Radiological differences between subtypes of WHO 2016 grade II-III gliomas: a systematic review and meta-analysis

Accuracy of MRI in Detecting 1p/19q Co-deletion Status of Gliomas: A Single-Center Retrospective Study

Background Oligodendrogliomas, rare brain tumors in the frontal lobe's white matter, are reshaped by molecular markers like isocitrate dehydrogenase mutations and 1p/19q co-deletion, influencing treatment outcomes. Despite the initial indolence, these tumors pose a significant risk, with a median survival of 10-12 years. Non-invasive alternatives, such as magnetic resonance imaging (MRI) for assessing T2-fluid-attenuated inversion recovery (FLAIR) mismatch and calcifications, provide insights into molecular subtypes and aid prognosis. Our study explored these features to predict the oligodendroglioma status and refine patient management to improve outcomes. Methods In this retrospective study, patient data identified patients with suspected central nervous system tumors undergoing MRI, revealing low-grade gliomas. Surgical biopsy and 1p/19q fluorescence in situ hybridization confirmed the co-deletion status. MRI was used to assess various morphological features. Statistical analyses included x2 tests, Fisher's exact tests, Kruskal-Wallis tests, and binary logistic regression models, with significance set at p < 0.05. Results Seventy-three patients (median age, 37 years) were stratified according to 1p/19q co-deletion. Most (61.6%) were 18-40 years old and mostly male (67.1%). Co-deletion cases, primarily frontal lobe lesions (67.6%), were unilateral (88.2%), with 55.9% non-circumscribed margins and 58.8% ill-defined contours. Smooth contrast enhancement and no necrosis were observed in 48.1% of 1p/19q co-deletion cases. Logistic regression analysis showed a significant association between ill-defined/irregular contours and 1p/19q co-deletion. Fisher's exact test confirmed this but raised concerns about the small sample size influencing the conclusions. Conclusions This study established a significant link between glioma tumor contour characteristics, particularly irregular and ill-defined contours, and the likelihood of 1p/19q co-deletion. Our findings underscore the clinical relevance of using tumor contours in treatment decisions and prognosis assessments.

Advances in diffuse glial tumors diagnosis

In recent decades, there have been significant advances in the diagnosis of diffuse gliomas, driven by the integration of novel technologies. These advancements have deepened our understanding of tumor oncogenesis, enabling a more refined stratification of the biological behavior of these neoplasms. This progress culminated in the fifth edition of the WHO classification of central nervous system (CNS) tumors in 2021. This comprehensive review article aims to elucidate these advances within a multidisciplinary framework, contextualized within the backdrop of the new classification. This article will explore morphologic pathology and molecular/genetics techniques (immunohistochemistry, genetic sequencing, and methylation profiling), which are pivotal in diagnosis, besides the correlation of structural neuroimaging radiophenotypes to pathology and genetics. It briefly reviews the usefulness of tractography and functional neuroimaging in surgical planning. Additionally, the article addresses the value of other functional imaging techniques such as perfusion MRI, spectroscopy, and nuclear medicine in distinguishing tumor progression from treatment-related changes. Furthermore, it discusses the advantages of evolving diagnostic techniques in classifying these tumors, as well as their limitations in terms of availability and utilization. Moreover, the expanding domains of data processing, artificial intelligence, radiomics, and radiogenomics hold great promise and may soon exert a substantial influence on glioma diagnosis. These innovative technologies have the potential to revolutionize our approach to these tumors. Ultimately, this review underscores the fundamental importance of multidisciplinary collaboration in employing recent diagnostic advancements, thereby hoping to translate them into improved quality of life and extended survival for glioma patients.

Adult-type and Pediatric-type Diffuse Gliomas : What the Neuroradiologist Should Know

The classification of diffuse gliomas into the adult type and the pediatric type is the new basis for the diagnosis and clinical evaluation. The knowledge for the neuroradiologist should not remain limited to radiological aspects but should be based additionally on the current edition of the World Health Organization (WHO) classification of tumors of the central nervous system (CNS). This classification defines the 11 entities of diffuse gliomas, which are included in the 3 large groups of adult-type diffuse gliomas, pediatric-type diffuse low-grade gliomas, and pediatric-type diffuse high-grade gliomas. This article provides a detailed overview of important molecular, morphological, and clinical aspects for all 11 entities, such as typical genetic alterations, age distribution, variability of the tumor localization, variability of histopathological and radiological findings within each entity, as well as currently available statistical information on prognosis and outcome. Important differential diagnoses are also discussed.

Clinical characteristics and radiological features of glioblastoma, IDH-wildtype, grade 4 with histologically lower-grade gliomas

The 2021 World Health Organization (WHO) classification of central nervous system tumors applied molecular criteria and further integrated histological and molecular diagnosis of gliomas. This classification allows for the diagnosis of isocitrate dehydrogenase wild-type (IDHwt) glioblastoma (GBM), and WHO grade 4 with histologically lower-grade gliomas (LrGGs), even in the absence of high-grade histopathologic features, such as necrosis and/or microvascular proliferation. They contain at least one of the following molecular features: epidermal growth factor receptor amplification, chromosome 7 gain/10 loss, or telomerase reverse transcriptase promoter mutation. In the imaging features at the time of histological diagnosis, a gliomatosis cerebri growth pattern was frequently observed in these tumors. Furthermore, this growth pattern was significantly higher in IDHwt GBM, WHO grade 4, with histological grade II gliomas. Although the exact prognosis of IDHwt GBM, WHO grade 4, with histologically LGGs remains unknown, its OS was approximately 1-2 years similar to that of histologically IDHwt GBM, WHO grade 4, despite histopathological features similar to IDHmut LrGGs. These findings reinforce the need for the analysis of molecular features, regardless of presenting similar clinical characteristics and imaging features to IDHmut LrGGs.

Imaging features of localized IDH wild-type histologically diffuse astrocytomas: a single-institution case series

Isocitrate dehydrogenase wild-type (IDHwt) diffuse astrocytomas feature highly infiltrative patterns, such as a gliomatosis cerebri growth pattern with widespread involvement. Among these tumors, localized IDHwt histologically diffuse astrocytomas are rarer than the infiltrative type. The aim of this study was to assess and describe the clinical, radiographic, histopathological, and molecular characteristics of this rare type of IDHwt histologically diffuse astrocytomas and thereby provide more information on how its features affect clinical prognoses and outcomes. We retrospectively analyzed the records of five patients with localized IDHwt histologically diffuse astrocytomas between July 2017 and January 2020. All patients were female, and their mean age at the time of the initial treatment was 55.0 years. All patients had focal disease that did not include gliomatosis cerebri or multifocal disease. All patients received a histopathological diagnosis of diffuse astrocytomas at the time of the initial treatment. For recurrent tumors, second surgeries were performed at a mean of 12.4 months after the initial surgery. A histopathological diagnosis of glioblastoma was made in four patients and one of gliosarcoma in one patient. The initial status of IDH1, IDH2, H3F3A, HIST1H3B, and BRAF was "wild-type" in all patients. TERT promoter mutations (C250T or C228T) were detected in four patients. No tumors harbored a 1p/19q codeletion, EGFR amplification, or chromosome 7 gain/10 loss (+ 7/ - 10). We assessed clinical cases of localized IDHwt histologically diffuse astrocytomas that resulted in malignant recurrence and a poor clinical prognosis similar to that of glioblastomas. Our case series suggests that even in patients with histologically diffuse astrocytomas and those who present with radiographic imaging findings suggestive of a localized tumor mass, physicians should consider the possibility of IDHwt histologically diffuse astrocytomas.

Imaging-based stratification of adult gliomas prognosticates survival and correlates with the 2021 WHO classification

BACKGROUND

Because of the lack of global accessibility, delay, and cost-effectiveness of genetic testing, there is a clinical need for an imaging-based stratification of gliomas that can prognosticate survival and correlate with the 2021-WHO classification.

METHODS

In this retrospective study, adult primary glioma patients with pre-surgery/pre-treatment MRI brain images having T2, FLAIR, T1, T1 post-contrast, DWI sequences, and survival information were included in TCIA training-dataset (n = 275) and independent validation-dataset (n = 200). A flowchart for imaging-based stratification of adult gliomas(IBGS) was created in consensus by three authors to encompass all adult glioma types. Diagnostic features used were T2-FLAIR mismatch sign, central necrosis with peripheral enhancement, diffusion restriction, and continuous cortex sign. Roman numerals (I, II, and III) denote IBGS types. Two independent teams of three and two radiologists, blinded to genetic, histology, and survival information, manually read MRI into three types based on the flowchart. Overall survival-analysis was done using age-adjusted Cox-regression analysis, which provided both hazard-ratio (HR) and area-under-curve (AUC) for each stratification system(IBGS and 2021-WHO). The sensitivity and specificity of each IBSG type were analyzed with cross-table to identify the corresponding 2021-WHO genotype.

RESULTS

Imaging-based stratification was statistically significant in predicting survival in both datasets with good inter-observer agreement (age-adjusted Cox-regression, AUC > 0.5, k > 0.6, p < 0.001). IBGS type-I, type-II, and type-III gliomas had good specificity in identifying IDHmut 1p19q-codel oligodendroglioma (training - 97%, validation - 85%); IDHmut 1p19q non-codel astrocytoma (training - 80%, validation - 85.9%); and IDHwt glioblastoma (training - 76.5%, validation- 87.3%) respectively (p-value < 0.01).

CONCLUSIONS

Imaging-based stratification of adult diffuse gliomas predicted patient survival and correlated well with 2021-WHO glioma classification.

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.

Combining Radiology and Pathology for Automatic Glioma Classification

Subtype classification is critical in the treatment of gliomas because different subtypes lead to different treatment options and postoperative care. Although many radiological- or histological-based glioma classification algorithms have been developed, most of them focus on single-modality data. In this paper, we propose an innovative two-stage model to classify gliomas into three subtypes (i.e., glioblastoma, oligodendroglioma, and astrocytoma) based on radiology and histology data. In the first stage, our model classifies each image as having glioblastoma or not. Based on the obtained non-glioblastoma images, the second stage aims to accurately distinguish astrocytoma and oligodendroglioma. The radiological images and histological images pass through the two-stage design with 3D and 2D models, respectively. Then, an ensemble classification network is designed to automatically integrate the features of the two modalities. We have verified our method by participating in the MICCAI 2020 CPM-RadPath Challenge and won 1st place. Our proposed model achieves high performance on the validation set with a balanced accuracy of 0.889, Cohen’s Kappa of 0.903, and an F1-score of 0.943. Our model could advance multimodal-based glioma research and provide assistance to pathologists and neurologists in diagnosing glioma subtypes. The code has been publicly available online at https://github.com/Xiyue-Wang/1st-in-MICCAI2020-CPM.

Deep cross-view co-regularized representation learning for glioma subtype identification

The new subtypes of diffuse gliomas are recognized by the World Health Organization (WHO) on the basis of genotypes, e.g., isocitrate dehydrogenase and chromosome arms 1p/19q, in addition to the histologic phenotype. Glioma subtype identification can provide valid guidances for both risk-benefit assessment and clinical decision. The feature representations of gliomas in magnetic resonance imaging (MRI) have been prevalent for revealing underlying subtype status. However, since gliomas are highly heterogeneous tumors with quite variable imaging phenotypes, learning discriminative feature representations in MRI for gliomas remains challenging. In this paper, we propose a deep cross-view co-regularized representation learning framework for glioma subtype identification, in which view representation learning and multiple constraints are integrated into a unified paradigm. Specifically, we first learn latent view-specific representations based on cross-view images generated from MRI via a bi-directional mapping connecting original imaging space and latent space, and view-correlated regularizer and output-consistent regularizer in the latent space are employed to explore view correlation and derive view consistency, respectively. We further learn view-sharable representations which can explore complementary information of multiple views by projecting the view-specific representations into a holistically shared space and enhancing via adversary learning strategy. Finally, the view-specific and view-sharable representations are incorporated for identifying glioma subtype. Experimental results on multi-site datasets demonstrate the proposed method outperforms several state-of-the-art methods in detection of glioma subtype status.

Fully automated hybrid approach to predict the IDH mutation status of gliomas via deep learning and radiomics

BACKGROUND

Glioma prognosis depends on isocitrate dehydrogenase (IDH) mutation status. We aimed to predict the IDH status of gliomas from preoperative MR images using a fully automated hybrid approach with convolutional neural networks (CNNs) and radiomics.

METHODS

We reviewed 1166 preoperative MR images of gliomas (grades II-IV) from Severance Hospital (n = 856), Seoul National University Hospital (SNUH; n = 107), and The Cancer Imaging Archive (TCIA; n = 203). The Severance set was subdivided into the development (n = 727) and internal test (n = 129) sets. Based on T1 postcontrast, T2, and fluid-attenuated inversion recovery images, a fully automated model was developed that comprised a CNN for tumor segmentation (Model 1) and CNN-based classifier for IDH status prediction (Model 2) that uses a hybrid approach based on 2D tumor images and radiomic features from 3D tumor shape and loci guided by Model 1. The trained model was tested on internal (a subset of the Severance set) and external (SNUH and TCIA) test sets.

RESULTS

The CNN for tumor segmentation (Model 1) achieved a dice coefficient of 0.86-0.92 across datasets. Our hybrid model achieved accuracies of 93.8%, 87.9%, and 78.8%, with areas under the receiver operating characteristic curves of 0.96, 0.94, and 0.86 and areas under the precision-recall curves of 0.88, 0.82, and 0.81 in the internal test, SNUH, and TCIA sets, respectively.

CONCLUSIONS

Our fully automated hybrid model demonstrated the potential to be a highly reproducible and generalizable tool across different datasets for the noninvasive prediction of the IDH status of gliomas.

Histogram-based analysis of cerebral blood flow using arterial spin labeling MRI in de novo brain gliomas: relationship to histopathologic grade and molecular markers

PURPOSE

We developed multiple histogram-based CBF indices and evaluated their association with histopathologic grade in de novo brain tumor patients. Furthermore, the associations between these advanced CBF indices and molecular markers, including IDH1 mutation, ATRX loss, and 1p/19q co-deletion were also investigated.

METHODS

Thirteen de novo brain tumor patients (age 21-68 years, 9 M/4F) who were enrolled in our prospective study were scanned on 3 T MRI using a pCASL perfusion sequence following IRB-approved written informed consent. All patients have since undergone surgical intervention with tissue sampling for histopathologic tumor grading and molecular marker assessment. Tumor region of interest (ROI) were manually delineated on FLAIR images including the full extent of the tumor and peritumoral edema. Fourteen rCBF indices were derived from the histogram of the voxels with the ROI. Multi-linear regression was then used to compare rCBF indices with histopathologic tumor grade and molecular markers.

RESULTS

Averaged rCBF in top 10 and top 20 voxels (p < 0.004), but not the entire tumor ROI, was positively associated with WHO tumor grade. After accounting for tumor grade, the presence of 1p/19q co-deletion was associated with higher rCBF in top voxels, as well as with standard deviation of rCBF in the tumor ROI (p < 0.001). ATRX retention was related to higher rCBF, and this effect appears to be present in both higher-perfusion (p < 0.004) and low-perfusion (p < 0.05) voxels. IDH mutation was not significantly associated with any of the CBF indices investigated.

CONCLUSION

ASL MRI may provide useful supplemental noninvasive imaging assessment of brain tumor grade and molecular marker status.