Low-grade Gliomas Research Articles

Multimodal Deep Learning Improves Recurrence Risk Prediction in Pediatric Low-Grade Gliomas.

Postoperative recurrence risk for pediatric low-grade gliomas (pLGGs) is challenging to predict by conventional clinical, radiographic, and genomic factors. We investigated if deep learning of MRI tumor features could improve postoperative pLGG risk stratification. We used pre-trained deep learning (DL) tool designed for pLGG segmentation to extract pLGG imaging features from preoperative T2-weighted MRI from patients who underwent surgery (DL-MRI features). Patients were pooled from two institutions: Dana Farber/Boston Children's Hospital (DF/BCH) and the Children's Brain Tumor Network (CBTN). We trained three DL logistic hazard models to predict postoperative event-free survival (EFS) probabilities with 1) clinical features, 2) DL-MRI features, and 3) multimodal (clinical and DL-MRI features). We evaluated the models with a time-dependent Concordance Index (Ctd) and risk group stratification with Kaplan Meier plots and log-rank tests. We developed an automated pipeline integrating pLGG segmentation and EFS prediction with the best model. Of the 396 patients analyzed (median follow-up: 85 months, range: 1.5-329 months), 214 (54%) underwent gross total resection and 110 (28%) recurred. The multimodal model improved EFS prediction compared to the DL-MRI and clinical models (Ctd: 0.85 (95% CI: 0.81-0.93), 0.79 (95% CI: 0.70-0.88), and 0.72 (95% CI: 0.57-0.77), respectively). The multimodal model improved risk-group stratification (3-year EFS for predicted high-risk: 31% versus low-risk: 92%, p<0.0001). DL extracts imaging features that can inform postoperative recurrence prediction for pLGG. Multimodal DL improves postoperative risk stratification for pLGG and may guide postoperative decision-making. Larger, multicenter training data may be needed to improve model generalizability.

Comparative Analysis of Brain Coping Mechanisms in Small Left-Hemisphere Lesions: Incidental vs. Symptomatic Gliomas.

Incidentally discovered low-grade gliomas (iLGGs) are very rare and little is still known about their associated functional imaging activation patterns, white-matter status, and plasticity potential. Recent studies shed light on several clinical factors responsible for the good clinical status observed in these patients versus those with their symptomatic counterpart (sLGGs), including small volume. Comparisons were typically carried out by comparing iLGGs with the wider and more heterogeneous sLGG group. In this study, we investigated whether iLGGs affect the brain differently from comparably small sLGGs. Starting from a sample of 13 patients with iLGG, in the current comparative cross-sectional study, we identified a group of patients with sLGGs, primarily matched by lesion volume. We looked for potential differences between the two groups in language-related functional and structural parameters (the fMRI network associated with naming and white-matter fascicles). The t-test did not show significant differences in the fMRI network, but these emerged when performing masking. No significant differences were observed at the white-matter level. Given that small volumes characterized both groups and that demographic variables were comparable, too, we hypothesized that differences between the two groups could be attributed to alternative lesion-related parameters. We discussed these findings from clinical and neurosurgical perspectives.

Upper brainstem pediatric low-grade gliomas: review and neuroendoscopic approach.

Pediatric brain tumors, particularly those affecting the brainstem, present a significant challenge due to their intricate anatomical location and diverse classification. This review explores the classification, anatomical considerations, and surgical approaches for pediatric brainstem tumors, focusing on recent updates from the World Health Organization (WHO) classification. Brainstem tumors encompass a spectrum from diffuse gliomas to focal intrinsic and exophytic types, each presenting unique clinical and surgical challenges. Surgical strategies have evolved with advancements in neuroimaging and surgical techniques, emphasizing approaches such as neuroendoscopy and tailored incisions to minimize damage to critical structures. Despite the complexities involved, recent developments offer promising outcomes in tumor resection and patient management, highlighting ongoing advancements in neurosurgical care for pediatric brain tumors.

Prognostic value of anoikis-related genes revealed using multi-omics analysis and machine learning based on lower-grade glioma features and tumor immune microenvironment

BackgroundThe investigation explores the involvement of anoikis-related genes (ARGs) in lower-grade glioma (LGG), seeking to provide fresh insights into the disease's underlying mechanisms and to identify potential targets for therapy. MethodsWe applied unsupervised clustering techniques to categorize LGG patients into distinct molecular subtypes based on ARGs with prognostic significance. Additionally, various machine learning algorithms were employed to pinpoint genes most strongly correlated with patient outcomes, which were then used to develop and assess risk profiles. ResultsOur analysis identified two distinct molecular subtypes of LGG, each with significantly different prognoses. Patients in Cluster 2 had a median survival of 2.036 years, markedly shorter than the 7.994 years observed in Cluster 1 (P < 0.001). We also constructed a six-gene ARG signature that efficiently classified patients into two risk categories, showing median survival durations of 4.084 years for the high-risk group and 10.304 years for the low-risk group (P < 0.001). Significantly, the immune profiles, tumor mutation characteristics, and drug sensitivity varied greatly among these risk groups. The high-risk group was characterized by a “cold” tumor microenvironment (TME), a lower IDH1 mutation rate (61.7 % vs. 91.4 %), a higher TP53 mutation rate (53.7 % vs. 38.9 %), and greater sensitivity to targeted therapies such as QS11 and PF-562271. Furthermore, our nomogram, integrating risk scores with clinicopathological features, demonstrated strong predictive accuracy for clinical outcomes in LGG patients, with an AUC of 0.903 for the first year. The robustness of this prognostic model was further validated through internal cross-validation and across three external cohorts. ConclusionsThe evidence from our research suggests that ARGs could potentially serve as reliable indicators for evaluating immunotherapy effectiveness and forecasting clinical results in patients with LGG.

Accurate low and high grade glioma classification using free water eliminated diffusion tensor metrics and ensemble machine learning

Glioma, a predominant type of brain tumor, can be fatal. This necessitates an early diagnosis and effective treatment strategies. Current diagnosis is based on biopsy, prompting the need for non invasive neuroimaging alternatives. Diffusion tensor imaging (DTI) is a promising method for studying the pathophysiological impact of tumors on white matter (WM) tissue. Single-shell DTI studies in brain glioma patients have not accounted for free water (FW) contamination due to tumors. This study aimed to (a) assess the efficacy of a two-compartment DTI model that accounts for FW contamination and (b) identify DTI-based biomarkers to classify low-grade glioma (LGG) and high-grade glioma (HGG) patients. DTI data from 86 patients (LGG n = 39, HGG n = 47) were obtained using a routine clinical imaging protocol. DTI metrics of tumorous regions and normal-appearing white matter (NAWM) were evaluated. Advanced stacked-based ensemble learning was employed to classify LGG and HGG patients using both single- and two-compartment DTI model measures. The DTI metrics of the two-compartment model outperformed those of the standard single-compartment DTI model in terms of sensitivity, specificity, and area under the curve of receiver operating characteristic (AUC-ROC) score in classifying LGG and HGG patients. Four features (out of 16 features), namely fractional anisotropy (FA) of the edema and core region and FA and mean diffusivity of the NAWM region, showed superior performance (sensitivity = 92%, specificity = 90%, and AUC-ROC = 90%) in classifying LGG and HGG. This demonstrates that both tumorous and NAWM regions may be differentially affected in LGG and HGG patients. Our results demonstrate the significance of using a two-compartment DTI model that accounts for FW contamination by improving diagnostic accuracy. This improvement may eventually aid in planning treatment strategies for glioma patients.

Identification of DNA methylation-regulated WEE1 with potential implications in prognosis and immunotherapy for low-grade glioma.

WEE1 is a critical kinase in the DNA damage response pathway and has been shown to be effective in treating serous uterine cancer. However, its role in gliomas, specifically low-grade glioma (LGG), remains unclear. The impact of DNA methylation on WEE1 expression and its correlation with the immune landscape in gliomas also need further investigation. This study used data from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), and Gene Expression Omnibus (GEO) and utilized various bioinformatics tools to analyze gene expression, survival, gene correlation, immune score, immune infiltration, genomic alterations, tumor mutation burden, microsatellite instability, clinical characteristics of glioma patients, WEE1 DNA methylation, prognostic analysis, single-cell gene expression distribution in glioma tissue samples, and immunotherapy response prediction based on WEE1 expression. WEE1 was upregulated in LGG and glioblastoma (GBM), but it had a more significant prognostic impact in LGG compared to other cancers. High WEE1 expression was associated with poorer prognosis in LGG, particularly when combined with wild-type IDH. The WEE1 inhibitor MK-1775 effectively inhibited the proliferation and migration of LGG cell lines, which were more sensitive to WEE1 inhibition. DNA methylation negatively regulated WEE1, and high DNA hypermethylation of WEE1 was associated with better prognosis in LGG than in GBM. Combining WEE1 inhibition and DNA methyltransferase inhibition showed a synergistic effect. Additionally, downregulation of WEE1 had favorable predictive value in immunotherapy response. Co-expression network analysis identified key genes involved in WEE1-mediated regulation of immune landscape, differentiation, and metastasis in LGG. Our study shows that WEE1 is a promising indicator for targeted therapy and prognosis evaluation. Notably, significant differences were observed in the role of WEE1 between LGG and GBM. Further investigation into WEE1 inhibition, either in combination with DNA methyltransferase inhibition or immunotherapy, is warranted in the context of LGG.

Extra-temporal pediatric low-grade gliomas and epilepsy.

Low-grade gliomas, especially glioneuronal tumors, are a common cause of epilepsy in children. Seizures associated with low-grade pediatric tumors are medically refractory and present a significant burden to patients. Often, morbidity and patients´ quality of life are determined rather by the control of seizures than the oncological process itself and the resolution of epilepsy represents an important part in the treatment of LGGs. The pathogenesis of tumor-related seizures in focal LGG tumors is multifactorial, and mechanisms differ probably among patients and tumor types. Pediatric low-grade tumors associated with epilepsy include a series of neoplasms that have a pure astrocytic or glioneuronal lineage. They are usually benign tumors with a neocortical localization typically in the temporal lobes, but also in other supratentorial locations. Gangliogliomas and dysembryoplastic neuroepithelial tumors (DNET) are the most common entities together with astrocytic gliomas (pilocytic astrocytomas and pleomorphic xanthoastrocytoma) and angiocentric gliomas, and dual pathology is found in up to 40% of glioneuronal tumors. The treatment of low-grade gliomas and associated epilepsy is based mainly on resection and the extent of surgery is the main predictor of postoperative seizure control in patients with a LGG. Long-term epilepsy-associated tumors (LEATs) tend to be well-circumscribed, and therefore, the chances for a complete resection and epilepsy control with a safe approach are very high. New treatments have emerged as alternatives to open microsurgical approaches, including laser thermal ablation or the use of BRAF inhibitors. Future advances in identifying seizure-related biomarkers and molecular tumor pathways will facilitate targeted treatment strategies that will have a deep impact both in oncologic and epilepsy outcomes.

Single-cell dissection of the human blood-brain barrier and glioma blood-tumor barrier

The blood-brain barrier (BBB) serves as a crucial vascular specialization, shielding and nourishing brain neurons and glia while impeding drug delivery. Here, we conducted single-cell mRNA sequencing of human cerebrovascular cells from 13 surgically resected glioma samples and adjacent normal brain tissue. The transcriptomes of 103,230 cells were mapped, including 57,324 endothelial cells (ECs) and 27,703 mural cells (MCs). Both EC and MC transcriptomes originating from lower-grade glioma were indistinguishable from those of normal brain tissue, whereas transcriptomes from glioblastoma (GBM) displayed a range of abnormalities. Among these, we identified LOXL2-dependent collagen modification as a common GBM-dependent trait and demonstrated that inhibiting LOXL2 enhanced chemotherapy efficacy in both murine and human patient-derived xenograft (PDX) GBM models. Our comprehensive single-cell RNA sequencing-based molecular atlas of the human BBB, coupled with insights into its perturbations in GBM, holds promise for guiding future investigations into brain health, pathology, and therapeutic strategies.

Comprehensive exploration on the role of base excision repair genes in modulating immune infiltration in low-grade glioma

IntroductionGlioma is a brain tumour occurring in all age groups but common in adults. Despite advances in the understanding of tumours, we cannot improve the survival of the patients and do not have an appropriate biomarker for progression and prognosis prediction. The base excision repair mechanism maintains the integrity of the genome, preventing tumour formation. However, continuous chemical damage to the cells results in mutations that escape the repair mechanism and support tumour growth. The tumour microenvironment in cancer is crucial in determining the tumour growth, development, and response to treatments. The present study explored the significance of Base Excision Repair genes (BER) in modulating the tumour microenvironment. MethodsWe used the publically available data sets from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) to explore the role of the base excision repair gene in the modulating tumour microenvironment. The data was analysed for the expression of base excision repair genes, their correlation with the immune markers, their prognostic potential, and enrichment analysis to understand the pathways they modulate in low-grade glioma (LGG) progression. ResultsThe analysis showed BER genes contribute an integral role in the overall and disease-free survival of LGG. Genes like MUTYH, PNKP, UNG and XRCC1 showed a correlation with the immune infiltration levels and a significant correlation with various immune markers associated with different immune cells, including tumour-associated macrophages. MUTYH, UNG and XRCC1 correlated with IDH1 mutation status, and functional enrichment analysis showed that these genes are enriched in several pathways like Wnt, PD-1 and Integrin signalling. ConclusionOur findings suggest that the BER genes MUTYH, PNKP, UNG and XRCC1 can potentially be prognostic biomarkers and highly correlate with the immune cells of the tumour microenvironment.

Pan-cancer analysis identifies venous thromboembolism-related genes F3, PLAT, and C1S as potential prognostic biomarkers for glioblastoma and lower grade glioma

Venous thromboembolism (VTE) is a prevalent complication among patients with cancer, contributing significantly to morbidity and mortality. However, the relationship between VTE-related genes (VRGs) and their potential impact on prognosis, immune response, and therapeutic targets in various cancer types remains unclear. Based on the coagulation and complement pathways, we identified hub VRGs that play a role in regulating the immune response in cancer. Specifically, coagulation factor III (F3), plasminogen activator (PLAT) and complement C1s (C1S) were identified as genes that exhibit high expression levels, positively correlating with tumor stemness and copy number variations, while inversely correlating with methylation levels, in particular cancer types. Pan-cancer survival analysis revealed detrimental effects of these VRGs in several cancer types, notably in glioblastoma and lower grade glioma (GMBLGG). Further analysis using receiver operating characteristic (ROC) curves demonstrated a high accuracy of F3, PLAT and C1S in predicting outcomes in GBMLGG, with area under the curve (AUC) values ranging from 0.78 to 0.9. Validation of the prognostic value of these three genes in GMBLGG was conducted using an independent Gene Expression Omnibus (GEO) dataset. Additionally, gene–drug association analysis identified ciclosporin, ouabain and 6- mercaptopurine, which all exhibit immunosuppressive properties, as potential therapeutic options for tumor patients exhibiting high F3, PLAT or C1S expression, respectively. In summary, our findings provide a bioinformatics perspective on VRGs in pan-cancer, highlighting the pivotal roles of F3, PLAT and C1S, which could potentially be therapeutically exploited and targeted in several cancers, especially in GBMLGG.

Subjective, but not objective, language functions predict fatigue in patients with lower-grade gliomas during longitudinal follow-up

Purpose To investigate the course of fatigue, subjective and objective language functions in patients with lower-grade gliomas during the first year of disease. Further, to examine if subjective and objective language variables predicted ratings of fatigue. Methods Fatigue was assessed with the Fatigue Severity Scale, subjective language with self-reported word-finding, expression of thoughts, reading and writing from the Functional Assessment of Cancer Therapy – Brain, and objective language with standardized tests. Mixed models were used to investigate changes in variables over time and predictors of fatigue. Results Twenty-three patients with gliomas (WHO 1–3) were included. Average ratings of fatigue did not change significantly, but altering patterns were observed. Subjective concerns about word-finding and expression of thoughts increased significantly during follow-up. The regression analyses showed that concerns about abilities to read and write significantly predicted fatigue. The results indicated that less concerns about reading and writing were associated with lower levels of fatigue. Conclusions Patients with lower-grade gliomas report fatigue and language concerns throughout the first year. Concerns about reading and writing call for careful consideration as they seem to provoke fatigue. The findings underline the importance of the patients’ perspective in treatment and follow-up.

A disulfidptosis-associated long noncoding RNA signature to predict low-grade glioma classification, prognosis, tumor microenvironment, and therapy regimens: Observational study.

This study aimed to investigate the function of disulfidptosis-associated long noncoding RNAs (DAlncRNAs) in low-grade gliomas (LGG) through bioinformatics analysis and construct a signature to predict the classification, prognosis, tumor microenvironment, and selection of immunotherapy and chemotherapy in LGG. Genomic, clinical, and mutational information of 526 patients with LGG was retrieved from The Cancer Genome Atlas repository. A nonnegative matrix factorization algorithm was applied to classify patients with LGG. Univariate, LASSO regression, and multivariate Cox regression analyses were performed to determine prognostic DAlncRNAs. Following the median risk score, we defined the sample as a high-risk (HR) or low-risk group. Finally, survival, receiver operating characteristic curve, risk curve, principal component, independent prognosis, risk difference, functional enrichment, tumor microenvironment, immune cell infiltration, mutation, and drug sensitivity analyses were performed. Patients were classified into C1 and C2 subtypes associated with disulfidptosis. Eight prognostic DAlncRNAs (AC003035.2, AC010157.2, AC010273.3, AC011444.3, AC092667.1, AL450270.1, AL645608.2, and LINC01571) were identified, and a prognostic signature of LGG was developed. The DAlncRNA-based signature was found to be an independent prognostic factor in patients with LGG, thereby constructing a nomogram. In addition, in the HR group, immune function was more active and the tumor mutation burden was higher. The patients were mainly composed of subtype C2, and their prognosis was worse. Immunotherapy and chemotherapy were predicted in the HR and low-risk groups, respectively. Our study, based on DAlncRNAs, highlights 2 disulfidptosis-associated LGG subtypes with different prognostic and immune characteristics and creates a novel disulfidptosis-associated prognostic signature, which may inform the classification, prognosis, molecular pathogenesis, and therapeutic strategies for patients with LGG.

Expressions of the satellite repeat HSAT5 and transposable elements are implicated in disease progression and survival in glioma.

The glioma genome encompasses a complex array of dysregulatory events, presenting a formidable challenge in managing this devastating disease. Despite the widespread distribution of repeat and transposable elements across the human genome, their involvement in glioma's molecular pathology and patient survival remains largely unexplored. In this study, we aimed to characterize the links between the expressions of repeat/transposable elements with disease progression and survival in glioma patients. Hence, we analyzed the expression levels of satellite repeats and transposons along with genes in low-grade glioma (LGG) and high-grade glioma (HGG). Endogenous transposable elements LTR5 and HERV_a-int exhibited higher expression in HGG patients, along with immune response-related genes. Altogether, 16 transposable elements were associated with slower progression of disease in LGG patients. Conversely, 22 transposons and the HSAT5 satellite repeat were linked to a shorter event-free survival in HGG patients. Intriguingly, our weighted gene coexpression network analysis (WGCNA) disclosed that the HSAT5 satellite repeat resided in the same module network with genes implicated in chromosome segregation and nuclear division; potentially hinting at its contribution to disease pathogenesis. Collectively, we report for the first time that repeat and/or transposon expression could be related to disease progression and survival in glioma. The expressions of these elements seem to exert a protective effect during LGG-to-HGG progression, whereas they could have a detrimental impact once HGG is established. The results presented herein could serve as a foundation for further experimental work aimed at elucidating the molecular regulation of glioma genome.

Application of U-Net Network Utilizing Multiattention Gate for MRI Segmentation of Brain Tumors.

Studies have shown that the type of low-grade glioma is associated with its shape. The traditional diagnostic method involves extraction of the tumor shape from MRIs and diagnosing the type of glioma based on corresponding relationship between the glioma shape and type. This method is affected by the MRI background, tumor pixel size, and doctors' professional level, leading to misdiagnoses and missed diagnoses. With the help of deep learning algorithms, the shape of a glioma can be automatically segmented, thereby assisting doctors to focus more on the diagnosis of glioma and improving diagnostic efficiency. The segmentation of glioma MRIs using traditional deep learning algorithms exhibits limited accuracy, thereby impeding the effectiveness of assisting doctors in the diagnosis. The primary objective of our research is to facilitate the segmentation of low-grade glioma MRIs for medical practitioners through the utilization of deep learning algorithms. In this study, a UNet glioma segmentation network that incorporates multiattention gates was proposed to address this limitation. The UNet-based algorithm in the coding part integrated the attention gate into the hierarchical structure of the network to suppress the features of irrelevant regions and reduce the feature redundancy. In the decoding part, by adding attention gates in the fusion process of low- and high-level features, important feature information was highlighted, model parameters were reduced, and model sensitivity and accuracy were improved. The network model performed image segmentation on the glioma MRI dataset, and the accuracy and average intersection ratio (mIoU) of the algorithm segmentation reached 99.7%, 87.3%, 99.7%, and 87.6%. Compared with the UNet, PSPNet, and Attention UNet network models, this network model has obvious advantages in accuracy, mIoU, and loss convergence. It can serve as a standard for assisting doctors in diagnosis.

Pan-Cancer Analysis Reveals Long Non-coding RNA (lncRNA) Embryonic Stem Cell-Related Gene (ESRG) as a Promising Diagnostic and Prognostic Biomarker.

Embryonic stem cell-related gene (ESRG; also known as HESRG) is a long non-coding RNA (lncRNA). It is involved in the regulation of human pluripotent stem cells (hPSCs) self-renewal. ESRG gene has the ability to interact with chromatins, different RNA types, and RNA binding proteins (RBP); thus making ESRG be considered an oncogenic lncRNA, where its expression is detected in various tumor tissues. This study aimed to evaluate the prospective diagnostic and prognostic values of ESRG in various human cancers. The expression of ESRG in various cancers was analyzed using the Gene Expression Profiling Interactive Analysis (GEPIA), Tumor Immune Estimation Resource (TIMER), and University of Alabama at Birmingham Cancer Data Analysis Portal (UALCAN) databases. Moreover, the correlation between the expression of ESRG and clinical pathological parameters was analyzed using UALCAN. The effect of ESRG expression on the survival outcome was evaluated using Kaplan-Meier plotter, UALCAN, GEPIA, and TIMER. The correlation between ESRG expression and immune cell infiltration was studied by TIMER. Additionally, the genetic alterations were investigated cBioportal. Our findings were validated using the GEO2R database. Our results showed ESRG to be significantly up-regulated incolon adenocarcinoma (COAD) and lung squamous cell carcinoma (LUSC) with p<0.001, in addition to rectum adenocarcinoma (READ), and uterine carcinosarcoma (UCEC) withp<0.01. Regarding pathogenic stages, there was a significant upregulation in stages 2, 3, and 4 compared to normal in COAD and stages 1, 2, and 3 for LUSC patients. The combined prognostic analysis showed that the up-regulated expression of ESRG was associated with better survival outcomes in patients with brain lower-grade glioma (LGG). Our results demonstrate a significant negative correlation between ESRG expression and the abundance of CD8+T cells in COAD, READ, LUSC, and UCEC. Additionally, ESRG was mutated in 0.77 (<1%) of the queried samples, and the most prevalent ESRG mutations are deep deletion mutations, followed by amplification. Analysis of ESRG across various cancer types elucidated its potential to be used as a diagnostic biomarker in COAD, LUSC, READ, and UCEC and a promising prognostic biomarker in LGG. Our findings provide useful insights for future research.

MR-guided laser interstitial thermal therapy in the treatment of brain tumors and epilepsy.

MR-guided Laser Interstitial Thermal Therapy (MRgLITT) is a minimally invasive neurosurgical technique increasingly used for the treatment of drug-resistant epilepsy and brain tumors. Utilizing near-infrared light energy delivery guided by real-time MRI thermometry, MRgLITT enables precise ablation of targeted brain tissues, resulting in limited corridor-related morbidity and expedited postoperative recovery. Since receiving CE marking in 2018, the adoption of MRgLITT has expanded to more than 40 neurosurgical centers across Europe. In epilepsy treatment, MRgLITT can be applied to various types of focal lesional epilepsy, including mesial temporal lobe epilepsy, hypothalamic hamartoma, focal cortical dysplasias, periventricular heterotopias, cavernous malformations, dysembryoplastic neuroepithelial tumors (DNET), low-grade gliomas, tuberous sclerosis, and in disconnective surgeries. In neuro-oncology, MRgLITT is used for treating newly diagnosed and recurrent primary brain tumors, brain metastases, and radiation necrosis. This comprehensive review presents an overview of the current evidence and technical considerations for the use of MRgLITT in treating various pathologies associated with drug-resistant epilepsy and brain tumors.

An MRI multi-sequence feature imputation and fusion mutual-aid model based on sequence deletion for differentiation of high-grade from low-grade glioma

To evaluate the performance of magnetic resonance imaging (MRI) multi-sequence feature imputation and fusion mutual model based on sequence deletion in differentiating high-grade glioma (HGG) from low-grade glioma (LGG). We retrospectively collected multi-sequence MR images from 305 glioma patients, including 189 HGG patients and 116 LGG patients. The region of interest (ROI) of T1-weighted images (T1WI), T2-weighted images (T2WI), T2 fluid attenuated inversion recovery (T2_FLAIR) and post-contrast enhancement T1WI (CE_T1WI) were delineated to extract the radiomics features. A mutual-aid model of MRI multi-sequence feature imputation and fusion based on sequence deletion was used for imputation and fusion of the feature matrix with missing data. The discriminative ability of the model was evaluated using 5-fold cross-validation method and by assessing the accuracy, balanced accuracy, area under the ROC curve (AUC), specificity, and sensitivity. The proposed model was quantitatively compared with other non-holonomic multimodal classification models for discriminating HGG and LGG. Class separability experiments were performed on the latent features learned by the proposed feature imputation and fusion methods to observe the classification effect of the samples in twodimensional plane. Convergence experiments were used to verify the feasibility of the model. For differentiation of HGG from LGG with a missing rate of 10%, the proposed model achieved accuracy, balanced accuracy, AUC, specificity, and sensitivity of 0.777, 0.768, 0.826, 0.754 and 0.780, respectively. The fused latent features showed excellent performance in the class separability experiment, and the algorithm could be iterated to convergence with superior classification performance over other methods at the missing rates of 30% and 50%. The proposed model has excellent performance in classification task of HGG and LGG and outperforms other non-holonomic multimodal classification models, demonstrating its potential for efficient processing of non-holonomic multimodal data.

Utilizing machine learning to tailor radiotherapy and chemoradiotherapy for low-grade glioma patients.

There is ongoing uncertainty about the effectiveness of various adjuvant treatments for low-grade gliomas (LGGs). Machine learning (ML) models that predict individual treatment effects (ITE) and provide treatment recommendations could help tailor treatments to each patient's needs. We sought to discern the individual suitability of radiotherapy (RT) or chemoradiotherapy (CRT) in LGG patients using ML models. Ten ML models, trained to infer ITE in 4,042 LGG patients, were assessed. We compared patients who followed treatment recommendations provided by the models with those who did not. To mitigate the risk of treatment selection bias, we employed inverse probability treatment weighting (IPTW). The Balanced Survival Lasso-Network (BSL) model showed the most significant protective effect among all the models we tested (hazard ratio (HR): 0.52, 95% CI, 0.41-0.64; IPTW-adjusted HR: 0.58, 95% CI, 0.45-0.74; the difference in restricted mean survival time (DRMST): 9.11, 95% CI, 6.19-12.03; IPTW-adjusted DRMST: 9.17, 95% CI, 6.30-11.83). CRT presented a protective effect in the 'recommend for CRT' group (IPTW-adjusted HR: 0.60, 95% CI, 0.39-0.93) yet presented an adverse effect in the 'recommend for RT' group (IPTW-adjusted HR: 1.64, 95% CI, 1.19-2.25). Moreover, the models predict that younger patients and patients with overlapping lesions or tumors crossing the midline are better suited for CRT (HR: 0.62, 95% CI, 0.42-0.91; IPTW-adjusted HR: 0.59, 95% CI, 0.36-0.97). Our findings underscore the potential of the BSL model in guiding the choice of adjuvant treatment for LGGs patients, potentially improving survival time. This study emphasizes the importance of ML in customizing patient care, understanding the nuances of treatment selection, and advancing personalized medicine.

The Transtemporal Isthmus Approach for Insular Glioma Surgery.

Maximal and safe removal of insular gliomas by a transinsular cortex approach is challenging. In this article, a new transtemporal isthmus approach to resect insular gliomas is presented. We retrospectively examined 53 patients with insular glioma who underwent resection through the temporal isthmus approach using magnetic resonance imaging and functional neuronavigation guidance and intraoperative electrophysiological monitoring. Extent of resection was determined using intraoperative magnetic resonance imaging. Fifty-three patients were included for analysis, 30 men and 23 women. The median (range) age was 45 (26-70) years. Tumor laterality was left in 22 patients and right in 31. All tumors involved at least zone III or IV (Berger-Sanai classification system), including zones I-IV were involved in 29 (54.7%) and zones III and IV in 17 (32.0%). Among the 37 low-grade gliomas, preoperative median (IQR) volume was 45.7 (31.8, 60.3) cm 3 , and gross total resection was achieved in 24 (64.9%). Among the 16 high-grade gliomas, preoperative median (IQR) volume was 45.3 (40.1, 54.0) cm 3 , and gross total resection was achieved in 14 (87.5%). The median (IQR) extent of resection of the whole group was 100% (89%-100%). The median (IQR) postoperative Karnofsky performance score 3 months after surgery was 90 (80-90). Mean temporal isthmus width was significantly higher in the affected side (involving tumor) than the contralateral one (21.6 vs 11.3 mm; 95% CI: 9.3 to 11.3, P < .01). Muscle strength was grade 4 or higher, and speech was nearly normal in all patients 3 months after surgery. Insular glioma surgery using the transtemporal isthmus approach can achieve safe and maximum resection. A widened temporal isthmus provides a surgical pathway for transisthmic resection of insular tumor.

MAPK pathway alterations in polymorphous low-grade neuroepithelial tumor of the young: diagnostic considerations.

Polymorphous low-grade neuroepithelial tumor of the young (PLNTY) is a recently recognised tumor type with indolent behaviourwith characteristic imaging and histomolecular features. We describe the clinical, imaging, histo-molecular features of 15 cases diagnosed as low-grade glioma suggestive of PLNTY,over a period of 3years.Immunohistochemistry (IHC) and fluorescence in situ hybridisation were used to assess molecular alterations. The tumors were seen predominantly in children (range 5-65years). Most of the patients presented with history of seizures. Imaging revealed cortical-subcortical well demarcated solid-cystic tumor with intratumoral calcification. Histopathologyrevealed a low-grade tumor with oligodendroglia-Iike cells admixed with astrocytic cells immunopositive for CD34. BRAF p.V600E mutations and FGFR2 breakapart were observed in six cases each, while three showed FGFR3 breakapart. FGFR2 breakapart positive PLNTY were seen in children exclusively. The majority of cases were seizure free post-surgery, except two patients who succumbed to the illness. PLNTY, needs to be considered as a prime differential diagnosis in a solid-cystic tumor in a young patient with history of seizures. Characteristic clinical features, radiology, histomorphology with an IHC panel of OLIG2, GFAP and CD34 correlates with one of the MAPK alterations in PLNTY (BRAF p.V600E, FGFR2/3 gene rearrangement). In a resource limited setting, this limited panel may be sufficient for a correlative diagnosis.