Table_2_Nomogram for Predicting Facial Nerve Outcomes After Surgical Resection of Vestibular Schwannoma.docx

ObjectiveThe facial nerve (FN) outcomes after vestibular schwannoma surgery seriously affect the social psychology and quality of life of patients. More and more attention has been paid to the protection of FN function. This study aimed to identify significant prognostic factors for FN outcomes after vestibular schwannoma surgery and create a new nomogram for predicting the rates of poor FN outcomes.MethodsData from patients who had undergone operations for vestibular schwannoma between 2015 and 2020 were retrieved retrospectively and patients were divided into good and poor FN outcomes groups according to postoperative nerve function. The nomogram for predicting the risk of poor FN outcomes was constructed from the results of the univariate logistic regression analysis and the multivariate logistic regression analysis of the influencing factors for FN outcomes after surgical resection of vestibular schwannoma.ResultsA total of 392 participants were enrolled. The univariate logistic regression analysis revealed that age, tumor size, cystic features of tumors, cerebrospinal fluid (CSF) cleft sign, tumor adhesion to the nerve, learning curve, and FN position were statistically significant. The multivariate logistic regression analysis showed that age, tumor size, cystic features of tumors, CSF cleft sign, tumor adhesion to the nerve, learning curve, and FN position were independent factors. The nomogram model was constructed according to these indicators. At the last follow-up examination, a good FN outcome was observed in 342 patients (87.2%) and only 50 patients (12.8%) was presented with poor FN function. Application of the nomogram in the validation cohort still gave good discrimination [area under the curve (AUC), 0.806 (95% CI, 0.752–0.861)] and good calibration.ConclusionThis study has presented a reliable and valuable nomogram that can accurately predict the occurrence of poor FN outcomes after surgery in patients. This tool is easy to use and could assist doctors in establishing clinical decision-making for individual patients.</p

Table_1_Nomogram for Predicting Facial Nerve Outcomes After Surgical Resection of Vestibular Schwannoma.docx

ObjectiveThe facial nerve (FN) outcomes after vestibular schwannoma surgery seriously affect the social psychology and quality of life of patients. More and more attention has been paid to the protection of FN function. This study aimed to identify significant prognostic factors for FN outcomes after vestibular schwannoma surgery and create a new nomogram for predicting the rates of poor FN outcomes.MethodsData from patients who had undergone operations for vestibular schwannoma between 2015 and 2020 were retrieved retrospectively and patients were divided into good and poor FN outcomes groups according to postoperative nerve function. The nomogram for predicting the risk of poor FN outcomes was constructed from the results of the univariate logistic regression analysis and the multivariate logistic regression analysis of the influencing factors for FN outcomes after surgical resection of vestibular schwannoma.ResultsA total of 392 participants were enrolled. The univariate logistic regression analysis revealed that age, tumor size, cystic features of tumors, cerebrospinal fluid (CSF) cleft sign, tumor adhesion to the nerve, learning curve, and FN position were statistically significant. The multivariate logistic regression analysis showed that age, tumor size, cystic features of tumors, CSF cleft sign, tumor adhesion to the nerve, learning curve, and FN position were independent factors. The nomogram model was constructed according to these indicators. At the last follow-up examination, a good FN outcome was observed in 342 patients (87.2%) and only 50 patients (12.8%) was presented with poor FN function. Application of the nomogram in the validation cohort still gave good discrimination [area under the curve (AUC), 0.806 (95% CI, 0.752–0.861)] and good calibration.ConclusionThis study has presented a reliable and valuable nomogram that can accurately predict the occurrence of poor FN outcomes after surgery in patients. This tool is easy to use and could assist doctors in establishing clinical decision-making for individual patients.</p

Image_1_Nomogram for Predicting Facial Nerve Outcomes After Surgical Resection of Vestibular Schwannoma.TIF

ObjectiveThe facial nerve (FN) outcomes after vestibular schwannoma surgery seriously affect the social psychology and quality of life of patients. More and more attention has been paid to the protection of FN function. This study aimed to identify significant prognostic factors for FN outcomes after vestibular schwannoma surgery and create a new nomogram for predicting the rates of poor FN outcomes.MethodsData from patients who had undergone operations for vestibular schwannoma between 2015 and 2020 were retrieved retrospectively and patients were divided into good and poor FN outcomes groups according to postoperative nerve function. The nomogram for predicting the risk of poor FN outcomes was constructed from the results of the univariate logistic regression analysis and the multivariate logistic regression analysis of the influencing factors for FN outcomes after surgical resection of vestibular schwannoma.ResultsA total of 392 participants were enrolled. The univariate logistic regression analysis revealed that age, tumor size, cystic features of tumors, cerebrospinal fluid (CSF) cleft sign, tumor adhesion to the nerve, learning curve, and FN position were statistically significant. The multivariate logistic regression analysis showed that age, tumor size, cystic features of tumors, CSF cleft sign, tumor adhesion to the nerve, learning curve, and FN position were independent factors. The nomogram model was constructed according to these indicators. At the last follow-up examination, a good FN outcome was observed in 342 patients (87.2%) and only 50 patients (12.8%) was presented with poor FN function. Application of the nomogram in the validation cohort still gave good discrimination [area under the curve (AUC), 0.806 (95% CI, 0.752–0.861)] and good calibration.ConclusionThis study has presented a reliable and valuable nomogram that can accurately predict the occurrence of poor FN outcomes after surgery in patients. This tool is easy to use and could assist doctors in establishing clinical decision-making for individual patients.</p

The prevalence and clinical features of molecular subtypes in anaplastic gliomas.

<p>(A) astrocytomas, (B) oligoastrocytomas and (C) oligodendrocytomas.</p

The prevalence and clinical features of the molecular subtypes in glioblastomas.

<p>The prevalence and clinical features of the molecular subtypes in glioblastomas.</p

Prevalence and Clinicopathologic Characteristics of the Molecular Subtypes in Malignant Glioma: A Multi-Institutional Analysis of 941 Cases

<div><p>Background</p><p>Glioblastoma can be classified into four distinct molecular subtypes (Proneural, Neural, Classical and Mesenchymal), based on gene expression profiling. This study aimed to investigate the prevalence, clinicopathologic features and overall survival (OS) of the four molecular subtypes among all malignant gliomas.</p><p>Methods</p><p>A total of 941 gene expression arrays with clinical data were obtained from the Rembrandt, GSE16011 and CGGA datasets. Molecular subtypes were predicted with a prediction analysis of microarray.</p><p>Results</p><p>Among 941 malignant gliomas, 32.73% were Proneural, 15.09% Neural, 19.77% Classical and 32.41% Mesenchymal. The Proneural and Neural subtypes occurred largely in low-grade gliomas, while the Classical and Mesenchymal subtypes were more frequent in high-grade gliomas. A survival analysis showed that the Proneural subtype displayed a good prognosis, Neural had an intermediate correlation with overall survival, Mesenchymal had a worse prognosis than Neural, and Classical had the worst clinical outcome. Furthermore, oligodendrocytomas were preferentially assigned to the Proneural subtype, while the Mesenchymal subtype included a higher percentage of astrocytomas, compared with oligodendrocytomas. Additionally, nearly all classical gliomas harbored EGFR amplifications. Classical anaplastic gliomas have similar clinical outcomes as their glioblastoma counterparts and should be treated more aggressively.</p><p>Conclusions</p><p>Molecular subtypes exist stably in all histological malignant gliomas subtypes. This could be an important improvement to histological diagnoses for both prognosis evaluations and clinical outcome predictions.</p></div

The prevalence and clinical features of the molecular subtypes in low-grade gliomas.

<p>(A) astrocytomas, (B) oligoastrocytomas and (C) oligodendrocytomas.</p

Survival analysis according to the histology and tumor molecular subtypes in high-grade gliomas.

<p>Survival analysis according to the histology and tumor molecular subtypes in high-grade gliomas.</p

The prevalence of EGFR amplification in the molecular subtypes of malignant gliomas (A) and the ROC curve of EGFR amplification as a potential diagnostic marker of Classical gliomas (B).

<p>The prevalence of EGFR amplification in the molecular subtypes of malignant gliomas (A) and the ROC curve of EGFR amplification as a potential diagnostic marker of Classical gliomas (B).</p

The prevalence and clinical features of the molecular subtypes in all malignant gliomas.

<p>(A) Distribution of the TCGA molecular subtypes in all malignant gliomas; (B) survival analysis according to the TCGA molecular subtypes in all malignant gliomas.</p