Enhancing predictability of IDH mutation status in glioma patients at initial diagnosis: a comparative analysis of radiomics from MRI, [18F]FET PET, and TSPO PET.

PURPOSE: According to the World Health Organization classification for tumors of the central nervous system, mutation status of the isocitrate dehydrogenase (IDH) genes has become a major diagnostic discriminator for gliomas. Therefore, imaging-based prediction of IDH mutation status is of high interest for individual patient management. We compared and evaluated the diagnostic value of radiomics derived from dual positron emission tomography (PET) and magnetic resonance imaging (MRI) data to predict the IDH mutation status non-invasively. METHODS: Eighty-seven glioma patients at initial diagnosis who underwent PET targeting the translocator protein (TSPO) using [18F]GE-180, dynamic amino acid PET using [18F]FET, and T1-/T2-weighted MRI scans were examined. In addition to calculating tumor-to-background ratio (TBR) images for all modalities, parametric images quantifying dynamic [18F]FET PET information were generated. Radiomic features were extracted from TBR and parametric images. The area under the receiver operating characteristic curve (AUC) was employed to assess the performance of logistic regression (LR) classifiers. To report robust estimates, nested cross-validation with five folds and 50 repeats was applied. RESULTS: TBRGE-180 features extracted from TSPO-positive volumes had the highest predictive power among TBR images (AUC 0.88, with age as co-factor 0.94). Dynamic [18F]FET PET reached a similarly high performance (0.94, with age 0.96). The highest LR coefficients in multimodal analyses included TBRGE-180 features, parameters from kinetic and early static [18F]FET PET images, age, and the features from TBRT2 images such as the kurtosis (0.97). CONCLUSION: The findings suggest that incorporating TBRGE-180 features along with kinetic information from dynamic [18F]FET PET, kurtosis from TBRT2, and age can yield very high predictability of IDH mutation status, thus potentially improving early patient management.

Distinguishing IDH mutation status in gliomas using FTIR-ATR spectra of peripheral blood plasma indicating clear traces of protein amyloid aggregation.

BACKGROUND: Glioma is a primary brain tumor and the assessment of its molecular profile in a minimally invasive manner is important in determining treatment strategies. Among the molecular abnormalities of gliomas, mutations in the isocitrate dehydrogenase (IDH) gene are strong predictors of treatment sensitivity and prognosis. In this study, we attempted to non-invasively diagnose glioma development and the presence of IDH mutations using multivariate analysis of the plasma mid-infrared absorption spectra for a comprehensive and sensitive view of changes in blood components associated with the disease and genetic mutations. These component changes are discussed in terms of absorption wavenumbers that contribute to differentiation. METHODS: Plasma samples were collected at our institutes from 84 patients with glioma (13 oligodendrogliomas, 17 IDH-mutant astrocytoma, 7 IDH wild-type diffuse glioma, and 47 glioblastomas) before treatment initiation and 72 healthy participants. FTIR-ATR spectra were obtained for each plasma sample, and PLS discriminant analysis was performed using the absorbance of each wavenumber in the fingerprint region of biomolecules as the explanatory variable. This data was used to distinguish patients with glioma from healthy participants and diagnose the presence of IDH mutations. RESULTS: The derived classification algorithm distinguished the patients with glioma from healthy participants with 83% accuracy (area under the curve (AUC) in receiver operating characteristic (ROC) = 0.908) and diagnosed the presence of IDH mutation with 75% accuracy (AUC = 0.752 in ROC) in cross-validation using 30% of the total test data. The characteristic changes in the absorption spectra suggest an increase in the ratio of ß-sheet structures in the conformational composition of blood proteins of patients with glioma. Furthermore, these changes were more pronounced in patients with IDH-mutant gliomas. CONCLUSIONS: The plasma infrared absorption spectra could be used to diagnose gliomas and the presence of IDH mutations in gliomas with a high degree of accuracy. The spectral shape of the protein absorption band showed that the ratio of ß-sheet structures in blood proteins was significantly higher in patients with glioma than in healthy participants, and protein aggregation was a distinct feature in patients with glioma with IDH mutations.

IDH1 mutation is associated with improved resection rates, progression-free survival and overall survival in patients with anaplastic astrocytomas.

PURPOSE: The introduction of molecular markers in to the diagnosis of gliomas has changed the therapeutic approach to this tumors. The aim of this study was to examine the impact of surgery on anaplastic astrocytomas (AA), which has not previously been fully elucidated. METHODS: This was a retrospective study involving a total of 143 patients who underwent surgery for primary AA in our department between 1995 and 2020. RESULTS: Total tumor resection was achieved more often in patients with IDH-mutant tumors (41.09%) than in patients with IDH-wildtype tumors (30.76%). The median PFS was 1876 days for patients with IDH1 mutations and 238 days for patients with IDH-wildtype tumors. The 1-, 3-, 5- and 10-year PFS were longer in patients with total tumor resection and IDH-mutant AA (86.2%, 69%, 65.5% and 44.8%, respectively) than in patients with subtotal tumor resection and IDH-mutant AA (83.3%, 55.6%, 41.7% and 25%, respectively) and even longer compared to all IDH-wildtype tumors. The median OS was 2472 days for patients with IDH1 mutations and 434 days for patients with IDH-wildtype tumors. The 3-, 5- and 10-year OS times were longer in patients with total tumor resection and IDH-mutant AA (89.2%, 85.2% and 72.6%, respectively) than in patients with subtotal tumor resection and IDH-mutant AA (85.9%, 73.7% and 52.6%, respectively) and were even longer compared to all IDH-wildtype tumors. CONCLUSION: Total tumor resection is more common with IDH-mutant AA than with IDH-wildtype tumors. Patients with IDH-mutant AA had significantly better PFS and OS after total tumor resection than after subtotal tumor resection and biopsy.

Antiepileptic Strategies for Patients with Primary and Metastatic Brain Tumors.

OPINION STATEMENT: Seizure activity is common in patients with primary and metastatic brain tumors, affecting more than 50% of cases over the course of their disease. Several mechanisms contribute to brain tumor-related epilepsy (BTRE), including a pro-inflammatory environment, excessive secretion of glutamate and an increase in neuronal excitatory tone, reduction of GABAergic inhibitory activity, and an increase in 2-hydroxygluturate production in isocitrate dehydrogenase mutant tumors. After a verified seizure in a brain tumor patient, the consensus is that BTRE has developed, and it is necessary to initiate an antiepileptic drug (AED). It is not recommended to initiate AED prophylaxis. Second- and third-generation AEDs are the preferred options for initiation, due to a lack of hepatic enzyme induction and reduced likelihood for drug-drug interactions, especially in regard to neoplastic treatment. The efficacy of appropriate AEDs for patients with BTRE is fairly equivalent, although some data suggests that levetiracetam may be slightly more active in suppressing seizures than other AEDs. The consensus among most Neuro-Oncology providers is to initiate levetiracetam monotherapy after a first seizure in a brain tumor patient, as long as the patient does not have any psychiatric co-morbidities. If levetiracetam is not tolerated well or is ineffective, other appropriate initial AED options for monotherapy or as an add-on anticonvulsant include lacosamide, valproic acid, briviracetam, lamotrigine, and perampanel.

Development of End-to-End AI-Based MRI Image Analysis System for Predicting IDH Mutation Status of Patients with Gliomas: Multicentric Validation.

Radiogenomics has shown potential to predict genomic phenotypes from medical images. The development of models using standard-of-care pre-operative MRI images, as opposed to advanced MRI images, enables a broader reach of such models. In this work, a radiogenomics model for IDH mutation status prediction from standard-of-care MRIs in patients with glioma was developed and validated using multicentric data. A cohort of 142 (wild-type: 32.4%) patients with glioma retrieved from the TCIA/TCGA was used to train a logistic regression model to predict the IDH mutation status. The model was evaluated using retrospective data collected in two distinct hospitals, comprising 36 (wild-type: 63.9%) and 53 (wild-type: 75.5%) patients. Model development utilized ROC analysis. Model discrimination and calibration were used for validation. The model yielded an AUC of 0.741 vs. 0.716 vs. 0.938, a sensitivity of 0.784 vs. 0.739 vs. 0.875, and a specificity of 0.657 vs. 0.692 vs. 1.000 on the training, test cohort 1, and test cohort 2, respectively. The assessment of model fairness suggested an unbiased model for age and sex, and calibration tests showed a p < 0.05. These results indicate that the developed model allows the prediction of the IDH mutation status in gliomas using standard-of-care MRI images and does not appear to hold sex and age biases.

Plasma ctDNA liquid biopsy of IDH1, TERTp, and EGFRvIII mutations in glioma.

Background: Circulating tumor DNA has emerging clinical applications in several cancers; however, previous studies have shown low sensitivity in glioma. We investigated if 3 key glioma gene mutations IDH1, TERTp, and EGFRvIII could be reliably detected in plasma by droplet digital polymerase chain reaction (ddPCR) thereby demonstrating the potential of this technique for glioma liquid biopsy. Methods: We analyzed 110 glioma patients from our biobank with a total of 359 plasma samples (median 4 samples per patient). DNA was isolated from plasma and analyzed for IDH1, TERTp, and EGFRvIII mutations using ddPCR. Results: Total cfDNA was significantly associated with tumor grade, tumor volume, and both overall and progression-free survival for all gliomas as well as the grade 4 glioblastoma subgroup, but was not reliably associated with changes in tumor volume/progression during the patients' postoperative time course. IDH1 mutation was detected with 84% overall sensitivity across all plasma samples and 77% in the preoperative samples alone; however, IDH1 mutation plasma levels were not associated with tumor progression or survival. IDH1m plasma levels were not associated with pre- or postsurgery progression or survival. The TERTp C228T mutation was detected in the plasma ctDNA in 88% but the C250T variant in only 49% of samples. The EGFRvIII mutation was detected in plasma in 5 out of 7 patients (71%) with tissue EGFRvIII mutations in tumor tissue. Conclusions: Plasma ctDNA mutations detected with ddPCR provide excellent diagnostic sensitivity for IDH1, TERTp-C228T, and EGFRvIII mutations in glioma patients. Total cfDNA may also assist with prognostic information. Further studies are needed to validate these findings and the clinical role of ctDNA in glioma.

Volumetric Response and Survival of Patients With Bulky IDH-Mutated Grade 3 Glioma Managed With FET-FDG-Guided Integrated Boost IMRT.

AIMS: Despite relatively favourable outcomes associated with IDH-mutant grade 3 gliomas, many patients present with diffuse non-enhancing disease involving multiple brain regions, prompting concern over both durable disease control and the morbidity associated with large volume radiation therapy. This study audits volumetric response, survival and functional outcomes in this 'large volume' subgroup that undergoes intensity modulated radiation therapy (IMRT). MATERIALS AND METHODS: From a prospective database of 187 patients with IDH-mutant grade 3 gliomas managed with IMRT between 2008 and 2020, recorded PTV was divided into quartiles. The top quartile, termed the 'large volume cohort' (LVC), was identified. IMRT involved FET-FDG guided integrated boost (59.4/54Gy in 33 fractions). Manual volumetric segmentation of baseline, four months and 13 months post-IMRT tumour were performed for T1, T2 and T1gd MRI sequences. The primary endpoint was volumetric reduction on the T1 and T2 sequences at 13 months and analysed with relapse-free survival (RFS) and overall survival (OS). Morbidity endpoints were assessed at year four post-IMRT and included performance status (ECOG PS) and employment outcomes. RESULTS: The fourth quartile (LVC) identified 44 patients for whom volumetric analysis was available. The LVC had median PTV of 320cm3 compared to 186.2cm3 for the total group. Anaplastic astrocytoma and oligodendroglioma were equally distributed and tumour sites were frontal (54%), temporal (18%) and parietal lobes (16%). Median follow-up for survivors was 71.5 months. Projected 10-year RFS and OS in LVC was 40% and 62%, compared to 53% and 62% respectively in the overall cohort. The RFS (p = 0.06) and OS (p = 0.65) of the LVC was not significantly different to other PTV quartiles; however the impact of PTV volume reached significance when analysed as a continuous variable (RFS p < 0.01; OS p = 0.02). Median T1 volumes were 26.1cm3, 8.0cm3 and 5.3cm3 at months +0, +3 and +12, respectively. The corresponding T2 volumes were 120.8cm3, 29.1cm3 and 26.3cm3. The median T1 and T2 volume reductions were 77% (q1-3: 57-92%) and 78% (q1-3: 60-85%) at 13 months post-IMRT. Initial T2 volume was associated with worse RFS (p = 0.04) but not OS (p = 0.96). There was no association between median T2 volume reduction and RFS (p = 0.77). For patients assessable at year 4 post-IMRT, no late CTCAE Grade 3/4 toxicity events were recognised. 92% of patients were ECOG PS 0-1, 45% were employed at prior capacity and 28% were working with impairment. CONCLUSION: Patients with large volume IDH-mutant Grade 3 glioma demonstrated significant tumour reduction post-IMRT, and good long-term outcomes with respect to survival and functional status. Although larger IMRT volumes were associated with poorer RFS, this was also associated with the initial volume of non-enhancing tumour.

Extracranial metastatic oligodendroglioma with molecular progression, case presentation.

BACKGROUND: Extraneural metastasis of central nervous system tumors is generally rare and most often reported in glioblastomas and medulloblastomas, whereas oligodendrogliomas seem to have the lowest risk of extracranial metastasis. Given its infrequent occurrence, both the diagnosis and therapy of metastatic oligodendroglioma is often challenging. CASE PRESENTATION: This case study presents an oligodendroglioma, the isocitrate dehydrogenase 1 (IDH1) mutant, 1p/19q-codeleted tumor with bone marrow metastasis. The significance of this case lies in the comprehensive molecular analysis conducted for both the primary tumor and the metastasis. Chromosome 7 trisomy and chromosome 10 monosomy (+ 7/-10) were detected in the metastasis indicating molecular progression, which, to the best of our knowledge, has not been previously documented in metastatic oligodendroglioma. CONCLUSIONS: This case study serves additional information for better understanding of the metastatic capabilities of CNS tumors.

[Malignant transformation of Ollier disease-related multiple glioma with IDH1 p.R132C mutation].

A 21-year-old man who was diagnosed with Ollier disease at the age of 1 year developed incidental multiple gliomas at the age of 15 years. Subsequently, the multiple gliomas enlarged and the patient underwent three surgical removals. Genetic analysis revealed the IDH1 p.R132C mutation in the gliomas, and histopathology showed malignant transformation. Despite multimodality treatment, the gliomas could not be controlled, and the patient died at the age of 23 years. Ollier disease is a rare disease with IDH1/2 mutations and is often associated with gliomas. However, there are very few reports on genetic analysis of IDH1/2 mutations and long-term follow-up in Ollier disease-related gliomas. Genetic analysis of IDH mutations may contribute to the elucidation of its pathogenesis. The cross-departmental collaboration is required for long-term follow-up of Ollier disease-related gliomas.

Two-Stage Training Framework Using Multicontrast MRI Radiomics for IDH Mutation Status Prediction in Glioma.

Purpose To develop a radiomics framework for preoperative MRI-based prediction of isocitrate dehydrogenase (IDH) mutation status, a crucial glioma prognostic indicator. Materials and Methods Radiomics features (shape, first-order statistics, and texture) were extracted from the whole tumor or the combination of nonenhancing, necrosis, and edema regions. Segmentation masks were obtained via the federated tumor segmentation tool or the original data source. Boruta, a wrapper-based feature selection algorithm, identified relevant features. Addressing the imbalance between mutated and wild-type cases, multiple prediction models were trained on balanced data subsets using random forest or XGBoost and assembled to build the final classifier. The framework was evaluated using retrospective MRI scans from three public datasets (The Cancer Imaging Archive [TCIA, 227 patients], the University of California San Francisco Preoperative Diffuse Glioma MRI dataset [UCSF, 495 patients], and the Erasmus Glioma Database [EGD, 456 patients]) and internal datasets collected from the University of Texas Southwestern Medical Center (UTSW, 356 patients), New York University (NYU, 136 patients), and University of Wisconsin-Madison (UWM, 174 patients). TCIA and UTSW served as separate training sets, while the remaining data constituted the test set (1617 or 1488 testing cases, respectively). Results The best performing models trained on the TCIA dataset achieved area under the receiver operating characteristic curve (AUC) values of 0.89 for UTSW, 0.86 for NYU, 0.93 for UWM, 0.94 for UCSF, and 0.88 for EGD test sets. The best performing models trained on the UTSW dataset achieved slightly higher AUCs: 0.92 for TCIA, 0.88 for NYU, 0.96 for UWM, 0.93 for UCSF, and 0.90 for EGD. Conclusion This MRI radiomics-based framework shows promise for accurate preoperative prediction of IDH mutation status in patients with glioma. Keywords: Glioma, Isocitrate Dehydrogenase Mutation, IDH Mutation, Radiomics, MRI Supplemental material is available for this article. Published under a CC BY 4.0 license. See also commentary by Moassefi and Erickson in this issue.

SEOM-GEINO clinical guidelines for grade 2 gliomas (2023).

The 2021 World Health Organization (WHO) classification has updated the definition of grade 2 gliomas and the presence of isocitrate dehydrogenase (IDH) mutation has been deemed the cornerstone of diagnosis. Though slow-growing and having a low proliferative index, grade 2 gliomas are incurable by surgery and complementary treatments are vital to improving prognosis. This guideline provides recommendations on the multidisciplinary treatment of grade 2 astrocytomas and oligodendrogliomas based on the best evidence available.

Advancing noninvasive glioma classification with diffusion radiomics: Exploring the impact of signal intensity normalization.

Background: This study investigates the influence of diffusion-weighted Magnetic Resonance Imaging (DWI-MRI) on radiomic-based prediction of glioma types according to molecular status and assesses the impact of DWI intensity normalization on model generalizability. Methods: Radiomic features, compliant with image biomarker standardization initiative standards, were extracted from preoperative MRI of 549 patients with diffuse glioma, known IDH, and 1p19q-status. Anatomical sequences (T1, T1c, T2, FLAIR) underwent N4-Bias Field Correction (N4) and WhiteStripe normalization (N4/WS). Apparent diffusion coefficient (ADC) maps were normalized using N4 or N4/z-score. Nine machine-learning algorithms were trained for multiclass prediction of glioma types (IDH-mutant 1p/19q codeleted, IDH-mutant 1p/19q non-codeleted, IDH-wild type). Four approaches were compared: Anatomical, anatomical + ADC naive, anatomical + ADC N4, and anatomical + ADC N4/z-score. The University of California San Francisco (UCSF)-glioma dataset (n = 409) was used for external validation. Results: Naïve-Bayes algorithms yielded overall the best performance on the internal test set. Adding ADC radiomics significantly improved AUC from 0.79 to 0.86 (P = .011) for the IDH-wild-type subgroup, but not for the other 2 glioma subgroups (P > .05). In the external UCSF dataset, the addition of ADC radiomics yielded a significantly higher AUC for the IDH-wild-type subgroup (P ≤ .001): 0.80 (N4/WS anatomical alone), 0.81 (anatomical + ADC naive), 0.81 (anatomical + ADC N4), and 0.88 (anatomical + ADC N4/z-score) as well as for the IDH-mutant 1p/19q non-codeleted subgroup (P < .012 each). Conclusions: ADC radiomics can enhance the performance of conventional MRI-based radiomic models, particularly for IDH-wild-type glioma. The benefit of intensity normalization of ADC maps depends on the type and context of the used data.

Accuracy of Radiomics in Predicting IDH Mutation Status in Diffuse Gliomas: A Bivariate Meta-Analysis.

Purpose To perform a systematic review and meta-analysis assessing the predictive accuracy of radiomics in the noninvasive determination of isocitrate dehydrogenase (IDH) status in grade 4 and lower-grade diffuse gliomas. Materials and Methods A systematic search was performed in the PubMed, Scopus, Embase, Web of Science, and Cochrane Library databases for relevant articles published between January 1, 2010, and July 7, 2021. Pooled sensitivity and specificity across studies were estimated. Risk of bias was evaluated using Quality Assessment of Diagnostic Accuracy Studies-2, and methods were evaluated using the radiomics quality score (RQS). Additional subgroup analyses were performed according to tumor grade, RQS, and number of sequences used (PROSPERO ID: CRD42021268958). Results Twenty-six studies that included 3280 patients were included for analysis. The pooled sensitivity and specificity of radiomics for the detection of IDH mutation were 79% (95% CI: 76, 83) and 80% (95% CI: 76, 83), respectively. Low RQS scores were found overall for the included works. Subgroup analyses showed lower false-positive rates in very low RQS studies (RQS < 6) (meta-regression, z = -1.9; P = .02) compared with adequate RQS studies. No substantial differences were found in pooled sensitivity and specificity for the pure grade 4 gliomas group compared with the all-grade gliomas group (81% and 86% vs 79% and 79%, respectively) and for studies using single versus multiple sequences (80% and 77% vs 79% and 82%, respectively). Conclusion The pooled data showed that radiomics achieved good accuracy performance in distinguishing IDH mutation status in patients with grade 4 and lower-grade diffuse gliomas. The overall methodologic quality (RQS) was low and introduced potential bias. Keywords: Neuro-Oncology, Radiomics, Integration, Application Domain, Glioblastoma, IDH Mutation, Radiomics Quality Scoring Supplemental material is available for this article. Published under a CC BY 4.0 license.

Dissecting the prognostic signature of patients with astrocytoma isocitrate dehydrogenase-mutant grade 4: a large multicenter, retrospective study.

BACKGROUND: The World Health Organization (WHO) 2021 classification of central nervous system (CNS) tumors classified astrocytoma isocitrate dehydrogenase-mutant (A IDHm) with either microvascular proliferation and/or necrosis or homozygous deletion of CDKN2A/B as CNS grade 4 (CNS WHO G4), introducing a distinct entity and posing new challenges to physicians for appropriate management and prognostication. PATIENTS AND METHODS: We retrospectively collected information about patients diagnosed with A IDHm CNS WHO G4 at three reference neuro-oncological Italian centers and correlated them with survival. RESULTS: A total of 133 patients were included. Patients were young (median age 41 years) and most received post-operative treatment including chemo-radiation (n = 101) and/or temozolomide maintenance (n = 112). With a median follow-up of 51 months, the median overall survival (mOS) was 31.2 months, with a 5-year survival probability of 26%. In the univariate analysis, complete resection (mOS: 40.2 versus 26.3 months, P = 0.03), methyl-guaninemethyltransferase (MGMT) promoter methylation (mOS: 40.7 versus 18 months, P = 0.0136), and absence of telomerase reverse transcriptase (TERT) promoter mutation (mOS: 40.7 versus 18 months, P = 0.0003) correlated with better prognosis. In the multivariate models, lack of TERT promoter mutation [hazard ratio (HR) 0.23, 95% confidence interval (CI) 0.07-0.82, P = 0.024] and MGMT methylation (HR 0.40, 95% CI 0.20-0.81, P = 0.01) remained associated with improved survival. CONCLUSIONS: This is the largest experience in Western countries exploring the prognostic signature of patients with A IDHm CNS G4. Our results show that MGMT promoter methylation and TERT promoter mutation may impact clinical outcomes. This may support physicians in prognostication, clinical management, and design of future studies of this distinct diagnostic entity.

Comparison of MRI Sequences to Predict IDH Mutation Status in Gliomas Using Radiomics-Based Machine Learning.

OBJECTIVES: Regarding the 2021 World Health Organization (WHO) classification of central nervous system (CNS) tumors, the isocitrate dehydrogenase (IDH) mutation status is one of the most important factors for CNS tumor classification. The aim of our study is to analyze which of the commonly used magnetic resonance imaging (MRI) sequences is best suited to obtain this information non-invasively using radiomics-based machine learning models. We developed machine learning models based on different MRI sequences and determined which of the MRI sequences analyzed yields the highest discriminatory power in predicting the IDH mutation status. MATERIAL AND METHODS: In our retrospective IRB-approved study, we used the MRI images of 106 patients with histologically confirmed gliomas. The MRI images were acquired using the T1 sequence with and without administration of a contrast agent, the T2 sequence, and the Fluid-Attenuated Inversion Recovery (FLAIR) sequence. To objectively compare performance in predicting the IDH mutation status as a function of the MRI sequence used, we included only patients in our study cohort for whom MRI images of all four sequences were available. Seventy-one of the patients had an IDH mutation, and the remaining 35 patients did not have an IDH mutation (IDH wild-type). For each of the four MRI sequences used, 107 radiomic features were extracted from the corresponding MRI images by hand-delineated regions of interest. Data partitioning into training data and independent test data was repeated 100 times to avoid random effects associated with the data partitioning. Feature preselection and subsequent model development were performed using Random Forest, Lasso regression, LDA, and Naïve Bayes. The performance of all models was determined with independent test data. RESULTS: Among the different approaches we examined, the T1-weighted contrast-enhanced sequence was found to be the most suitable for predicting IDH mutations status using radiomics-based machine learning models. Using contrast-enhanced T1-weighted MRI images, our seven-feature model developed with Lasso regression achieved a mean area under the curve (AUC) of 0.846, a mean accuracy of 0.792, a mean sensitivity of 0.847, and a mean specificity of 0.681. The administration of contrast agents resulted in a significant increase in the achieved discriminatory power. CONCLUSIONS: Our analyses show that for the prediction of the IDH mutation status using radiomics-based machine learning models, among the MRI images acquired with the commonly used MRI sequences, the contrast-enhanced T1-weighted images are the most suitable.

Multi-omics reveals the impact of cancer-associated fibroblasts on the prognosis and treatment response of adult diffuse highest-grade gliomas.

Background: Cancer associated fibroblasts (CAF), an important cancer-promoting and immunosuppressive component of the tumor immune microenvironment (TIME), have recently been found to infiltrate adult diffuse highest-grade gliomas (ADHGG) (gliomas of grade IV). Methods: Gene expression and clinical data of ADHGG patients were obtained from the CGGA and TCGA databases. Consensus clustering was used to identify CAF subtypes based on CAF key genes acquired from single-cell omics and spatial transcriptomomics. CIBERSORT, ssGSEA, MCPcounter, and ESTIMATE analyses were used to assess the TIME of GBM. Survival analysis, drug sensitivity analysis, TCIA database, TIDE and cMap algorithms were used to compare the prognosis and treatment response between patients with different CAF subtypes. An artificial neural network (ANN) model based on random forest was constructed to exactly identify CAF subtypes, which was validated in a real-world patient cohort of ADHGG. Results: Consensus clustering classified ADHGG into two CAF subtypes. Compared with subtype B, patients with ADHGG subtype A had a poorer prognosis, worse responsiveness to immunotherapy and radiotherapy, higher CAF infiltration in TIME, but higher sensitivity to temozolomide. Furthermore, patients with subtype A had a much lower proportion of IDH mutations. Finally, the ANN model based on five genes (COL3A1, COL1A2, CD248, FN1, and COL1A1) could exactly discriminate CAF subtypes, and the validation of the real-world cohort indicated consistent results with the bioinformatics analyses. Conclusion: This study revealed a novel CAF subtype to distinguish ADHGG patients with different prognosis and treatment responsiveness, which may be helpful for accurate clinical decision-making of ADHGG.

Glioma Grade and Molecular Markers: Comparing Machine-Learning Approaches Using VASARI (Visually AcceSAble Rembrandt Images) Radiological Assessment.

OBJECTIVES: This study aimed to leverage Visually AcceSAble Rembrandt Images (VASARI) radiological features, extracted from magnetic resonance imaging (MRI) scans, and machine-learning techniques to predict glioma grade, isocitrate dehydrogenase (IDH) mutation status, and O6-methylguanine-DNA methyltransferase (MGMT) methylation. METHODOLOGY: A retrospective evaluation was undertaken, analyzing MRI and molecular data from 107 glioma patients treated at a tertiary hospital. Patients underwent MRI scans using established protocols and were evaluated based on VASARI criteria. Tissue samples were assessed for glioma grade and underwent molecular testing for IDH mutations and MGMT methylation. Four machine learning models, namely, Random Forest, Elastic-Net, multivariate adaptive regression spline (MARS), and eXtreme Gradient Boosting (XGBoost), were trained on 27 VASARI features using fivefold internal cross-validation. The models' predictive performances were assessed using the area under the curve (AUC), sensitivity, and specificity. RESULTS: For glioma grade prediction, XGBoost exhibited the highest AUC (0.978), sensitivity (0.879), and specificity (0.964), with f6 (proportion of non-enhancing) and f12 (definition of enhancing margin) as the most important predictors. In predicting IDH mutation status, XGBoost achieved an AUC of 0.806, sensitivity of 0.364, and specificity of 0.880, with f1 (tumor location), f12, and f30 (perpendicular diameter to f29) as primary predictors. For MGMT methylation, XGBoost displayed an AUC of 0.580, sensitivity of 0.372, and specificity of 0.759, highlighting f29 (longest diameter) as the key predictor. CONCLUSIONS: This study underscores the robust potential of combining VASARI radiological features with machine learning models in predicting glioma grade, IDH mutation status, and MGMT methylation. The best and most balanced performance was achieved using the XGBoost model. While the prediction of glioma grade showed promising results, the sensitivity in discerning IDH mutations and MGMT methylation still leaves room for improvement. Follow-up studies with larger datasets and more advanced artificial intelligence techniques can further refine our understanding and management of gliomas.

Comprehensive analysis of the REST transcription factor regulatory networks in IDH mutant and IDH wild-type glioma cell lines and tumors.

The RE1-silencing transcription factor (REST) acts either as a repressor or activator of transcription depending on the genomic and cellular context. REST is a key player in brain cell differentiation by inducing chromatin modifications, including DNA methylation, in a proximity of its binding sites. Its dysfunction may contribute to oncogenesis. Mutations in IDH1/2 significantly change the epigenome contributing to blockade of cell differentiation and glioma development. We aimed at defining how REST modulates gene activation and repression in the context of the IDH mutation-related phenotype in gliomas. We studied the effects of REST knockdown, genome wide occurrence of REST binding sites, and DNA methylation of REST motifs in IDH wild type and IDH mutant gliomas. We found that REST target genes, REST binding patterns, and TF motif occurrence proximal to REST binding sites differed in IDH wild-type and mutant gliomas. Among differentially expressed REST targets were genes involved in glial cell differentiation and extracellular matrix organization, some of which were differentially methylated at promoters or gene bodies. REST knockdown differently impacted invasion of the parental or IDH1 mutant glioma cells. The canonical REST-repressed gene targets showed significant correlation with the GBM NPC-like cellular state. Interestingly, results of REST or KAISO silencing suggested the interplay between these TFs in regulation of REST-activated and repressed targets. The identified gene regulatory networks and putative REST cooperativity with other TFs, such as KAISO, show distinct REST target regulatory networks in IDH-WT and IDH-MUT gliomas, without concomitant DNA methylation changes. We conclude that REST could be an important therapeutic target in gliomas.