Exploration of neuroanatomical characteristics to differentiate prodromal Alzheimer's disease from cognitively unimpaired amyloid-positive individuals.

Differentiating clinical stages based solely on positive findings from amyloid PET is challenging. We aimed to investigate the neuroanatomical characteristics at the whole-brain level that differentiate prodromal Alzheimer's disease (AD) from cognitively unimpaired amyloid-positive individuals (CU A+) in relation to amyloid deposition and regional atrophy. We included 45 CU A+ participants and 135 participants with amyloid-positive prodromal AD matched 1:3 by age, sex, and education. All participants underwent 18F-florbetaben positron emission tomography and 3D structural T1-weighted magnetic resonance imaging. We compared the standardized uptake value ratios (SUVRs) and volumes in 80 regions of interest (ROIs) between CU A+ and prodromal AD groups using independent t-tests, and employed the least absolute selection and shrinkage operator (LASSO) logistic regression model to identify ROIs associated with prodromal AD in relation to amyloid deposition, regional atrophy, and their interaction. After applying False Discovery Rate correction at < 0.1, there were no differences in global and regional SUVR between CU A+ and prodromal AD groups. Regional volume differences between the two groups were observed in the amygdala, hippocampus, entorhinal cortex, insula, parahippocampal gyrus, and inferior temporal and parietal cortices. LASSO logistic regression model showed significant associations between prodromal AD and atrophy in the entorhinal cortex, inferior parietal cortex, both amygdalae, and left hippocampus. The mean SUVR in the right superior parietal cortex (beta coefficient = 0.0172) and its interaction with the regional volume (0.0672) were also selected in the LASSO model. The mean SUVR in the right superior parietal cortex was associated with an increased likelihood of prodromal AD (Odds ratio [OR] 1.602, p = 0.014), particularly in participants with lower regional volume (OR 3.389, p < 0.001). Only regional volume differences, not amyloid deposition, were observed between CU A+ and prodromal AD. The reduced volume in the superior parietal cortex may play a significant role in the progression to prodromal AD through its interaction with amyloid deposition in that region.

Prediction of treatment response after stereotactic radiosurgery of brain metastasis using deep learning and radiomics on longitudinal MRI data.

We developed artificial intelligence models to predict the brain metastasis (BM) treatment response after stereotactic radiosurgery (SRS) using longitudinal magnetic resonance imaging (MRI) data and evaluated prediction accuracy changes according to the number of sequential MRI scans. We included four sequential MRI scans for 194 patients with BM and 369 target lesions for the Developmental dataset. The data were randomly split (8:2 ratio) for training and testing. For external validation, 172 MRI scans from 43 patients with BM and 62 target lesions were additionally enrolled. The maximum axial diameter (Dmax), radiomics, and deep learning (DL) models were generated for comparison. We evaluated the simple convolutional neural network (CNN) model and a gated recurrent unit (Conv-GRU)-based CNN model in the DL arm. The Conv-GRU model performed superior to the simple CNN models. For both datasets, the area under the curve (AUC) was significantly higher for the two-dimensional (2D) Conv-GRU model than for the 3D Conv-GRU, Dmax, and radiomics models. The accuracy of the 2D Conv-GRU model increased with the number of follow-up studies. In conclusion, using longitudinal MRI data, the 2D Conv-GRU model outperformed all other models in predicting the treatment response after SRS of BM.

Ventral Anterior Cingulate Atrophy as a Predisposing Factor for Transient Global Amnesia.

Background and Purpose: This study aimed to evaluate the brain magnetic resonance imaging (MRI) of patients with acute transient global amnesia (TGA) using volumetric analysis to verify whether the brains of TGA patients have pre-existing structural abnormalities. Methods: We evaluated the brain MRI data from 87 TGA patients and 20 age- and sex-matched control subjects. We included brain MRIs obtained from TGA patients within 72 hours of symptom onset to verify the pre-existence of structural change. For voxel-based morphometric analyses, statistical parametric mapping was employed to analyze the structural differences between patients with TGA and control subjects. Results: TGA patients exhibited significant volume reductions in the bilateral ventral anterior cingulate cortices (corrected p<0.05). Conclusions: TGA patients might have pre-existing structural changes in bilateral ventral anterior cingulate cortices prior to TGA attacks.

Effects of sleep quality on diurnal variation of brain volume in older adults: A retrospective cross-sectional study.

AIM: Brain volume is influenced by several factors that can change throughout the day. In addition, most of these factors are influenced by sleep quality. This study investigated diurnal variation in brain volume and its relation to overnight sleep quality. METHODS: We enrolled 1,003 healthy Koreans without any psychiatric disorders aged 60 years or older. We assessed sleep quality and average wake time using the Pittsburgh Sleep Quality Index, and divided sleep quality into good, moderate, and poor groups. We estimated the whole and regional brain volumes from three-dimensional T1-weighted brain MRI scans. We divided the interval between average wake-up time and MRI acquisition time (INT) into tertile groups: short (INT1), medium (INT2), and long (INT3). RESULTS: Whole and regional brain volumes showed no significance with respect to INT. However, the `interaction between INT and sleep quality showed significance for whole brain, cerebral gray matter, and cerebrospinal fluid volumes (p < .05). The INT2 group showed significantly lower volumes of whole brain, whole gray matter, cerebral gray matter, cortical gray matter, subcortical gray matter, and cerebrospinal fluid than the INT1 and INT3 groups only in the individuals with good sleep quality. CONCLUSION: Human brain volume changes significantly within a day associated with overnight sleep in the individuals with good sleep quality.

Exploring shared neural substrates underlying cognition and gait variability in adults without dementia.

BACKGROUND: High gait variability is associated with neurodegeneration and cognitive impairments and is predictive of cognitive impairment and dementia. The objective of this study was to identify cortical or subcortical structures of the brain shared by gait variability measured using a body-worn tri-axial accelerometer (TAA) and cognitive function. METHODS: This study is a part of a larger population-based cohort study on cognitive aging and dementia. The study included 207 participants without dementia, with a mean age of 72.6, and 45.4% of them are females. We conducted standardized diagnostic interview including a detailed medical history, physical and neurological examinations, and laboratory tests for cognitive impairment. We obtained gait variability during walking using a body-worn TAA along and measured cortical thickness and subcortical volume from brain magnetic resonance (MR) images. We cross-sectionally investigated the cortical and subcortical neural structures associated with gait variability and the shared neural substrates of gait variability and cognitive function. RESULTS: Higher gait variability was associated with the lower cognitive function and thinner cortical gray matter but not smaller subcortical structures. Among the clusters exhibiting correlations with gait variability, one that included the inferior temporal, entorhinal, parahippocampal, fusiform, and lingual regions in the left hemisphere was also associated with global cognitive and verbal memory function. Mediation analysis results revealed that the cluster's cortical thickness played a mediating role in the association between gait variability and cognitive function. CONCLUSION: Gait variability and cognitive function may share neural substrates, specifically in regions related to memory and visuospatial navigation.

Textural and Volumetric Changes of the Temporal Lobes in Semantic Variant Primary Progressive Aphasia and Alzheimer's Disease.

BACKGROUND: Texture analysis may capture subtle changes in the gray matter more sensitively than volumetric analysis. We aimed to investigate the patterns of neurodegeneration in semantic variant primary progressive aphasia (svPPA) and Alzheimer's disease (AD) by comparing the temporal gray matter texture and volume between cognitively normal controls and older adults with svPPA and AD. METHODS: We enrolled all participants from three university hospitals in Korea. We obtained T1-weighted magnetic resonance images and compared the gray matter texture and volume of regions of interest (ROIs) between the groups using analysis of variance with Bonferroni posthoc comparisons. We also developed models for classifying svPPA, AD and control groups using logistic regression analyses, and validated the models using receiver operator characteristics analysis. RESULTS: Compared to the AD group, the svPPA group showed lower volumes in five ROIs (bilateral temporal poles, and the left inferior, middle, and superior temporal cortices) and higher texture in these five ROIs and two additional ROIs (right inferior temporal and left entorhinal cortices). The performances of both texture- and volume-based models were good and comparable in classifying svPPA from normal cognition (mean area under the curve [AUC] = 0.914 for texture; mean AUC = 0.894 for volume). However, only the texture-based model achieved a good level of performance in classifying svPPA and AD (mean AUC = 0.775 for texture; mean AUC = 0.658 for volume). CONCLUSION: Texture may be a useful neuroimaging marker for early detection of svPPA in older adults and its differentiation from AD.

Association Between Changes in Bioelectrical Impedance Analysis (BIA) Parameter and the Clinical Outcomes in Patients With Acute Heart Failure.

BACKGROUND: Volume overload is associated not only with clinical manifestations but also with poor outcomes of heart failure (HF). However, there is an unmet need for effective methods for serial monitoring of volume status during HF hospitalization. The aim of this study was to evaluate the prognostic implication of serial measurement of bioelectrical impedance analysis (BIA) in patients hospitalized with acute HF. METHODS: This study is a retrospective observational study and screened 310 patients hospitalized due to acute decompensated HF between November 2021 and September 2022. Among them, 116 patients with acute HF who underwent BIA at the time of admission and at discharge were evaluated. We investigated the correlation between change of BIA parameters and the primary composite outcome (in-hospital mortality or rehospitalization for worsening HF within one month). RESULTS: The median (interquartile range) age was 77 years (67-82 years). The mean left ventricular ejection fraction was 40.7 ± 14.6% and 55.8% of HF patients have HF with reduced ejection fraction. The body water composition (intracellular water [ICW], extracellular water [ECW], and total body water [TBW]) showed a statistically significant correlation with body mass index and LV chamber sizes. Furthermore, the ratio of ECW to TBW (ECW/TBW), as an edema index showed a significant correlation with natriuretic peptide levels. Notably, the change of the edema index during hospitalization (ΔECW/TBW) showed a significant correlation with the primary outcome. The area under the curve of ΔECW/TBW for predicting primary outcome was 0.71 (95% confidence interval [CI], 0.61-0.79; P = 0.006). When patients were divided into two groups based on the median value of ΔECW/TBW, the group of high and positive ΔECW/TBW (+0.3% to +5.1%) had a significantly higher risk of the primary outcome (23.2% vs. 8.3%, adjusted odds ratio, 4.8; 95% CI, 1.2-19.3; P = 0.029) than those with a low and negative ΔECW/TBW (-5.3% to +0.2%). CONCLUSION: BIA is a noninvasive and effective method to evaluate the volume status during the hospitalization of HF patients. The high and positive value of ΔECW/TBW during hospitalization was associated with poor outcomes in patients with HF.

Diffuse Leptomeningeal Glioneuronal Tumor with FGFR1 Mutation in a 29-Year-Old Male.

This study reports on diffuse leptomeningeal glioneuronal tumor (DL-GNT) in a 29-year-old male. DL-GNT is a rare central nervous system (CNS) tumor mostly seen in children and only few cases have been reported in adult patients. Our patient presented with a chronic headache that lasted for five months. MR imaging showed mild hydrocephalus, multiple rim-enhancing nodular lesions in the suprasellar cistern, diffuse leptomeningeal enhancement in the lumbosacral area, and multiple small non-enhancing cyst-appearing lesions not suppressed on fluid attenuated inversion recovery (FLAIR) images in the bilateral basal ganglia, thalami, and cerebral hemispheres. Under the impression of germ cell tumor with leptomeningeal seeding, the patient underwent trans-sphenoidal tumor removal. DL-GNT was pathologically confirmed and FGFR1 mutation was detected through a next-generation sequencing test. In conclusion, a combination of leptomeningeal enhancement and multiple parenchymal non-enhancing cyst-appearing lesions not suppressed on FLAIR images may be helpful for differential diagnosis despite overlapping imaging features with many other CNS diseases that have leptomeningeal enhancement.

Glymphatic function assessment in Parkinson's disease using diffusion tensor image analysis along the perivascular space.

INTRODUCTION: Glymphatic dysfunction can contribute to α-synucleinopathies. We examined glymphatic function in idiopathic Parkinson's disease (PD) utilizing Diffusion Tensor Image Analysis aLong the Perivascular Space (DTI-ALPS). METHODS: This study enrolled consecutive patients diagnosed with de novo PD between June 2017 and March 2019 who underwent brain DTI with concurrent 123I-2ß-carbomethoxy-3ß-(4-iodophenyl)-N-(3-fluoropropyl)-nortropane (123I-FP-CIT) SPECT, and age- and sex-matched controls. From DTI-ALPS, the ALPS-index was calculated as a ratio of diffusivities along the x-axis in the region of neural fibers passing vertically to the diffusivities perpendicular to them, which reflected perivascular water motion at the lateral ventricular body level. The ALPS-index of the PD and control groups was compared using Student's t-test; its correlations with clinical scores for motor and cognition (UPDRS-III, MMSE, and MoCA) and striatal dopamine transporter uptake measured by 123I-FP-CIT specific binding ratios (SBRs) were examined using a correlation coefficient. RESULTS: In all, 54 patients in the de novo PD group (31 women, 23 men; mean age, 68.9 ± 9.4 years) and 54 in the control group (mean age, 69.0 ± 10.5 years) were included. The ALPS-index was lower in the PD group than in the controls (1.51 ± 0.22 versus 1.66 ± 0.20; P < 0.001). In the PD group, the ALPS-index negatively correlated with the UPDRS-III score (r = -0.526), and positively correlated with the MMSE (r = 0.377) and MoCA scores (r = 0.382) (all, P < 0.05). No correlation was observed between the ALPS-index and striatal 123I-FP-CIT SBRs (P > 0.05). CONCLUSIONS: DTI-ALPS can reveal glymphatic dysfunction in patients with PD, whose severity correlated with motor and cognitive dysfunction, but not striatal dopamine transporter uptake.

A Nationwide Web-Based Survey of Neuroradiologists' Perceptions of Artificial Intelligence Software for Neuro-Applications in Korea.

OBJECTIVE: We aimed to investigate current expectations and clinical adoption of artificial intelligence (AI) software among neuroradiologists in Korea. MATERIALS AND METHODS: In April 2022, a 30-item online survey was conducted by neuroradiologists from the Korean Society of Neuroradiology (KSNR) to assess current user experiences, perceptions, attitudes, and future expectations regarding AI for neuro-applications. Respondents with experience in AI software were further investigated in terms of the number and type of software used, period of use, clinical usefulness, and future scope. Results were compared between respondents with and without experience with AI software through multivariable logistic regression and mediation analyses. RESULTS: The survey was completed by 73 respondents, accounting for 21.9% (73/334) of the KSNR members; 72.6% (53/73) were familiar with AI and 58.9% (43/73) had used AI software, with approximately 86% (37/43) using 1-3 AI software programs and 51.2% (22/43) having up to one year of experience with AI software. Among AI software types, brain volumetry software was the most common (62.8% [27/43]). Although 52.1% (38/73) assumed that AI is currently useful in practice, 86.3% (63/73) expected it to be useful for clinical practice within 10 years. The main expected benefits were reducing the time spent on repetitive tasks (91.8% [67/73]) and improving reading accuracy and reducing errors (72.6% [53/73]). Those who experienced AI software were more familiar with AI (adjusted odds ratio, 7.1 [95% confidence interval, 1.81-27.81]; P = 0.005). More than half of the respondents with AI software experience (55.8% [24/43]) agreed that AI should be included in training curriculums, while almost all (95.3% [41/43]) believed that radiologists should coordinate to improve its performance. CONCLUSION: A majority of respondents experienced AI software and showed a proactive attitude toward adopting AI in clinical practice, suggesting that AI should be incorporated into training and active participation in AI development should be encouraged.

Deep learning regressor model based on nigrosome MRI in Parkinson syndrome effectively predicts striatal dopamine transporter-SPECT uptake.

PURPOSE: Nigrosome imaging using susceptibility-weighted imaging (SWI) and dopamine transporter imaging using 123I-2ß-carbomethoxy-3ß-(4-iodophenyl)-N-(3-fluoropropyl)-nortropane (123I-FP-CIT) single-photon emission computerized tomography (SPECT) can evaluate Parkinsonism. Nigral hyperintensity from nigrosome-1 and striatal dopamine transporter uptake are reduced in Parkinsonism; however, quantification is only possible with SPECT. Here, we aimed to develop a deep-learning-based regressor model that can predict striatal 123I-FP-CIT uptake on nigrosome magnetic resonance imaging (MRI) as a biomarker for Parkinsonism. METHODS: Between February 2017 and December 2018, participants who underwent 3 T brain MRI including SWI and 123I-FP-CIT SPECT based on suspected Parkinsonism were included. Two neuroradiologists evaluated the nigral hyperintensity and annotated the centroids of nigrosome-1 structures. We used a convolutional neural network-based regression model to predict striatal specific binding ratios (SBRs) measured via SPECT using the cropped nigrosome images. The correlation between measured and predicted SBRs was evaluated. RESULTS: We included 367 participants (203 women (55.3%); age, 69.0 ± 9.2 [range, 39-88] years). Random data from 293 participants (80%) were used for training. In the test set (74 participants [20%]), the measured and predicted 123I-FP-CIT SBRs were significantly lower with the loss of nigral hyperintensity (2.31 ± 0.85 vs. 2.44 ± 0.90) than with intact nigral hyperintensity (4.16 ± 1.24 vs. 4.21 ± 1.35, P < 0.01). The sorted measured 123I-FP-CIT SBRs and the corresponding predicted values were significantly and positively correlated (ρc = 0.7443; 95% confidence interval, 0.6216-0.8314; P < 0.01). CONCLUSION: A deep learning-based regressor model effectively predicted striatal 123I-FP-CIT SBRs based on nigrosome MRI with high correlation using manually-measured values, enabling nigrosome MRI as a biomarker for nigrostriatal dopaminergic degeneration in Parkinsonism.

Altered Brain Glymphatic Flow at Diffusion-Tensor MRI in Rapid Eye Movement Sleep Behavior Disorder.

Background Brain glymphatic dysfunction may contribute to the development of α-synucleinopathies. Yet, noninvasive imaging and quantification remain lacking. Purpose To examine glymphatic function of the brain in isolated rapid eye movement sleep behavior disorder (RBD) and its relevance to phenoconversion with use of diffusion-tensor imaging (DTI) analysis along the perivascular space (ALPS). Materials and Methods This prospective study included consecutive participants diagnosed with RBD, age- and sex-matched control participants, and participants with Parkinson disease (PD) who were enrolled and examined between May 2017 and April 2020. All study participants underwent 3.0-T brain MRI including DTI, susceptibility-weighted and susceptibility map-weighted imaging, and/or dopamine transporter imaging using iodine 123-2ß-carbomethoxy-3ß-(4-iodophenyl)-N-(3-fluoropropyl)-nortropane SPECT at the time of participation. Phenoconversion status to α-synucleinopathies was unknown at the time of MRI. Participants were regularly followed up and monitored for any signs of α-synucleinopathies. The ALPS index reflecting glymphatic activity was calculated by a ratio of the diffusivities along the x-axis in the projection and association neural fibers to the diffusivities perpendicular to them and compared according to the groups with use of the Kruskal-Wallis and Mann-Whitney U tests. The phenoconversion risk in participants with RBD was evaluated according to the ALPS index with use of a Cox proportional hazards model. Results Twenty participants diagnosed with RBD (12 men; median age, 73 years [IQR, 66-76 years]), 20 control participants, and 20 participants with PD were included. The median ALPS index was lower in the group with RBD versus controls (1.53 vs 1.72; P = .001) but showed no evidence of a difference compared with the group with PD (1.49; P = .68). The conversion risk decreased with an increasing ALPS index (hazard ratio, 0.57 per 0.1 increase in the ALPS index [95% CI: 0.35, 0.93]; P = .03). Conclusion DTI-ALPS in RBD demonstrated a more severe reduction of glymphatic activity in individuals with phenoconversion to α-synucleinopathies. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Filippi and Balestrino in this issue.

A Case-Control Clinical Trial on a Deep Learning-Based Classification System for Diagnosis of Amyloid-Positive Alzheimer's Disease.

OBJECTIVE: A deep learning-based classification system (DLCS) which uses structural brain magnetic resonance imaging (MRI) to diagnose Alzheimer's disease (AD) was developed in a previous recent study. Here, we evaluate its performance by conducting a single-center, case-control clinical trial. METHODS: We retrospectively collected T1-weighted brain MRI scans of subjects who had an accompanying measure of amyloid-beta (Aß) positivity based on a 18F-florbetaben positron emission tomography scan. The dataset included 188 Aß-positive patients with mild cognitive impairment or dementia due to AD, and 162 Aß-negative controls with normal cognition. We calculated the sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC) of the DLCS in the classification of Aß-positive AD patients from Aß-negative controls. RESULTS: The DLCS showed excellent performance, with sensitivity, specificity, positive predictive value, negative predictive value, and AUC of 85.6% (95% confidence interval [CI], 79.8-90.0), 90.1% (95% CI, 84.5-94.2), 91.0% (95% CI, 86.3-94.1), 84.4% (95% CI, 79.2-88.5), and 0.937 (95% CI, 0.911-0.963), respectively. CONCLUSION: The DLCS shows promise in clinical settings where it could be routinely applied to MRI scans regardless of original scan purpose to improve the early detection of AD.

[MRI Findings in Parkinson's Disease: Radiologic Assessment of Nigrostriatal Degeneration]. / 파킨슨병의 ìžê¸°ê³µëª ì˜ìƒ 소견: í‘ì§ˆì„ ì¡°ì²´ 변성의 ì˜ìƒí•™ì  평가.

Parkinson's disease (PD) is a movement disorder that develops due to degenerative loss of dopaminergic cells in the substantia nigra of the midbrain. Recent advances in MRI techniques have demonstrated various imaging findings that can reflect the underlying pathophysiological processes occurring in Parkinson's disease. Many imaging studies have shown that such findings can assist in the diagnosis of Parkinson's disease and its differentiation from atypical parkinsonism. In this review, we present MRI techniques that can be used in clinical assessment, such as nigrosome imaging and neuromelanin imaging, and we provide the detailed imaging features of Parkinson's disease reflecting nigrostriatal degeneration.

Serum neurofilament light chain levels are correlated with the infarct volume in patients with acute ischemic stroke.

Neurofilament light chains (NfLs) are promising biomarkers of neuroaxonal damage in stroke patients. We investigated the correlations between NfL levels and infarct volume, initial stroke severity, and functional outcomes at discharge in patients with acute ischemic stroke. We prospectively included 15 patients with first-ever acute ischemic stroke and 8 age- and sex-matched healthy controls without other neurological disorders. Serum NfL levels were measured using the single-molecule array (Simoa) technique twice within 24 hours of admission (NfL1D) and on the seventh hospital day (NfL7D) in patients with stroke and once in healthy controls. We assessed the infarct volume on diffusion-weighted magnetic resonance imaging using the free software ITK-SNAP. Serum NfL1D levels in stroke patients were significantly higher (28.4 pg/mL; interquartile range [IQR], 43.0) than in healthy controls (14.5 pg/mL; IQR, 3.2; P = .005). Temporal pattern analyses demonstrated that NfL7D levels were increased (114.0 pg/mL; IQR, 109.6) compared to NfL1D levels in all stroke patients (P = .001). There was a strong correlation between NfL7D levels and infarct volume (R = 0.67, P = .007). The difference between NfL1D and NfL7D (NfLdiff levels) was strongly correlated with the infarct volume (R = 0.63; P = .013). However, there was no statistically significant correlation between NfL levels and the initial stroke severity or functional outcomes at discharge. NfL levels in the subacute stage of stroke and the NfL difference between admission and 7th day of hospital were correlated with infarct volume in patients with acute ischemic stroke.

Magnetic Resonance Imaging in Diplopia: Neural Pathway, Imaging, and Clinical Correlation.

The role of magnetic resonance imaging (MRI) in diplopia is to diagnose various diseases that occur along the neural pathway governing eye movement. However, the lesions are frequently small and subtle and are therefore difficult to detect on MRI. This article presents representative cases of diseases that cause diplopia. The purpose of this article was to 1) describe the anatomy of the neural pathway governing eye movement, 2) recommend optimal MRI targets and protocols for the diagnosis of diseases causing diplopia, 3) correlate MRI findings with misalignment of the eyes (i.e., strabismus), and 4) help familiarize the reader with the imaging diagnosis of diplopia.

Predictive accuracy of T2-FLAIR mismatch sign for the IDH-mutant, 1p/19q noncodeleted low-grade glioma: An updated systematic review and meta-analysis.

BACKGROUND: The T2-fluid-attenuated inversion recovery (FLAIR) mismatch sign, has been considered a highly specific imaging biomarker of IDH-mutant, 1p/19q noncodeleted low-grade glioma. This systematic review and meta-analysis aimed to evaluate the diagnostic performance of T2-FLAIR mismatch sign for prediction of a patient with IDH-mutant, 1p/19q noncodeleted low-grade glioma, and identify the causes responsible for the heterogeneity across the included studies. METHODS: A systematic literature search in the Ovid-MEDLINE and EMBASE databases was performed for studies reporting the relevant topic before November 17, 2020. The pooled sensitivity and specificity values with their 95% confidence intervals were calculated using bivariate random-effects modeling. Meta-regression analyses were also performed to determine factors influencing heterogeneity. RESULTS: For all the 10 included cohorts from 8 studies, the pooled sensitivity was 40% (95% confidence interval [CI] 28-53%), and the pooled specificity was 100% (95% CI 95-100%). In the hierarchic summary receiver operating characteristic curve, the difference between the 95% confidence and prediction regions was relatively large, indicating heterogeneity among the studies. Higgins I2 statistics demonstrated considerable heterogeneity in sensitivity (I2 = 83.5%) and considerable heterogeneity in specificity (I2 = 95.83%). Among the potential covariates, it seemed that none of factors was significantly associated with study heterogeneity in the joint model. However, the specificity was increased in studies with all the factors based on the differences in the composition of the detailed tumors. CONCLUSIONS: The T2-FLAIR mismatch sign is near-perfect specific marker of IDH mutation and 1p/19q noncodeletion.