Background: Meningioma is the most common primary tumor of the central nervous system. Preoperative diagnosis of high-grade meningioma is helpful for the selection of treatment options. The aim of our study is to establish a diagnostic nomogram model for preoperative prediction of the pathological grade of meningioma.Methods: The predictive model was established from a cohort of 215 clinicopathologically confirmed meningioma between January 2012 and December 2017. Radiomic features were collected from preoperative magnetic resonance imaging (MRI) and computed tomography of patients with meningioma. The least absolute shrinkage and selection operator (LASSO) regression model was used for data dimension reduction and feature selection. Multivariate logistic regression was used to build a predictive model and presented as a nomogram. The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. Internal validation was evaluated using bootstrapping validation.Results: High-grade meningioma was observed in 47 patients (22%). The predictors included in the nomogram were tumor-brain interface, bone invasion, and tumor location. The final diagnostic model exhibited good calibration and discrimination with a C-index of 0.874 (95% confidence interval: 0.818-0.929) and a higher C-index of 0.868 in internal validation. Decision curve analysis (DCA) indicated that the nomogram is very useful in clinical practice. Conclusions:This study provides a nomogram model with tumor-brain interface, bone invasion, and tumor location that can effectively predict the preoperative pathological grading of patients with meningioma and thus help clinicians provide more reasonable treatment strategies for meningioma patients.

Purpose Tumor microenvironment (TME) affects the occurrence and progression of low-grade glioma (LGG). The aim of this study is to identify TME-related genes that influence prognosis in LGG patients and to explore their function and role in tumor immunity. Patients and Methods The TME components of LGG samples in the Cancer Genome Atlas (TCGA) database were identified by the ESTIMATE method, and differentially expressed genes (DEGs) with significant differences in immune scores and stromal scores were screened out. The core genes of DEGs were screened out by protein–protein interaction (PPI) network. Furthermore, immune-related target genes significantly correlated with prognosis were identified. Survival analysis and correlation analysis showed the correlation between target genes and clinical features and prognosis. The expression differences of target genes were verified by external database Chinese Glioma Genome Atlas (CGGA). CIBERSORT software identified the proportion of tumor-infiltrating immune cells (TICs) that were significantly related to target genes. Gene set enrichment analysis (GSEA) could enrich the main functions related to high and low expression of target genes. Results A total of 1567 DEGs were screened out from 529 LGG samples in the TCGA database, and 146 immune-related genes affecting prognosis were found. A total of 403 core genes were obtained from PPI network. The target gene interferon regulatory factor 7 (IRF7) was significantly associated with prognosis and clinical features of the tumor. The CGGA database verified the relationship between high and low expression groups of IRF7 and prognosis. GSEA indicated that IRF7 was mainly enriched in immune-related activities, significantly correlated with T cells CD8, macrophages M1, macrophages M2 and monocytes. Conclusion The IRF7 is involved in immune responses in TME of LGG, which in turn influenced tumor occurrence and progression. IRF7 can act as a potential biomarker for prognosis in patients with LGG and provide a target for tumor immunotherapy.

Objective: The extensive bone infiltration and carpet-like growth characteristics of spheno-orbital meningioma (SOM) make it hard to remove entirely, and recurrence and proptosis are the main reasons for reoperation. The authors report 20 cases of surgical treatment for recurrence of SOM, including surgical technique and symptom improvement. Methods: The clinical data and follow-up results of 20 cases of recurrent SOM at our institution from 2000 to 2017 were retrospectively analyzed. Results: All of the 20 patients with recurrence had received at least one operation before admission, with a mean age of 56 years and 70% female. The mean follow-up time was 36 months (172 months). All patients mainly showed symptoms such as proptosis and headache, and were found to be affected by supraorbital fissure during the operation. in 17 patients with recurrence, the affected sphenoid wing became tumor-like hyperplasia. Patients with extraocular muscle involvement have obvious protrusion and are often accompanied by diplopia. After surgical removal of the tumor, the symptoms of proptosis in 19 patients were significantly improved. During the follow-up, only 3 cases of proptosis recurred. After 15 patients underwent Simpson grade IV resection, 4 patients (27%) relapsed again. Five patients underwent Simpson III resection, and only 1 patient (20%) had tumor recurrence 18th months after surgery, and no proptosis recurred. Conclusions: The complete surgical removal of recurrent SOM is practically impossible. The main direction of surgical treatment should be to improve the symptoms of proptosis.