Isocitrate dehydrogenase mutation is associated with tumor location and magnetic resonance imaging characteristics in astrocytic neoplasms

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-IDH mut ).

METHODS

A retrospective analysis of 22 patients diagnosed with BSG-IDH mut 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-IDH mut 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.

Glioma lateralization: Focus on the anatomical localization and the distribution of molecular alterations (Review)

It is well known how the precise localization of glioblastoma multiforme (GBM) predicts the direction of tumor spread in the surrounding neuronal structures. The aim of the present review is to reveal the lateralization of GBM by evaluating the anatomical regions where it is frequently located as well as the main molecular alterations observed in different brain regions. According to the literature, the precise or most frequent lateralization of GBM has yet to be determined. However, it can be said that GBM is more frequently observed in the frontal lobe. Tractus and fascicles involved in GBM appear to be focused on the corticospinal tract, superior longitudinal I, II and III fascicles, arcuate fascicle long segment, frontal strait tract, and inferior fronto‑occipital fasciculus. Considering the anatomical features of GBM and its brain involvement, it is logical that the main brain regions involved are the frontal‑temporal‑parietal‑occipital lobes, respectively. Although tumor volumes are higher in the right hemisphere, it has been determined that the prognosis of patients diagnosed with cancer in the left hemisphere is worse, probably reflecting the anatomical distribution of some detrimental alterations such as TP53 mutations, PTEN loss, EGFR amplification, and MGMT promoter methylation. There are theories stating that the right hemisphere is less exposed to external influences in its development as it is responsible for the functions necessary for survival while tumors in the left hemisphere may be more aggressive. To shed light on specific anatomical and molecular features of GBM in different brain regions, the present review article is aimed at describing the main lateralization pathways as well as gene mutations or epigenetic modifications associated with the development of brain tumors.

Relationship between molecular characteristics of glioblastoma multiforme and the subventricular zone

OBJECTIVE

This study aims to assess the relationship between the molecular characteristics of glioblastoma multiforme (GBM) and the subventricular zone (SVZ).

MATERIAL AND METHODS

Eligible patients had their data anonymously collected from an institutional database, including age, sex, preoperative performance status, the extent of tumour resection, anatomical location, IDH mutation and MGMT methylation status. An Institutional picture archiving and communications system was used for volumetric and morphometric analysis. All measurements were made on T1-weighted magnetic resonance images with gadolinium contrast enhancement. IDH wild-type and mutant GBMs were stratified by MGMT methylation status. The relationship between tumour volume, distance from the tumour's enhancing edge and the tumour's geometric centre to the SVZ and their molecular characteristics were assessed.

RESULTS

Fifty IDH wild-type GBMs were studied. Twenty-three were MGMT methylated, Twenty-seven were unmethylated. IDH wild-type MGMT methylated GBMs were significantly associated with a tumour's enhancing boundary being contiguous to the SVZ (P < 0.001). Ninety percent of tumours contiguous to the SVZ were wild-type methylated (n = 18) and 10% were unmethylated (n = 2). Mean GBM geometric centre distance to SVZ was significantly less for methylated wild-type GBMs compared to unmethylated (P = 0.025) and median GBM distance from the tumour's edge of enhancement to the SVZ was significantly shorter in methylated tumours compared to unmethylated (P < 0.001). Mean and median distances to SVZ from the edge of enhancement was 3.8 millimetres (mm) and 0 mm, respectively, for wild-type methylated GBMs, while for unmethylated wild-types, 14.6 mm, and 12.5 mm. There was no anatomical localisation of IDH wild-type GBMs by MGMT methylation status to a cerebral hemisphere or lobe.

CONCLUSION

IDH wild-type GBMs contiguous to the SVZ are highly likely to be MGMT methylated. Replication by further studies is required to affirm our results and conclusion.

Towards consistency in pediatric brain tumor measurements: Challenges, solutions, and the role of artificial intelligence-based segmentation

MR imaging is central to the assessment of tumor burden and changes over time in neuro-oncology. Several response assessment guidelines have been set forth by the Response Assessment in Pediatric Neuro-Oncology (RAPNO) working groups in different tumor histologies; however, the visual delineation of tumor components using MRIs is not always straightforward, and complexities not currently addressed by these criteria can introduce inter- and intra-observer variability in manual assessments. Differentiation of non-enhancing tumors from peritumoral edema, mild enhancement from absence of enhancement, and various cystic components can be challenging; particularly given a lack of sufficient and uniform imaging protocols in clinical practice. Automated tumor segmentation with artificial intelligence (AI) may be able to provide more objective delineations, but rely on accurate and consistent training data created manually (ground truth). Herein, this paper reviews existing challenges and potential solutions to identifying and defining subregions of pediatric brain tumors (PBTs) that are not explicitly addressed by current guidelines. The goal is to assert the importance of defining and adopting criteria for addressing these challenges, as it will be critical to achieving standardized tumor measurements and reproducible response assessment in PBTs, ultimately leading to more precise outcome metrics and accurate comparisons among clinical studies.

The Relationship Between the Molecular Phenotypes of Brain Gliomas and the Imaging Features and Sensitivity of Radiotherapy and Chemotherapy

Gliomas are the most common primary malignant tumors of the brain, accounting for about 80% of all central nervous system malignancies. With the development of molecular biology, the molecular phenotypes of gliomas have been shown to be closely related to the process of diagnosis and treatment. The molecular phenotype of glioma also plays an important role in guiding treatment plans and evaluating treatment effects and prognosis. However, due to the heterogeneity of the tumors and the trauma associated with the surgical removal of tumor tissue, the application of molecular phenotyping in glioma is limited. With the development of imaging technology, functional magnetic resonance imaging (MRI) can provide structural and function information about tumors in a noninvasive and radiation-free manner. MRI is very important for the diagnosis of intracranial lesions. In recent years, with the development of the technology for tumor molecular diagnosis and imaging, the use of molecular phenotype information and imaging procedures to evaluate the treatment outcome of tumors has become a hot topic. By reviewing the related literature on glioma treatment and molecular typing that has been published in the past 20 years, and referring to the latest 2020 NCCN treatment guidelines, summarizing the imaging characteristic and sensitivity of radiotherapy and chemotherapy of different molecular phenotypes of glioma. In this article, we briefly review the imaging characteristics of different molecular phenotypes in gliomas and their relationship with radiosensitivity and chemosensitivity of gliomas.

Targeting Isocitrate Dehydrogenase (IDH) in Solid Tumors: Current Evidence and Future Perspectives

The isocitrate dehydrogenase 1 and 2 (IDH1 and IDH2) enzymes are involved in key metabolic processes in human cells, regulating differentiation, proliferation, and oxidative damage response. IDH mutations have been associated with tumor development and progression in various solid tumors such as glioma, cholangiocarcinoma, chondrosarcoma, and other tumor types and have become crucial markers in molecular classification and prognostic assessment. The intratumoral and serum levels of D-2-hydroxyglutarate (D-2-HG) could serve as diagnostic biomarkers for identifying IDH mutant (IDHmut) tumors. As a result, an increasing number of clinical trials are evaluating targeted treatments for IDH1/IDH2 mutations. Recent studies have shown that the focus of these new therapeutic strategies is not only the neomorphic activity of the IDHmut enzymes but also the epigenetic shift induced by IDH mutations and the potential role of combination treatments. Here, we provide an overview of the current knowledge about IDH mutations in solid tumors, with a particular focus on available IDH-targeted treatments and emerging results from clinical trials aiming to explore IDHmut tumor-specific features and to identify the clinical benefit of IDH-targeted therapies and their combination strategies. An insight into future perspectives and the emerging roles of circulating biomarkers and radiomic features is also included.

Clinical outcome, radiological findings, and genetic features of IDH-mutant brainstem glioma in adults

BACKGROUND

With the recent advent of genetic testing, IDH-mutant glioma has been found among adult brainstem gliomas. However, the clinical outcome and prognosis of IDH-mutant brainstem gliomas in adults have not been elucidated. This study aimed to investigate the clinical outcome, radiological findings, and genetic features of adult patients with IDH-mutant diffuse brainstem gliomas.

METHODS

Data from adult patients with brainstem glioma at Hokkaido University Hospital between 2006 and 2022 were retrospectively analyzed. Patient characteristics, treatment methods, genetic features, and prognosis were evaluated.

RESULTS

Of 12 patients with brainstem glioma with proven histopathology, 4 were identified with IDH mutation. All patients underwent local radiotherapy with 54 Gray in 27 fractions combined with chemotherapy with temozolomide. Three patients had IDH1 R132H mutation and one had IDH2 R172G mutation. The median progression-free survival and overall survival were 68.4 months and 85.2 months, respectively, longer than that for IDH-wildtype gliomas (5.6 months and 12.0 months, respectively). At the time of initial onset, contrast-enhanced lesions were observed in two of the four cases in magnetic resonance imaging.

CONCLUSION

As some adult brainstem gliomas have IDH mutations, and a clearly different prognosis from those with IDH-wildtype, biopsies are proactively considered to confirm the genotype.

Genetic Prognostic Factors in Adult Diffuse Gliomas: A 10-Year Experience at a Single Institution

Gliomas are primary brain lesions involving cerebral structures without well-defined boundaries and constitute the most prevalent central nervous system (CNS) neoplasms. Among gliomas, glioblastoma (GB) is a glioma of the highest grade and is associated with a grim prognosis. We examined how clinical variables and molecular profiles may have affected overall survival (OS) over the past ten years. A retrospective study was conducted at Sina Hospital in Tehran, Iran and examined patients with confirmed glioma diagnoses between 2012 and 2020. We evaluated the correlation between OS in GB patients and sociodemographic as well as clinical factors and molecular profiling based on IDH1, O-6-Methylguanine-DNA Methyltransferase (MGMT), TERTp, and epidermal growth factor receptor (EGFR) amplification (EGFR-amp) status. Kaplan-Meier and multivariate Cox regression models were used to assess patient survival. A total of 178 patients were enrolled in the study. The median OS was 20 months, with a 2-year survival rate of 61.0%. Among the 127 patients with available IDH measurements, 100 (78.7%) exhibited mutated IDH1 (IDH1-mut) tumors. Of the 127 patients with assessed MGMT promoter methylation (MGMTp-met), 89 (70.1%) had MGMT methylated tumors. Mutant TERTp (TERTp-mut) was detected in 20 out of 127 cases (15.7%), while wildtype TERTp (wildtype TERTp-wt) was observed in 107 cases (84.3%). Analyses using multivariable models revealed that age at histological grade (p < 0.0001), adjuvant radiotherapy (p < 0.018), IDH1 status (p < 0.043), and TERT-p status (p < 0.014) were independently associated with OS. Our study demonstrates that patients with higher tumor histological grades who had received adjuvant radiotherapy exhibited IDH1-mut or presented with TERTp-wt experienced improved OS. Besides, an interesting finding showed an association between methylation of MGMTp and TERTp status with tumor location.

Noninvasive Isocitrate Dehydrogenase 1 Status Prediction in Grade II/III Glioma Based on Magnetic Resonance Images: A Transfer Learning Strategy

OBJECTIVE

The aim of this study was to evaluate transfer learning combined with various convolutional neural networks (TL-CNNs) in predicting isocitrate dehydrogenase 1 ( IDH1 ) status of grade II/III gliomas.

METHODS

Grade II/III glioma patients diagnosed at the Tangdu Hospital (August 2009 to May 2017) were retrospectively enrolled, including 54 patients with IDH1 mutant and 56 patients with wild-type IDH1 . Convolutional neural networks, AlexNet, GoogLeNet, ResNet, and VGGNet were fine-tuned with T2-weighted imaging (T2WI), fluid attenuation inversion recovery (FLAIR), and contrast-enhanced T1-weighted imaging (T1CE) images. The single-modal networks were integrated with averaged sigmoid probabilities, logistic regression, and support vector machine. FLAIR-T1CE-fusion (FC-fusion), T2WI-T1CE-fusion (TC-fusion), and FLAIR-T2WI-T1CE-fusion (FTC-fusion) were used for fine-tuning TL-CNNs.

RESULTS

IDH1 -mutant prediction accuracies using AlexNet, GoogLeNet, ResNet, and VGGNet achieved 70.0% (AUC = 0.660), 65.0% (AUC = 0.600), 70.0% (AUC = 0.700), and 80.0% (AUC = 0.730) for T2WI images, 70.0% (AUC = 0.660), 70.0% (AUC = 0.620), 70.0% (AUC = 0.710), and 80.0% (AUC = 0.720) for FLAIR images, and 73.7% (AUC = 0.744), 73.7% (AUC = 0.656), 73.7% (AUC = 0.633), and 73.7% (AUC = 0.700) for T1CE images, respectively. The highest AUC (0.800) was achieved using VGGNet and FC-fusion images.

CONCLUSIONS

TL-CNNs (especially VGGNet) had a potential predictive value for IDH1 -mutant status of grade II/III gliomas.

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.

Correlating MRI features with additional genetic markers and patient survival in histological grade 2-3 IDH-mutant astrocytomas

PURPOSE

The increasing importance of molecular markers for classification and prognostication of diffuse gliomas has prompted the use of imaging features to predict genotype ("radiogenomics"). CDKN2A/B homozygous deletion has only recently been added to the diagnostic paradigm for IDH[isocitrate dehydrogenase]-mutant astrocytomas; thus, associated radiogenomic literature is sparse. There is also little data on whether different IDH mutations are associated with different imaging appearances. Furthermore, given that molecular status is now generally obtained routinely, the additional prognostic value of radiogenomic features is less clear. This study correlated MRI features with CDKN2A/B status, IDH mutation type and survival in histological grade 2-3 IDH-mutant brain astrocytomas.

METHODS

Fifty-eight grade 2-3 IDH-mutant astrocytomas were identified, 50 with CDKN2A/B results. IDH mutations were stratified into IDH1-R132H and non-canonical mutations. Background and survival data were obtained. Two neuroradiologists independently assessed the following MRI features: T2-FLAIR mismatch (<25%, 25-50%, >50%), well-defined tumour margins, contrast-enhancement (absent, wispy, solid) and central necrosis.

RESULTS

8/50 tumours with CDKN2A/B results demonstrated homozygous deletion; slightly shorter survival was not significant (p=0.571). IDH1-R132H mutations were present in 50/58 (86%). No MRI features correlated with CDKN2A/B status or IDH mutation type. T2-FLAIR mismatch did not predict survival (p=0.977), but well-defined margins predicted longer survival (HR 0.36, p=0.008), while solid enhancement predicted shorter survival (HR 3.86, p=0.004). Both correlations remained significant on multivariate analysis.

CONCLUSION

MRI features did not predict CDKN2A/B homozygous deletion, but provided additional positive and negative prognostic information which correlated more strongly with prognosis than CDKN2A/B status in our cohort.

MRI-Based Radiomics Combined with Deep Learning for Distinguishing IDH-Mutant WHO Grade 4 Astrocytomas from IDH-Wild-Type Glioblastomas

This study aimed to investigate the potential of quantitative radiomic data extracted from conventional MR images in discriminating IDH-mutant grade 4 astrocytomas from IDH-wild-type glioblastomas (GBMs). A cohort of 57 treatment-naïve patients with IDH-mutant grade 4 astrocytomas (n = 23) and IDH-wild-type GBMs (n = 34) underwent anatomical imaging on a 3T MR system with standard parameters. Post-contrast T1-weighted and T2-FLAIR images were co-registered. A semi-automatic segmentation approach was used to generate regions of interest (ROIs) from different tissue components of neoplasms. A total of 1050 radiomic features were extracted from each image. The data were split randomly into training and testing sets. A deep learning-based data augmentation method (CTGAN) was implemented to synthesize 200 datasets from the training sets. A total of 18 classifiers were used to distinguish two genotypes of grade 4 astrocytomas. From generated data using 80% training set, the best discriminatory power was obtained from core tumor regions overlaid on post-contrast T1 using the K-best feature selection algorithm and a Gaussian naïve Bayes classifier (AUC = 0.93, accuracy = 0.92, sensitivity = 1, specificity = 0.86, PR_AUC = 0.92). Similarly, high diagnostic performances were obtained from original and generated data using 50% and 30% training sets. Our findings suggest that conventional MR imaging-based radiomic features combined with machine/deep learning methods may be valuable in discriminating IDH-mutant grade 4 astrocytomas from IDH-wild-type GBMs.

Diffusion Weighted Imaging in Neuro-Oncology: Diagnosis, Post-Treatment Changes, and Advanced Sequences-An Updated Review

DWI is an imaging technique commonly used for the assessment of acute ischemia, inflammatory disorders, and CNS neoplasia. It has several benefits since it is a quick, easily replicable sequence that is widely used on many standard scanners. In addition to its normal clinical purpose, DWI offers crucial functional and physiological information regarding brain neoplasia and the surrounding milieu. A narrative review of the literature was conducted based on the PubMed database with the purpose of investigating the potential role of DWI in the neuro-oncology field. A total of 179 articles were included in the study.

IDH-mutant Astrocytoma Arising in the Brainstem with Symptom Improvement by Foramen Magnum Decompression: A Case Report

Diffusely infiltrative midline gliomas are known to have a poor prognosis. The standard treatment for typical diffuse midline glioma in the pons is local radiotherapy as surgical resection is inappropriate. This case reports a brainstem glioma in which stereotactic biopsy and foramen magnum decompression were concomitantly performed to confirm the diagnosis and improve symptoms. A 23-year-old woman was referred to our department with a chief complaint of headache for six months. Magnetic resonance imaging (MRI) showed diffuse T2 hyperintense swelling of the brainstem with the pons as the main locus. Enlargement of the lateral ventricles was observed because of cerebrospinal fluid obstruction out of the posterior fossa. This was atypical for a diffuse midline glioma in terms of the longstanding slow progression of symptoms and patient age. Stereotactic biopsy was performed for diagnosis, and foramen magnum decompression (FMD) was concomitantly performed to treat the obstructive hydrocephalus. The histological diagnosis was astrocytoma, IDH-mutant. Post-surgery, the patient's symptoms were relieved, and she was discharged on the fifth day after surgery. The hydrocephalus was resolved, and the patient returned to normal life without any symptoms. The tumor size follow-up with MRI demonstrated no marked change for 12 months. Even though diffuse midline glioma is considered to have a poor prognosis, clinicians should contemplate if it is atypical. In atypical cases like the one described herein, surgical treatment may contribute to pathological diagnosis and symptom improvement.

Noninvasive Classification of Glioma Subtypes Using Multiparametric MRI to Improve Deep Learning

Background: Deep learning (DL) methods can noninvasively predict glioma subtypes; however, there is no set paradigm for the selection of network structures and input data, including the image combination method, image processing strategy, type of numeric data, and others. Purpose: To compare different combinations of DL frameworks (ResNet, ConvNext, and vision transformer (VIT)), image preprocessing strategies, magnetic resonance imaging (MRI) sequences, and numerical data for increasing the accuracy of DL models for differentiating glioma subtypes prior to surgery. Methods: Our dataset consisted of 211 patients with newly diagnosed gliomas who underwent preoperative MRI with standard and diffusion-weighted imaging methods. Different data combinations were used as input for the three different DL classifiers. Results: The accuracy of the image preprocessing strategies, including skull stripping, segment addition, and individual treatment of slices, was 5%, 10%, and 12.5% higher, respectively, than that of the other strategies. The accuracy increased by 7.5% and 10% following the addition of ADC and numeric data, respectively. ResNet34 exhibited the best performance, which was 5% and 17.5% higher than that of ConvNext tiny and VIT-base, respectively. Data Conclusions: The findings demonstrated that the addition of quantitatively numeric data, ADC images, and effective image preprocessing strategies improved model accuracy for datasets of similar size. The performance of ResNet was superior for small or medium datasets.

Diffuse glioma, not otherwise specified: imaging-based risk stratification achieves histomolecular-level prognostication

OBJECTIVES

To determine whether imaging-based risk stratification enables prognostication in diffuse glioma, NOS (not otherwise specified).

METHODS

Data from 220 patients classified as diffuse glioma, NOS, between January 2011 and December 2020 were retrospectively included. Two neuroradiologists analyzed pre-surgical CT and MRI to assign gliomas to the three imaging-based risk types considering well-known imaging phenotypes (e.g., T2/FLAIR mismatch). According to the 2021 World Health Organization classification, the three risk types included (1) low-risk, expecting oligodendroglioma, isocitrate dehydrogenase (IDH)-mutant, and 1p/19q-codeleted; (2) intermediate-risk, expecting astrocytoma, IDH-mutant; and (3) high-risk, expecting glioblastoma, IDH-wildtype. Progression-free survival (PFS) and overall survival (OS) were estimated for each risk type. Time-dependent receiver operating characteristic analysis using 10-fold cross-validation with 100-fold bootstrapping was used to compare the performance of an imaging-based survival model with that of a historical molecular-based survival model published in 2015, created using The Cancer Genome Archive data.

RESULTS

Prognostication according to the three imaging-based risk types was achieved for both PFS and OS (log-rank test, p < 0.001). The imaging-based survival model showed high prognostic value, with areas under the curves (AUCs) of 0.772 and 0.650 for 1-year PFS and OS, respectively, similar to the historical molecular-based survival model (AUC = 0.74 for PFS and 0.87 for OS). The imaging-based survival model achieved high long-term performance in both 3-year PFS (AUC = 0.806) and 5-year OS (AUC = 0.812).

CONCLUSION

Imaging-based risk stratification achieved histomolecular-level prognostication in diffuse glioma, NOS, and could aid in guiding patient referral for insufficient or unsuccessful molecular diagnosis.

KEY POINTS

• Three imaging-based risk types enable distinct prognostication in diffuse glioma, NOS (not otherwise specified). • The imaging-based survival model achieved similar prognostic performance as a historical molecular-based survival model. • For long-term prognostication of 3 and 5 years, the imaging-based survival model showed high performance.

Factors Involved in Preoperative Edema in High-Grade Gliomas

Background Expansion of preoperative edema (PE) is an independent poor prognostic factor in high-grade gliomas. Evaluation of PE provides important information that can be readily obtained from magnetic resonance imaging (MRI), but there are few reports on factors associated with PE. The goal of this study was to identify factors contributing to PE in Grade 3 (G3) and Grade 4 (G4) gliomas. Methodology PE was measured in 141 pathologically proven G3 and G4 gliomas, and factors with a potential relationship with PE were examined in univariate and multivariate analyses. The following eight explanatory variables were used: age, sex, Karnofsky performance status (KPS), location of the glioma, tumor diameter, pathological grade, isocitrate dehydrogenase (IDH)-1-R132H status, and Ki-67 index. Overall survival (OS) and progression-free survival (PFS) were calculated in groups divided by PE (<1 vs. ≥1 cm) and by factors with a significant correlation with PE in multivariate analysis. Results In univariate analysis, age (p = 0.013), KPS (p = 0.012), pathology grade (p = 0.004), and IDH1-R132H status (p = 0.0003) were significantly correlated with PE. In multivariate analysis, only IDH1-R132H status showed a significant correlation (p = 0.036), with a regression coefficient of -0.42. The median follow-up period in survivors was 38.9 months (range: 1.2-131.7 months). The one-, two-, and three-year OS rates for PE <1 vs. ≥1 cm were 77% vs. 68%, 67% vs. 44%, and 63% vs. 24% (p = 0.0001), respectively, and those for IDH1-R132H mutated vs. wild-type cases were 85% vs. 67%, 85% vs. 40%, and 81% vs. 21% (p < 0.0001), respectively. The one-, two-, and three-year PFS rates for PE <1 vs. ≥1 cm were 77% vs. 49%, 64% vs. 24%, and 50% vs. 18% (p = 0.0002), respectively, and those for IDH1-R132H mutated vs. wild-type cases were 85% vs. 48%, 77% vs. 23%, and 73% vs. 14% (p < 0.0001), respectively. Conclusions IDH1-R132H status was found to be a significant contributor to PE. Cases with PE <1 cm and those with the IDH1-R132H mutation clearly had a better prognosis.

Molecular subtype classification of low-grade gliomas using magnetic resonance imaging-based radiomics and machine learning

In 2016, the World Health Organization (WHO) updated the glioma classification by incorporating molecular biology parameters, including low-grade glioma (LGG). In the new scheme, LGGs have three molecular subtypes: isocitrate dehydrogenase (IDH)-mutated 1p/19q-codeleted, IDH-mutated 1p/19q-noncodeleted, and IDH-wild type 1p/19q-noncodeleted entities. This work proposes a model prediction of LGG molecular subtypes using magnetic resonance imaging (MRI). MR images were segmented and converted into radiomics features, thereby providing predictive information about the brain tumor classification. With 726 raw features obtained from the feature extraction procedure, we developed a hybrid machine learning-based radiomics by incorporating a genetic algorithm and eXtreme Gradient Boosting (XGBoost) classifier, to ascertain 12 optimal features for tumor classification. To resolve imbalanced data, the synthetic minority oversampling technique (SMOTE) was applied in our study. The XGBoost algorithm outperformed the other algorithms on the training dataset by an accuracy value of 0.885. We continued evaluating the XGBoost model, then achieved an overall accuracy of 0.6905 for the three-subtype classification of LGGs on an external validation dataset. Our model is among just a few to have resolved the three-subtype LGG classification challenge with high accuracy compared with previous studies performing similar work.

Classification and Diagnosis of Adult Glioma: A Scoping Review

Gliomas are primary central nervous system tumors that arise from glial progenitor cells. Gliomas have been classically classified morphologically based on their histopathological characteristics. However, with recent advances in cancer genomics, molecular profiles have now been integrated into the classification and diagnosis of gliomas. In this review article, we discuss the clinical features, imaging findings, and molecular profiles of adult-type diffuse gliomas based on the new 2021 World Health Organization Classifications of Tumors of the central nervous system.

Noninvasive Determination of the IDH Status of Gliomas Using MRI and MRI-Based Radiomics: Impact on Diagnosis and Prognosis

Gliomas are the most common primary malignant brain tumors in adults. The fifth edition of the WHO Classification of Tumors of the Central Nervous System, published in 2021, provided molecular and practical approaches to CNS tumor taxonomy. Currently, molecular features are essential for differentiating the histological subtypes of gliomas, and recent studies have emphasized the importance of isocitrate dehydrogenase (IDH) mutations in stratifying biologically distinct subgroups of gliomas. IDH plays a significant role in gliomagenesis, and the association of IDH status with prognosis is very clear. Recently, there has been much progress in conventional MR imaging (cMRI), advanced MR imaging (aMRI), and radiomics, which are widely used in the study of gliomas. These advances have resulted in an improved correlation between MR signs and IDH mutation status, which will complement the prediction of the IDH phenotype. Although imaging cannot currently substitute for genetic tests, imaging findings have shown promising signs of diagnosing glioma subtypes and evaluating the efficacy and prognosis of individualized molecular targeted therapy. This review focuses on the correlation between MRI and MRI-based radiomics and IDH gene-phenotype prediction, discussing the value and application of these techniques in the diagnosis and evaluation of the prognosis of gliomas.

Swin Transformer Improves the IDH Mutation Status Prediction of Gliomas Free of MRI-Based Tumor Segmentation

Background: Deep learning (DL) could predict isocitrate dehydrogenase (IDH) mutation status from MRIs. Yet, previous work focused on CNNs with refined tumor segmentation. To bridge the gap, this study aimed to evaluate the feasibility of developing a Transformer-based network to predict the IDH mutation status free of refined tumor segmentation. Methods: A total of 493 glioma patients were recruited from two independent institutions for model development (TCIA; N = 259) and external test (AHXZ; N = 234). IDH mutation status was predicted directly from T2 images with a Swin Transformer and conventional ResNet. Furthermore, to investigate the necessity of refined tumor segmentation, seven strategies for the model input image were explored: (i) whole tumor slice; (ii-iii) tumor mask and/or not edema; (iv-vii) tumor bounding box of 0.8, 1.0, 1.2, 1.5 times. Performance comparison was made among the networks of different architectures along with different image input strategies, using area under the curve (AUC) and accuracy (ACC). Finally, to further boost the performance, a hybrid model was built by incorporating the images with clinical features. Results: With the seven proposed input strategies, seven Swin Transformer models and seven ResNet models were built, respectively. Based on the seven Swin Transformer models, an averaged AUC of 0.965 (internal test) and 0.842 (external test) were achieved, outperforming 0.922 and 0.805 resulting from the seven ResNet models, respectively. When a bounding box of 1.0 times was used, Swin Transformer (AUC = 0.868, ACC = 80.7%), achieved the best results against the one that used tumor segmentation (Tumor + Edema, AUC = 0.862, ACC = 78.5%). The hybrid model that integrated age and location features into images yielded improved performance (AUC = 0.878, Accuracy = 82.0%) over the model that used images only. Conclusions: Swin Transformer outperforms the CNN-based ResNet in IDH prediction. Using bounding box input images benefits the DL networks in IDH prediction and makes the IDH prediction free of refined glioma segmentation feasible.

Radiological Prediction of Isocitrate Dehydrogenase (IDH) Mutational Status and Pathological Verification for Lower-Grade Astrocytomas

Background and objective

The isocitrate dehydrogenase (IDH) status of patients with World Health Organization (WHO) grade II or III astrocytoma is essential for understanding its biological features and determining therapeutic strategies. This study aimed to use radiological analysis to predict the IDH status of patients with lower-grade astrocytomas and to verify the pathological implications.

Methods

In this study, 47 patients with grade II (17 cases) or III astrocytomas (30 cases), based on 2016 WHO Classification, underwent methionine (MET) positron emission tomography (PET) and magnetic resonance spectroscopy (MRS) on the same day between January 2013 and June 2020. The patients were retrospectively assessed. Immunohistochemistry showed 23 cases of IDH-mutant and 24 of IDH-wildtype. Based on fluid-attenuated recovery inversion (FLAIR)/T2 imaging, three doctors blinded to clinical data independently allocated 18 patients to the clear boundary group between the tumor and the normal brain and 29 to the unclear boundary group. The peak ratios of N-acetylaspartate (NAA)/creatine (Cr), choline (Cho)/Cr, and Cho/NAA and the tumor-to-normal region (T/N) ratio for maximum accumulation in MET-PET were calculated. For statistical analysis, Fisher’s exact test was used to assess associations between two variables, and the Mann-Whitney U test to compare the values between the IDH-wildtype and IDH-mutant groups. The optimal cut-off values of MET T/N ratio and MRS parameters for discriminating IDH-wildtype from IDH-mutant were obtained using receiver operating characteristics curves.

Results

The unclear boundary group had significantly more IDH-wildtype cases than the clear boundary group (P<0.001). The IDH-wildtype group had significantly lower Cho/Cr (<1.84) and Cho/NAA (<1.62) ratios (P=0.02 and P=0.047, respectively) and a higher MET T/N ratio (>1.44, P=0.02) than the IDH-mutant group. The odds for the IDH-wildtype were 0.22 for patients who fulfilled none of the four criteria, including boundary status and three ratios, and 0.9 for all four criteria.

Conclusions

These results suggest that the combination of MRI, MRS, and MET-PET examination could be helpful for the prediction of IDH status in WHO grade II/III gliomas.

A Targeted Next-Generation Sequencing Panel to Genotype Gliomas

Gliomas account for the majority of primary brain tumors. Glioblastoma is the most common and malignant type. Based on their extreme molecular heterogeneity, molecular markers can be used to classify gliomas and stratify patients into diagnostic, prognostic, and therapeutic clusters. In this work, we developed and validated a targeted next-generation sequencing (NGS) approach to analyze variants or chromosomal aberrations correlated with tumorigenesis and response to treatment in gliomas. Our targeted NGS analysis covered 13 glioma-related genes (ACVR1, ATRX, BRAF, CDKN2A, EGFR, H3F3A, HIST1H3B, HIST1H3C, IDH1, IDH2, P53, PDGFRA, PTEN), a 125 bp region of the TERT promoter, and 54 single nucleotide polymorphisms (SNPs) along chromosomes 1 and 19 for reliable assessment of their copy number alterations (CNAs). Our targeted NGS approach provided a portrait of gliomas’ molecular heterogeneity with high accuracy, specificity, and sensitivity in a single workflow, enabling the detection of variants associated with unfavorable outcomes, disease progression, and drug resistance. These preliminary results support its use in routine diagnostic neuropathology.

IDH2: A novel biomarker for environmental exposure in blood circulatory system disorders (Review)

As the risk of harmful environmental exposure is increasing, it is important to find suitable targets for the diagnosis and treatment of the diseases caused. Isocitrate dehydrogenase 2 (IDH2) is an enzyme located in the mitochondria; it plays an important role in numerous cell processes, including maintaining redox homeostasis, participating in the tricarboxylic acid cycle and indirectly taking part in the transmission of the oxidative respiratory chain. IDH2 mutations promote progression in acute myeloid leukemia, glioma and other diseases. The present review mainly summarizes the role and mechanism of IDH2 with regard to the biological effects, such as the mitophagy and apoptosis of animal or human cells, caused by environmental pollution such as radiation, heavy metals and other environmental exposure factors. The possible mechanisms of these biological effects are described in terms of IDH2 expression, reduced nicotine adenine dinucleotide phosphate content and reactive oxygen species level, among other variables. The impact of environmental pollution on human health is increasingly attracting attention. IDH2 may therefore become useful as a potential diagnostic and therapeutic target for environmental exposure-induced diseases.

Increased Ascorbate Content of Glioblastoma Is Associated With a Suppressed Hypoxic Response and Improved Patient Survival

Glioblastoma multiforme is a challenging disease with limited treatment options and poor survival. Glioblastoma tumours are characterised by hypoxia that activates the hypoxia inducible factor (HIF) pathway and controls a myriad of genes that drive cancer progression. HIF transcription factors are regulated at the post-translation level via HIF-hydroxylases. These hydroxylases require oxygen and 2-oxoglutarate as substrates, and ferrous iron and ascorbate as cofactors. In this retrospective observational study, we aimed to determine whether ascorbate played a role in the hypoxic response of glioblastoma, and whether this affected patient outcome. We measured the ascorbate content and members of the HIF-pathway of clinical glioblastoma samples, and assessed their association with clinicopathological features and patient survival. In 37 samples (37 patients), median ascorbate content was 7.6 μg ascorbate/100 mg tissue, range 0.8 – 20.4 μg ascorbate/100 mg tissue. In tumours with above median ascorbate content, HIF-pathway activity as a whole was significantly suppressed (p = 0.005), and several members of the pathway showed decreased expression (carbonic anhydrase-9 and glucose transporter-1, both p < 0.01). Patients with either lower tumour HIF-pathway activity or higher tumour ascorbate content survived significantly longer than patients with higher HIF-pathway or lower ascorbate levels (p = 0.011, p = 0.043, respectively). Median survival for the low HIF-pathway score group was 362 days compared to 203 days for the high HIF-pathway score group, and median survival for the above median ascorbate group was 390 days, compared to the below median ascorbate group with 219 days. The apparent survival advantage associated with higher tumour ascorbate was more prominent for the first 8 months following surgery. These associations are promising, suggesting an important role for ascorbate-regulated HIF-pathway activity in glioblastoma that may impact on patient survival.

Prediction of IDH Mutation Status in High-grade Gliomas Using DWI and High T1-weight DSC-MRI

RATIONALE AND OBJECTIVES

We aimed to evaluate the diagnostic performance of diffusion-weighted imaging (DWI) and dynamic susceptibility contrast-enhanced (DSC) magnetic resonance imaging (MRI) parameters in the noninvasive prediction of the isocitrate dehydrogenase (IDH) mutation status in high-grade gliomas (HGGs).

MATERIALS AND METHODS

A total of 58 patients with histopathologically proved HGGs were included in this retrospective study. All patients underwent multiparametric MRI on 3-T, including DSC-MRI and DWI before surgery. The mean apparent diffusion coefficient (ADC), relative maximum cerebral blood volume (rCBV), and percentage signal recovery (PSR) of the tumor core were measured and compared depending on the IDH mutation status and tumor grade. The Mann-Whitney U test was used to detect statistically significant differences in parameters between IDH-mutant-type (IDH-m-type) and IDH-wild-type (IDH-w-type) HGGs. Receiver operating characteristic curve (ROC) analysis was performed to evaluate the diagnostic performance.

RESULTS

The rCBV was significantly higher, and the PSR value was significantly lower in IDH-w-type tumors than in the IDH-m group (p = 0.002 and <0.001, respectively).The ADC value in IDH-w-type tumors was significantly lower compared with the one in IDH-m types (p = 0.023), but remarkable overlaps were found between the groups. The PSR showed the best diagnostic performance with an AUC of 0.938 and with an accuracy rate of 0.87 at the optimal cutoff value of 86.85. The combination of the PSR and the rCBV for the identification of the IDH mutation status increased the discrimination ability at the AUC level of 0.955. In terms of each tumor grade, the PSR and rCBV showed significant differences between the IDH-m and IDH-w groups (p ≤0.001).

CONCLUSION

The rCBV and PSR from DSC-MRI may be feasible noninvasive imaging parameters for predicting the IDH mutation status in HGGs. The standardization of the imaging protocol is indispensable to the utility of DSC perfusion MRI in wider clinical usage.

Clinical and radiographic characteristics of diffuse astrocytic glioma, IDH-wildtype, with molecular features of glioblastoma: a single institution review

PURPOSE

Genetic analyses of gliomas have identified key molecular features that impact treatment paradigms beyond conventional histomorphology. Despite at-times lower grade histopathologic appearances, IDH-wildtype infiltrating gliomas expressing certain molecular markers behave like higher-grade tumors. For IDH-wildtype infiltrating gliomas lacking traditional features of glioblastoma, these markers form the basis for the novel diagnosis of diffuse astrocytic glioma, IDH-wildtype (wt), with molecular features of glioblastoma (GBM), WHO grade-IV (DAG-G). However, given the novelty of this approach to diagnosis, literature detailing the exact clinical, radiographic, and histopathologic findings associated with these tumors remain in development.

METHODS

Data for 25 patients matching the DAG-G diagnosis were obtained from our institution's retrospective database. Information regarding patient demographics, treatment regimens, radiographic imaging, and genetic pathology were analyzed to determine association with clinical outcomes.

RESULTS

The initial radiographic findings, histopathology, and symptomatology of patients with DAG-G were similar to lower-grade astrocytomas (WHO grade 2/3). Overall survival (OS) and progression free survival (PFS) associated with our cohort, however, were similar to that of IDH-wt GBM, indicating a more severe clinical course than expected from other associated features (15.1 and 5.39 months respectively).

CONCLUSION

Despite multiple features similar to lower-grade gliomas, patients with DAG-G experience clinical courses similar to GBM. Such findings reinforce the need for biopsy and subsequent analysis of molecular features associated with any astrocytoma regardless of presenting characteristics.

The diagnostic value of contrast enhancement on MRI in diffuse and anaplastic gliomas

PURPOSE

We evaluated differentiations in gadolinium contrast enhancement (CE) between low-grade WHO °II and high-grade WHO °III gliomas in conventional MRI, which have been repeatedly questioned.

METHODS

Ninety-nine patients, who underwent first resection of WHO°II and °III gliomas, were retrospectively retrieved from a prospective database. The quantitative metric volume of Gd-CE in T1-weighted pre-operative MRI was measured using volumetric segmentation.

RESULTS

The OR to detect CE in anaplastic gliomas was seven times higher than that in diffuse gliomas (CI95% 2.8-17.2, p<0.0001). No CE was seen in 50% (8/16) of focal anaplastic and in 28% (10/36) of entirely anaplastic gliomas. CE was present in 21% (10/47) of diffuse gliomas. Anaplasia correlated with a larger CE volume (r=0.49, p<0.0001) and provided additional 4 cm³ of CE volume compared to entirely diffuse tumors. The OR to have CE was 3.6 times for IDH1 wild-type tumors (CI95% 1.3-10.2, p=0.05) and 4.8 for tumors with ATRX expression (CI95% 1.3-17.2, p=0.05). In all sub-groups, at least a quarter of cases showed no CE at all and there were cases with present CE.

CONCLUSION

CE is associated with higher odds of unfavorable prognostic features like anaplasia, wild-type IDH1 and retained ATRX. There was no CE in one-fourth of anaplastic gliomas and half of gliomas with focal anaplasia.

A radiomics model based on DCE-MRI and DWI may improve the prediction of estimating IDH1 mutation and angiogenesis in gliomas

PURPOSE

To investigate the value of a radiomics model based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion weighted imaging (DWI) in estimating isocitrate dehydrogenase 1 (IDH1) mutation and angiogenesis in gliomas.

METHOD

One hundred glioma patients with DCE-MRI and DWI were enrolled in this study (training and validation groups with a ratio of 7:3). The IDH1 genotypes and expression of vascular endothelial growth factor (VEGF) in gliomas were assessed by immunohistochemistry. Radiomics features were extracted by an open source software (3DSlicer) and reduced using Least absolute shrinkage and selection operator (Lasso). The support vector machine (SVM) model was developed based on the most useful predictive radiomics features. The conventional model was built by the selected clinical and morphological features. Finally, a combined model including radiomics signature, age and enhancement degree was established. Receiver operator characteristic (ROC) curve was implemented to assess the diagnostic performance of the three models.

RESULTS

For IDH1 mutation, the combined model achieved the highest area under curve (AUC) in comparison with the SVM and conventional models (training group, AUC = 0.967, 0.939 and 0.906; validation group, AUC = 0.909, 0.880 and 0.842). Furthermore, the SVM model showed good diagnostic performance in estimating gliomas VEGF expression (validation group, AUC = 0.919).

CONCLUSIONS

The radiomics model based on DCE-MRI and DWI can have a considerable effect on the evaluation of IDH1 mutation and angiogenesis in gliomas.

Variations in the management of diffuse low-grade gliomas-A Scandinavian multicenter study

Background

Early extensive surgery is a cornerstone in treatment of diffuse low-grade gliomas (DLGGs), and an additional survival benefit has been demonstrated from early radiochemotherapy in selected “high-risk” patients. Still, there are a number of controversies related to DLGG management. The objective of this multicenter population-based cohort study was to explore potential variations in diagnostic work-up and treatment between treating centers in 2 Scandinavian countries with similar public health care systems.

Methods

Patients screened for inclusion underwent primary surgery of a histopathologically verified diffuse WHO grade II glioma in the time period 2012 through 2017. Clinical and radiological data were collected from medical records and locally conducted research projects, whereupon differences between countries and inter-hospital variations were explored.

Results

A total of 642 patients were included (male:female ratio 1:4), and annual age-standardized incidence rates were 0.9 and 0.8 per 100 000 in Norway and Sweden, respectively. Considerable inter-hospital variations were observed in preoperative work-up, tumor diagnostics, surgical strategies, techniques for intraoperative guidance, as well as choice and timing of adjuvant therapy.

Conclusions

Despite geographical population-based case selection, similar health care organizations, and existing guidelines, there were considerable variations in DLGG management. While some can be attributed to differences in clinical implementation of current scientific knowledge, some of the observed inter-hospital variations reflect controversies related to diagnostics and treatment. Quantification of these disparities renders possible identification of treatment patterns associated with better or worse outcomes and may thus represent a step toward more uniform evidence-based care.

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.

Improving the noninvasive classification of glioma genetic subtype with deep learning and diffusion-weighted imaging

BACKGROUND

Diagnostic classification of diffuse gliomas now requires an assessment of molecular features, often including IDH-mutation and 1p19q-codeletion status. Because genetic testing requires an invasive process, an alternative noninvasive approach is attractive, particularly if resection is not recommended. The goal of this study was to evaluate the effects of training strategy and incorporation of biologically relevant images on predicting genetic subtypes with deep learning.

METHODS

Our dataset consisted of 384 patients with newly-diagnosed gliomas who underwent preoperative MR imaging with standard anatomical and diffusion-weighted imaging, and 147 patients from an external cohort with anatomical imaging. Using tissue samples acquired during surgery, each glioma was classified into IDH-wildtype (IDHwt), IDH-mutant/1p19q-noncodeleted (IDHmut-intact), and IDH-mutant/1p19q-codeleted (IDHmut-codel) subgroups. After optimizing training parameters, top performing convolutional neural network (CNN) classifiers were trained, validated, and tested using combinations of anatomical and diffusion MRI with either a 3-class or tiered structure. Generalization to an external cohort was assessed using anatomical imaging models.

RESULTS

The best model used a 3-class CNN containing diffusion-weighted imaging as an input, achieving 85.7% (95% CI:[77.1,100]) overall test accuracy and correctly classifying 95.2%, 88.9%, 60.0% of the IDHwt, IDHmut-intact, and IDHmut-codel tumors. In general, 3-class models outperformed tiered approaches by 13.5-17.5%, and models that included diffusion-weighted imaging were 5-8.8% more accurate than those that used only anatomical imaging.

CONCLUSION

Training a classifier to predict both IDH-mutation and 1p19q-codeletion status outperformed a tiered structure that first predicted IDH-mutation, then1p19q-codeletion. Including ADC, a surrogate marker of cellularity, more accurately captured differences between subgroups.

Lower-Grade Gliomas: An Epidemiological Voxel-Based Analysis of Location and Proximity to Eloquent Regions

Background

Glioma is the most common intra-axial tumor, and its location relative to critical areas of the brain is important for treatment decision-making. Studies often report tumor location based on anatomical taxonomy alone since the estimation of eloquent regions requires considerable knowledge of functional neuroanatomy and is, to some degree, a subjective measure. An unbiased and reproducible method to determine tumor location and eloquence is desirable, both for clinical use and for research purposes.

Objective

To report on a voxel-based method for assessing anatomical distribution and proximity to eloquent regions in diffuse lower-grade gliomas (World Health Organization grades 2 and 3).

Methods

A multi-institutional population-based dataset of adult patients (≥18 years) histologically diagnosed with lower-grade glioma was analyzed. Tumor segmentations were registered to a standardized space where two anatomical atlases were used to perform a voxel-based comparison of the proximity of segmentations to brain regions of traditional clinical interest.

Results

Exploring the differences between patients with oligodendrogliomas, isocitrate dehydrogenase (IDH) mutated astrocytomas, and patients with IDH wild-type astrocytomas, we found that the latter were older, more often had lower Karnofsky performance status, and that these tumors were more often found in the proximity of eloquent regions. Eloquent regions are found slightly more frequently in the proximity of IDH-mutated astrocytomas compared to oligodendrogliomas. The regions included in our voxel-based definition of eloquence showed a high degree of association with performing biopsy compared to resection.

Conclusion

We present a simple, robust, unbiased, and clinically relevant method for assessing tumor location and eloquence in lower-grade gliomas.

CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2014-2018

The Central Brain Tumor Registry of the United States (CBTRUS), in collaboration with the CDC and NCI, is the largest population-based registry focused exclusively on primary brain and other central nervous system (CNS) tumors in the United States (US) and represents the entire US population. This report contains the most up-to-date population-based data on primary brain tumors available and supersedes all previous reports in terms of completeness and accuracy and is the first CBTRUS Report to provide the distribution of molecular markers for selected brain and CNS tumor histologies. All rates are age-adjusted using the 2000 US standard population and presented per 100,000 population. The average annual age-adjusted incidence rate (AAAIR) of all malignant and non-malignant brain and other CNS tumors was 24.25 (Malignant AAAIR=7.06, Non-malignant AAAIR=17.18). This overall rate was higher in females compared to males (26.95 versus 21.35) and non-Hispanics compared to Hispanics (24.68 versus 22.12). The most commonly occurring malignant brain and other CNS tumor was glioblastoma (14.3% of all tumors and 49.1% of malignant tumors), and the most common non-malignant tumor was meningioma (39% of all tumors and 54.5% of non-malignant tumors). Glioblastoma was more common in males, and meningioma was more common in females. In children and adolescents (age 0-19 years), the incidence rate of all primary brain and other CNS tumors was 6.21. An estimated 88,190 new cases of malignant and non-malignant brain and other CNS tumors are expected to be diagnosed in the US population in 2021 (25,690 malignant and 62,500 non-malignant). There were 83,029 deaths attributed to malignant brain and other CNS tumors between 2014 and 2018. This represents an average annual mortality rate of 4.43 per 100,000 and an average of 16,606 deaths per year. The five-year relative survival rate following diagnosis of a malignant brain and other CNS tumor was 66.9%, for a non-malignant brain and other CNS tumors the five-year relative survival rate was 92.1%.

Advanced Imaging and Computational Techniques for the Diagnostic and Prognostic Assessment of Malignant Gliomas

ABSTRACT

Advanced imaging techniques provide a powerful tool to assess the intratumoral and intertumoral heterogeneity of gliomas. Advances in the molecular understanding of glioma subgroups may allow improved diagnostic assessment combining imaging and molecular tumor features, with enhanced prognostic utility and implications for patient treatment. In this article, a comprehensive overview of the physiologic basis for conventional and advanced imaging techniques is presented, and clinical applications before and after treatment are discussed. An introduction to the principles of radiomics and the advanced integration of imaging, clinical outcomes, and genomic data highlights the future potential for this field of research to better stratify and select patients for standard as well as investigational therapies.

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.

Multiparametric MRI Features Predict the SYP Gene Expression in Low-Grade Glioma Patients: A Machine Learning-Based Radiomics Analysis

Purpose

Synaptophysin (SYP) gene expression levels correlate with the survival rate of glioma patients. This study aimed to explore the feasibility of applying a multiparametric magnetic resonance imaging (MRI) radiomics model composed of a convolutional neural network to predict the SYP gene expression in patients with glioma.

Method

Using the TCGA database, we examined 614 patients diagnosed with glioma. First, the relationship between the SYP gene expression level and outcome of survival rate was investigated using partial correlation analysis. Then, 7266 patches were extracted from each of the 108 low-grade glioma patients who had available multiparametric MRI scans, which included preoperative T1-weighted images (T1WI), T2-weighted images (T2WI), and contrast-enhanced T1WI images in the TCIA database. Finally, a radiomics features-based model was built using a convolutional neural network (ConvNet), which can perform autonomous learning classification using a ROC curve, accuracy, recall rate, sensitivity, and specificity as evaluation indicators.

Results

The expression level of SYP decreased with the increase in the tumor grade. With regard to grade II, grade III, and general patients, those with higher SYP expression levels had better survival rates. However, the SYP expression level did not show any significant association with the outcome in Level IV patients.

Conclusion

Our multiparametric MRI radiomics model constructed using ConvNet showed good performance in predicting the SYP gene expression level and prognosis in low-grade glioma patients.

Radiogenomics of Gliomas

The 2016 World Health Organization brain tumor classification is based on genomic and molecular profile of tumor tissue. These characteristics have improved understanding of the brain tumor and played an important role in treatment planning and prognostication. There is an ongoing effort to develop noninvasive imaging techniques that provide insight into tissue characteristics at the cellular and molecular levels. This article focuses on the molecular characteristics of gliomas, transcriptomic subtypes, and radiogenomic studies using semantic and radiomic features. The limitations and future directions of radiogenomics as a standalone diagnostic tool also are discussed.

Machine Learning for the Prediction of Molecular Markers in Glioma on Magnetic Resonance Imaging: A Systematic Review and Meta-Analysis

BACKGROUND

Molecular characterization of glioma has implications for prognosis, treatment planning, and prediction of treatment response. Current histopathology is limited by intratumoral heterogeneity and variability in detection methods. Advances in computational techniques have led to interest in mining quantitative imaging features to noninvasively detect genetic mutations.

OBJECTIVE

To evaluate the diagnostic accuracy of machine learning (ML) models in molecular subtyping gliomas on preoperative magnetic resonance imaging (MRI).

METHODS

A systematic search was performed following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines to identify studies up to April 1, 2020. Methodological quality of studies was assessed using the Quality Assessment for Diagnostic Accuracy Studies (QUADAS)-2. Diagnostic performance estimates were obtained using a bivariate model and heterogeneity was explored using metaregression.

RESULTS

Forty-four original articles were included. The pooled sensitivity and specificity for predicting isocitrate dehydrogenase (IDH) mutation in training datasets were 0.88 (95% CI 0.83-0.91) and 0.86 (95% CI 0.79-0.91), respectively, and 0.83 to 0.85 in validation sets. Use of data augmentation and MRI sequence type were weakly associated with heterogeneity. Both O6-methylguanine-DNA methyltransferase (MGMT) gene promoter methylation and 1p/19q codeletion could be predicted with a pooled sensitivity and specificity between 0.76 and 0.83 in training datasets.

CONCLUSION

ML application to preoperative MRI demonstrated promising results for predicting IDH mutation, MGMT methylation, and 1p/19q codeletion in glioma. Optimized ML models could lead to a noninvasive, objective tool that captures molecular information important for clinical decision making. Future studies should use multicenter data, external validation and investigate clinical feasibility of ML models.

Adult isocitrate dehydrogenase-mutant brainstem glioma: illustrative case

BACKGROUND

Adult brainstem gliomas are rare entities that demonstrate heterogeneous biology and appear to be distinct from both their pediatric counterparts and adult supratentorial gliomas. Although the role of histone 3 mutations is being increasingly understood in this disease, the effect of isocitrate dehydrogenase (IDH) mutations remains unclear, largely because of limited data.

OBSERVATIONS

The authors present the case of a 29-year-old male with an IDH1-mutant, World Health Organization grade III anaplastic astrocytoma in the dorsal medulla, and they provide a review of the available literature on adult IDH-mutant brainstem glioma. The authors have amassed a cohort of 15 such patients, 7 of whom have survival data available. Median survival is 56 months in this small cohort, which is similar to that for IDH wild-type adult brainstem gliomas.

LESSONS

The authors' work reenforces previous literature suggesting that the role of IDH mutation in glioma differs between brainstem and supratentorial lesions. Therefore, the authors advocate that adult brainstem gliomas be studied in terms of major molecular subgroups (including IDH mutant) because these gliomas may exhibit fundamental differences from each other, from pediatric brainstem gliomas, and from adult supratentorial gliomas.

CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2013-2017

The Central Brain Tumor Registry of the United States (CBTRUS), in collaboration with the Centers for Disease Control (CDC) and National Cancer Institute (NCI), is the largest population-based registry focused exclusively on primary brain and other central nervous system (CNS) tumors in the United States (US) and represents the entire US population. This report contains the most up-to-date population-based data on primary brain tumors (malignant and non-malignant) and supersedes all previous CBTRUS reports in terms of completeness and accuracy. All rates (incidence and mortality) are age-adjusted using the 2000 US standard population and presented per 100,000 population. The average annual age-adjusted incidence rate (AAAIR) of all malignant and non-malignant brain and other CNS tumors was 23.79 (Malignant AAAIR=7.08, non-Malignant AAAIR=16.71). This rate was higher in females compared to males (26.31 versus 21.09), Blacks compared to Whites (23.88 versus 23.83), and non-Hispanics compared to Hispanics (24.23 versus 21.48). The most commonly occurring malignant brain and other CNS tumor was glioblastoma (14.5% of all tumors), and the most common non-malignant tumor was meningioma (38.3% of all tumors). Glioblastoma was more common in males, and meningioma was more common in females. In children and adolescents (age 0-19 years), the incidence rate of all primary brain and other CNS tumors was 6.14. An estimated 83,830 new cases of malignant and non-malignant brain and other CNS tumors are expected to be diagnosed in the US in 2020 (24,970 malignant and 58,860 non-malignant). There were 81,246 deaths attributed to malignant brain and other CNS tumors between 2013 and 2017. This represents an average annual mortality rate of 4.42. The 5-year relative survival rate following diagnosis of a malignant brain and other CNS tumor was 23.5% and for a non-malignant brain and other CNS tumor was 82.4%.

Fluid attenuation in non-contrast-enhancing tumor (nCET): an MRI Marker for Isocitrate Dehydrogenase (IDH) mutation in Glioblastoma

PURPOSE

The WHO 2016 update classifies glioblastomas (WHO grade IV) according to isocitrate dehydrogenase (IDH) gene mutation status. We aimed to determine MRI-based metrics for predicting IDH mutation in glioblastoma.

METHODS

This retrospective study included glioblastoma cases (n = 199) with known IDH mutation status and pre-operative MRI (T1WI, T2WI, FLAIR, contrast-enhanced T1W1 at minimum). Two neuroradiologists determined the following MRI metrics: (1) primary lobe of involvement (frontal or non-frontal); (2) presence/absence of contrast-enhancement; (3) presence/absence of necrosis; (4) presence/absence of fluid attenuation in the non-contrast-enhancing tumor (nCET); (5) maximum width of peritumoral edema (cm); (6) presence/absence of multifocal disease. Inter-reader agreement was determined. After resolving discordant measurements, multivariate association between consensus MRI metrics/patient age and IDH mutation status was determined.

RESULTS

Among 199 glioblastomas, 16 were IDH-mutant. Inter-reader agreement was calculated for contrast-enhancement (ĸ = 0.49 [- 0.11-1.00]), necrosis (ĸ = 0.55 [0.34-0.76]), fluid attenuation in nCET (ĸ = 0.83 [0.68-0.99]), multifocal disease (ĸ = 0.55 [0.39-0.70]), and primary lobe (ĸ = 0.85 [0.80-0.91]). Mean difference for peritumoral edema width between readers was 0.3 cm [0.2-0.5], p < 0.001. Multivariate analysis uncovered significant associations between IDH-mutation and fluid attenuation in nCET (OR 82.9 [19.22, ∞], p < 0.001), younger age (OR 0.93 [0.86, 0.98], p = 0.009), frontal lobe location (OR 11.08 [1.14, 352.97], p = 0.037), and less peritumoral edema (OR 0.15 [0, 0.65], p = 0.044).

CONCLUSIONS

Conventional MRI metrics and patient age predict IDH-mutation status in glioblastoma. Among MRI markers, fluid attenuation in nCET represents a novel marker with high inter-reader agreement that is strongly associated with Glioblastoma, IDH-mutant.

Qualitative and Quantitative MRI Analysis in IDH1 Genotype Prediction of Lower-Grade Gliomas: A Machine Learning Approach

Purpose

Preoperative prediction of isocitrate dehydrogenase 1 (IDH1) mutation in lower-grade gliomas (LGGs) is crucial for clinical decision-making. This study aimed to examine the predictive value of a machine learning approach using qualitative and quantitative MRI features to identify the IDH1 mutation in LGGs.

Materials and Methods

A total of 102 LGG patients were allocated to training (n = 67) and validation (n = 35) cohorts and were subject to Visually Accessible Rembrandt Images (VASARI) feature extraction (23 features) from conventional multimodal MRI and radiomics feature extraction (56 features) from apparent diffusion coefficient maps. Feature selection was conducted using the maximum Relevance Minimum Redundancy method and 0.632+ bootstrap method. A machine learning model to predict IDH1 mutation was then established using a random forest classifier. The predictive performance was evaluated using receiver operating characteristic (ROC) curves.

Results

After feature selection, the top 5 VASARI features were enhancement quality, deep white matter invasion, tumor location, proportion of necrosis, and T1/FLAIR ratio, and the top 10 radiomics features included 3 histogram features, 3 gray-level run-length matrix features, and 3 gray-level size zone matrix features and one shape feature. Using the optimal VASARI or radiomics feature sets for IDH1 prediction, the trained model achieved an area under the ROC curve (AUC) of 0.779 ± 0.001 or 0.849 ± 0.008 on the validation cohort, respectively. The fusion model that integrated outputs of both optimal VASARI and radiomics models improved the AUC to 0.879.

Conclusion

The proposed machine learning approach using VASARI and radiomics features can predict IDH1 mutation in LGGs.

Diffuse astrocytic glioma, IDH-Wildtype, with molecular features of glioblastoma, WHO grade IV: A single-institution case series and review

OBJECTIVE

In 2018, cIMPACT-NOW update 3 concluded that WHO grade II/III IDH-wildtype diffuse astrocytomas that contain TERT promoter mutations, chromosome 7 gain/10 loss, and/or EGFR amplification, correspond to a WHO grade IV diagnosis and should be classified as Diffuse astrocytic glioma, IDH-wildtype, with molecular features of glioblastoma, WHO grade IV (DAG-G). We present a single-institution series of patients with DAG-G and IDH-mutant astrocytomas and compare their clinical, molecular, and radiographic characteristics.

METHODS

Patient data was retrospectively extracted from the EMR for all patients undergoing surgical biopsy/resection of a diffuse astrocytoma at our institution from 2018 to 2020. Clinical presentation, molecular alterations, radiographic appearance, surgery, and survival were reviewed for each patient.

RESULTS

Six DAG-G patients were identified in our cohort. All patients had diffuse disease, and presented with expansile, T2 hyperintense lesions with minimal enhancement. Compared to patients with classic IDH-mutant astrocytomas, mean age for DAG-G patients was older (68 vs 33 years, p < 0.0001), tumors were more diffuse (p = 0.02), with patients more likely to present with focal deficits and receive a biopsy only (p = 0.005). Overall survival was significantly shorter for DAG-G patients (p = 0.03).

CONCLUSION

Patients with DAG-G are more likely to be older than typical IDH-mutant diffuse astrocytoma patients. They are more likely to present with tumors in a diffuse pattern with focal deficits. When such patients are encountered, prompt biopsy/resection to confirm the diagnosis and immediate initiation of adjuvant therapy is recommended, as the disease progression and overall prognosis is similar to glioblastoma.

Analysis of morphological characteristics of IDH-mutant/wildtype brain tumors using whole-lesion phenotype analysis

BACKGROUND

Although IDH-mutant tumors aggregate to the frontotemporal regions, the clustering pattern of IDH-wildtype tumors is less clear. As voxel-based lesion-symptom mapping (VLSM) has several limitations for solid lesion mapping, a new technique, whole-lesion phenotype analysis (WLPA), is developed. We utilize WLPA to assess spatial clustering of tumors with IDH mutation from The Cancer Genome Atlas and The Cancer Imaging Archive.

METHODS

The degree of tumor clustering segmented from T1 weighted images is measured to every other tumor by a function of lesion similarity to each other via the Hausdorff distance. Each tumor is ranked according to the degree to which its neighboring tumors show identical phenotypes, and through a permutation technique, significant tumors are determined. VLSM was applied through a previously described method.

RESULTS

A total of 244 patients of mixed-grade gliomas (WHO II-IV) are analyzed, of which 150 were IDH-wildtype and 139 were glioblastomas. VLSM identifies frontal lobe regions that are more likely associated with the presence of IDH mutation but no regions where IDH-wildtype was more likely to be present. WLPA identifies both IDH-mutant and -wildtype tumors exhibit statistically significant spatial clustering.

CONCLUSION

WLPA may provide additional statistical power when compared with VLSM without making several potentially erroneous assumptions. WLPA identifies tumors most likely to exhibit particular phenotypes, rather than producing anatomical maps, and may be used in conjunction with VLSM to understand the relationship between tumor morphology and biologically relevant tumor phenotypes.

Non-Invasive Estimation of Glioma IDH1 Mutation and VEGF Expression by Histogram Analysis of Dynamic Contrast-Enhanced MRI

Objectives

To investigate whether glioma isocitrate dehydrogenase (IDH) 1 mutation and vascular endothelial growth factor (VEGF) expression can be estimated by histogram analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).

Methods

Chinese Glioma Genome Atlas (CGGA) database was wined for differential expression of VEGF in gliomas with different IDH genotypes. The VEGF expression and IDH1 genotypes of 56 glioma samples in our hospital were assessed by immunohistochemistry. Preoperative DCE-MRI data of glioma samples were reviewed. Regions of interest (ROIs) covering tumor parenchyma were delineated. Histogram parameters of volume transfer constant (K^trans) and volume of extravascular extracellular space per unit volume of tissue (V_e) derived from DCE-MRI were obtained. Histogram parameters of K^trans, V_e and VEGF expression of IDH1 mutant type (IDH1^mut) gliomas were compared with the IDH1 wildtype (IDH1^wt) gliomas. Receiver operating characteristic (ROC) curve analysis was performed to differentiate IDH1^mut from IDH1^wt gliomas. The correlation coefficients were determined between histogram parameters of K^trans, V_e and VEGF expression in gliomas.

Results

In CGGA database, VEGF expression in IDH^mut gliomas was lower as compared to wildtype counterpart. The immunohistochemistry of glioma samples in our hospital also confirmed the results. Comparisons demonstrated statistically significant differences in histogram parameters of K^transand V_e [mean, standard deviation (SD), 50th, 75th, 90th. and 95th percentile] between IDH1^mutand IDH1^wtgliomas (P < 0.05, respectively). ROC curve analysis revealed that 50th percentile of K^trans (0.019 min⁻¹) and V_e (0.039) provided the perfect combination of sensitivity and specificity in differentiating gliomas with IDH1^mutfrom IDH1^wt. Irrespective of IDH1 mutation, histogram parameters of K^transand V_e were correlated with VEGF expression in gliomas (P < 0.05, respectively).

Conclusions

VEGF expression is significantly lower in IDH1^mut gliomas as compared to the wildtype counterpart, and it is non-invasively predictable with histogram analysis of DCE-MRI.

Automated MRI based pipeline for segmentation and prediction of grade, IDH mutation and 1p19q co-deletion in glioma

In the WHO glioma classification guidelines grade (glioblastoma versus lower-grade glioma), IDH mutation and 1p/19q co-deletion status play a central role as they are important markers for prognosis and optimal therapy planning. Currently, diagnosis requires invasive surgical procedures. Therefore, we propose an automatic segmentation and classification pipeline based on routinely acquired pre-operative MRI (T1, T1 postcontrast, T2 and/or FLAIR). A 3D U-Net was designed for segmentation and trained on the BraTS 2019 training dataset. After segmentation, the 3D tumor region of interest is extracted from the MRI and fed into a CNN to simultaneously predict grade, IDH mutation and 1p19q co-deletion. Multi-task learning allowed to handle missing labels and train one network on a large dataset of 628 patients, collected from The Cancer Imaging Archive and BraTS databases. Additionally, the network was validated on an independent dataset of 110 patients retrospectively acquired at the Ghent University Hospital (GUH). Segmentation performance calculated on the BraTS validation set shows an average whole tumor dice score of 90% and increased robustness to missing image modalities by randomly excluding input MRI during training. Classification area under the curve scores are 93%, 94% and 82% on the TCIA test data and 94%, 86% and 87% on the GUH data for grade, IDH and 1p19q status respectively. We developed a fast, automatic pipeline to segment glioma and accurately predict important (molecular) markers based on pre-therapy MRI.

MRI Radiomic Features to Predict IDH1 Mutation Status in Gliomas: A Machine Learning Approach using Gradient Tree Boosting

Patients with gliomas, isocitrate dehydrogenase 1 (IDH1) mutation status have been studied as a prognostic indicator. Recent advances in machine learning (ML) have demonstrated promise in utilizing radiomic features to study disease processes in the brain. We investigate whether ML analysis of multiparametric radiomic features from preoperative Magnetic Resonance Imaging (MRI) can predict IDH1 mutation status in patients with glioma. This retrospective study included patients with glioma with known IDH1 status and preoperative MRI. Radiomic features were extracted from Fluid-Attenuated Inversion Recovery (FLAIR) and Diffused Weighted Imaging (DWI). The dataset was split into training, validation, and testing sets by stratified sampling. Synthetic Minority Oversampling Technique (SMOTE) was applied to the training sets. eXtreme Gradient Boosting (XGBoost) classifiers were trained, and the hyperparameters were tuned. Receiver operating characteristic curve (ROC), accuracy, and f1-scores were collected. A total of 100 patients (age: 55 ± 15, M/F 60/40); with IDH1 mutant (n = 22) and IDH1 wildtype (n = 78) were included. The best performance was seen with a DWI-trained XGBoost model, which achieved ROC with Area Under the Curve (AUC) of 0.97, accuracy of 0.90, and f1-score of 0.75 on the test set. The FLAIR-trained XGBoost model achieved ROC with AUC of 0.95, accuracy of 0.90, f1-score of 0.75 on the test set. A model that was trained on combined FLAIR-DWI radiomic features did not provide incremental accuracy. The results show that a XGBoost classifier using multiparametric radiomic features derived from preoperative MRI can predict IDH1 mutation status with > 90% accuracy.