Intracranial pressure monitoring in neurosurgery: the present situation and prospects.

Intracranial pressure (ICP) is one of the most important indexes in neurosurgery. It is essential for doctors to determine the numeric value and changes of ICP, whether before or after an operation. Although external ventricular drainage (EVD) is the gold standard for monitoring ICP, more and more novel monitoring methods are being applied clinically.Invasive wired ICP monitoring is still the most commonly used in practice. Meanwhile, with the rise and development of various novel technologies, non-invasive types and invasive wireless types are gradually being used clinically or in the testing phase, as a complimentary approach of ICP management. By choosing appropriate monitoring methods, clinical neurosurgeons are able to obtain ICP values safely and effectively under particular conditions.This article introduces diverse monitoring methods and compares the advantages and disadvantages of different monitoring methods. Moreover, this review may enable clinical neurosurgeons to have a broader view of ICP monitoring.

Alternations and Applications of the Structural and Functional Connectome in Gliomas: A Mini-Review.

In the central nervous system, gliomas are the most common, but complex primary tumors. Genome-based molecular and clinical studies have revealed different classifications and subtypes of gliomas. Neuroradiological approaches have non-invasively provided a macroscopic view for surgical resection and therapeutic effects. The connectome is a structural map of a physical object, the brain, which raises issues of spatial scale and definition, and it is calculated through diffusion magnetic resonance imaging (MRI) and functional MRI. In this study, we reviewed the basic principles and attributes of the structural and functional connectome, followed by the alternations of connectomes and their influences on glioma. To extend the applications of connectome, we demonstrated that a series of multi-center projects still need to be conducted to systemically investigate the connectome and the structural-functional coupling of glioma. Additionally, the brain-computer interface based on accurate connectome could provide more precise structural and functional data, which are significant for surgery and postoperative recovery. Besides, integrating the data from different sources, including connectome and other omics information, and their processing with artificial intelligence, together with validated biological and clinical findings will be significant for the development of a personalized surgical strategy.

Recurrence- and Malignant Progression-Associated Biomarkers in Low-Grade Gliomas and Their Roles in Immunotherapy.

Despite a generally better prognosis than high-grade glioma (HGG), recurrence and malignant progression are the main causes for the poor prognosis and difficulties in the treatment of low-grade glioma (LGG). It is of great importance to learn about the risk factors and underlying mechanisms of LGG recurrence and progression. In this study, the transcriptome characteristics of four groups, namely, normal brain tissue and recurrent LGG (rLGG), normal brain tissue and secondary glioblastoma (sGBM), primary LGG (pLGG) and rLGG, and pLGG and sGBM, were compared using Chinese Glioma Genome Atlas (CGGA) and Genotype-Tissue Expression Project (GTEx) databases. In this study, 296 downregulated and 396 upregulated differentially expressed genes (DEGs) with high consensus were screened out. Univariate Cox regression analysis of data from The Cancer Genome Atlas (TCGA) yielded 86 prognostically relevant DEGs; a prognostic prediction model based on five key genes (HOXA1, KIF18A, FAM133A, HGF, and MN1) was established using the least absolute shrinkage and selection operator (LASSO) regression dimensionality reduction and multivariate Cox regression analysis. LGG was divided into high- and low-risk groups using this prediction model. Gene Set Enrichment Analysis (GSEA) revealed that signaling pathway differences in the high- and low-risk groups were mainly seen in tumor immune regulation and DNA damage-related cell cycle checkpoints. Furthermore, the infiltration of immune cells in the high- and low-risk groups was analyzed, which indicated a stronger infiltration of immune cells in the high-risk group than that in the low-risk group, suggesting that an immune microenvironment more conducive to tumor growth emerged due to the interaction between tumor and immune cells. The tumor mutational burden and tumor methylation burden in the high- and low-risk groups were also analyzed, which indicated higher gene mutation burden and lower DNA methylation level in the high-risk group, suggesting that with the accumulation of genomic mutations and epigenetic changes, tumor cells continued to evolve and led to the progression of LGG to HGG. Finally, the value of potential therapeutic targets for the five key genes was analyzed, and findings demonstrated that KIF18A was the gene most likely to be a potential therapeutic target. In conclusion, the prediction model based on these five key genes can better identify the high- and low-risk groups of LGG and lay a solid foundation for evaluating the risk of LGG recurrence and malignant progression.

Identification of the novel prognostic biomarker, MLLT11, reveals its relationship with immune checkpoint markers in glioma.

Aim: This study aimed to explore the expression pattern of MLLT11 under different pathological features, evaluate its prognostic value for glioma patients, reveal the relationship between MLLT11 mRNA expression and immune cell infiltration in the tumor microenvironment (TME), and provide more evidence for the molecular diagnosis of glioma and immunotherapy. Methods: Using large-scale bioinformatic approach and RNA sequencing (RNA-seq) data from public databases The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), and The Gene Expression Omnibus (GEO)), we investigated the relationship between MLLT11 mRNA levels and pathologic characteristics. The distribution in the different subtypes was observed based on Verhaak bulk and Neftel single-cell classification. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were used for bioinformatic analysis. Kaplan-Meier survival analysis and Cox regression analysis were used for survival analysis. Correlation analyses were performed between MLLT11 expression and 22 immune cells and immune checkpoints in the TME. Results: We found that MLLT11 expression is decreased in high-grade glioma tissues; we further verified this result by RT-PCR, Western blotting, and immunohistochemistry using our clinical samples. According to the Verhaak classification, high MLLT11 expression is mostly clustered in pro-neutral (PN) and neutral (NE) subtypes, while in the Neftel classification, MLLT11 mainly clustered in neural progenitor-like (NPC-like) neoplastic cells. Survival analysis revealed that low levels of MLLT11 expression are associated with a poorer prognosis; MLLT11 was identified as an independent prognostic factor in multivariate Cox regression analyses. Functional enrichment analyses of MLLT11 with correlated expression indicated that low MLLT11 expression is associated with the biological process related to the extracellular matrix, and the high expression group is related to the synaptic structure. Correlation analyses suggest that declined MLLT11 expression is associated with increased macrophage infiltration in glioma, especially M2 macrophage, and verified by RT-PCR, Western blotting, and immunohistochemistry using our clinical glioma samples. MLLT11 had a highly negative correlation with immune checkpoint inhibitor (ICI) genes including PDCD1, PD-L1, TIM3(HAVCR2), and PD-L2 (PDCD1LG2). Conclusion: MLLT11 plays a crucial role in the progression of glioma and has the potential to be a new prognostic marker for glioma.

Classification of Gliomas and Germinomas of the Basal Ganglia by Transfer Learning.

Background: Germ cell tumors (GCTs) are neoplasms derived from reproductive cells, mostly occurring in children and adolescents at 10 to 19 years of age. Intracranial GCTs are classified histologically into germinomas and non-germinomatous germ cell tumors. Germinomas of the basal ganglia are difficult to distinguish based on symptoms or routine MRI images from gliomas, even for experienced neurosurgeons or radiologists. Meanwhile, intracranial germinoma has a lower incidence rate than glioma in children and adults. Therefore, we established a model based on pre-trained ResNet18 with transfer learning to better identify germinomas of the basal ganglia. Methods: This retrospective study enrolled 73 patients diagnosed with germinoma or glioma of the basal ganglia. Brain lesions were manually segmented based on both T1C and T2 FLAIR sequences. The T1C sequence was used to build the tumor classification model. A 2D convolutional architecture and transfer learning were implemented. ResNet18 from ImageNet was retrained on the MRI images of our cohort. Class activation mapping was applied for the model visualization. Results: The model was trained using five-fold cross-validation, achieving a mean AUC of 0.88. By analyzing the class activation map, we found that the model's attention was focused on the peri-tumoral edema region of gliomas and tumor bulk for germinomas, indicating that differences in these regions may help discriminate these tumors. Conclusions: This study showed that the T1C-based transfer learning model could accurately distinguish germinomas from gliomas of the basal ganglia preoperatively.

Multivariate Analysis and Validation of the Prognostic Factors for Skull Base Chordoma.

Background: Skull base chordoma is a rare tumor with low-grade malignancy and a high recurrence rate, the factors affecting the prognosis of patients need to be further studied. For that, we investigated prognostic factors of skull base chordoma through the database of the Surveillance, Epidemiology, and End Results (SEER) program, and validated in an independent data set from the Xiangya Hospital. Methods: Six hundred and forty-three patients diagnosed with skull base chordoma were obtained from the SEER database (606 patients) and the Xiangya Hospital (37 patients). Categorical variables were selected by Chi-square test with a statistical difference. Survival curves were constructed by Kaplan-Meier analysis and compared by log-rank test. Univariate and multivariate Cox regression analyses were used to explore the prognostic factors. Propensity score matching (PSM) analysis was undertaken to reduce the substantial bias between gross total resection (GTR) and subtotal resection (STR) groups. Furthermore, clinical data of 37 patients from the Xiangya Hospital were used as validation cohorts to check the survival impacts of the extent of resection and adjuvant radiotherapy on prognosis. Results: We found that age at diagnosis, primary site, disease stage, surgical treatment, and tumor size was significantly associated with the prognosis of skull base chordoma. PSM analysis revealed that there was no significant difference in the OS between GTR and STR (p = 0.157). Independent data set from the Xiangya Hospital proved no statistical difference in OS between GTR and STR groups (p = 0.16), but the GTR group was superior to the STR group for progression-free survival (PFS) (p = 0.048). Postoperative radiotherapy does not improve OS (p = 0.28), but it can prolong PFS (p = 0.0037). Nomograms predicting 5- and 10-year OS and DSS were constructed based on statistically significant factors identified by multivariate Cox analysis. Age, primary site, tumor size, surgical treatment, and disease stage were included as prognostic predictors in the nomograms with good performance. Conclusions: We identified age, tumor size, surgery, primary site, and tumor stage as main factors affecting the prognosis of the skull base chordoma. Resection of the tumor as much as possible while ensuring safety, combined with postoperative radiotherapy may be the optimum treatment for skull base chordoma.