T2-FLAIR mismatch in isocitrate dehydrogenase mutant astrocytomas: Variability and evolution

The T2-FLAIR mismatch sign in oncologic neuroradiology: History, current use, emerging data, and future directions

The T2-Fluid-Attenuated Inversion Recovery (T2-FLAIR) mismatch sign is a radiogenomic marker that is easily discernible on preoperative conventional MR imaging. Application of strict criteria (adult population, cerebral hemisphere location, and classic imaging morphology) permits the noninvasive preoperative diagnosis of isocitrate dehydrogenase (IDH)-mutant 1p/19q-non-codeleted diffuse astrocytoma with near-perfect specificity, albeit with variably low sensitivity. This leads to improved preoperative planning and patient counseling. More recent research has shown that the application of less strict criteria compromises the near-perfect specificity of the sign but remains adequate for ruling out IDH-wildtype (glioblastoma) phenotype, which bears a far grimmer prognosis compared to IDH-mutant diffuse astrocytic disease. In this review, we elaborate on the various definitions of the T2-FLAIR mismatch sign present in the literature, illustrate these with images obtained at a comprehensive cancer center, discuss the potential of the mismatch sign for application to certain pediatric-type brain tumors, namely dysembryoplastic neuroepithelial tumor and diffuse midline glioma, and elaborate upon the clinical, histologic, and molecular associations of the T2-FLAIR mismatch sign as recognized to date. Finally, the sign's correlates in diffusion- and perfusion-weighted imaging are presented, and opportunities to further maximize the diagnostic and prognostic applications of the sign in the context of the 2021 revision of the WHO Classification of Central Nervous System Tumors are discussed.

Brainstem Gliomas With Isocitrate Dehydrogenase Mutation: Natural History, Clinical-Radiological Features, Management Strategy, and Long-Term Outcome

BACKGROUND AND OBJECTIVES

This study aimed to investigate the clinical, radiological, pathological features, treatment options, and outcomes of isocitrate dehydrogenase (IDH)-mutant brainstem gliomas (BSG-IDHmut).

METHODS

A retrospective analysis of 22 patients diagnosed with BSG-IDHmut and treated at our institution from January 2011 to January 2017 was performed. Their clinical, radiological data, and long-term outcomes were collected and analyzed.

RESULTS

The median age of patients was 38.5 years, with a male predominance (63.6%). All patients had IDH1 and TP53 mutations, with noncanonical IDH mutations in 59.1% of cases, 06-methylguanine-DNA methyltransferase promoter methylation in 55.6%, and alpha-thalassemia mental retardation X-linked loss in 63.2%, respectively. Tumors were primarily located in the pontine-medullary oblongata (54.5%) and frequently involved the pontine brachium (50%). Most tumors exhibited ill-defined boundaries (68.2%), no T2-FLAIR mismatch (100%), and no contrast enhancement (86.3%). Two radiological growth patterns were also identified: focal and extensively infiltrative, which were associated with the treatment strategy when tumor recurred. Seven patients (31.8%) received surgery only and 15 (68.2%) surgery plus other therapy. The median overall survival was 124.8 months, with 1-year, 2-year, 5-year, and 10-year survival rates of 81.8%, 68.2%, 54.5%, and 13.6%, respectively. Six patients experienced tumor recurrence, and all retained their radiological growth patterns, with 2 transformed into central nervous system World Health Organization grade 4.

CONCLUSION

BSG-IDHmut represents a unique subgroup of brainstem gliomas with distinctive features and more favorable prognosis compared with other brainstem gliomas. Further research is required to better understand the molecular mechanisms and optimize treatment strategies for this rare and complex disease.

A Fully Automated Deep-Learning Model for Predicting the Molecular Subtypes of Posterior Fossa Ependymomas Using T2-Weighted Images

PURPOSE

We aimed to develop and validate a deep learning (DL) model to automatically segment posterior fossa ependymoma (PF-EPN) and predict its molecular subtypes [Group A (PFA) and Group B (PFB)] from preoperative MR images.

EXPERIMENTAL DESIGN

We retrospectively identified 227 PF-EPNs (development and internal test sets) with available preoperative T2-weighted (T2w) MR images and molecular status to develop and test a 3D nnU-Net (referred to as T2-nnU-Net) for tumor segmentation and molecular subtype prediction. The network was externally tested using an external independent set [n = 40; subset-1 (n = 31) and subset-2 (n =9)] and prospectively enrolled cases [prospective validation set (n = 27)]. The Dice similarity coefficient was used to evaluate the segmentation performance. Receiver operating characteristic analysis for molecular subtype prediction was performed.

RESULTS

For tumor segmentation, the T2-nnU-Net achieved a Dice score of 0.94 ± 0.02 in the internal test set. For molecular subtype prediction, the T2-nnU-Net achieved an AUC of 0.93 and accuracy of 0.89 in the internal test set, an AUC of 0.99 and accuracy of 0.93 in the external test set. In the prospective validation set, the model achieved an AUC of 0.93 and an accuracy of 0.89. The predictive performance of T2-nnU-Net was superior or comparable to that of demographic and multiple radiologic features (AUCs ranging from 0.87 to 0.95).

CONCLUSIONS

A fully automated DL model was developed and validated to accurately segment PF-EPNs and predict molecular subtypes using only T2w MR images, which could help in clinical decision-making.

Identifying IDH-mutant and 1p/19q noncodeleted astrocytomas from nonenhancing gliomas: Manual recognition followed by artificial intelligence recognition

Background

The T2-FLAIR mismatch sign (T2FM) has nearly 100% specificity for predicting IDH-mutant and 1p/19q noncodeleted astrocytomas (astrocytomas). However, only 18.2%-56.0% of astrocytomas demonstrate a positive T2FM. Methods must be considered for distinguishing astrocytomas from negative T2FM gliomas. In this study, positive T2FM gliomas were manually distinguished from nonenhancing gliomas, and then a support vector machine (SVM) classification model was used to distinguish astrocytomas from negative T2FM gliomas.

Methods

Nonenhancing gliomas (regardless of pathological type or grade) diagnosed between January 2022 and October 2022 (N = 300) and November 2022 and March 2023 (N = 196) will comprise the training and validation sets, respectively. Our method for distinguishing astrocytomas from nonenhancing gliomas was examined and validated using the training set and validation set.

Results

The specificity of T2FM for predicting astrocytomas was 100% in both the training and validation sets, while the sensitivity was 42.75% and 67.22%, respectively. Using a classification model of SVM based on radiomics features, among negative T2FM gliomas, the accuracy was above 85% when the prediction score was greater than 0.70 in identifying astrocytomas and above 95% when the prediction score was less than 0.30 in identifying nonastrocytomas.

Conclusions

Manual screening of positive T2FM gliomas, followed by the SVM classification model to differentiate astrocytomas from negative T2FM gliomas, may be a more effective method for identifying astrocytomas in nonenhancing gliomas.

Beyond T2-FLAIR mismatch sign in isocitrate dehydrogenase mutant 1p19q non-codeleted astrocytoma: Analysis of tumor core and evolution with multiparametric magnetic resonance imaging

Background

The T2-FLAIR mismatch sign is an imaging correlate for isocitrate dehydrogenase (IDH)-mutant 1p19q non-codeleted astrocytomas. However, it is only seen in a part of the cases at certain stages. Many of the tumors likely lose T2 homogeneity as they grow in size, and become heterogenous. The aim of this study was to investigate the timecourse of T2-FLAIR mismatch sign, and assess intratumoral heterogeneity using multiparametric magnetic resonance imaging techniques.

Methods

A total of 128 IDH-mutant gliomas were retrospectively analyzed. Observers blinded to molecular status used strict criteria to select T2-FLAIR mismatch astrocytomas. Pre-biopsy and follow-up standard structural sequences of T2, FLAIR and apparent diffusion coefficient, MR spectroscopy (both single- and multi-voxel techniques), and DSC perfusion were observed.

Results

Nine T2-FLAIR mismatch astrocytomas were identified. 7 had MR spectroscopy and perfusion data. The smallest astrocytomas began as rounded T2 homogeneous lesions without FLAIR suppression, and developed T2-FLAIR mismatch during follow-up with falls in NAA and raised Cho/Cr ratio. Larger tumors at baseline with T2-FLAIR mismatch signs developed intratumoral heterogeneity, and showed elevated Cho/Cr ratio and raised relative cerebral blood volume (rCBV). The highest levels of intratumoral Cho/Cr and rCBV changes were located within the tumor core, and this area signifies the progression of the tumors toward high grade.

Conclusions

T2-FLAIR mismatch sign is seen at a specific stage in the development of astrocytoma. By assessing the subsequent heterogeneity, MR spectroscopy and perfusion imaging are able to predict the progression of the tumor towards high grade, thereby can assist targeting for biopsy and selective debulking.

Neuro-oncology Treatment Strategies for Primary Glial Tumors

Primary brain tumors underwent reclassification in the 2021 World Health Organization update, relying on molecular findings (especially isocitrate dehydrogenase mutations and chromosomal changes in 1p, 19q, gain of chromosome 7 and loss of chromosome 10). Newer entities have also been described including histone 3 mutant midline gliomas. These updated pathologic classifications improve prognostication and reliable diagnosis, but may confuse interpretation of prior clinical trials and require reclassification of patients diagnosed in the past. For patients over seventy, multiple studies have now confirmed the utility of shorter courses of radiation, and the risk of post-operative delirium. Ongoing studies are comparing proton to photon radiation. Long term follow up of prior clinical trials have confirmed the roles and length of chemotherapy (mainly temozolomide) in different tumors, as well as the wearable novottf device. New oral isocitrate dehydrogenase inhibitors have also shown efficacy in clinical trials.

Development and validation of a machine learning algorithm for predicting diffuse midline glioma, H3 K27-altered, H3 K27 wild-type high-grade glioma, and primary CNS lymphoma of the brain midline in adults

OBJECTIVE

Preoperative diagnosis of diffuse midline glioma, H3 K27-altered (DMG-A) and midline high-grade glioma without H3 K27 alteration (DMG-W), as well as midline primary CNS lymphoma (PCNSL) in adults, is challenging but crucial. The aim of this study was to develop a model for predicting these three entities using machine learning (ML) algorithms.

METHODS

Thirty-three patients with DMG-A, 35 with DMG-W, and 35 with midline PCNSL were retrospectively enrolled in the study. Radiomics features were extracted from contrast-enhanced T1-weighted MR images. Two radiologists evaluated the conventional MRI features of the tumors, such as shape. Patient age, tumor volume, and conventional MRI features were considered clinical features. The data set was randomly stratified into 70% training and 30% testing cohorts. Predictive models based on the clinical features, radiomics features, and integration of clinical and radiomics features were established through ML. The performances of the models were evaluated by calculating the area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity. Subsequently, 10 patients with DMG-A, 10 with DMG-W, and 12 with PCNSL were enrolled from another institution to validate the established models.

RESULTS

The predictive models based on clinical features, radiomics features, and the integration of clinical and radiomics features through the support vector machine algorithm had the optimal accuracies in the training, testing, and validation cohorts, and the accuracies in the testing cohort were 0.871, 0.892, and 0.903, respectively. Age, 2 radiomics features, and 3 conventional MRI features were the 6 most significant features in the established integrated model.

CONCLUSIONS

The integrated prediction model established by ML provides high discriminatory accuracy for predicting DMG-A, DMG-W, and midline PCNSL in adults.

The sinuous, wave-like intratumoral-wall sign is a sensitive and specific radiological biomarker for oligodendrogliomas

OBJECTIVES

The purpose of this study was to investigate the clinical utility of the sinuous, wave-like intratumoral-wall (SWITW) sign on T2WI in diagnosing isocitrate dehydrogenase (IDH) mutant and 1p/19q codeleted (IDHmut-Codel) oligodendrogliomas, for which a relatively conservative resection strategy might be sufficient due to a better response to chemoradiotherapy and favorable prognosis.

METHODS

Imaging data from consecutive adult patients with diffuse lower-grade gliomas (LGGs, histological grades 2-3) in Beijing Tiantan Hospital (December 1, 2013, to October 31, 2021, BTH set, n = 711) and the Cancer Imaging Archive (TCIA) LGGs set (n = 117) were used to develop and validate our findings. Two independent observers assessed the SWITW sign and some well-reported discriminative radiological features to establish a practical diagnostic strategy.

RESULTS

The SWITW sign showed satisfying sensitivity (0.684 and 0.722 for BTH and TCIA sets) and specificity (0.938 and 0.914 for BTH and TCIA sets) in defining IDHmut-Codels, and the interobserver agreement was substantial (κ 0.718 and 0.756 for BTH and TCIA sets). Compared to calcification, the SWITW sign improved the sensitivity by 0.28 (0.404 to 0.684) in the BTH set, and 81.0% (277/342) of IDHmut-Codel cases demonstrated SWITW and/ or calcification positivity. Combining the SWITW sign, calcification, low ADC values, and other discriminative features, we established a concise and reliable diagnostic protocol for IDHmut-Codels.

CONCLUSIONS

The SWITW sign was a sensitive and specific imaging biomarker for IDHmut-Codels. The integrated protocol provided an explicable, efficient, and reproducible method for precise preoperative diagnosis, which was essential to guide individualized surgical plan-making.

KEY POINTS

• The SWITW sign was a sensitive and specific imaging biomarker for IDHmut-Codel oligodendrogliomas. • The SWITW sign was more sensitive than calcification and an integrated strategy could improve diagnostic sensitivity for IDHmut-Codel oligodendrogliomas. • Combining SWITW, calcification, low ADC values, and other discriminative features could make a precise preoperative diagnosis for IDHmut-Codel oligodendrogliomas.

T2-Fluid-Attenuated Inversion Recovery Mismatch Sign in Grade II and III Gliomas: Is There a Coexisting T2-Diffusion-Weighted Imaging Mismatch?

OBJECTIVE

To determine whether the T2 fluid-attenuated inversion recovery (T2-FLAIR) mismatch sign in diffuse gliomas is associated with an equivalent pattern of disparity in signal intensities when comparing T2- and diffusion-weighted imaging (DWI).

METHODS

The level of correspondence between T2-FLAIR and T2-DWI evaluations in 34 World Health Organization grade II/III gliomas and interreader agreement among 3 neuroradiologists were assessed by calculating intraclass correlation coefficient and κ statistics, respectively. Tumoral apparent diffusion coefficient values were compared using t test.

RESULTS

There was an almost perfect correspondence between the 2 mismatch signs (intraclass correlation coefficient = 0.824 [95% confidence interval, 0.68-0.91]) that were associated with higher mean tumoral apparent diffusion coefficient (P < 0.01). Interreader agreement was substantial for T2-FLAIR (Fleiss κ = 0.724) and moderate for T2-DWI comparisons (Fleiss κ = 0.589) (P < 0.001).

CONCLUSIONS

The T2-FLAIR mismatch sign is usually reflected by a distinct microstructural pattern on DWI. The management of this tumor subtype may benefit from specifically tailored imaging assessments.

Radiogenomic association between the T2-FLAIR mismatch sign and IDH mutation status in adult patients with lower-grade gliomas: an updated systematic review and meta-analysis

OBJECTIVES

To reveal a radiogenomic correlation between the presence of the T2-fluid-attenuated inversion recovery resection (T2-FLAIR) mismatch sign on MR images and isocitrate dehydrogenase (IDH) mutation status in adult patients with lower-grade gliomas (LGGs).

METHODS

A web-based systemic search for eligible literature up to April 13, 2021, was conducted on PubMed, Embase, and the Cochrane Library databases by two independent reviewers. This study was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. We included studies evaluating the accuracy of the T2-FLAIR mismatch sign in diagnosing the IDH mutation in adult patients with LGGs. The T2-FLAIR mismatch sign was defined as a T2-hyperintense lesion that is hypointense on FLAIR except for a hyperintense rim.

RESULTS

Fourteen studies (n = 1986) were finally identified. The mean age of patients in the included studies ranged from 38.5 to 56 years. The pooled area under the curve (AUC), sensitivity, and specificity were obtained for each molecular profile: IDHmut-Codel: 0.46 (95% confidence interval [CI]: 0.42-0.50), 1% (95%CI: 0-7%), and 69% (95%CI: 62-75%), respectively; IDHmut-Noncodel: 0.75 (95%CI: 0.71-0.79), 42% (95%CI: 34-50%), and 99% (95%CI: 96-100%), respectively; IDH-Mutation regardless of 1p/19q codeletion status: 0.77 (95%CI: 0.73-0.80), 29% (95%CI: 21-40%), and 99% (95%CI: 92-100%), respectively.

CONCLUSIONS

The T2-FLAIR mismatch sign was an insensitive but highly specific marker for IDHmut-Noncodel and IDH-Mutation LGGs, whereas it was not a useful marker for IDHmut-Codel LGGs. The findings might identify the T2-FLAIR mismatch sign as a non-invasive imaging biomarker for the selection of patients with IDH-mutant LGGs.

KEY POINTS

• The T2-FLAIR mismatch sign was not a sensitive sign for IDH mutation in LGGs. • The T2-FLAIR mismatch sign was related to IDHmut-Noncodel with a specificity of 99%. • The pooled specificity (69%) of the T2-FLAIR mismatch sign for IDHmut-Codel was low.

Radiomics and Qualitative Features From Multiparametric MRI Predict Molecular Subtypes in Patients With Lower-Grade Glioma

Background

Isocitrate dehydrogenase (IDH) mutation and 1p19q codeletion status have been identified as significant markers for therapy and prognosis in lower-grade glioma (LGG). The current study aimed to construct a combined machine learning-based model for predicting the molecular subtypes of LGG, including (1) IDH wild-type astrocytoma (IDHwt), (2) IDH mutant and 1p19q non-codeleted astrocytoma (IDHmut-noncodel), and (3) IDH-mutant and 1p19q codeleted oligodendroglioma (IDHmut-codel), based on multiparametric magnetic resonance imaging (MRI) radiomics, qualitative features, and clinical factors.

Methods

A total of 335 patients with LGG (WHO grade II/III) were retrospectively enrolled. The sum of 5,929 radiomics features were extracted from multiparametric MRI. Selected robust, non-redundant, and relevant features were used to construct a random forest model based on a training cohort (n = 269) and evaluated on a testing cohort (n = 66). Meanwhile, preoperative MRIs of all patients were scored in accordance with Visually Accessible Rembrandt Images (VASARI) annotations and T2-fluid attenuated inversion recovery (T2-FLAIR) mismatch sign. By combining radiomics features, qualitative features (VASARI annotations and T2-FLAIR mismatch signs), and clinical factors, a combined prediction model for the molecular subtypes of LGG was built.

Results

The 17-feature radiomics model achieved area under the curve (AUC) values of 0.6557, 0.6830, and 0.7579 for IDHwt, IDHmut-noncodel, and IDHmut-codel, respectively, in the testing cohort. Incorporating qualitative features and clinical factors into the radiomics model resulted in improved AUCs of 0.8623, 0.8056, and 0.8036 for IDHwt, IDHmut-noncodel, and IDHmut-codel, with balanced accuracies of 0.8924, 0.8066, and 0.8095, respectively.

Conclusion

The combined machine learning algorithm can provide a method to non-invasively predict the molecular subtypes of LGG preoperatively with excellent predictive performance.

Isocitrate Dehydrogenase Mutant Grade II and III Glial Neoplasms

Mutations in isocitrate dehydrogenase (IDH) 1 or IDH2 occur in most of the adult low-grade gliomas and, less commonly, in cholangiocarcinoma, chondrosarcoma, acute myeloid leukemia, and other human malignancies. Cancer-associated mutations alter the function of the enzyme, resulting in production of R(-)-2-hydroxyglutarate and broad epigenetic dysregulation. Small molecule IDH inhibitors have received regulatory approval for the treatment of IDH mutant (mIDH) leukemia and are under development for the treatment of mIDH solid tumors. This article provides a current view of mIDH adult astrocytic and oligodendroglial tumors, including their clinical presentation and treatment, and discusses novel approaches and challenges toward improving the treatment of these tumors.

Exploring MRI Characteristics of Brain Diffuse Midline Gliomas With the H3 K27M Mutation Using Radiomics

Objectives

To explore the magnetic resonance imaging (MRI) characteristics of brain diffuse midline gliomas with the H3 K27M mutation (DMG-M) using radiomics.

Materials and Methods

Thirty patients with diffuse midline gliomas, including 16 with the H3 K27M mutant and 14 with wild type tumors, were retrospectively included in this study. A total of 272 radiomic features were initially extracted from MR images of each tumor. Principal component analysis, univariate analysis, and three other feature selection methods, including variance thresholding, recursive feature elimination, and the elastic net, were used to analyze the radiomic features. Based on the results, related visually accessible features of the tumors were further evaluated.

Results

Patients with DMG-M were younger than those with diffuse midline gliomas with H3 K27M wild (DMG-W) (median, 25.5 and 48 years old, respectively; p=0.005). Principal component analysis showed that there were obvious overlaps in the first two principal components for both DMG-M and DMG-W tumors. The feature selection results showed that few features from T2-weighted images (T2WI) were useful for differentiating DMG-M and DMG-W tumors. Thereafter, four visually accessible features related to T2WI were further extracted and analyzed. Among these features, only cystic formation showed a significant difference between the two types of tumors (OR=7.800, 95% CI 1.476-41.214, p=0.024).

Conclusions

DMGs with and without the H3 K27M mutation shared similar MRI characteristics. T2W sequences may be valuable, and cystic formation a useful MRI biomarker, for diagnosing brain DMG-M.

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.