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

Risk Estimation in Non-Enhancing Glioma: Introducing a Clinical Score

The preoperative grading of non-enhancing glioma (NEG) remains challenging. Herein, we analyzed clinical and magnetic resonance imaging (MRI) features to predict malignancy in NEG according to the 2021 WHO classification and developed a clinical score, facilitating risk estimation. A discovery cohort (2012-2017, n = 72) was analyzed for MRI and clinical features (T2/FLAIR mismatch sign, subventricular zone (SVZ) involvement, tumor volume, growth rate, age, Pignatti score, and symptoms). Despite a "low-grade" appearance on MRI, 81% of patients were classified as WHO grade 3 or 4. Malignancy was then stratified by: (1) WHO grade (WHO grade 2 vs. WHO grade 3 + 4) and (2) molecular criteria (IDH^mut WHO grade 2 + 3 vs. IDH^wt glioblastoma + IDH^mut astrocytoma WHO grade 4). Age, Pignatti score, SVZ involvement, and T2/FLAIR mismatch sign predicted malignancy only when considering molecular criteria, including IDH mutation and CDKN2A/B deletion status. A multivariate regression confirmed age and T2/FLAIR mismatch sign as independent predictors (p = 0.0009; p = 0.011). A "risk estimation in non-enhancing glioma" (RENEG) score was derived and tested in a validation cohort (2018-2019, n = 40), yielding a higher predictive value than the Pignatti score or the T2/FLAIR mismatch sign (AUC of receiver operating characteristics = 0.89). The prevalence of malignant glioma was high in this series of NEGs, supporting an upfront diagnosis and treatment approach. A clinical score with robust test performance was developed that identifies patients at risk for malignancy.

Deep learning for prediction of isocitrate dehydrogenase mutation in gliomas: a critical approach, systematic review and meta-analysis of the diagnostic test performance using a Bayesian approach

Background

Conventionally, identifying isocitrate dehydrogenase (IDH) mutation in gliomas is based on histopathological analysis of tissue specimens acquired via stereotactic biopsy or definitive resection. Accurate pre-treatment prediction of IDH mutation status using magnetic resonance imaging (MRI) can guide clinical decision-making. We aim to evaluate the diagnostic performance of deep learning (DL) to determine IDH mutation status in gliomas.

Methods

A systematic search of Cochrane Library, Web of Science, Medline, and Scopus was conducted to identify relevant publications until August 1, 2021. Articles were included if all the following criteria were met: (I) patients with histopathologically confirmed World Health Organization (WHO) grade II, III, or IV gliomas; (II) histopathological examination with the IDH mutation; (III) DL was used to predict the IDH mutation status; (IV) sufficient data for reconstruction of confusion matrices in terms of the diagnostic performance of the DL algorithms; and (V) original research articles. Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) and Checklist for Artificial Intelligence in Medical Imaging (CLAIM) was used to assess the studies' quality. Bayes theorem was utilized to calculate the posttest probability.

Results

Four studies with a total of 1,295 patients were included. In the training set, the pooled sensitivity, specificity, and area under the summary receiver operating characteristic (SROC) curve were 93.9%, 90.9% and 0.958, respectively. In the validation set, the pooled sensitivity, specificity, and area under the SROC curve were 90.8%, 85.5% and 0.939, respectively. With a known pretest probability of 80.2%, the Bayes theorem yielded a posttest probability of 97.6% and 96.0% for a positive test and 27.0% and 30.6% for a negative test for training sets and validation sets, respectively.

Discussion

This is the first meta-analysis that summarizes the diagnostic performance of DL in predicting IDH mutation status in gliomas via the Bayes theorem. DL algorithms demonstrate excellent diagnostic performance in predicting IDH mutation in gliomas. Radiomic features associated with IDH mutation, and its underlying pathophysiology extracted from advanced MRI may improve prediction probability. However, more studies are required to optimize and increase its reliability. Limitations include obtaining some data via email and lack of training and test sets statistics.

Preoperative Diagnosis and Molecular Characterization of Gliomas With Liquid Biopsy and Radiogenomics

Gliomas are a heterogenous group of central nervous system tumors with different outcomes and different therapeutic needs. Glioblastoma, the most common subtype in adults, has a very poor prognosis and disabling consequences. The World Health Organization (WHO) classification specifies that the typing and grading of gliomas should include molecular markers. The molecular characterization of gliomas has implications for prognosis, treatment planning, and prediction of treatment response. At present, gliomas are diagnosed via tumor resection or biopsy, which are always invasive and frequently risky methods. In recent years, however, substantial advances have been made in developing different methods for the molecular characterization of tumors through the analysis of products shed in body fluids. Known as liquid biopsies, these analyses can potentially provide diagnostic and prognostic information, guidance on choice of treatment, and real-time information on tumor status. In addition, magnetic resonance imaging (MRI) is another good source of tumor data; radiomics and radiogenomics can link the imaging phenotypes to gene expression patterns and provide insights to tumor biology and underlying molecular signatures. Machine and deep learning and computational techniques can also use quantitative imaging features to non-invasively detect genetic mutations. The key molecular information obtained with liquid biopsies and radiogenomics can be useful not only in the diagnosis of gliomas but can also help predict response to specific treatments and provide guidelines for personalized medicine. In this article, we review the available data on the molecular characterization of gliomas using the non-invasive methods of liquid biopsy and MRI and suggest that these tools could be used in the future for the preoperative diagnosis of gliomas.

T2-fluid-attenuated inversion recovery mismatch sign in lower grade gliomas: correlation with pathological and molecular findings

After the new molecular-based classification was reported to be useful for predicting prognosis, the T2-fluid-attenuated inversion recovery (FLAIR) mismatch sign has gained interest as one of the promising methods for detecting lower grade gliomas (LGGs) with isocitrate dehydrogenase (IDH) mutations and chromosome 1p/19q non-codeletion (IDH mut-Noncodel) with high specificity. Although all institutions could use T2-FLAIR mismatch sign without any obstacles, this sign was not completely helpful because of its low sensitivity. In this study, we attempted to uncover the mechanism of T2-FLAIR mismatch sign for clarifying the cause of this sign's low sensitivity. Among 99 patients with LGGs, 22 were T2-FLAIR mismatch sign-positive (22%), and this sign as a marker of IDH mut-Noncodel showed a sensitivity of 55.6% and specificity of 96.8%. Via pathological analyses, we could provide evidence that not only microcystic changes but the enlarged intercellular space was associated with T2-FLAIR mismatch sign (p = 0.017). As per the molecular analyses, overexpression of mTOR-related genes (m-TOR, RICTOR) were detected as the molecular events correlated with T2-FLAIR mismatch sign (p = 0.020, 0.030. respectively). Taken together, we suggested that T2-FLAIR mismatch sign could pick up the IDH mut-Noncodel LGGs with enlarged intercellular space or that with overexpression of mTOR-related genes.

Predicting 1p/19q co-deletion status from magnetic resonance imaging using deep learning in adult-type diffuse lower-grade gliomas: a discovery and validation study

Determination of 1p/19q co-deletion status is important for the classification, prognostication, and personalized therapy in diffuse lower-grade gliomas (LGG). We developed and validated a deep learning imaging signature (DLIS) from preoperative magnetic resonance imaging (MRI) for predicting the 1p/19q status in patients with LGG. The DLIS was constructed on a training dataset (n = 330) and validated on both an internal validation dataset (n = 123) and a public TCIA dataset (n = 102). The receiver operating characteristic (ROC) analysis and precision recall curves (PRC) were used to measure the classification performance. The area under ROC curves (AUC) of the DLIS was 0.999 for training dataset, 0.986 for validation dataset, and 0.983 for testing dataset. The F1-score of the prediction model was 0.992 for training dataset, 0.940 for validation dataset, and 0.925 for testing dataset. Our data suggests that DLIS could be used to predict the 1p/19q status from preoperative imaging in patients with LGG. The imaging-based deep learning has the potential to be a noninvasive tool predictive of molecular markers in adult diffuse gliomas.

Predictive Accuracy of T2-FLAIR mismatch sign for the IDH-mutant, 1p/19q non-codeleted 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.

A Review of Radiomics and Deep Predictive Modeling in Glioma Characterization

Recent developments in glioma categorization based on biological genotypes and application of computational machine learning or deep learning based predictive models using multi-modal MRI biomarkers to assess these genotypes provides potential assurance for optimal and personalized treatment plans and efficacy. Artificial intelligence based quantified assessment of glioma using MRI derived hand-crafted or auto-extracted features have become crucial as genomic alterations can be associated with MRI based phenotypes. This survey integrates all the recent work carried out in state-of-the-art radiomics, and Artificial Intelligence based learning solutions related to molecular diagnosis, prognosis, and treatment monitoring with the aim to create a structured resource on radiogenomic analysis of glioma. Challenges such as inter-scanner variability, requirement of benchmark datasets, prospective validations for clinical applicability are discussed with further scope for designing optimal solutions for glioma stratification with immediate recommendations for further diagnostic decisions and personalized treatment plans for glioma patients.

The T2-FLAIR Mismatch Sign as an Imaging Indicator of IDH-Mutant, 1p/19q Non-Codeleted Lower Grade Gliomas: A Systematic Review and Diagnostic Accuracy Meta-Analysis

The study's objective was the evaluation of the diagnostic accuracy of the T2-FLAIR mismatch sign in terms of diagnosing IDH-mutant non-codeleted (IDHmut-Noncodel) lower grade gliomas (LGG) of the brain. We searched the MEDLINE, Scopus and Cochrane Central databases. The last database search was performed on 12 April 2021. Studies that met the following were included: MRI scan assessing the presence of T2-FLAIR mismatch sign, and available IDH mutation and 1p/19q codeletion status. The quality of studies was assessed using the QUADAS-2 tool. Twelve studies involving 14 cohorts were included in the quantitative analysis. The diagnostic odds ratio [DOR (95% confidence interval; CI)] was estimated at 34.42 (20.95, 56.56), P_z < 0.01. Pooled sensitivity and specificity (95% CI) were estimated at 40% (31-50%; P_z = 0.05) and 97% (93-99%; P_z < 0.01), respectively. The likelihood ratio (LR; 95% CI) for a positive test was 11.39 (6.10, 21.29; P_z < 0.01) and the LR (95% CI) for a negative test was 0.40 (0.24, 0.65; P_z < 0.01).The T2-FLAIR mismatch sign is a highly specific biomarker for the diagnosis of IDHmut-Noncodel LGGs. However, the test was found positive in some other tumors and had a high number of false negative results. The diagnostic accuracy of the mismatch sign might be improved when combined with further imaging parameters.

MRI biomarkers in neuro-oncology

The central role of MRI in neuro-oncology is undisputed. The technique is used, both in clinical practice and in clinical trials, to diagnose and monitor disease activity, support treatment decision-making, guide the use of focused treatments and determine response to treatment. Despite recent substantial advances in imaging technology and image analysis techniques, clinical MRI is still primarily used for the qualitative subjective interpretation of macrostructural features, as opposed to quantitative analyses that take into consideration multiple pathophysiological features. However, the field of quantitative imaging and imaging biomarker development is maturing. The European Imaging Biomarkers Alliance (EIBALL) and Quantitative Imaging Biomarkers Alliance (QIBA) are setting standards for biomarker development, validation and implementation, as well as promoting the use of quantitative imaging and imaging biomarkers by demonstrating their clinical value. In parallel, advanced imaging techniques are reaching the clinical arena, providing quantitative, commonly physiological imaging parameters that are driving the discovery, validation and implementation of quantitative imaging and imaging biomarkers in the clinical routine. Additionally, computational analysis techniques are increasingly being used in the research setting to convert medical images into objective high-dimensional data and define radiomic signatures of disease states. Here, I review the definition and current state of MRI biomarkers in neuro-oncology, and discuss the clinical potential of quantitative image analysis techniques.

Characterisation of isocitrate dehydrogenase gene mutant WHO grade 2 and 3 gliomas: MRI predictors of 1p/19q co-deletion and tumour grade

AIM

To identify imaging predictors of molecular subtype and tumour grade in patients with isocitrate dehydrogenase (IDH) gene mutant (IDH_mut) World Health Organization (WHO) grade 2 or 3 gliomas.

MATERIALS AND METHODS

Patients with histologically confirmed WHO grade 2 or 3 IDH_mut gliomas between 2016 and 2019 were included in the study. Magnetic resonance imaging (MRI) images were evaluated for the presence or absence of potential imaging predictors of tumour subtype, such as T2/fluid attenuated inversion recovery (FLAIR) signal match, and these factors were examined using regression analysis. On perfusion imaging, the maximum relative cerebral blood volume (rCBV_max) was evaluated as a potential predictor of tumour grade. The performance of two experienced neuroradiologists in correctly predicting tumour type on MRI was evaluated.

RESULTS

Eighty-five patients were included in the study. The presence of T2/FLAIR signal match >50% of tumour volume (p<0.01) and intratumoural susceptibility (p=0.02) were independent predictors of 1p/19q co-deletion. Mean rCBV max was significantly higher in WHO grade 3 astrocytomas (p=0.04) than WHO grade 2 astrocytomas. The consensus prediction of 1p/19q co-deletion status by two neuroradiologists of tumour was 95% sensitive and 86% specific.

CONCLUSION

The presence of matched T2/FLAIR signal could be used to identify tumour subtype when biopsy is inconclusive or genetic analysis is unavailable. rCBV_max predicted astrocytoma grade. Experienced neuroradiologists predict tumour subtype with good sensitivity and specificity.

Update on neuroimaging in brain tumours

PURPOSE OF REVIEW

To highlight some of the recent advances in magnetic resonance imaging (MRI), in terms of acquisition, analysis, and interpretation for primary diagnosis, treatment planning, and surveillance of patients with a brain tumour.

RECENT FINDINGS

The rapidly emerging field of radiomics associates large numbers of imaging features with clinical characteristics. In the context of glioma, attempts are made to correlate such imaging features with the tumour genotype, using so-called radiogenomics. The T2-fluid attenuated inversion recovery (FLAIR) mismatch sign is an easy to apply imaging feature for identifying isocitrate dehydrogenase-mutant 1p/19q intact glioma with very high specificity.For treatment planning, resting state functional MRI (fMRI) may become as powerful as task-based fMRI. Functional ultrasound has shown the potential to identify functionally active cortex during surgery.For tumour response assessment automated techniques have been developed. Multiple new guidelines have become available, including those for adult and paediatric glioma and for leptomeningeal metastases, as well as on brain metastasis and perfusion imaging.

SUMMARY

Neuroimaging plays a central role but still often falls short on essential questions. Advanced imaging acquisition and analysis techniques hold great promise for answering such questions, and are expected to change the role of neuroimaging for patient management substantially in the near future.

Imaging of brain tumors in children: the basics-a narrative review

Primary pediatric brain tumors comprise a broad group of neoplasm subtypes that can be categorized based on their histological and molecular features according to the 2016 World Health Organization (WHO) classification of central nervous system (CNS) tumors. The majority of the pediatric brain tumors demonstrate a singular preference for this age group and have a unique molecular profile. The separation of certain tumor entities, including different types of embryonal tumors, low-grade gliomas, and high-grade gliomas, may have a significant impact by guiding appropriate treatment for these children and potentially changing their outcomes. Currently, the focus of the imaging diagnostic studies is to follow the molecular updates, searching for potential imaging patterns that translate this information in molecular profile results, therefore helping the final diagnosis. Due to the high impact of accurate diagnosis in this context, the scientific community has presented extensive research on imaging pediatric tumors in recent years. This article summarizes the key characteristics of the imaging features of the most common primary childhood brain tumors, categorizing them according to the recent WHO classification update, which is based on each of their molecular profiles. The purpose of this review article is to familiarize radiologists with their key imaging features and thereby improve diagnostic accuracy.

The Histopathologic and Radiologic Features of T2-FLAIR Mismatch Sign in IDH-Mutant 1p/19q Non-codeleted Astrocytomas

OBJECTIVE

The T2-FLAIR mismatch sign is a useful imaging sign in clinical magnetic resonance imaging studies for detecting isocitrate dehydrogenase (IDH)-mutant 1p/19q non-codeleted astrocytomas. However, the association between the mismatch sign and pathologic findings is poorly understood. Therefore, the aim of this study was to elucidate the relationship of histopathologic and radiologic features with the mismatch sign in IDH-mutant 1p/19q non-codeleted astrocytomas.

METHODS

We divided 17 IDH-mutant 1p/19q non-codeleted patients into 2 groups according to mismatch sign presence (WITH, n = 9; WITHOUT, n = 8) and retrospectively analyzed their pathologic findings and apparent diffusion coefficient (ADC) values. We also compared these findings between the tumor Core (central area) and Rim (marginal area).

RESULTS

In the pathologic analysis, Core of the WITH group contained numerous microcysts whereas Rim had abundant neuroglial fibrils and cellularity. In contrast, Core of the WITHOUT group had highly concentrated neuroglial fibrils. In ADC analysis, Core of the WITH group had significantly higher ADC values compared with Rim (P < 0.001). However, there was no significant difference between Core and Rim in the WITHOUT group (P = 0.12). The WITH group had a significantly higher Core/Rim ratio of ADC values compared with the WITHOUT group (P < 0.001).

CONCLUSIONS

This study provides evidence that a region-dependent microstructural difference could reflect the mismatch sign in IDH-mutant 1p/19q non-codeleted astrocytomas. Core of the mismatch sign characteristically had microcystic changes accompanied by higher ADC values, whereas Rim had abundant neuroglial fibrils and cellularity accompanied by lower ADC values.

IDH-Mutant Low-grade Glioma: Advances in Molecular Diagnosis, Management, and Future Directions

PURPOSE OF REVIEW

IDH-mutant low-grade gliomas (LGG) have emerged as a distinct clinical and molecular entity with unique treatment considerations. Here, we review updates in IDH-mutant LGG diagnosis and classification, imaging biomarkers, therapies, and neurocognitive and patient-reported outcomes.

RECENT FINDINGS

CDKN2A/B homozygous deletion in IDH-mutant astrocytoma is associated with shorter survival, similar to WHO grade 4. The T2-FLAIR mismatch, a highly specific but insensitive sign, is diagnostic of IDH-mutant astrocytoma. Maximal safe resection is currently indicated in all LGG cases. Radiotherapy with subsequent PCV (procarbazine, lomustine, vincristine) provides longer overall survival compared to radiotherapy alone. Temozolomide in place of PCV is reasonable, but high-level evidence is still lacking. LGG adjuvant treatment has important quality of life and neurocognitive side effects that should be considered. Although incurable, IDH-mutant LGG have a favorable survival compared to IDH-WT glioma. Recent advances in molecular-based classification, imaging, and targeted therapies will hopefully improve survival and quality of life.

Comparative evaluation of intracranial oligodendroglioma and astrocytoma of similar grades using conventional and T1-weighted DCE-MRI

PURPOSE

This retrospective study was performed on a 3T MRI to determine the unique conventional MR imaging and T1-weighted DCE-MRI features of oligodendroglioma and astrocytoma and investigate the utility of machine learning algorithms in their differentiation.

METHODS

Histologically confirmed, 81 treatment-naïve patients were classified into two groups as per WHO 2016 classification: oligodendroglioma (n = 16; grade II, n = 25; grade III) and astrocytoma (n = 10; grade II, n = 30; grade III). The differences in tumor morphology characteristics were evaluated using Z-test. T1-weighted DCE-MRI data were analyzed using an in-house built MATLAB program. The mean 90th percentile of relative cerebral blood flow, relative cerebral blood volume corrected, volume transfer rate from plasma to extracellular extravascular space, and extravascular extracellular space volume values were evaluated using independent Student's t test. Support vector machine (SVM) classifier was constructed to differentiate two groups across grade II, grade III, and grade II+III based on statistically significant features.

RESULTS

Z-test signified only calcification among conventional MR features to categorize oligodendroglioma and astrocytoma across grade III and grade II+III tumors. No statistical significance was found in the perfusion parameters between two groups and its subtypes. SVM trained on calcification also provided moderate accuracy to differentiate oligodendroglioma from astrocytoma.

CONCLUSION

We conclude that conventional MR features except calcification and the quantitative T1-weighted DCE-MRI parameters fail to discriminate between oligodendroglioma and astrocytoma. The SVM could not further aid in their differentiation. The study also suggests that the presence of more than 50% T2-FLAIR mismatch may be considered as a more conclusive sign for differentiation of IDH mutant astrocytoma.

Impact of Inversion Time for FLAIR Acquisition on the T2-FLAIR Mismatch Detectability for IDH-Mutant, Non-CODEL Astrocytomas

The current research tested the hypothesis that inversion time (TI) shorter than 2,400 ms under 3T for FLAIR can improve the diagnostic accuracy of the T2-FLAIR mismatch sign for identifying IDHmt, non-CODEL astrocytomas. We prepared three different cohorts; 94 MRI from 76 IDHmt, non-CODEL Lower-grade gliomas (LrGGs), 33 MRI from 31 LrGG under the restriction of FLAIR being acquired with TI < 2,400 ms for 3T or 2,016 ms for 1.5T, and 112 MRI from 112 patients from the TCIA/TCGA dataset for LrGG. The presence or absence of the “T2-FLAIR mismatch sign” was evaluated, and we compared diagnostic accuracies according to TI used for FLAIR acquisition. The T2-FLAIR mismatch sign was more frequently positive when TI was shorter than 2,400 ms under 3T for FLAIR acquisition (p = 0.0009, Fisher’s exact test). The T2-FLAIR mismatch sign was positive only for IDHmt, non-CODEL astrocytomas even if we confined the cohort with FLAIR acquired with shorter TI (p = 0.0001, Fisher’s exact test). TCIA/TCGA dataset validated that the sensitivity, specificity, PPV, and NPV of the T2-FLAIR mismatch sign to identify IDHmt, non-CODEL astrocytomas improved from 31, 90, 79, and 51% to 67, 94, 92, and 74%, respectively and the area under the curve of ROC improved from 0.63 to 0.87 when FLAIR was acquired with shorter TI. We revealed that TI for FLAIR impacts the T2-FLAIR mismatch sign’s diagnostic accuracy and that FLAIR scanned with TI < 2,400 ms in 3T is necessary for LrGG imaging.

The T2-FLAIR mismatch sign as a predictor of IDH-mutant, 1p/19q-noncodeleted lower-grade gliomas: a systematic review and diagnostic meta-analysis

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.

The Role of the T2-FLAIR Mismatch Sign as an Imaging Marker of IDH Status in a Mixed Population of Low- and High-Grade Gliomas

Our study evaluated the role of the T2-fluid-attenuated inversion recovery (FLAIR) mismatch sign in detecting isocitrate dehydrogenase (IDH) mutations based on a mixed sample of 24 patients with low- and high- grade gliomas. The association between the two was realized using univariate and multivariate logistic regression analysis. There was a substantial agreement between the two raters for the detection of the T2-FLAIR mismatch sign (Cohen's kappa coefficient was 0.647). The T2-FLAIR mismatch sign when co-registered with the degree of tumor homogeneity were significant predictors of the IDH status (OR 29.642; 95% CI 1.73-509.15, p = 0.019). The probability of being IDH mutant in the presence of T2-FLAIR mismatch sign was as high as 92.9% (95% CI 63-99%). The sensitivity and specificity of T2-FLAIR mismatch sign in the detection of the IDH mutation was 88.9% and 86.7%, respectively. The T2-FLAIR mismatch sign may be an easy to use and helpful tool in recognizing IDH mutant patients, particularly if formal IDH testing is not available. We suggest that the adoption of a protocol based on imaging and histological data for optimal glioma characterization could be very helpful.

Thin-Slice Magnetic Resonance Imaging-Based Radiomics Signature Predicts Chromosomal 1p/19q Co-deletion Status in Grade II and III Gliomas

Objective: Chromosomal 1p/19q co-deletion is recognized as a diagnostic, prognostic, and predictive biomarker in lower grade glioma (LGG). This study aims to construct a radiomics signature to non-invasively predict the 1p/19q co-deletion status in LGG.

Methods: Ninety-six patients with pathology-confirmed LGG were retrospectively included and randomly assigned into training (n = 78) and validation (n = 18) dataset. Three-dimensional contrast-enhanced T1 (3D-CE-T1)-weighted magnetic resonance (MR) images and T2-weighted MR images were acquired, and simulated-conventional contrast-enhanced T1 (SC-CE-T1)-weighted images were generated. One hundred and seven shape, first-order, and texture radiomics features were extracted from each imaging modality and selected using the least absolute shrinkage and selection operator on the training dataset. A 3D-radiomics signature based on 3D-CE-T1 and T2-weighted features and a simulated-conventional (SC) radiomics signature based on SC-CE-T1 and T2-weighted features were established using random forest. The radiomics signatures were validated independently and evaluated using receiver operating characteristic (ROC) curves. Tumors with IDH mutations were also separately assessed.

Results: Four radiomics features were selected to construct the 3D-radiomics signature and displayed accuracies of 0.897 and 0.833, areas under the ROC curves (AUCs) of 0.940 and 0.889 in the training and validation datasets, respectively. The SC-radiomics signature was constructed with 4 features, but the AUC values were lower than that of the 3D signature. In the IDH-mutated subgroup, the 3D-radiomics signature presented AUCs of 0.950–1.000.

Conclusions: The MRI-based radiomics signature can differentiate 1p/19q co-deletion status in LGG with or without predetermined IDH status. 3D-CE-T1-weighted radiomics features are more favorable than SC-CE-T1-weighted features in the establishment of radiomics signatures.

Clinicopathological analysis of T2-FLAIR mismatch sign in lower-grade gliomas

T2-FLAIR mismatch sign is known as a highly specific imaging marker of IDH-mutant astrocytomas. This study was intended to clarify what the T2-FLAIR mismatch sign represents by pathological analysis of lower-grade gliomas rediagnosed in accordance with the WHO 2016 classification. We retrospectively analyzed the records of 64 patients diagnosed with WHO grade II and III diffuse gliomas between June 2009 and November 2018. T2-FLAIR mismatch sign was found in 10 (45%) out of 22 patients with IDH-mutant astrocytoma, 1 (5%) out of 20 with oligodendroglioma, and 1 (5%) out of 22 with IDH-wild-type astrocytoma. T2-FLAIR mismatch sign as a marker of IDH-mutant astrocytomas showed positive predictive value of 83%. Among 22 patients with IDH-mutant astrocytomas, microcystic change was found in eight, of which seven showed T2-FLAIR mismatch sign. Microcystic change was significantly associated with T2-FLAIR mismatch sign (P < 0.01). From multi-sampling in a patient, abundant microcysts were observed upon HE staining of specimens from the T2-FLAIR mismatched region, while microcysts were hardly observed from the T2-FLAIR matched one. All three protoplasmic astrocytomas among our IDH-mutant astrocytomas presented T2-FLAIR mismatch sign. In conclusion, T2-FLAIR mismatch sign may reflect microcyst formation in IDH-mutant astrocytomas and be common in IDH-mutant protoplasmic astrocytoma.