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Background: This study evaluated whether generative artificial intelligence (AI)-based augmentation (GAA) can provide diverse and realistic imaging phenotypes and improve deep learning-based classification of isocitrate dehydrogenase (IDH) type in glioma compared with neuroradiologists., Methods: For model development, 565 patients (346 IDH-wildtype, 219 IDH-mutant) with paired contrast-enhanced T1 and FLAIR MRI scans were collected from tertiary hospitals and The Cancer Imaging Archive. Performance was tested on internal (119, 78 IDH-wildtype, 41 IDH-mutant [IDH1 and 2]) and external test sets (108, 72 IDH-wildtype, 36 IDH-mutant). GAA was developed using a score-based diffusion model and ResNet50 classifier. The optimal GAA was selected in comparison with the null model. Two neuroradiologists (R1, R2) assessed realism, diversity of imaging phenotypes, and predicted IDH mutation. The performance of a classifier trained with optimal GAA was compared with that of neuroradiologists using the area under the receiver operating characteristics curve (AUC). The effect of tumor size and contrast enhancement on GAA performance was tested., Results: Generated images demonstrated realism (Turing's test: 47.5-50.5%) and diversity indicating IDH type. Optimal GAA was achieved with augmentation with 110 000 generated slices (AUC: 0.938). The classifier trained with optimal GAA demonstrated significantly higher AUC values than neuroradiologists in both the internal (R1, P = .003; R2, P < .001) and external test sets (R1, P < .01; R2, P < .001). GAA with large-sized tumors or predominant enhancement showed comparable performance to optimal GAA (internal test: AUC 0.956 and 0.922; external test: 0.810 and 0.749)., Conclusions: The application of generative AI with realistic and diverse images provided better diagnostic performance than neuroradiologists for predicting IDH type in glioma., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Objectives: To evaluate the performance of natural language processing (NLP) models to predict isocitrate dehydrogenase (IDH) mutation status in diffuse glioma using routine MR radiology reports., Materials and Methods: This retrospective, multi-center study included consecutive patients with diffuse glioma with known IDH mutation status from May 2009 to November 2021 whose initial MR radiology report was available prior to pathologic diagnosis. Five NLP models (long short-term memory [LSTM], bidirectional LSTM, bidirectional encoder representations from transformers [BERT], BERT graph convolutional network [GCN], BioBERT) were trained, and area under the receiver operating characteristic curve (AUC) was assessed to validate prediction of IDH mutation status in the internal and external validation sets. The performance of the best performing NLP model was compared with that of the human readers., Results: A total of 1427 patients (mean age ± standard deviation, 54 ± 15; 779 men, 54.6%) with 720 patients in the training set, 180 patients in the internal validation set, and 527 patients in the external validation set were included. In the external validation set, BERT GCN showed the highest performance (AUC 0.85, 95% CI 0.81-0.89) in predicting IDH mutation status, which was higher than LSTM (AUC 0.77, 95% CI 0.72-0.81; p = .003) and BioBERT (AUC 0.81, 95% CI 0.76-0.85; p = .03). This was higher than that of a neuroradiologist (AUC 0.80, 95% CI 0.76-0.84; p = .005) and a neurosurgeon (AUC 0.79, 95% CI 0.76-0.84; p = .04)., Conclusion: BERT GCN was externally validated to predict IDH mutation status in patients with diffuse glioma using routine MR radiology reports with superior or at least comparable performance to human reader., Clinical Relevance Statement: Natural language processing may be used to extract relevant information from routine radiology reports to predict cancer genotype and provide prognostic information that may aid in guiding treatment strategy and enabling personalized medicine., Key Points: • A transformer-based natural language processing (NLP) model predicted isocitrate dehydrogenase mutation status in diffuse glioma with an AUC of 0.85 in the external validation set. • The best NLP models were superior or at least comparable to human readers in both internal and external validation sets. • Transformer-based models showed higher performance than conventional NLP model such as long short-term memory., (© 2023. The Author(s), under exclusive licence to European Society of Radiology.)

Objective: Imaging-based survival stratification of patients with gliomas is important for their management, and the 2021 WHO classification system must be clinically tested. The aim of this study was to compare integrative imaging- and pathology-based methods for survival stratification of patients with diffuse glioma., Materials and Methods: This study included diffuse glioma cases from The Cancer Genome Atlas (training set: 141 patients) and Asan Medical Center (validation set: 131 patients). Two neuroradiologists analyzed presurgical CT and MRI to assign gliomas to five imaging-based risk subgroups (1 to 5) according to well-known imaging phenotypes (e.g., T2/FLAIR mismatch) and recategorized them into three imaging-based risk groups, according to the 2021 WHO classification: group 1 (corresponding to risk subgroup 1, indicating oligodendroglioma, isocitrate dehydrogenase [IDH]-mutant, and 1p19q-co-deleted), group 2 (risk subgroups 2 and 3, indicating astrocytoma, IDH-mutant), and group 3 (risk subgroups 4 and 5, indicating glioblastoma, IDHwt). The progression-free survival (PFS) and overall survival (OS) were estimated for each imaging risk group, subgroup, and pathological diagnosis. Time-dependent area-under-the receiver operating characteristic analysis (AUC) was used to compare the performance between imaging-based and pathology-based survival model., Results: Both OS and PFS were stratified according to the five imaging-based risk subgroups ( P < 0.001) and three imaging-based risk groups ( P < 0.001). The three imaging-based groups showed high performance in predicting PFS at one-year (AUC, 0.787) and five-years (AUC, 0.823), which was similar to that of the pathology-based prediction of PFS (AUC of 0.785 and 0.837). Combined with clinical predictors, the performance of the imaging-based survival model for 1- and 3-year PFS (AUC 0.813 and 0.921) was similar to that of the pathology-based survival model (AUC 0.839 and 0.889)., Conclusion: Imaging-based survival stratification according to the 2021 WHO classification demonstrated a performance similar to that of pathology-based survival stratification, especially in predicting PFS., Competing Interests: Dr. Ji Eun Park and Dr. Ho Sung Kim, the contributing editors of the Korean Journal of Radiology, and Prof. Seo Young Park, the Statistical Consultant of Korean Journal of Radiology, were not involved in the editorial evaluation or decision to publish this article. All remaining authors have declared no conflicts of interest., (Copyright © 2023 The Korean Society of Radiology.)

Objectives: To determine whether imaging-based risk stratification enables prognostication in diffuse glioma, NOS (not otherwise specified)., Methods: Data from 220 patients classified as diffuse glioma, NOS, between January 2011 and December 2020 were retrospectively included. Two neuroradiologists analyzed pre-surgical CT and MRI to assign gliomas to the three imaging-based risk types considering well-known imaging phenotypes (e.g., T2/FLAIR mismatch). According to the 2021 World Health Organization classification, the three risk types included (1) low-risk, expecting oligodendroglioma, isocitrate dehydrogenase (IDH)-mutant, and 1p/19q-codeleted; (2) intermediate-risk, expecting astrocytoma, IDH-mutant; and (3) high-risk, expecting glioblastoma, IDH-wildtype. Progression-free survival (PFS) and overall survival (OS) were estimated for each risk type. Time-dependent receiver operating characteristic analysis using 10-fold cross-validation with 100-fold bootstrapping was used to compare the performance of an imaging-based survival model with that of a historical molecular-based survival model published in 2015, created using The Cancer Genome Archive data., Results: Prognostication according to the three imaging-based risk types was achieved for both PFS and OS (log-rank test, p < 0.001). The imaging-based survival model showed high prognostic value, with areas under the curves (AUCs) of 0.772 and 0.650 for 1-year PFS and OS, respectively, similar to the historical molecular-based survival model (AUC = 0.74 for PFS and 0.87 for OS). The imaging-based survival model achieved high long-term performance in both 3-year PFS (AUC = 0.806) and 5-year OS (AUC = 0.812)., Conclusion: Imaging-based risk stratification achieved histomolecular-level prognostication in diffuse glioma, NOS, and could aid in guiding patient referral for insufficient or unsuccessful molecular diagnosis., Key Points: • Three imaging-based risk types enable distinct prognostication in diffuse glioma, NOS (not otherwise specified). • The imaging-based survival model achieved similar prognostic performance as a historical molecular-based survival model. • For long-term prognostication of 3 and 5 years, the imaging-based survival model showed high performance., (© 2022. The Author(s), under exclusive licence to European Society of Radiology.)

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., (© 2021. European Society of Radiology.)

Background: The epidermal growth factor receptor (EGFR) amplification status of isocitrate dehydrogenase-wild-type (IDHwt) lower-grade gliomas (LGGs; grade II/III) is one of the key markers for diagnosing molecular glioblastoma. However, the association between EGFR status and imaging parameters is unclear., Objective: To identify noninvasive imaging parameters from diffusion-weighted and dynamic susceptibility contrast imaging for predicting the EGFR amplification status of IDHwt LGGs., Methods: A total of 86 IDHwt LGG patients with known EGFR amplification status (62 nonamplified and 24 amplified) from 3 tertiary institutions were included. Qualitative and quantitative imaging features, including histogram parameters from apparent diffusion coefficient (ADC), normalized cerebral blood volume (nCBV), and normalized cerebral blood flow (nCBF), were assessed. Univariable and multivariable logistic regression models were constructed., Results: On multivariable analysis, multifocal/multicentric distribution (odds ratio [OR] = 11.77, P = .006), mean ADC (OR = 0.01, P = .044), 5th percentile of ADC (OR = 0.01, P = .046), and 95th percentile of nCBF (OR = 1.24, P = .031) were independent predictors of EGFR amplification. The diagnostic performance of the model with qualitative imaging parameters increased significantly when quantitative imaging parameters were added, with areas under the curves of 0.81 and 0.93, respectively (P = .004)., Conclusion: The presence of multifocal/multicentric distribution patterns, lower mean ADC, lower 5th percentile of ADC, and higher 95th percentile of nCBF may be useful imaging biomarkers for EGFR amplification in IDHwt LGGs. Moreover, quantitative imaging biomarkers may add value to qualitative imaging parameters., (© Congress of Neurological Surgeons 2021.)

Objectives: To evaluate the diagnostic performance of the T2-FLAIR mismatch sign for prediction of isocitrate dehydrogenase (IDH)-mutant, 1p/19q-noncodeleted lower-grade gliomas (LGGs) and review studies with false positive results., Methods: The MEDLINE and EMBASE databases were searched up to March 13, 2020, to identify articles reporting the diagnostic performance of the T2-FLAIR mismatch sign for prediction of IDH-mutant, 1p/19q-noncodeleted LGGs (IDHmut-Noncodel) using the search terms (T2 FLAIR mismatch). Pooled sensitivity, specificity, and correlation coefficient for interobserver agreement were calculated., Results: Twelve studies including a total of 1053 patients were included. The median age was 43 (median; range, 14-56). The pooled sensitivity and specificity were 42% (95% CI, 28-58%) and 100% (95% CI, 88-100%), respectively. According to the HSROC curve, the area under the curve was 0.77 (95% CI, 0.73-0.80). Considerable heterogeneity was possible among the studies in terms of both sensitivity and specificity. A threshold effect was suggested and was considered to explain most of the heterogeneity. Four studies reported false positive results for the T2-FLAIR mismatch sign, including dysembryoplastic neuroepithelial tumor, pediatric-type gliomas, and non-neoplastic lesions. The 2 original articles with false positive results showed the highest sensitivities among the 10 studies included in the quantitative analysis, supporting the probability of the threshold effect. The pooled correlation coefficient was 0.87 (95% CI, 0.73-0.94)., Conclusions: The T2-FLAIR mismatch sign had a high specificity and interobserver agreement for the prediction of IDHmut-Noncodel. However, the sign demonstrated low sensitivity, and a few studies with false positive cases were also reported., Key Points: • The pooled sensitivity and specificity of the T2-FLAIR mismatch sign for prediction of IDH-mutant, 1p/19q-noncodeleted lower-grade gliomas were 42% and 100%, respectively. • Four studies reported false positive results. • The pooled correlation coefficient was 0.87, suggesting almost perfect interobserver agreement.

Purpose: To compare the ability of 2-hydroxyglutarate (2HG)-to-lipid and lactate (2HG/[lipid + lactate]) ratio with the ability of 2HG concentration alone to predict the isocitrate dehydrogenase (IDH) mutation status in patients with glioma., Materials and Methods: In this retrospective study, consecutive patients with histopathologically proven glioma were enrolled between July 2016 and February 2019. A total of 79 patients were enrolled (mean age, 44 years; 49 men). The 2HG concentration and other MR spectroscopic parameters were measured by single-voxel point-resolved spectroscopy before surgery. The diagnostic performance of the 2HG concentration and 2HG/(lipid + lactate) ratio were calculated. Internal validation was assessed by the bootstrap approach with 1000 bootstrap resamples. Differences in the predictive accuracy of 2HG/(lipid + lactate) ratio and 2HG concentration were determined by calculating the integrated discrimination improvement. The diagnostic accuracy (sensitivity, specificity, and area under the receiver operating characteristic curve [AUC]) of these measures was also compared separately in patients with glioblastomas and patients with lower-grade gliomas., Results: Of the 79 enrolled patients, 28 had IDH mutations and 51 had wild-type IDH . The sensitivity, specificity, and AUC of 2HG concentration for predicting IDH -mutant gliomas were 89% (25 of 28), 67% (34 of 51), and 0.80 (95% confidence interval [CI]: 0.70, 0.88; C statistic, 0.80), respectively. The sensitivity, specificity, and AUC of the 2HG/(lipid + lactate) ratio for predicting IDH -mutant gliomas were 79% (22 of 28), 92% (47 of 51), and 0.90 (95% CI: 0.81, 0.96; C statistics, 0.90), respectively. The optimal cutoff value for the 2HG/(lipid + lactate) ratio was 0.63. The 2HG/(lipid + lactate) ratio was significantly better for predicting IDH mutation status than the 2HG concentration alone ( P < .01). In glioblastoma, the 2HG/(lipid + lactate) ratio was also better for predicting IDH mutations than the 2HG concentration alone, with borderline significance ( P = .052). In lower-grade glioma, the 2HG/(lipid + lactate) ratio and the 2HG concentration showed comparable diagnostic performance ( P = .72)., Conclusion: The 2HG/(lipid + lactate) ratio is more accurate for predicting IDH mutation status in patients with glioma than the 2HG concentration alone. Keywords: Brain/Brain Stem, CNS, MR-Imaging, MR-Spectroscopy, Neoplasms-Primary, Neuro-Oncology© RSNA, 2020., (2020 by the Radiological Society of North America, Inc.)

Objective: To categorize the radiological patterns of recurrence after bevacizumab treatment and to derive the pooled proportions of patients with recurrent malignant glioma showing the different radiological patterns., Materials and Methods: A systematic literature search in the Ovid-MEDLINE and EMBASE databases was performed to identify studies reporting radiological recurrence patterns in patients with recurrent malignant glioma after bevacizumab treatment failure until April 10, 2019. The pooled proportions according to radiological recurrence patterns (geographically local versus non-local recurrence) and predominant tumor portions (enhancing tumor versus non-enhancing tumor) after bevacizumab treatment were calculated. Subgroup and meta-regression analyses were also performed., Results: The systematic review and meta-analysis included 17 articles. The pooled proportions were 38.3% (95% confidence interval [CI], 30.6-46.1%) for a geographical radiologic pattern of non-local recurrence and 34.2% (95% CI, 27.3-41.5%) for a non-enhancing tumor-predominant recurrence pattern. In the subgroup analysis, the pooled proportion of non-local recurrence in the patients treated with bevacizumab only was slightly higher than that in patients treated with the combination with cytotoxic chemotherapy (34.9% [95% CI, 22.8-49.4%] versus 22.5% [95% CI, 9.5-44.6%])., Conclusion: A substantial proportion of high-grade glioma patients show non-local or non-enhancing radiologic patterns of recurrence after bevacizumab treatment, which may provide insight into surrogate endpoints for treatment failure in clinical trials of recurrent high-grade glioma., Competing Interests: The authors have no potential conflicts of interest to disclose., (Copyright © 2020 The Korean Society of Radiology.)

Objectives: A combination of T2/FLAIR mismatch sign and advanced imaging parameters may improve the determination of molecular subtypes of diffuse lower-grade glioma. We assessed the diagnostic value of adding the apparent diffusion coefficient (ADC) and cerebral blood volume (CBV) to the T2/FLAIR mismatch sign for differentiation of the IDH mutation or 1p/19q codeletion., Materials and Methods: Preoperative conventional, diffusion-weighted, and dynamic susceptibility contrast imaging were performed on 110 patients with diffuse lower-grade gliomas. The study population was classified into three groups using molecular subtype, namely IDH mutation and 1p/19q codeletion (IDHmut-Codel), IDH wild type (IDHwt) and IDH mutation and no 1p/19q codeletion (IDHmut-Noncodel). T2/FLAIR mismatch sign and the histogram parameters of apparent diffusion coefficient (ADC) and normalised cerebral blood volume (nCBV) values were assessed. A multivariate logistic regression model was constructed to distinguish IDHmut-Noncodel from IDHmut-Codel and IDHwt and from IDHwt, and the performance was compared with that of single parameters using the area under the receiver operating characteristics curve (AUC)., Results: Positive visual T2/FLAIR mismatch sign and higher nCBV skewness were significant variables to distinguish IDHmut-Noncodel from the other two groups (AUC, 0.88; 95% CI, 0.81-0.96). A lower ADC10 was a significant variable for distinguishing IDHmut-Noncodel from the IDHwt group (AUC, 0.75; 95% CI, 0.62-0.89). Adding ADC or CBV histogram parameters to T2/FLAIR mismatch sign improved performance in distinguishing IDHmut-Noncodel from the other two groups (AUC 0.882 vs. AUC 0.810) or from IDHwt (AUC 0.923 vs. AUC 0.868)., Conclusions: The combination of the T2/FLAIR mismatch sign with ADC or CBV histogram parameters can improve the identification of IDHmut-Noncodel diffuse lower-grade gliomas, which can be easily applied in clinical practice., Key Points: • The combination of the T2/FLAIR mismatch sign with the ADC or CBV histogram parameters can improve the identification of IDHmut-Noncodel diffuse lower-grade gliomas. • The multivariable model showed a significantly better performance for distinguishing the IDHmut-Noncodel group from other diffuse lower-grade gliomas than the T2/FLAIR mismatch sign alone or any single parameter. • The IDHmut-Noncodel type was associated with intermediate treatment outcomes; therefore, the identification of IDHmut-Noncodel diffuse lower-grade gliomas could be helpful for determining the clinical approach.

Objectives: To investigate whether clinical condition, imaging session, and locations affect repeatability of amide proton transfer-weighted (APTw) magnetic resonance imaging (MRI) in the brain., Materials and Methods: Three APTw MRI data sets were acquired, involving two intrasession scans and one intersession scan for 19 healthy, 15 glioma, and 12 acute stroke adult participants (mean age 53.8, 54.6, and 68.5, respectively) on a 3T MR scanner. The mean APTw signals from five locations in healthy brain (supratentorial and infratentorial locations) and from entire tumor and stroke lesions (supratentorial location) were calculated. The within-subject coefficient of variation (wCV) and intraclass correlation coefficient (ICC) were calculated for each clinical conditions, image sessions, and anatomic locations. Differences in APTw signals between sessions were analyzed using repeated-measures analysis of variance., Results: The ICC and wCV were 0.96 (95% confidence interval [CI], 0.91-0.99) and 16.1 (12.6-21.3) in glioma, 0.93 (0.82-0.98) and 15.0 (11.4-20.6) in stroke, and 0.84 (0.72-0.91) and 34.0 (28.7-41.0) in healthy brain. There were no significant differences in APTw signal between three sessions, irrespective of disease condition and location. The ICC and wCV were 0.85 (0.68-0.94) and 27.4 (21.8-35.6) in supratentorial, and 0.44 (- 0.18 to 0.76) and 32.7 (25.9 to 42.9) in infratentorial locations. There were significant differences in APTw signal between supra- (mean, 0.49%; 95% CI, 0.38-0.61) and infratentorial locations (1.09%, 0.98-1.20; p < 0.001)., Conclusion: The repeatability of APTw signal was excellent in supratentorial locations, while it was poor in infratentorial locations due to severe B0 inhomogeneity and susceptibility which affects MTR asymmetry., Key Points: • In supratentorial locations, APTw MRI showed excellent intrasession and intersession repeatability in brains of healthy controls and patients with glioma, as well as in stroke-affected regions. • APTw MRI showed excellent repeatability in supratentorial locations, but poor repeatability in infratentorial locations. • Considering poor repeatability in the infratentorial locations, the use of APTw MRI in longitudinal assessment in infratentorial locations is not indicated.

Purpose: Grading of brain gliomas is of clinical importance, and noninvasive molecular imaging may help differentiate low- and high-grade gliomas. We aimed to evaluate the diagnostic performance of amide proton transfer-weighted (APTw) MRI for differentiating low- and high-grade gliomas on 3-T scanners., Methods: A systematic literature search of Ovid-MEDLINE and EMBASE was performed up to March 28, 2018. Original articles evaluating the diagnostic performance of APTw MRI for differentiating low- and high-grade gliomas were selected. The pooled sensitivity and specificity were calculated using a bivariate random-effects model. A coupled forest plot and a hierarchical summary receiver operating characteristic curve were obtained. Heterogeneity was investigated using Higgins inconsistency index (I 2 ) test. Meta-regression was performed., Results: Ten original articles with a total of 353 patients were included. High-grade gliomas showed significantly higher APT signal intensity than low-grade gliomas. The pooled sensitivity and specificity for the diagnostic performance of APTw MRI for differentiating low-grade and high-grade gliomas were 88% (95% CI, 77-94%) and 91% (95% CI, 82-96%), respectively. Higgins I 2 statistic demonstrated heterogeneity in the sensitivity (I 2  = 68.17%), whereas no heterogeneity was noted in the specificity (I 2  = 44.84%). In meta-regression, RF saturation power was associated with study heterogeneity. Correlation coefficients between APT signal intensity and Ki-67 cellular proliferation index ranged from 0.430 to 0.597, indicating moderate correlation. All studies showed excellent interobserver agreement., Conclusions: Although heterogeneous protocols were used, APTw MRI demonstrated excellent diagnostic performance for differentiating low- and high-grade gliomas. APTw MRI could be a reliable technique for glioma grading in clinical practice.

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.

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.

Objectives: Differentiation of glioma from brain metastasis is clinically crucial because it affects the clinical outcome of patients and alters patient management. Here, we present a systematic review and meta-analysis of the currently available data on perfusion magnetic resonance imaging (MRI) for differentiating glioma from brain metastasis, assessing MRI protocols and parameters., Methods: A computerised search of Ovid-MEDLINE and EMBASE databases was performed up to 3 October 2017, to find studies on the diagnostic performance of perfusion MRI for differentiating glioma from brain metastasis. Pooled summary estimates of sensitivity and specificity were obtained using hierarchical logistic regression modelling. We conducted meta-regression and subgroup analyses to explain the effects of the study heterogeneity., Results: Eighteen studies with 900 patients were included. The pooled sensitivity and specificity were 90% (95% CI, 84-94%) and 91% (95% CI, 84-95%), respectively. The area under the hierarchical summary receiver operating characteristic curve was 0.96 (95% CI, 0.94-0.98). The meta-regression showed that the percentage of glioma in the study population and the study design were significant factors affecting study heterogeneity. In a subgroup analysis including patients with glioblastoma only, the pooled sensitivity was 92% (95% CI, 84-97%) and the pooled specificity was 94% (95% CI, 85-98%)., Conclusions: Although various perfusion MRI techniques were used, the current evidence supports the use of perfusion MRI to differentiate glioma from brain metastasis. In particular, perfusion MRI showed excellent diagnostic performance for differentiating glioblastoma from brain metastasis., Key Points: • Perfusion MRI shows high diagnostic performance for differentiating glioma from brain metastasis. • The pooled sensitivity was 90% and pooled specificity was 91%. • Peritumoral rCBV derived from DSC is a relatively well-validated.

Objectives: To compare the diagnostic performance of amide proton transfer (APT) imaging and 11-C methionine positron emission tomography (MET-PET) for in vivo molecular imaging of protein metabolism in post-treatment gliomas., Materials and Methods: This study included 43 patients (12 low and 31 high grade) with post-treatment gliomas who underwent both APT and MET-PET imaging within 3 weeks. APT-weighted voxel values and semi-quantitative tumour-to-normal ratios (TNR) were obtained from tumour portions. The voxel-wise relationships between TNR and APT were assessed. The diagnostic performance for recurrence of high-grade gliomas was calculated, using the area under the receiver operating characteristic curve (AUC) with maximum (TNR max and APT max ) and 90% histogram values (TNR90 and APT90)., Results: A moderate positive correlation between TNR and APT was found in low-grade recurrences (r = 0.47, p < 0.001), but not in high-grade ones (r = -0.24, p < 0.001). For distinguishing recurrence in post-treatment high-grade gliomas, APT max (AUC, 0.88) and APT90 (AUC, 0.78-0.83) had a similar to better diagnostic performance than TNR max (AUC, 0.71, p = 0.08) or TNR90 (AUC, 0.53-0.59, p = 0.01-0.05)., Conclusions: In post-treatment high-grade gliomas, APT provides different regional information to MET-PET and provides higher diagnostic performance. This difference needs to be considered when using APT or MET-PET as a surrogate marker for tumour protein metabolism., Key Points: • APT and TNR values in low-grade recurrence showed a moderate voxel-wise correlation. • APT and TNR demonstrated regional differences in post-treatment high-grade gliomas. • APT90 showed better diagnostic performance than TNR90 in high-grade recurrence.

This study aims to evaluate the value of perfusion MRI as a predictive/prognostic biomarker and a pharmacodynamic biomarker in patients with recurrent glioma treated with a bevacizumab-based regimen. We identified thirteen literature reports that investigated dynamic susceptibility-contrast (DSC) MRI or dynamic contrast-enhanced (DCE) MRI for predicting the patient outcome and analyzing the anti-angiogenic effect of bevacizumab by performing a systematic search of MEDLINE and EMBASE. The relative cerebral volume (rCBV) of DSC-MRI is currently the most common perfusion MRI parameter used as a predictive/prognostic biomarker. Pooled hazard ratios between responders and non-responders, as determined by rCBV, were 0.46 (95 % CI 0.28-0.76) for progression-free survival from five articles with a total 226 patients and 0.47 (95 % CI 0.29-0.76) for overall survival from six articles with a total 247 patients, and thus indicating that rCBV is helpful for predicting disease progression and the eventual outcome after treatment. Regarding the pharmacodynamic value of perfusion MRI parameters derived from either DSC-MRI or DCE-MRI, most perfusion MRI parameters (rCBV, Ktrans, CBVmax, Kpsmax, fpv, Ve and Kep) demonstrated a consistent decrease on the follow-up MRI after treatment, indicating that perfusion MRI may be helpful for evaluating the anti-angiogenic effect of a bevacizumab-based treatment regimen. However, the lack of standardization of imaging acquisition and analysis techniques for various perfusion MRI parameters needs to be resolved in the future. Despite these unsolved issues, the current evidence favoring the use of perfusion MRI as a predictive/prognostic or pharmacodynamic biomarker should be considered in patients with glioma treated using a bevacizumab-based regimen.

Introduction: The need for information regarding functional alterations in patients with brain gliomas is increasing, but little is known about the functional consequences of focal brain tumors throughout the entire brain. Using resting-state functional MR imaging (rs-fMRI), this study assessed functional connectivity in patients with supratentorial brain gliomas with possible alterations in long-distance connectivity and network topology., Methods: Data from 36 patients with supratentorial brain gliomas and 12 healthy subjects were acquired using rs-fMRI. The functional connectivity matrix (FCM) was created using 32 pairs of cortical seeds on Talairach coordinates in each individual subject. Local and distant connectivity were calculated using z-scores in the individual patient's FCM, and the averaged FCM of patients was compared with that of healthy subjects. Weighted network analysis was performed by calculating local efficiency, global efficiency, clustering coefficient, and small-world topology, and compared between patients and healthy controls., Results: When comparing the averaged FCM of patients with that of healthy controls, the patients showed decreased long-distance, inter-hemispheric connectivity (0.32 ± 0.16 in patients vs. 0. 42 ± 0.15 in healthy controls, p = 0.04). In network analysis, patients showed increased local efficiency (p < 0.05), but global efficiency, clustering coefficient, and small-world topology were relatively preserved compared to healthy subjects., Conclusion: Patients with supratentorial brain gliomas showed decreased long-distance connectivity while increased local efficiency and preserved small-world topology. The results of this small case series may provide a better understanding of the alterations of functional connectivity in patients with brain gliomas across the whole brain scale.

Purpose: To correlate and compare diagnostic performance with amide proton transfer (APT) imaging as a tumor proliferation index with that with magnetic resonance (MR) spectroscopy in subgroups of patients with pre- and posttreatment glioma., Materials and Methods: This retrospective study was approved by the institutional review board. In 40 patients with pretreatment glioma and 25 patients with posttreatment glioma, correlation between APT asymmetry and the choline-to-creatine and choline-to-N-acetylaspartate ratios in corresponding voxels of interest was determined, and the 90% histogram cutoff of APT asymmetry values (APT90) for the entire solid portion of gliomas was calculated for diagnostic performance. Area under the receiver operating characteristic curve (AUC), leave-one-out cross validation, and intraclass correlation coefficients were analyzed., Results: The APT asymmetry values showed a moderate correlation (r = 0.49, P < .001) with the choline-to-creatine ratios and a mild correlation with the choline-to-N-acetyl-aspartate ratios (r = 0.32, P = .011) in the corresponding lesions. The APT90 showed comparable diagnostic accuracy for grading of gliomas (AUC, 0.81-0.84 vs 0.86; P = .582-.864) and superior accuracy for differentiation of tumor progression from treatment-related change (AUC, 0.89-0.90 vs 0.60; P = .031-.046) compared with those with MR spectroscopy. The cross-validated area under the curve and accuracy of the APT90 in posttreatment gliomas were 0.89-0.90 and 72%, respectively. The interreader agreement for APT90 was excellent in both pretreatment and posttreatment gliomas (intraclass correlation coefficient, 0.95 and 0.96, respectively)., Conclusion: APT imaging used as a tumor proliferation index showed moderate correlation with MR spectroscopic values and is a superior imaging method to MR spectroscopy, particularly for assessment of posttreatment gliomas., (© RSNA, 2015.)

Purpose: To determine whether semiquantitative histogram analysis of the normalized cerebral blood volume (CBV) for an entire contrast material-enhanced lesion could be used to predict the volume fraction of posttreatment high-grade glioma recurrence compared with posttreatment change., Materials and Methods: The institutional review board approved this retrospective study. Informed consent was obtained. Thirty-nine patients with pathologically proved predominant tumor recurrence (tumor recurrence group, tumor fraction > or =50% [n = 14]), mixed tumor and posttreatment change (mixed group, tumor fraction > or =20% and <50% [n = 10]), and predominant posttreatment change (treatment change group, tumor fraction <20% [n = 15]) were evaluated. Histogram parameters of normalized CBV-histogram width, peak height position (PHP), and maximum value (MV)-were measured in entire contrast-enhanced lesions and used as discriminative indexes. Ordered logistic regression was used to determine independent factors for predicting the diseases of posttreatment contrast-enhanced lesions. Leave-one-out cross-validation was used to determine diagnostic accuracy., Results: PHP was an independent predictive factor (P = .003) for differentiating contrast-enhanced lesions in patients with posttreatment gliomas. According to receiver operating characteristic curve analyses, PHP provided sensitivity of 90.2% and specificity of 91.1% for differentiating tumor recurrence from mixed and treatment change groups at an optimum threshold of 1.7 by using leave-one-out cross-validation. MV helped distinguish treatment change group from tumor recurrence and mixed groups at an optimum threshold of 2.6 (sensitivity, 96.5%; specificity, 93.1%)., Conclusion: PHP can be used to predict the volume fraction of posttreatment high-grade glioma recurrence., ((c) RSNA, 2010.)