Does location matter? Characterisation of the anatomic locations, molecular profiles, and clinical features of gliomas

Comparison of Early Postoperative Diffusion Weighted Magnetic Resonance Imaging Findings After Resection of Gliomas and Meningiomas

OBJECTIVE

Glioma and meningioma require vastly different surgical approaches, even if only involving a simple craniotomy procedure. Diffusion weighted imaging (DWI) is useful for the postoperative evaluation of ischemic damage. The present study evaluated the expected but unproven differences in DWI findings.

METHODS

A total of 41 patients with meningiomas and 63 with gliomas met the inclusion criteria for adult cases with superficial lesions treated through simple supratentorial craniotomy. Postoperative DWI findings of DWI-positive rate, DWI-positive area type, and relationship with neurological deficits were evaluated.

RESULTS

The DWI-positive rate (P = 0.01) and the proportion of rim-type lesions (P < 0.01) were significantly more common in gliomas. Patients with meningiomas and DWI-positive areas presented with higher rates of new neurological deficits (P < 0.01), and patients with meningiomas on the left side were more likely to develop new neurological deficits (P = 0.02). Patients with gliomas tended to develop new deficits with larger DWI-positive area volumes (P = 0.04).

CONCLUSIONS

Postoperative early DWI-positive rate and rim-type lesions are more common after glioma resection than meningioma resection. Larger volumes of DWI-positive areas may be associated with postoperative neurological symptoms in gliomas. DWI-positive finding is less common after meningioma than glioma resection but more likely to be associated with new neurological symptoms. These differences are important for adequate postoperative DWI evaluation of common supratentorial brain tumors.

A transformer-based multi-task deep learning model for simultaneous infiltrated brain area identification and segmentation of gliomas

BACKGROUND

The anatomical infiltrated brain area and the boundaries of gliomas have a significant impact on clinical decision making and available treatment options. Identifying glioma-infiltrated brain areas and delineating the tumor manually is a laborious and time-intensive process. Previous deep learning-based studies have mainly been focused on automatic tumor segmentation or predicting genetic/histological features. However, few studies have specifically addressed the identification of infiltrated brain areas. To bridge this gap, we aim to develop a model that can simultaneously identify infiltrated brain areas and perform accurate segmentation of gliomas.

METHODS

We have developed a transformer-based multi-task deep learning model that can perform two tasks simultaneously: identifying infiltrated brain areas segmentation of gliomas. The multi-task model leverages shaped location and boundary information to enhance the performance of both tasks. Our retrospective study involved 354 glioma patients (grades II-IV) with single or multiple brain area infiltrations, which were divided into training (N = 270), validation (N = 30), and independent test (N = 54) sets. We evaluated the predictive performance using the area under the receiver operating characteristic curve (AUC) and Dice scores.

RESULTS

Our multi-task model achieved impressive results in the independent test set, with an AUC of 94.95% (95% CI, 91.78-97.58), a sensitivity of 87.67%, a specificity of 87.31%, and accuracy of 87.41%. Specifically, for grade II-IV glioma, the model achieved AUCs of 95.25% (95% CI, 91.09-98.23, 84.38% sensitivity, 89.04% specificity, 87.62% accuracy), 98.26% (95% CI, 95.22-100, 93.75% sensitivity, 98.15% specificity, 97.14% accuracy), and 93.83% (95%CI, 86.57-99.12, 92.00% sensitivity, 85.71% specificity, 87.37% accuracy) respectively for the identification of infiltrated brain areas. Moreover, our model achieved a mean Dice score of 87.60% for the whole tumor segmentation.

CONCLUSIONS

Experimental results show that our multi-task model achieved superior performance and outperformed the state-of-the-art methods. The impressive performance demonstrates the potential of our work as an innovative solution for identifying tumor-infiltrated brain areas and suggests that it can be a practical tool for supporting clinical decision making.

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.

Gliomas with Downregulation of lncRNA SLC25A21-AS1 Carry a Dismal Prognosis and an Accelerated Progression in Cell Proliferation, Migration and Invasion

Glioma is one type of primary intracranial carcinoma with a relatively poor prognosis. We investigated the level of SLC25A21-AS1 in gliomas and the association with survival and progression in patients with glioma. Specimens of gliomas from patients were assessed by quantitative real-time polymerase chain reaction analysis of the SLC25A21-AS1 level (117 specimens). For prognostic value assessment, χ² test, Kaplan-Meier method with the log-rank test, and Multivariate survival analysis were performed. The direct targets for SLC25A21-AS1 were explored. The biological roles of SLC25A21-AS1 were investigated by manipulating the expression level of SLC25A21-AS1 in glioma cells. SLC25A21-AS1 was significantly downregulated in glioma specimens and cell lines compared to non-cancerous ones. Significant associations were found between SLC25A21-AS1 downregulation and WHO stage, IDH status, poor disease-free survival/overall survival. miR-221-3p/miR-222-3p were the target miRNAs for SLC25A21-AS1. Overexpression of SLC25A21-AS1 inhibited glioma cell growth, invasion, and migration while miR-221-3p/miR-222-3p-overexpressed groups could offset this effect. Downregulation of SLC25A21-AS1 in gliomas carries a universally poor prognosis. Overexpression of SLC25A21-AS1 inhibited glioma progression via miR-221-3p/miR-222-3p.

On the Prognosis of Multifocal Glioblastoma: An Evaluation Incorporating Volumetric MRI

Primary glioblastoma (GBM), IDH-wildtype, especially with multifocal appearance/growth (mGBM), is associated with very poor prognosis. Several clinical parameters have been identified to provide prognostic value in both unifocal GBM (uGBM) and mGBM, but information about the influence of radiological parameters on survival for mGBM cohorts is scarce. This study evaluated the prognostic value of several volumetric parameters derived from magnetic resonance imaging (MRI). Data from the Department of Neurosurgery, Leipzig University Hospital, were retrospectively analyzed. Patients treated between 2014 and 2019, aged older than 18 years and with adequate peri-operative MRI were included. Volumetric assessment was performed manually. One hundred and eighty-three patients were included. Survival of patients with mGBM was significantly shorter (p < 0.0001). Univariate analysis revealed extent of resection, adjuvant therapy regimen, residual tumor volume, tumor necrosis volume and ratio of tumor necrosis to initial volume as statistically significant for overall survival. In multivariate Cox regression, however, only EOR (for uGBM and the entire cohort) and adjuvant therapy were independently significant for survival. Decreased ratio of tumor necrosis to initial tumor volume and extent of resection were associated with prolonged survival in mGBM but failed to achieve statistical significance in multivariate analysis.