OBJECTIVE: Controversy surrounds the prognostic value of contrast-enhanced T1-weighted (T1CE) imaging-based subventricular zone (SVZ) classification in isocitrate dehydrogenase (IDH)-wildtype glioblastomas (GBMs). In this study, the authors aimed to assess the potential of incorporating FLAIR imaging into T1CE imaging-based classification for improving prognostic accuracy.
METHODS: A retrospective analysis was conducted on 281 patients with IDH-wildtype GBM. T1CE imaging-based classification was performed, and T2-weighted/FLAIR imaging was integrated to evaluate its prognostic estimation ability. Based on the relationship between the tumors and SVZ, patients were categorized into SVZ+ and SVZ- cohorts based on T1CE and T2-weighted/FLAIR imaging findings. Kaplan-Meier and Cox proportional hazards regression analyses were used to assess progression-free survival (PFS) and overall survival (OS), respectively. Patients were then categorized into three subgroups based on their combined classifications: group 1 (SVZ+ on T1CE and T2-weighted/FLAIR imaging), group 2 (SVZ- on T1CE but SVZ+ on T2-weighted/FLAIR imaging), and group 3 (SVZ- on T1CE and T2-weighted/FLAIR imaging). Subgroup analysis was used to evaluate differences in clinical and molecular factors as well as in prognoses.
RESULTS: The T1CE imaging-based classification failed to stratify OS between SVZ+ and SVZ- cohorts (16.0 vs 20.0 months, p = 0.36). Survival analysis revealed similar prognoses for patients in groups 1 and 2, and patients in group 2 exhibited worse OS compared with those in group 3 (19.0 vs 23.5 months, p = 0.024). Logistic regression identified lower Karnofsky Performance Status (KPS) (p = 0.011), tumor diameter (p = 0.002), and telomerase reverse transcriptase (TERT) promoter mutation (p = 0.003) to be associated with a higher incidence of group 2 GBMs. Additionally, T2-weighted/FLAIR imaging-based classification provided significant prognostic value (17.0 vs 23.5 months p = 0.021) and was found to be an independent prognostic factor in the Cox multivariate analysis (HR 1.79, 95% CI 1.08-2.96; p = 0.024).
CONCLUSIONS: This study underscores the limitations of T1CE imaging-based SVZ-associated classification in predicting prognosis for IDH-wildtype GBMs. The authors therefore propose an integrated approach that involves T2-weighted/FLAIR imaging that can provide improved prognostic ability. Notably, the presence of TERT promoter mutation was identified as a critical factor in nonenhancing tumor infiltration into the SVZ. Further validation through extensive cohort studies is recommended to confirm these findings.

UNASSIGNED: Glioma is the most common primary intracranial neoplasm with a relatively poor prognosis.
UNASSIGNED: Here, we present a unique case of a 53-year-old woman with two histopathologically distinct gliomas at the initial diagnosis. She presented with headaches and left limb weakness before admission, and magnetic resonance imaging (MRI) showed right frontal and basal ganglia area involvement combined with hemorrhage. The patient underwent a navigation-guided craniotomy for tumor removal. Pathological examination revealed the right frontal lobe lesion as a WHO grade II IDH-NOS astrocytoma, but the right parietal lobe lesion was a WHO grade IV IDH-mutant diffuse astrocytoma. Molecular detection of the parietal lesion revealed a point mutation at the R132 locus of the IDH1 gene, no mutation in the TERT promoter, amplification of the epidermal growth factor receptor, and a non-homozygous CDKN2A/B deletion.
UNASSIGNED: In-depth epigenomic analysis and molecular examination revealed that one patient had two different brain tumors, underscoring the importance of performing a comprehensive brain tumor workup.
UNASSIGNED: This unique case confirms that adjacent astrocytomas may have different molecular pathogenesis and provides novel insights into the development of gliomas.

BACKGROUND: The role of isocitrate dehydrogenase (IDH) mutation status for glioma stratification and prognosis is established. While structural magnetic resonance image (MRI) is a promising biomarker, it may not be sufficient for non-invasive characterisation of IDH mutation status. We investigated the diagnostic value of combined diffusion tensor imaging (DTI) and structural MRI enhanced by a deep radiomics approach based on convolutional neural networks (CNNs) and support vector machine (SVM), to determine the IDH mutation status in Central Nervous System World Health Organization (CNS WHO) grade 2-4 gliomas.
METHODS: This retrospective study analyzed the DTI-derived fractional anisotropy (FA) and mean diffusivity (MD) images and structural images including fluid attenuated inversion recovery (FLAIR), non-enhanced T1-, and T2-weighted images of 206 treatment-naïve gliomas, including 146 IDH mutant and 60 IDH-wildtype ones. The lesions were manually segmented by experienced neuroradiologists and the masks were applied to the FA and MD maps. Deep radiomics features were extracted from each subject by applying a pre-trained CNN and statistical description. An SVM classifier was applied to predict IDH status using imaging features in combination with demographic data.
RESULTS: We comparatively assessed the CNN-SVM classifier performance in predicting IDH mutation status using standalone and combined structural and DTI-based imaging features. Combined imaging features surpassed stand-alone modalities for the prediction of IDH mutation status [area under the curve (AUC) = 0.846; sensitivity = 0.925; and specificity = 0.567]. Importantly, optimal model performance was noted following the addition of demographic data (patients\' age) to structural and DTI imaging features [area under the curve (AUC) = 0.847; sensitivity = 0.911; and specificity = 0.617].
CONCLUSIONS: Imaging features derived from DTI-based FA and MD maps combined with structural MRI, have superior diagnostic value to that provided by standalone structural or DTI sequences. In combination with demographic information, this CNN-SVM model offers a further enhanced non-invasive prediction of IDH mutation status in gliomas.

OBJECTIVE: To assess the performance of hybrid multi-dimensional magnetic resonance imaging (HM-MRI) in quantifying hematoxylin and eosin (H&E) staining results, grading and predicting isocitrate dehydrogenase (IDH) mutation status of gliomas.
METHODS: Included were 71 glioma patients (mean age, 50.17 ± 13.38 years; 35 men). HM-MRI images were collected at five different echo times (80-200 ms) with seven b-values (0-3000 s/mm2). A modified three-compartment model with very-slow, slow and fast diffusion components was applied to calculate HM-MRI metrics, including fractions, diffusion coefficients and T2 values of each component. Pearson correlation analysis was performed between HM-MRI derived fractions and H&E staining derived percentages. HM-MRI metrics were compared between high-grade and low-grade gliomas, and between IDH-wild and IDH-mutant gliomas. Using receiver operational characteristic (ROC) analysis, the diagnostic performance of HM-MRI in grading and genotyping was compared with mono-exponential models.
RESULTS: HM-MRI metrics FDvery-slow and FDslow demonstrated a significant correlation with the H&E staining results (p < .05). Besides, FDvery-slow showed the highest area under ROC curve (AUC = 0.854) for grading, while Dslow showed the highest AUC (0.845) for genotyping. Furthermore, a combination of HM-MRI metrics FDvery-slow and T2Dslow improved the diagnostic performance for grading (AUC = 0.876).
CONCLUSIONS: HM-MRI can aid in non-invasive diagnosis of gliomas.

Isocitrate dehydrogenase-wildtype (IDHwt) glioblastoma (GBM) is one of the most aggressive primary brain tumors. The recurrence of GBM is almost inevitable. As an adjuvant option to surgery, intraoperative radiotherapy (IORT) is gaining increasing attention in the treatment of glioma. This study is aimed to evaluate the therapeutic efficacy of IORT on recurrent IDHwt GBM.
In total, 34 recurrent IDHwt GBM patients who received a second surgery were included in the analysis (17 in the surgery group and 17 in the surgery + IORT group).
The progression-free survival and overall survival after the second surgery were defined as PFS2 and OS2, respectively. The median PFS2 was 7.3 months (95% CI: 6.3-10.5) and 10.6 months (95% CI: 9.3-14.6) for those patients who received surgery and surgery + IORT, respectively. Patients in the surgery + IORT group also had a longer OS2 (12.8 months, 95% CI: 11.4-17.2) than those in the surgery group (9.3 months, 95% CI: 8.9-12.9). The Kaplan-Meier survival curves, analyzed by log-rank test, revealed a statistically significant difference in PFS2 and OS2 between both groups, suggesting that IORT plays an active role in the observed benefits for PFS2 and OS2. The effects of IORT on PFS2 and OS2 were further confirmed by multivariate Cox hazards regression analysis. Two patients in the surgery group developed distant glioma metastases, and no radiation-related complications were observed in the IORT group.
This study suggests that low-dose IORT may improve the prognosis of recurrent IDHwt GBM patients. Future prospective large-scale studies are needed to validate the efficacy and safety of IORT.

UNASSIGNED: Isocitrate dehydrogenase-wildtype glioblastoma (IDH-wildtype GBM) and IDH-mutant astrocytoma have distinct biological behaviors and clinical outcomes. The location of brain tumors is closely associated not only with clinical symptoms and prognosis but also with key molecular alterations such as IDH. Therefore, we hypothesize that the key brain regions influencing the prognosis of glioblastoma and astrocytoma are likely to differ. This study aims to (1) identify specific regions that are associated with the Karnofsky Performance Scale (KPS) or overall survival (OS) in IDH-wildtype GBM and IDH-mutant astrocytoma and (2) test whether the involvement of these regions could act as a prognostic indicator.
UNASSIGNED: A total of 111 patients with IDH-wildtype GBM and 78 patients with IDH-mutant astrocytoma from the Cancer Imaging Archive database were included in the study. Voxel-based lesion-symptom mapping (VLSM) was used to identify key brain areas for lower KPS and shorter OS. Next, we analyzed the structural and cognitive dysfunction associated with these regions. The survival analysis was carried out using Kaplan-Meier survival curves. Another 72 GBM patients and 48 astrocytoma patients from Harbin Medical University Cancer Hospital were used as a validation cohort.
UNASSIGNED: Tumors located in the insular cortex, parahippocampal gyrus, and middle and superior temporal gyrus of the left hemisphere tended to lead to lower KPS and shorter OS in IDH-wildtype GBM. The regions that were significantly correlated with lower KPS in IDH-mutant astrocytoma included the subcallosal cortex and cingulate gyrus. These regions were associated with diverse structural and cognitive impairments. The involvement of these regions was an independent predictor for shorter survival in both GBM and astrocytoma.
UNASSIGNED: This study identified the specific regions that were significantly associated with OS or KPS in glioma. The results may help neurosurgeons evaluate patient survival before surgery and understand the pathogenic mechanisms of glioma in depth.

UNASSIGNED: In adult diffuse glioma, preoperative detection of isocitrate dehydrogenase (IDH) status helps clinicians develop surgical strategies and evaluate patient prognosis. Here, we aim to identify an optimal machine-learning model for prediction of IDH genotyping by combining deep-learning (DL) signatures and conventional radiomics (CR) features as model predictors.
UNASSIGNED: In this study, a total of 486 patients with adult diffuse gliomas were retrospectively collected from our medical center (n=268) and the public database (TCGA, n=218). All included patients were randomly divided into the training and validation sets by using nested 10-fold cross-validation. A total of 6,736 CR features were extracted from four MRI modalities in each patient, namely T1WI, T1CE, T2WI, and FLAIR. The LASSO algorithm was performed for CR feature selection. In each MRI modality, we applied a CNN+LSTM-based neural network to extract DL features and integrate these features into a DL signature after the fully connected layer with sigmoid activation. Eight classic machine-learning models were analyzed and compared in terms of their prediction performance and stability in IDH genotyping by combining the LASSO-selected CR features and integrated DL signatures as model predictors. In the validation sets, the prediction performance was evaluated by using accuracy and the area under the curve (AUC) of the receiver operating characteristics, while the model stability was analyzed by using the relative standard deviation of the AUC (RSDAUC). Subgroup analyses of DL signatures and CR features were also individually conducted to explore their independent prediction values.
UNASSIGNED: Logistic regression (LR) achieved favorable prediction performance (AUC: 0.920 ± 0.043, accuracy: 0.843 ± 0.044), whereas support vector machine with the linear kernel (l-SVM) displayed low prediction performance (AUC: 0.812 ± 0.052, accuracy: 0.821 ± 0.050). With regard to stability, LR also showed high robustness against data perturbation (RSDAUC: 4.7%). Subgroup analyses showed that DL signatures outperformed CR features (DL, AUC: 0.915 ± 0.054, accuracy: 0.835 ± 0.061, RSDAUC: 5.9%; CR, AUC: 0.830 ± 0.066, accuracy: 0.771 ± 0.051, RSDAUC: 8.0%), while DL and DL+CR achieved similar prediction results.
UNASSIGNED: In IDH genotyping, LR is a promising machine-learning classification model. Compared with CR features, DL signatures exhibit markedly superior prediction values and discriminative capability.

Adult gliomas are divided into isocitrate dehydrogenase (IDH) wild-type and IDH mutant subtypes according to the new 2021 World Health Organization classification system. However, the local and systemic effects of IDH mutations on primary glioma patients are not well illustrated. Retrospective analysis, immune-cell infiltration analysis, meta-analysis, and immunohistochemistry assay were applied in the present study. The results from our cohort showed that IDH mutant gliomas own a lower proliferating rate compared to that in wild-type gliomas. Patients with mutant IDH exhibited a higher frequency of seizures in both our cohort and the cohort from the meta-analysis. Mutations in IDH result in lower levels of intra-tumour but higher levels of circulating CD4+ and CD8+ T lymphocytes. Levels of neutrophils in both intra-tumour and circulating blood were lower in IDH mutant gliomas. Moreover, IDH mutant glioma patients receiving radiotherapy in combination with chemotherapy exhibited better overall survival with respect to radiotherapy alone. Mutations in IDH alters the local and circulating immune microenvironment, and increases the sensitivity of tumour cell to chemotherapy.

Mutations in IDH1, IDH2, and TET2 are recurrently observed in myeloid neoplasms. IDH1 and IDH2 encode isocitrate dehydrogenase isoforms, which normally catalyze the conversion of isocitrate to α-ketoglutarate (α-KG). Oncogenic IDH1/2 mutations confer neomorphic activity, leading to the production of D-2-hydroxyglutarate (D-2-HG), a potent inhibitor of α-KG-dependent enzymes which include the TET methylcytosine dioxygenases. Given their mutual exclusivity in myeloid neoplasms, IDH1, IDH2, and TET2 mutations may converge on a common oncogenic mechanism. Contrary to this expectation, we observed that they have distinct, and even opposite, effects on hematopoietic stem and progenitor cells in genetically engineered mice. Epigenetic and single-cell transcriptomic analyses revealed that Idh2R172K and Tet2 loss-of-function have divergent consequences on the expression and activity of key hematopoietic and leukemogenic regulators. Notably, chromatin accessibility and transcriptional deregulation in Idh2R172K cells were partially disconnected from DNA methylation alterations. These results highlight unanticipated divergent effects of IDH1/2 and TET2 mutations, providing support for the optimization of genotype-specific therapies.

The IDH somatic mutation status is an important basis for the diagnosis and classification of gliomas. We proposed a \"6-Step\" general radiomics model to noninvasively predict the IDH mutation status by simultaneously tuning combined multi-sequence MRI and optimizing the full radiomics processing pipeline. Radiomic features (n = 3776) were extracted from multi-sequence MRI (T1, T2, FLAIR, and T1Gd) in low-grade gliomas (LGGs), and a total of 45,360 radiomics pipeline were investigated according to different settings. The predictive ability of the general radiomics model was evaluated with regards to accuracy, stability, and efficiency. Based on numerous experiments, we finally reached an optimal pipeline for classifying IDH mutation status, namely the T2+FLAIR combined multi-sequence with the wavelet image filter, mean data normalization, PCC dimension reduction, RFE feature selection, and SVM classifier. The mean and standard deviation of AUC, accuracy, sensitivity, and specificity were 0.873 ± 0.05, 0.876 ± 0.09, 0.875 ± 0.11, and 0.877 ± 0.15, respectively. Furthermore, 14 radiomic features that best distinguished the IDH mutation status of the T2+FLAIR multi-sequence were analyzed, and the gray level co-occurrence matrix (GLCM) features were shown to be of high importance. Apart from the promising prediction of the molecular subtypes, this study also provided a general tool for radiomics investigation.