Radiotherapy is a cornerstone for glioblastoma multiforme (GBM), yet its efficacy is severely hampered by intrinsic radioresistance, the molecular basis of which remains elusive, and overcoming this resistance poses a persistent challenge for GBM therapeutics. This study aims to identify novel key regulators of radiosensitivity in glioblastoma (GBM), with the goal of developing potent radiosensitizers. By analyzing patient cohorts, we identified prolactin-releasing hormone receptor (PRLHR) as a differentially expressed molecule, with significantly elevated levels in glioma specimens from the long PFS group compared to the short PFS group. This discovery prompted us to investigate PRLHR’s role in modulating GBM radiosensitivity and DNA repair. Using in vitro and in vivo models, we found PRLHR overexpression enhanced GBM radiosensitivity by promoting DNA damage accumulation and attenuating repair pathways. Mechanistically, PRLHR interacts with Y-box-binding protein-1 (YBX1), inhibiting its nuclear translocation to reduce nuclear YBX1 levels, thereby compromising DNA repair efficiency. We further demonstrated repressor element-1 binding transcription factor (REST) directly binds the PRLHR promoter to transcriptionally repress its expression. Notably, degradation of REST by the antagonist X5050 upregulated PRLHR, increased DNA damage, and enhanced the therapeutic efficacy of radiotherapy in preclinical models. Our findings delineate PRLHR as a critical regulator of GBM radiosensitivity and establish X5050 as a promising radiosensitizing agent, offering a novel precision strategy to overcome radioresistance.

Multimodal data integration reveals causal features often missed by single-modality analyses, offering a more comprehensive view of glioblastoma (GBM) complexity. We collected radiomic, pathomic, genomic, transcriptomic, and proteomic data from patients with IDH-wild-type GBM to construct a machine learning–based risk stratification model. While sample sizes varied across modalities, 147 patients with complete data across all five omics layers were used for integrative analysis. This approach identified two clinically distinct subgroups. The low-risk group, linked to favorable outcomes, showed enhanced neurodevelopmental signatures, increased neuronal infiltration, and more oligodendrocytes. In contrast, the high-risk group, associated with poor prognosis, exhibited strong proliferative signals and hyperactive cell cycle pathways. Downstream multi-omics analysis identified PDIA4, EIF3I, and RFT1 as potential prognostic biomarkers and therapeutic targets in high-risk GBM. These findings underscore the utility of multimodal machine learning in refining prognostic models, characterizing tumor heterogeneity, and informing personalized treatment strategies.

This study investigates the repurposing of BAF312 (Siponimod), an FDA-approved sphingosine-1-phosphate (S1P) receptor agonist for multiple sclerosis, as a dual-targeting therapeutic agent for glioma by inhibiting tumor growth and normalizing aberrant tumor vasculature. The clinical correlations between S1PR1/5 expression and glioma prognosis were analyzed using the GEPIA and HPA databases. The effects of BAF312 on tumor growth, cell cycle progression, apoptosis, and vascular remodeling were evaluated using orthotopic GL261 glioma models and glioma cell lines (U118MG, T98G, GL261). Techniques such as bioluminescent imaging, flow cytometry, Western blot analysis, and immunofluorescence staining were employed. High expression of S1PR1/5 was associated with improved survival in glioma patients. BAF312 inhibited glioma proliferation both in vitro and in vivo by arresting the G1-S transition through modulation of the Skp2-p27 axis and inducing apoptosis through p53-independent mitochondrial pathways. Additionally, BAF312 promoted tumor vascular normalization by enhancing pericyte coverage (PDGFRβ+), reducing vessel leakage (TER119+ RBCs), and altering vessel diameter distribution. Notably, BAF312 reduced CD8+ T cell infiltration, suggesting that its efficacy is predominantly mediated by tumor-intrinsic mechanisms. BAF312 exhibits significant anti-glioma activity through mechanisms involving cell cycle arrest, apoptosis induction, and vascular normalization, highlighting its potential as a repurposed therapeutic agent. This study offers preclinical evidence supporting the anti-glioma effects of BAF312; though further investigation is warranted to evaluate combinatorial efficacy with conventional therapies and address immunosuppressive effects limiting monotherapy application.

Tumors evolve to avoid immune destruction and establish an immunosuppressive microenvironment. Syngeneic mouse tumor models are critical for understanding tumor immune evasion and testing cancer immunotherapy. Derived from established mouse tumor cell lines that can already evade the immune system, these models cannot simulate early phases of immunoediting during initial tumorigenesis. We developed a syngeneic mouse teratoma model derived from noncancerous mouse embryonic stem cells and conducted a genome-wide CRISPR screen to identify genes that impact early phases of cancer immunoediting. We found that loss of pro-apoptotic tumor suppressor genes, including Trp53, increased necrosis in teratomas, releasing APOE lipid particles into the extracellular milieu. Infiltrating T cells drawn to tumor necrotic regions accumulated lipids and became dysfunctional. Blocking lipid uptake in T cells or reducing necrosis in teratomas by inactivating the mitochondrial permeability transition pore (mPTP) restored immunosurveillance. Because mouse teratomas were highly enriched for brain tissues, we next examined the tumor-immune interaction in human glioblastoma (GBM). Indeed, infiltrating T cells in TP53-mutated human GBM accumulated APOE and were dysfunctional. Anti-APOE and anti-PDCD1 antibodies synergistically boosted anti-GBM immunity and prolonged survival in mice. Our results link mPTP-mediated tumor necrosis to immune evasion and suggest that targeting the uptake of lipids released by necrotic tumor cells by infiltrating immune cells can enhance cancer immunotherapy.

This article aims to develop and validate a pathological prognostic model for predicting prognosis in patients with isocitrate dehydrogenase (IDH)-mutant gliomas and reveal the biological underpinning of the prognostic pathological features. The pathomic model was constructed based on whole slide images (WSIs) from a training set (N = 486) and evaluated on internal validation set (N = 209), HPPH validation set (N = 54), and TCGA validation set (N = 352). Biological implications of PathScore and individual pathomic features were identified by pathogenomics set (N =100). The WSI-based pathological signature was an independent prognostic factor. Incorporating the pathological features into a clinical model resulted in a pathological-clinical model that predicted survival better than either the pathological model or clinical model alone. Ten categories of pathways (metabolism, proliferation, immunity, DNA damage response, disease, migrate, protein modification, synapse, transcription and translation, and complex cellular functions) were significantly correlated with the WSI-based pathological features.

Gliomas are the most prevalent and aggressive neoplasms of the central nervous system, representing a major challenge for effective treatment and patient prognosis. This study identifies the proteasome subunit beta type-8 (PSMB8/LMP7) as a promising prognostic biomarker for glioma. Using a multiparametric radiomic model derived from preoperative magnetic resonance imaging (MRI), we accurately predicted PSMB8 expression levels. Notably, radiomic prediction of poor prognosis was highly consistent with elevated PSMB8 expression. Our findings demonstrate that PSMB8 depletion not only suppressed glioma cell proliferation and migration but also induced apoptosis via activation of the transforming growth factor beta (TGF-β) signaling pathway. This was supported by downregulation of key receptors (TGFBR1 and TGFBR2). Furthermore, interference with PSMB8 expression impaired phosphorylation and nuclear translocation of SMAD2/3, critical mediators of TGF-β signaling. Consequently, these molecular alterations resulted in reduced tumor progression and enhanced sensitivity to temozolomide (TMZ), a standard chemotherapeutic agent. Overall, our findings highlight PSMB8's pivotal role in glioma pathophysiology and its potential as a prognostic marker. This study also demonstrates the clinical utility of MRI radiomics for preoperative risk stratification and pre-diagnosis. Targeted inhibition of PSMB8 may represent a therapeutic strategy to overcome TMZ resistance and improve glioma patient outcomes.

Background:Prolyl 4-hydroxylase subunit beta (P4HB) has been linked to glioma progression and treatment resistance; however, its role in the tumor microenvironment regulation remains unclear. Materials and Methods:A total of 90 human glioma samples and 30 normal traumatic brain injury (TBI) tissues from patients undergoing surgical resection were collected from the First Affiliated Hospital of Zhengzhou University for immunohistochemical (IHC) staining. The signaling pathway mechanism regulating prognosis and tumor proliferation was explored through bioinformatics analysis and functional assays via P4HB knockdown. Results:The IHC analysis of 90 glioma and 30 TBI tissues revealed that P4HB expression correlated with tumor malignancy, especially the WHO grade 4 and grade 2 (P < 0.0001). Furthermore, transcriptomic data analysis identified P4HB as a prognostic marker associated with poor survival and tumor-associated macrophage infiltration (P < 0.05 in all cohorts). Using shRNA lentiviral constructs, stable P4HB knockdown glioblastoma (GBM) cell lines were generated. mRNA sequencing revealed significant downregulation of the interleukin (IL)-6/signal transducer and activator of transcription 3 (IL-6/STAT3) signaling axis. Functional studies showed that P4HB deficiency resulted in reduced IL-6 secretion (P < 0.0001), suppressed M2 polarization of tumor-associated microglia, and inhibited glioma cell growth both in vitro and in vivo, with IL-6 neutralization recapitulating these effects. Mechanistically, P4HB promoted STAT3 phosphorylation in microglia, driving their pro-tumorigenic M2 phenotype. Conclusion:These findings establish P4HB as a regulator of glioma progression via IL-6/STAT3-mediated microglial polarization, highlighting its potential as a therapeutic target for GBM.

Integrating multimodal data can uncover causal features hidden in single-modality analyses, offering a comprehensive understanding of disease complexity. This study introduces a multimodal fusion subtyping (MOFS) framework that integrates radiological, pathological, genomic, transcriptomic, and proteomic data from 122 patients with IDH-wildtype adult glioma, identifying three subtypes: MOFS1 (proneural) with favorable prognosis, elevated neurodevelopmental activity, and abundant neurocyte infiltration; MOFS2 (proliferative) with the worst prognosis, superior proliferative activity, and genome instability; MOFS3 (TME-rich) with intermediate prognosis, abundant immune and stromal components, and sensitive to anti-PD-1 immunotherapy. STRAP emerges as a prognostic biomarker and potential therapeutic target for MOFS2, associated with its proliferative phenotype. Stromal infiltration in MOFS3 serves as a crucial prognostic indicator, allowing for further prognostic stratification. Additionally, we develop a deep neural network (DNN) classifier based on radiological features to further enhance the clinical translatability, providing a non-invasive tool for predicting MOFS subtypes. Overall, these findings highlight the potential of multimodal fusion in improving the classification, prognostic accuracy, and precision therapy of IDH-wildtype glioma, offering an avenue for personalized management.

Current literature emphasizes surgical complexities and customized resection for managing insular gliomas; however, radiogenomic investigations into prognostic radiomic traits remain limited. We aimed to develop and validate a radiomic model using multiparametric magnetic resonance imaging (MRI) for prognostic prediction and to reveal the underlying biological mechanisms. Radiomic features from preoperative MRI were utilized to develop and validate a radiomic risk signature (RRS) for insular gliomas, validated through paired MRI and RNA-seq data (N = 39), to identify core pathways underlying the RRS and individual prognostic radiomic features. An 18-feature-based RRS was established for overall survival (OS) prediction. Gene set enrichment analysis (GSEA) and weighted gene coexpression network analysis (WGCNA) were used to identify intersectional pathways. In total, 364 patients with insular gliomas (training set, N = 295; validation set, N = 69) were enrolled. RRS was significantly associated with insular glioma OS (log-rank p = 0.00058; HR = 3.595, 95% CI:1.636-7.898) in the validation set. The radiomic-pathological-clinical model (R-P-CM) displayed enhanced reliability and accuracy in prognostic prediction. The radiogenomic analysis revealed 322 intersectional pathways through GSEA and WGCNA fusion; 13 prognostic radiomic features were significantly correlated with these intersectional pathways. The RRS demonstrated independent predictive value for insular glioma prognosis compared with established clinical and pathological profiles. The biological basis for prognostic radiomic indicators includes immune, proliferative, migratory, metabolic, and cellular biological function-related pathways.

AIMS:Although radiotherapy is a core treatment modality for various human cancers, including glioblastoma multiforme (GBM), its clinical effects are often limited by radioresistance. The specific molecular mechanisms underlying radioresistance are largely unknown, and the reduction of radioresistance is an unresolved challenge in GBM research.METHODS:We analyzed and verified the expression of nuclear autoantigenic sperm protein (NASP) in gliomas and its relationship with patient prognosis. We also explored the function of NASP in GBM cell lines. We performed further mechanistic experiments to investigate the mechanisms by which NASP facilitates GBM progression and radioresistance. An intracranial mouse model was used to verify the effectiveness of combination therapy.RESULTS:NASP was highly expressed in gliomas, and its expression was negatively correlated with the prognosis of glioma. Functionally, NASP facilitated GBM cell proliferation, migration, invasion, and radioresistance. Mechanistically, NASP interacted directly with annexin A2 (ANXA2) and promoted its nuclear localization, which may have been mediated by phospho-annexin A2 (Tyr23). The NASP/ANXA2 axis was involved in DNA damage repair after radiotherapy, which explains the radioresistance of GBM cells that highly express NASP. NASP overexpression significantly activated the signal transducer and activator of transcription 3 (STAT3) signaling pathway. The combination of WP1066 (a STAT3 pathway inhibitor) and radiotherapy significantly inhibited GBM growth in vitro and in vivo.CONCLUSION:Our findings indicate that NASP may serve as a potential biomarker of GBM radioresistance and has important implications for improving clinical radiotherapy.

INTRODUCTION:This study addresses the lack of systematic investigation into the prognostic value of hand-crafted radiomic features derived from diffusion tensor imaging (DTI) in isocitrate dehydrogenase (IDH) wild-type glioblastoma (GBM), as well as the limited understanding of the biological interpretation of individual DTI radiomic features and metrics.AIMS:To develop and validate a DTI-based radiomic model for predicting prognosis in patients with IDH wild-type GBM and reveal the biological underpinning of individual DTI radiomic features and metrics.RESULTS:The DTI-based radiomic signature was an independent prognostic factor (p < 0.001). Incorporating the radiomic signature into a clinical model resulted in a radiomic-clinical nomogram that predicted survival better than either the radiomic model or clinical model alone, with a better calibration and classification accuracy. Four categories of pathways (synapse, proliferation, DNA damage response, and complex cellular functions) were significantly correlated with the DTI-based radiomic features and DTI metrics.CONCLUSION:The prognostic radiomic features derived from DTI are driven by distinct pathways involved in synapse, proliferation, DNA damage response, and complex cellular functions of GBM.

Background We aimed to develop machine learning models for prediction of molecular subgroups (low-risk group and intermediate/high-risk group) and molecular marker ( KIAA1549-BRAF fusion) of pediatric low-grade gliomas (PLGGs) based on radiomic features extracted from multiparametric MRI. Methods 61 patients with PLGGs were included in this retrospective study, which were divided into a training set and an internal validation set at a ratio of 2:1 based on the molecular subgroups or the molecular marker. The patients were classified into low-risk and intermediate/high-risk groups, BRAF fusion positive and negative groups, respectively. We extracted 5929 radiomic features from multiparametric MRI. Thereafter, we removed redundant features, trained random forest models on the training set for predicting the molecular subgroups or the molecular marker, and validated their performance on the internal validation set. The performance of the prediction model was verified by 3-fold cross-validation. Results We constructed the classification model differentiating low-risk PLGGs from intermediate/high-risk PLGGs using 4 relevant features, with an AUC of 0.833 and an accuracy of 76.2% in the internal validation set. In the prediction model for predicting KIAA1549-BRAF fusion using 4 relevant features, an AUC of 0.818 and an accuracy of 81.0% were achieved in the internal validation set. Conclusions The current study demonstrates that MRI radiomics is able to predict molecular subgroups of PLGGs and KIAA1549-BRAF fusion with satisfying sensitivity. Trial registration This study was retrospectively registered at clinicaltrials.gov (NCT04217018).

Current diagnosis of glioma types requires combining both histological features and molecular characteristics, which is an expensive and time-consuming procedure. Determining the tumor types directly from whole-slide images (WSIs) is of great value for glioma diagnosis. This study presents an integrated diagnosis model for automatic classification of diffuse gliomas from annotation-free standard WSIs. Our model is developed on a training cohort ( n = 1362) and a validation cohort ( n = 340), and tested on an internal testing cohort ( n = 289) and two external cohorts ( n = 305 and 328, respectively). The model can learn imaging features containing both pathological morphology and underlying biological clues to achieve the integrated diagnosis. Our model achieves high performance with area under receiver operator curve all above 0.90 in classifying major tumor types, in identifying tumor grades within type, and especially in distinguishing tumor genotypes with shared histological features. This integrated diagnosis model has the potential to be used in clinical scenarios for automated and unbiased classification of adult-type diffuse gliomas.

Background To develop and validate a conventional MRI-based radiomic model for predicting prognosis in patients with IDH wild-type glioblastoma (GBM) and reveal the biological underpinning of the radiomic phenotypes. Methods A total of 801 adult patients (training set, N = 471; internal validation set, N = 239; external validation set, N = 91) diagnosed with IDH wild-type GBM were included. A 20-feature radiomic risk score (Radscore) was built for overall survival (OS) prediction by univariate prognostic analysis and least absolute shrinkage and selection operator (LASSO) Cox regression in the training set. GSEA and WGCNA were applied to identify the intersectional pathways underlying the prognostic radiomic features in a radiogenomic analysis set with paired MRI and RNA-seq data (N = 132). The biological meaning of the conventional MRI sequences was revealed using a Mantel test. Results Radscore was demonstrated to be an independent prognostic factor (P < 0.001). Incorporating the Radscore into a clinical model resulted in a radiomic-clinical nomogram predicting survival better than either the Radscore model or the clinical model alone, with better calibration and classification accuracy (a total net reclassification improvement of 0.403, P < 0.001). Three pathway categories (proliferation, DNA damage response, and immune response) were significantly correlated with the prognostic radiomic phenotypes. Conclusion Our findings indicated that the prognostic radiomic phenotypes derived from conventional MRI are driven by distinct pathways involved in proliferation, DNA damage response, and immunity of IDH wild-type GBM.

Ochratoxin A (OTA) is a common mycotoxin, and it is a significant threat to human health throughout the food chain. In this study, a sensitive and specific fluorescent sensor based on magnetic separation technology combined with chain displacement amplification was developed for fast and easy detection of OTA in food. The designed strand displacement amplification can improve the sensitivity for the detection, and the magnetic nanomaterials can provide a large surface area, thus enhancing the capture efficiency of the target from the sample. Based on those designs, the experimental results showed that the proposed method displayed excellent performance. The linearity range was 0.5- 128.0 ng/mL. The detection limit was 0.125 ng/mL; the relative standard deviations were 3.92-7.71%. Additionally, the developed method was satisfactorily applied to determine OTA in wheat, corn, and red wine samples at three spiked levels (1.0, 8.0, and 64.0 ng/mL). The recoveries ranged from 85.45 to 107.8% for wheat flour, 101.34 to 108.35% for corn flour, and 91.15 to 93.80% for red wine, respectively. Compared with high-performance liquid chromatography, the proposed method showed a lower limit of detection and equal recovery. Hence, the designed method is a potential and good detecting tool for OTA residue analysis in complex matrix samples.

To investigate whether radiomic features extracted from dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) can improve the prediction of the molecular subtypes of adult diffuse gliomas, and to further develop and validate a multimodal radiomic model by integrating radiomic features from conventional and perfusion MRI. We extracted 1197 radiomic features from each sequence of conventional MRI and DSC-PWI, respectively. The Boruta algorithm was used for feature selection and combination, and a three-class random forest method was applied to construct the models. We also constructed a combined model by integrating radiomic features and clinical metrics. The models’ diagnostic performance for discriminating the molecular subtypes (IDH wild type [IDHwt], IDH mutant and 1p/19q-noncodeleted [IDHmut-noncodel], and IDH mutant and 1p/19q-codeleted [IDHmut-codel]) was compared using AUCs in the validation set. We included 272 patients (training set, n = 166; validation set, n = 106) with grade II–IV gliomas (mean age, 48.7 years; range, 19–77 years). The proportions of the molecular subtypes were 66.2

Purpose: Early identification of early mortality for glioblastoma (GBM) patients based on laboratory findings at the time of diagnosis could improve the overall survival. The study aimed to explore preoperative factors associated with higher risk of early death (within 1 year after surgery) for isocitrate dehydrogenase (IDH) -wild-type (wt) GBM patients. Patients and Methods: We conducted a retrospective analysis of 194 IDH-wt GBM patients who underwent standard treatment. The probability of dying within 1 year after gross total resection (GTR) was defined as the end point "early mortality". Retrospective collection of predictive factors including clinical characteristics and laboratory data at diagnosis. Results: Median follow-up time after GTR was 16 months (3-41 months). Forty-two patients died within 1 year after surgery (1-year mortality rate: 21.6%). All potential predictive factors were assessed on univariate analyses, which revealed the following factors as associated with higher risk of early death: older age (P = 0.013), occurrence of non-seizures symptoms (P = 0.042), special tumor positions (P = 0.046), higher neutrophil-to-lymphocyte ratio (NLR) (P = 0.015), higher red blood cell distribution width (RDW) (P = 0.019), higher lactate dehydrogenase (LDH) (P = 0.005), and higher fibrinogen (FIB) (P = 0.044). In a multivariate analysis, tumor location (P = 0.012), NLR (P = 0.032) and LDH (P = 0.002) were independent predictors of early mortality. The C-index of the nomogram was 0.795. The calibration curve showed good agreement between prediction by nomogram and actual observation. Conclusion: Tumor location, preoperative elevated NLR and serum LDH level were independent predictors for 1-year mortality after GTR. We indicate that increased preoperative NLR or LDH may guide patients to review head magnetic resonance imaging (MRI) more frequently and regularly to monitor tumor progression.

目的 分析腱糖蛋白C(TNC)在脑胶质瘤中的表达及临床意义.方法 回顾性分析中国脑胶质瘤基因组图谱(CGGA)数据库中胶质瘤患者转录组数据和临床资料,采用SPSS 22.0统计软件分析TNC在胶质瘤组织中的表达特点,分析TN C表达水平与患者临床特征及预后的关系.结果 TN C在胶质瘤组织中表达水平高于正常对照脑组织(P<0.05);TNC表达水平在胶质瘤组织中随世界卫生组织(WHO)病理分级增高而升高(P<0.05).TNC在异柠檬酸脱氢酶(IDH)基因野生型、年龄>42岁、O6-甲基鸟嘌呤DNA甲基转移酶(MGMT)基因启动子非甲基化患者的脑胶质瘤组织中高表达(P<0.05).TNC高表达组患者总生存期均短于TNC低表达组患者(P<0.05).多因素Cox回归分析结果显示,病理分级、术后化疗、1p/19q染色体共缺失和TNC表达水平是脑胶质瘤患者生存期的独立影响因素(P<0.05).结论 TN C在脑胶质瘤中的表达水平与脑胶质瘤的恶性程度相关,可作为脑胶质瘤的诊断标志物及预后预测因子.

-BACKGROUND: Carbamazepine (CBZ) is the first-line therapy for trigeminal neuralgia (TN), and microvascular decompression (MVD) is considered to be an effective surgical treatment for TN. However, the effect of preoperative CBZ treatment on MVD outcome is not clear. -METHODS: From 2013 to 2019, 63 patients with classical TN underwent MVD at the First Affiliated Hospital of Zhengzhou University, China. Data were collected through telephone follow-up and electronic medical records in April 2020. Short-term surgical outcome and long-term follow-up data were estimated by univariate and multivariate analysis. -RESULTS: Multivariate analysis indicated that preoperative CBZ treatment was not a significant predictor for short-term outcomes of MVD (P 0.05). Multivariate analysis for the long-term outcome of MVD indicated that preoperative CBZ treatment could predict postoperative recurrence of TN (P < 0.05). -CONCLUSIONS: For patients with classical TN, a longer preoperative medication history of CBZ treatment had no significant effect on short-term outcome of MVD, but CBZ treatment was associated with a poor long-term outcome following MVD.

This study attempts to explore the radiomics-based features of multi-parametric magnetic resonance imaging (MRI) and construct a machine-learning model to predict the blood supply in vestibular schwannoma preoperatively. By retrospectively collecting the preoperative MRI data of patients with vestibular schwannoma, patients were divided into poor and rich blood supply groups according to the intraoperative recording. Patients were divided into training and test cohorts (2:1), randomly. Stable features were retained by intra-group correlation coefficients (ICCs). Four feature selection methods and four classification methods were evaluated to construct favorable radiomics classifiers. The mean area under the curve (AUC) obtained in the test set for different combinations of feature selecting methods and classifiers was calculated separately to compare the performance of the models. Obtain and compare the best combination results with the performance of differentiation through visual observation in clinical diagnosis. 191 patients were included in this study. 3918 stable features were extracted from each patient. Least absolute shrinkage and selection operator (LASSO) and logistic regression model was selected as the optimal combinations after comparing the AUC calculated by models, which predicted the blood supply of vestibular schwannoma by K-Fold cross-validation method with a mean AUC = 0.88 and F1-score = 0.83. Radiomics machine-learning classifiers can accurately predict the blood supply of vestibular schwannoma by preoperative MRI data.