC) parameters and pathological grades, as well as IDH gene phenotypes of gliomas.

Methods

Preoperative MRI data from 166 glioma patients with pathological confirmation were retrospectively analyzed to compare the differences of MRI characteristics and ADC parameters between the low-grade and high-grade gliomas (LGGs vs. HGGs), IDH mutant and wild-type gliomas (IDH^mut vs. IDH^wt). Multivariate models were constructed to predict the pathological grades and IDH gene phenotypes of gliomas and the performance was assessed by the receiver operating characteristic (ROC) analysis.

Results

Two multivariable logistic regression models were developed by incorporating age, ADC parameters, and MRI morphological characteristics to predict pathological grades, and IDH gene phenotypes of gliomas, respectively. The Noninvasive Grading Model classified tumor grades with areas under the ROC curve (AUROC) of 0.934 (95% CI=0.895-0.973), sensitivity of 91.2%, and specificity of 78.6%. The Noninvasive IDH Genotyping Model differentiated IDH types with an AUROC of 0.857 (95% CI=0.787-0.926), sensitivity of 88.2%, and specificity of 63.8%.

Conclusion

MRI features were correlated with glioma grades and IDH mutation status. Multivariable logistic regression models combined with MRI morphological characteristics and ADC parameters may provide a noninvasive and preoperative approach to predict glioma grades and IDH mutation status.

Prediction of IDH Mutation Status in High-grade Gliomas Using DWI and High T1-weight DSC-MRI

RATIONALE AND OBJECTIVES

We aimed to evaluate the diagnostic performance of diffusion-weighted imaging (DWI) and dynamic susceptibility contrast-enhanced (DSC) magnetic resonance imaging (MRI) parameters in the noninvasive prediction of the isocitrate dehydrogenase (IDH) mutation status in high-grade gliomas (HGGs).

MATERIALS AND METHODS

A total of 58 patients with histopathologically proved HGGs were included in this retrospective study. All patients underwent multiparametric MRI on 3-T, including DSC-MRI and DWI before surgery. The mean apparent diffusion coefficient (ADC), relative maximum cerebral blood volume (rCBV), and percentage signal recovery (PSR) of the tumor core were measured and compared depending on the IDH mutation status and tumor grade. The Mann-Whitney U test was used to detect statistically significant differences in parameters between IDH-mutant-type (IDH-m-type) and IDH-wild-type (IDH-w-type) HGGs. Receiver operating characteristic curve (ROC) analysis was performed to evaluate the diagnostic performance.

RESULTS

The rCBV was significantly higher, and the PSR value was significantly lower in IDH-w-type tumors than in the IDH-m group (p = 0.002 and <0.001, respectively).The ADC value in IDH-w-type tumors was significantly lower compared with the one in IDH-m types (p = 0.023), but remarkable overlaps were found between the groups. The PSR showed the best diagnostic performance with an AUC of 0.938 and with an accuracy rate of 0.87 at the optimal cutoff value of 86.85. The combination of the PSR and the rCBV for the identification of the IDH mutation status increased the discrimination ability at the AUC level of 0.955. In terms of each tumor grade, the PSR and rCBV showed significant differences between the IDH-m and IDH-w groups (p ≤0.001).

CONCLUSION

The rCBV and PSR from DSC-MRI may be feasible noninvasive imaging parameters for predicting the IDH mutation status in HGGs. The standardization of the imaging protocol is indispensable to the utility of DSC perfusion MRI in wider clinical usage.

Reproducible imaging-based prediction of molecular subtype and risk stratification of gliomas across different experience levels using a structured reporting system

OBJECTIVES

To determine reproducible MRI parameters predictive of molecular subtype and risk stratification in glioma and develop a structured reporting system.

METHODS

All study patients were initially diagnosed with glioma, 141 from the Cancer Genome Atlas and 131 from our tertiary institution, as training and validation sets, respectively. Images were analyzed by three neuroradiologists with 1-7 years of experience. MRI features including contrast enhancement pattern, necrosis, margin, edema, T2/FLAIR mismatch, internal cyst, and cerebral blood volume higher than normal cortex were reported using a structured reporting system. The pathology was stratified into five risk types: (1) oligodendroglioma, isocitrate dehydrogenase [IDH]-mutant, 1p19q co-deleted; (2) diffuse astrocytoma, IDH-mutant, grade II-III; (3) glioblastoma, IDH-mutant, grade IV; (4) diffuse astrocytoma, IDH-wild, grade II-III; and (5) glioblastoma, IDH-wild, grade IV. Significant predictors were selected using multivariate logistic regression, and diagnostic performance was tested using a validation set.

RESULTS

Reproducible imaging parameters exhibiting > 50% agreement across readers included the presence of necrosis, T2/FLAIR mismatch, internal cyst, and predominant contrast enhancement. In the validation set, prediction of risk type 5 exhibited the highest diagnostic performance with AUCs of 0.92 (reader 1) and 0.93 (reader 2) with predominant enhancement, followed by risk type 2 with AUCs of 0.95 and 0.95 with T2/FLAIR mismatch sign and no necrosis, and risk type 1 with AUCs of 0.84 and 0.83 with internal cyst or necrosis. Risk types 3 and 4 were difficult to visually predict.

CONCLUSIONS

Imaging parameters with high reproducibility enabling prediction of IDH-wild-type glioblastoma, IDH-mutant/1p19q co-deletion oligodendroglioma, and IDH-mutant diffuse astrocytoma were identified.

KEY POINTS

• Reproducible MRI parameters for determining molecular subtypes of glioma included the presence of necrosis, T2/FLAIR mismatch, internal cyst, and predominant contrast enhancement. • IDH-wild type glioblastoma, IDH-mutant/1p19q co-deletion oligodendroglioma, and IDH-mutant low-grade astrocytoma were identified using MRI parameters with high inter-reader reproducibility. • Identification of IDH-wild type low-grade glioma and IDH-mutant glioblastoma was difficult by visual analysis.

Magnetic resonance imaging criteria for prediction of isocitrate dehydrogenase (IDH) mutation status in patients with grade II-III astrocytoma and oligodendroglioma

BACKGROUND

IDH mutation status is an important prognostic marker for glial tumors, which is detected immunohistochemically after surgery. Since this method is invasive, easy and noninvasive magnetic resonance imaging (MRI) methods have recently been used in predicting the IDH mutation status. However, there is currently no standard MRI technique to predict IDH mutation. We analyzed the value of conventional MRI to predict IDH mutation and its effect on survival among grade II-III astrocytoma and oligodendroglioma patients.

MATERIAL AND METHODS

We included WHO grade II-III astrocytoma and oligodendroglioma patients who underwent surgery at Bahcesehir University Goztepe Medical Park Hospital. All patients were analyzed according to their immunohistochemical IDH mutation status. Preoperative conventional MRI studies with respect to their location, diffusion restriction, contrast enhancement, calcification and hemorrhage on susceptibility-weighted image (SWI) or T2*- weighted imaging (T2*WI), and T2 -FLAIR mismatch properties were retrospectively assessed by a neuroradiologist. The relation between MRI characteristics and IDH mutation was analyzed using a chi-square test. The sensitivity and specificity of radiological IDH mutation were determined by ROC analysis. The impact of IDH mutation on survival was also analyzed by Kaplan-Meier tests.

RESULTS

IDH mutation was found to be positive in 82.5% of tumors histopathologically and 54.4% radiologically. The sensitivity and specificity were 63.8% and 90%, respectively (Area under the curve/AUC = 0.369, p = 0.08). IDH wild gliomas were predominantly diffusion-restricted tumors. IDH mutant tumors were less likely to have contrast enhancement and had lower grades compared to the IDH wild tumors. The median survival time could not be reached and the overall survival was not related to any tumor characteristics or IDH mutation.

CONCLUSIONS

Conventional MRI predicts IDH-mutation status in Grade II-III astrocytoma and oligodendroglioma. Contrast-enhancement and restricted diffusion were strongly associated with grade III astrocytoma and oligodendroglioma, IDH-wild type. Location, T2-FLAIR mismatch, and SWI did not contribute to making a decision on the IDH mutation status. There was no significant difference between the survival times of patients and their IDH status.

Fluid attenuation in non-contrast-enhancing tumor (nCET): an MRI Marker for Isocitrate Dehydrogenase (IDH) mutation in Glioblastoma

PURPOSE

The WHO 2016 update classifies glioblastomas (WHO grade IV) according to isocitrate dehydrogenase (IDH) gene mutation status. We aimed to determine MRI-based metrics for predicting IDH mutation in glioblastoma.

METHODS

This retrospective study included glioblastoma cases (n = 199) with known IDH mutation status and pre-operative MRI (T1WI, T2WI, FLAIR, contrast-enhanced T1W1 at minimum). Two neuroradiologists determined the following MRI metrics: (1) primary lobe of involvement (frontal or non-frontal); (2) presence/absence of contrast-enhancement; (3) presence/absence of necrosis; (4) presence/absence of fluid attenuation in the non-contrast-enhancing tumor (nCET); (5) maximum width of peritumoral edema (cm); (6) presence/absence of multifocal disease. Inter-reader agreement was determined. After resolving discordant measurements, multivariate association between consensus MRI metrics/patient age and IDH mutation status was determined.

RESULTS

Among 199 glioblastomas, 16 were IDH-mutant. Inter-reader agreement was calculated for contrast-enhancement (ĸ = 0.49 [- 0.11-1.00]), necrosis (ĸ = 0.55 [0.34-0.76]), fluid attenuation in nCET (ĸ = 0.83 [0.68-0.99]), multifocal disease (ĸ = 0.55 [0.39-0.70]), and primary lobe (ĸ = 0.85 [0.80-0.91]). Mean difference for peritumoral edema width between readers was 0.3 cm [0.2-0.5], p < 0.001. Multivariate analysis uncovered significant associations between IDH-mutation and fluid attenuation in nCET (OR 82.9 [19.22, ∞], p < 0.001), younger age (OR 0.93 [0.86, 0.98], p = 0.009), frontal lobe location (OR 11.08 [1.14, 352.97], p = 0.037), and less peritumoral edema (OR 0.15 [0, 0.65], p = 0.044).

CONCLUSIONS

Conventional MRI metrics and patient age predict IDH-mutation status in glioblastoma. Among MRI markers, fluid attenuation in nCET represents a novel marker with high inter-reader agreement that is strongly associated with Glioblastoma, IDH-mutant.

Neuro-Oncology and Radiogenomics: Time to Integrate?

Radiogenomics aims to predict genetic markers based on imaging features. The critical importance of molecular markers in the diagnosis and management of intracranial gliomas has led to a rapid growth in radiogenomics research, with progressively increasing complexity. Despite the advances in the techniques being examined, there has been little translation into the clinical domain. This has resulted in a growing disconnect between cutting-edge research and assimilation into clinical practice, though the fundamental goal is for these techniques to improve patient care. The goal of this review, therefore, is to discuss possible clinical scenarios in which the addition of radiogenomics may aid patient management. This includes facilitating patient counseling, determining optimal patient management when complete molecular characterization is not possible, reclassifying tumors, and overcoming some of the limitations of histologic assessment. The review also discusses considerations for selecting relevant radiogenomic features based on the clinical setting.

Automated apparent diffusion coefficient analysis for genotype prediction in lower grade glioma: association with the T2-FLAIR mismatch sign

PURPOSE

The prognosis of lower grade glioma (LGG) patients depends (in large part) on both isocitrate dehydrogenase (IDH) gene mutation and chromosome 1p/19q codeletion status. IDH-mutant LGG without 1p/19q codeletion (IDHmut-Noncodel) often exhibit a unique imaging appearance that includes high apparent diffusion coefficient (ADC) values not observed in other subtypes. The purpose of this study was to develop an ADC analysis-based approach that can automatically identify IDHmut-Noncodel LGG.

METHODS

Whole-tumor ADC metrics, including fractional tumor volume with ADC > 1.5 × 10^-3mm²/s (V_ADC>1.5), were used to identify IDHmut-Noncodel LGG in a cohort of N = 134 patients. Optimal threshold values determined in this dataset were then validated using an external dataset containing N = 93 cases collected from The Cancer Imaging Archive. Classifications were also compared with radiologist-identified T2-FLAIR mismatch sign and evaluated concurrently to identify added value from a combined approach.

RESULTS

V_ADC>1.5 classified IDHmut-Noncodel LGG in the internal cohort with an area under the curve (AUC) of 0.80. An optimal threshold value of 0.35 led to sensitivity/specificity = 0.57/0.93. Classification performance was similar in the validation cohort, with V_ADC>1.5 ≥ 0.35 achieving sensitivity/specificity = 0.57/0.91 (AUC = 0.81). Across both groups, 37 cases exhibited positive T2-FLAIR mismatch sign-all of which were IDHmut-Noncodel. Of these, 32/37 (86%) also exhibited V_ADC>1.5 ≥ 0.35, as did 23 additional IDHmut-Noncodel cases which were negative for T2-FLAIR mismatch sign.

CONCLUSION

Tumor subregions with high ADC were a robust indicator of IDHmut-Noncodel LGG, with V_ADC>1.5 achieving > 90% classification specificity in both internal and validation cohorts. V_ADC>1.5 exhibited strong concordance with the T2-FLAIR mismatch sign and the combination of both parameters improved sensitivity in detecting IDHmut-Noncodel LGG.

Conventional MRI features of adult diffuse glioma molecular subtypes: a systematic review

PURPOSE

Molecular parameters have become integral to glioma diagnosis. Much of radiogenomics research has focused on the use of advanced MRI techniques, but conventional MRI sequences remain the mainstay of clinical assessments. The aim of this research was to synthesize the current published data on the accuracy of standard clinical MRI for diffuse glioma genotyping, specifically targeting IDH and 1p19q status.

METHODS

A systematic search was performed in September 2019 using PubMed and the Cochrane Library, identifying studies on the diagnostic value of T1 pre-/post-contrast, T2, FLAIR, T2*/SWI and/or 3-directional diffusion-weighted imaging sequences for the prediction of IDH and/or 1p19q status in WHO grade II-IV diffuse astrocytic and oligodendroglial tumours as defined in the WHO 2016 Classification of CNS Tumours.

RESULTS

Forty-four studies including a total of 5286 patients fulfilled the inclusion criteria. Correlations between key glioma molecular markers, namely IDH and 1p19q, and distinctive MRI findings have been established, including tumour location, signal composition (including the T2-FLAIR mismatch sign) and apparent diffusion coefficient values.

CONCLUSION

Consistent trends have emerged indicating that conventional MRI is valuable for glioma genotyping, particularly in presumed lower grade glioma. However, due to limited interobserver testing, the reproducibility of qualitatively assessed visual features remains an area of uncertainty.

Differentiating between glioblastomas with and without isocitrate dehydrogenase gene mutation by findings on conventional magnetic resonance images

Various studies using advanced techniques have estimated the isocitrate dehydrogenase (IDH) gene mutation status in glioblastoma (GBM) from preoperative images. However, it is important to be able to predict mutation status using conventional MRI, which is more widely used in clinical practice. In this study, we examined the features of GBM with and without IDH gene mutation on conventional MRI. Twenty-three patients with GBM in whom IDH gene mutation status had been pathologically and molecularly confirmed in tumor specimens were included. The cases were divided into an IDH-wildtype group (n = 17) and an IDH-mutant group (n = 6). We retrospectively compared the following imaging parameters between the two groups: tumor location (superficial or deep), borders on T2-weighted images (regular or irregular), borders of enhancing lesions (regular or irregular), number of lesions showing contrast enhancement (solitary or multiple), presence or absence of intralesional bleeding, and presence or absence of a low-grade glioma in the background around the enhancing lesion. IDH-wildtype tumors were significantly more likely to be superficial than were IDH-mutant tumors (p < 0.05). Enhancing lesions in the IDH-wildtype group were less likely to have an irregular border (p = 0.059). Low-grade glioma was a background lesion in 5 patients (83.3%) in the IDH-mutant group and 9 (52.9%) in the IDH-wildtype group. The IDH mutation status is likely to be wildtype in patients with superficial GBM in which the enhancing lesion has a regular border and when low-grade glioma is not found as a background lesion on MRI.

Precision of preoperative diagnosis in patients with brain tumor - A prospective study based on "top three list" of differential diagnosis for 1061 patients

Background:

Accurate diagnosis of brain tumor is crucial for adequate surgical strategy. Our institution follows a comprehensive preoperative evaluation based on clinical and imaging information.

Methods:

To assess the precision of preoperative diagnosis, we compared the “top three list” of differential diagnosis (the first, second, and third diagnoses according to the WHO 2007 classification including grading) of 1061 brain tumors, prospectively and consecutively registered in preoperative case conferences from 2010 to the end of 2017, with postoperative pathology reports.

Results:

The correct diagnosis rate (sensitivity) of the first diagnosis was 75.8% in total. The sensitivity of the first diagnosis was high (84–94%) in hypothalamic-pituitary and extra-axial tumors, 67–75% in intra-axial tumors, and relatively low (29–42%) in intraventricular and pineal region tumors. Among major three intra-axial tumors, the sensitivity was highest in brain metastasis: 83.8% followed by malignant lymphoma: 81.4% and glioblastoma multiforme: 73.1%. Sensitivity was generally low (≦60%) in other gliomas. These sensitivities generally improved when the second and third diagnoses were included; 86.3% in total. Positive predictive value (PPV) was 76.9% in total. All the three preoperative diagnoses were incorrect in 3.4% (36/1061) of cases even when broader brain tumor classification was applied.

Conclusion:

Our institutional experience on precision of preoperative diagnosis appeared around 75% of sensitivity and PPV for brain tumor. Sensitivity improved by 10% when the second and third diagnoses were included. Neurosurgeons should be aware of these features of precision in preoperative differential diagnosis of a brain tumor for better surgical strategy and to adequately inform the patients.

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

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 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.

2-Hydroxyglutarate MR spectroscopy for prediction of isocitrate dehydrogenase mutant glioma: a systemic review and meta-analysis using individual patient data

Background

Noninvasive and accurate modality to predict isocitrate dehydrogenase (IDH) mutant glioma may have great potential in routine clinical practice. We aimed to investigate the diagnostic performance of 2-hydroxyglutarate (2HG) magnetic resonance spectroscopy (MRS) for prediction of IDH mutant glioma and provide an optimal cutoff value for 2HG.

Methods

A systematic literature search of Ovid-MEDLINE and EMBASE was performed to identify original articles investigating the diagnostic performance of 2HG MRS up to March 20, 2018. Pooled sensitivity and specificity were calculated using a bivariate random-effects model. Subgroup analysis and meta-regression were performed to explain heterogeneity effects. An optimal cutoff value for 2HG was calculated from studies providing individual patient data.

Results

Fourteen original articles with 460 patients were included. The pooled sensitivity and specificity for the diagnostic performance of 2HG MRS for prediction of IDH mutant glioma were 95% (95% CI, 85-98%) and 91% (95% CI, 83-96%), respectively. The Higgins I2 statistic demonstrated that heterogeneity was present in the sensitivity (I2 = 50.69%), but not in the specificity (I2 = 30.37%). In the meta-regression, echo time (TE) was associated with study heterogeneity. Among the studies using point-resolved spectroscopy (PRESS), a long TE (97 ms) resulted in higher sensitivity (92%) and specificity (97%) than a short TE (30-35 ms; sensitivity of 90%, specificity of 88%; P < 0.01). The optimal 2HG cutoff value of 2HG using individual patient data was 1.76 mM.

Conclusion

2HG MRS demonstrated excellent specificity for prediction of IDH mutant glioma, with TE being associated with heterogeneity in the sensitivity.

Key Points

1. HG MRS has excellent diagnostic performance in the prediction of IDH mutant glioma. 2. The pooled sensitivity was 95% and the pooled specificity was 91%. 3. Echo time was associated with study heterogeneity in the meta-regression.

Interrelationships between molecular subtype, anatomical location, and extent of resection in diffuse glioma: a systematic review and meta-analysis

Background

The introduction of the 2016 WHO Classification of Tumors of the Central Nervous System has resulted in tumor groupings with improved prognostic value for diffuse glioma patients. Molecular subtype, primarily based on IDH-mutational status and 1p/19q-status, is a strong predictor of survival. It is unclear to what extent this finding may be mediated by differences in anatomical location and surgical resectability among molecular subgroups. Our aim was to elucidate possible correlations between (1) molecular subtype and anatomical location and (2) molecular subtype and extent of resection.

Methods

We performed a systematic review of literature searching for studies on molecular subtype in relation to anatomical location and extent of resection. Only original data concerning adult participants suffering from cerebral diffuse glioma were included. Studies adopting similar outcomes measures were included in our meta-analysis.

Results

In the systematic analysis for research questions 1 and 2, totals of 20 and 9 studies were included, respectively. Study findings demonstrated that IDH-mutant tumors were significantly more frequently located in the frontal lobe and less often in the temporal lobe compared with IDH-wildtype gliomas. Within the IDH-mutant group, 1p/19q-codeleted tumors were associated with more frequent frontal and less frequent temporal localization compared with 1p/19q-intact tumors. In IDH-mutant gliomas, greater extent of resection was achieved than in IDH-wildtype tumors.

Conclusions

Genetic profile of diffuse cerebral glioma influences their anatomical location and seems to affect tumor resectability.

Molecular Subtype Classification in Lower-Grade Glioma with Accelerated DTI

BACKGROUND AND PURPOSE

Image-based classification of lower-grade glioma molecular subtypes has substantial prognostic value. Diffusion tensor imaging has shown promise in lower-grade glioma subtyping but currently requires lengthy, nonstandard acquisitions. Our goal was to investigate lower-grade glioma classification using a machine learning technique that estimates fractional anisotropy from accelerated diffusion MR imaging scans containing only 3 diffusion-encoding directions.

MATERIALS AND METHODS

Patients with lower-grade gliomas (n = 41) (World Health Organization grades II and III) with known isocitrate dehydrogenase (IDH) mutation and 1p/19q codeletion status were imaged preoperatively with DTI. Whole-tumor volumes were autodelineated using conventional anatomic MR imaging sequences. In addition to conventional ADC and fractional anisotropy reconstructions, fractional anisotropy estimates were computed from 3-direction DTI subsets using DiffNet, a neural network that directly computes fractional anisotropy from raw DTI data. Differences in whole-tumor ADC, fractional anisotropy, and estimated fractional anisotropy were assessed between IDH-wild-type and IDH-mutant lower-grade gliomas with and without 1p/19q codeletion. Multivariate classification models were developed using whole-tumor histogram and texture features from ADC, ADC + fractional anisotropy, and ADC + estimated fractional anisotropy to identify the added value provided by fractional anisotropy and estimated fractional anisotropy.

RESULTS

ADC (P = .008), fractional anisotropy (P < .001), and estimated fractional anisotropy (P < .001) significantly differed between IDH-wild-type and IDH-mutant lower-grade gliomas. ADC (P < .001) significantly differed between IDH-mutant gliomas with and without codeletion. ADC-only multivariate classification predicted IDH mutation status with an area under the curve of 0.81 and codeletion status with an area under the curve of 0.83. Performance improved to area under the curve = 0.90/0.94 for the ADC + fractional anisotropy classification and to area under the curve = 0.89/0.89 for the ADC + estimated fractional anisotropy classification.

CONCLUSIONS

Fractional anisotropy estimates made from accelerated 3-direction DTI scans add value in classifying lower-grade glioma molecular status.

Non-Contrast-Enhancing Tumor: A New Frontier in Glioblastoma Research

There is a growing understanding of the prognostic importance of non-contrast-enhancing tumor in glioblastoma, and recent attempts at more aggressive management of this component using neurosurgical resection and radiosurgery have been shown to prolong survival. Optimizing these therapeutic strategies requires an understanding of the features that can distinguish non-contrast-enhancing tumor from other processes, in particular vasogenic edema; however, the limited and heterogeneous manner in which it has been defined in the literature limits clinical translation. This review covers pertinent literature on our growing understanding of non-contrast-enhancing tumor and focuses on key conventional MR imaging features for improving its delineation. Such features include subtle differences in the degree of FLAIR hyperintensity, gray matter involvement, and focal mass effect. Improved delineation of tumor from edema will facilitate more aggressive management of this component and potentially realize associated survival benefits.

Multiregional radiomics profiling from multiparametric MRI: Identifying an imaging predictor of IDH1 mutation status in glioblastoma

Purpose

Isocitrate dehydrogenase 1 (IDH1) has been proven as a prognostic and predictive marker in glioblastoma (GBM) patients. The purpose was to preoperatively predict IDH mutation status in GBM using multiregional radiomics features from multiparametric magnetic resonance imaging (MRI).

Methods

In this retrospective multicenter study, 225 patients were included. A total of 1614 multiregional features were extracted from enhancement area, non‐enhancement area, necrosis, edema, tumor core, and whole tumor in multiparametric MRI. Three multiregional radiomics models were built from tumor core, whole tumor, and all regions using an all‐relevant feature selection and a random forest classification for predicting IDH1. Four single‐region models and a model combining all‐region features with clinical factors (age, sex, and Karnofsky performance status) were also built. All models were built from a training cohort (118 patients) and tested on an independent validation cohort (107 patients).

Results

Among the four single‐region radiomics models, the edema model achieved the best accuracy of 96% and the best F1‐score of 0.75 while the non‐enhancement model achieved the best area under the receiver operating characteristic curve (AUC) of 0.88 in the validation cohort. The overall performance of the tumor‐core model (accuracy 0.96, AUC 0.86 and F1‐score 0.75) and the whole‐tumor model (accuracy 0.96, AUC 0.88 and F1‐score 0.75) was slightly better than the single‐regional models. The 8‐feature all‐region radiomics model achieved an improved overall performance of an accuracy 96%, an AUC 0.90, and an F1‐score 0.78. Among all models, the model combining all‐region imaging features with age achieved the best performance of an accuracy 97%, an AUC 0.96, and an F1‐score 0.84.

Conclusions

The radiomics model built with multiregional features from multiparametric MRI has the potential to preoperatively detect the IDH1 mutation status in GBM patients. The multiregional model built with all‐region features performed better than the single‐region models, while combining age with all‐region features achieved the best performance.

Imaging prediction of isocitrate dehydrogenase (IDH) mutation in patients with glioma: a systemic review and meta-analysis

OBJECTIVES

To evaluate the imaging features of isocitrate dehydrogenase (IDH) mutant glioma and to assess the diagnostic performance of magnetic resonance imaging (MRI) for prediction of IDH mutation in patients with glioma.

METHODS

A systematic search of Ovid-MEDLINE and EMBASE up to 10 October 2017 was conducted to find relevant studies. The search terms combined synonyms for 'glioma', 'IDH mutation' and 'MRI'. Studies evaluating the imaging features of IDH mutant glioma and the diagnostic performance of MRI for prediction of IDH mutation in patients with glioma were selected. The pooled summary estimates of sensitivity and specificity and their 95% confidence intervals (CIs) were calculated using a bivariate random-effects model. The results of multiple subgroup analyses are reported.

RESULTS

Twenty-eight original articles in a total of 2,146 patients with glioma were included. IDH mutant glioma showed frontal lobe predominance, less contrast enhancement, well-defined border, high apparent diffusion coefficient (ADC) value and low relative cerebral blood volume (rCBV) value. For the meta-analysis that included 18 original articles, the summary sensitivity was 86% (95% CI, 79%-91%) and the summary specificity was 87% (95% CI, 78-92%). In a subgroup analysis, the summary sensitivity of 2-hydroxyglutarate magnetic resonance spectroscopy (MRS) [96% (95% CI, 91-100%)] was higher than the summary sensitivities of other imaging modalities.

CONCLUSIONS

IDH mutant glioma consistently demonstrated less aggressive imaging features than IDH wild-type glioma. Despite the variety of different MRI techniques used, MRI showed the potential to non-invasively predict IDH mutation in patients with glioma. 2-Hydroxyglutarate MRS shows higher pooled sensitivity than other imaging modalities.

KEY POINTS

• IDH mutant glioma showed frontal lobe predominance, less contrast enhancement, well-defined border, high ADC value, and low rCBV value. • The diagnostic performance of MRI for prediction of IDH mutation in patients with glioma is within a clinically acceptable range, the summary sensitivity was 86% (95% CI, 79-91%) and the summary specificity was 87% (95% CI, 78-92%). • In a subgroup analysis, the summary sensitivity of 2-hydroxyglutarate MRS [96% (95% CI, 91-100%)] was higher than the summary sensitivities of other imaging modalities.

Conventional and advanced magnetic resonance imaging in patients with high-grade glioma

Magnetic resonance imaging is integral to the care of patients with high-grade gliomas. Anatomic detail can be acquired with conventional structural imaging, but newer approaches also add capabilities to interrogate image-derived physiologic and molecular characteristics of central nervous system neoplasms. These advanced imaging techniques are increasingly employed to generate biomarkers that better reflect tumor burden and therapy response. The following is an overview of current strategies based on advanced magnetic resonance imaging that are used in the assessment of high-grade glioma patients with an emphasis on how novel imaging biomarkers can potentially advance patient care.

MRI Features Can Predict 1p/19q Status in Intracranial Gliomas

BACKGROUND AND PURPOSE

The 2016 revision of the World Health Organization Classification of Tumors of the Central Nervous System mandates codeletion of chromosomes 1p and 19q for the diagnosis of oligodendroglioma. We studied whether conventional MR imaging features could predict 1p/19q status.

MATERIALS AND METHODS

Patients with previous 1p/19q testing were identified through pathology department records, typically performed on the basis of an oligodendroglial component on routine histology; 69 patients met the inclusion criteria. Preoperative imaging of patients with grade II or III gliomas was retrospectively assessed by 2 neuroradiologists, blinded to the 1p/19q status. Thirteen MR imaging features were first assessed in a small initial cohort (n = 10), after which the criteria were narrowed for the remaining patients as a validation cohort.

RESULTS

There was 85% agreement between radiologists for the overall prediction of 1p/19q status in the validation cohort, with an accuracy of 84%. The presence of >50% T2-FLAIR mismatch and calcification was found to be the most useful for predicting 1p/19q status. The >50% T2-FLAIR mismatch variable was demonstrated in 14 tumors and had 100% specificity for identifying a noncodeleted tumor (P = .001), with 97% interobserver correlation. Calcification was visualized in 7 tumors, 6 of which were 1p/19q codeleted (specificity, 97%; P = .006), with 100% interobserver correlation.

CONCLUSIONS

The presence of >50% T2-FLAIR mismatch is highly predictive of a noncodeleted tumor, while calcifications suggest a 1p/19q codeleted tumor. If formal 1p/19q testing is not possible, a combined MR imaging-histologic assessment may improve the diagnostic accuracy over histology alone.

Residual Convolutional Neural Network for the Determination of IDH Status in Low- and High-Grade Gliomas from MR Imaging

Purpose: Isocitrate dehydrogenase (IDH) mutations in glioma patients confer longer survival and may guide treatment decision making. We aimed to predict the IDH status of gliomas from MR imaging by applying a residual convolutional neural network to preoperative radiographic data.Experimental Design: Preoperative imaging was acquired for 201 patients from the Hospital of University of Pennsylvania (HUP), 157 patients from Brigham and Women's Hospital (BWH), and 138 patients from The Cancer Imaging Archive (TCIA) and divided into training, validation, and testing sets. We trained a residual convolutional neural network for each MR sequence (FLAIR, T2, T1 precontrast, and T1 postcontrast) and built a predictive model from the outputs. To increase the size of the training set and prevent overfitting, we augmented the training set images by introducing random rotations, translations, flips, shearing, and zooming.Results: With our neural network model, we achieved IDH prediction accuracies of 82.8% (AUC = 0.90), 83.0% (AUC = 0.93), and 85.7% (AUC = 0.94) within training, validation, and testing sets, respectively. When age at diagnosis was incorporated into the model, the training, validation, and testing accuracies increased to 87.3% (AUC = 0.93), 87.6% (AUC = 0.95), and 89.1% (AUC = 0.95), respectively.Conclusions: We developed a deep learning technique to noninvasively predict IDH genotype in grade II-IV glioma using conventional MR imaging using a multi-institutional data set. Clin Cancer Res; 24(5); 1073-81. ©2017 AACR.

Morphologic patterns of noncontrast-enhancing tumor in glioblastoma correlate with IDH1 mutation status and patient survival

Glioblastomas with a substantial proportion of noncontrast-enhancing tumour (nCET) have a variety of imaging appearances. We aimed to determine whether glioblastomas demonstrating a substantial proportion (>33%) of nCET can be sub-classified by different morphologic pattern of nCET. We then assessed whether this improves the ability of MRI to predict isocitrate dehydrogenase-1 (IDH1) mutation status and whether this has prognostic significance independent of IDH1 mutation status. Pre-operative MRIs of patients with a new diagnosis of glioblastoma were reviewed. Tumours with >33% nCET were sub-classified by the dominant morphologic pattern of nCET: mass-like expansion, white matter dissemination, grey matter dissemination or a combination. IDH1 mutation status (by immunohistochemistry) and survival were compared for each pattern. 153 patients met the inclusion criteria, of whom 34 patients demonstrated >33% nCET. 10 patients had a significant mass-like component, either as the dominant pattern (n=4) or as part of a mixed pattern (n=6). The 10 patients with a significant mass-like component had longer survival than those without (median 387days, compared to 241days), though this was not statistically significant (p=0.242). Three patients had R132H-IDH1 mutations and >33% nCET, and all three had a mass-like component. Using the presence of a mass-like component of nCET for predicting IDH1 mutation status improved the positive predictive value, specificity and overall accuracy of MRI. Classification of nCET by morphologic pattern improves the ability of MRI to predict IDH1 mutations and may provide useful prognostic information.

Tumor location and patient age predict biological signatures of high-grade gliomas

Prognostic factors for high-grade gliomas include patient age, IDH1 mutation, MGMT methylation, and Ki67 value. We assessed the predictive role of topographic location of gliomas for their biological signatures. Collecting all neuroradiological and histological data of patients with histologically proven HGG, we performed a retrospective monocentric study. A predictive value of frontal location for a lower Ki67 value (especially in the left hemisphere) and mutation of IDH1 (especially in the right hemisphere) was found. Temporal location was predictive for IDH1 wild-type. Involvement of the parietal lobe was found to be predictive of methylated MGMT, while insular lobe involvement predicted an unmethylated MGMT. There was no statistically significant difference of IDH1 mutation and MGMT methylation between left and right sides.