胶质瘤患者术后影响生存的因素及其评估模型

Influencing factors for postoperative survival of patients with glioma and establishment of a prognostic model

摘要:

目的 分析胶质瘤患者术后影响生存的因素，并建立精准的预后模型，为临床治疗决策和预后评估提供可靠依据。方法 收集胶质瘤患者的临床资料，包括年龄、性别、肿瘤部位、病理分级、手术切除程度、术后放疗、化疗等多方面信息。运用统计分析方法，确定影响患者术后生存的危险因素。基于这些因素，利用机器学习算法建立预后模型，并通过内部验证和外部验证对模型进行全面评估。结果 年龄、病理分级、手术切除程度、术后放疗、化疗等因素与患者术后生存时间相关。多因素分析进一步揭示病理分级、手术切除程度、术后放疗等为影响患者术后生存的影响因素。所建立的预后模型具有卓越的预测性能，能够准确评估患者的预后。结论 病理分级、手术切除程度、术后放疗等是胶质瘤患者术后生存的重要影响因素。建立的预后模型为临床医生提供了科学的预后评估工具。

Abstract:

Objective To investigate the influencing factors for postoperative survival of patients with glioma， to establish an accurate prognostic model， and to provide a reliable basis for clinical treatment decision-making and prognostic evaluation.Methods Related clinical data were collected from the patients with glioma， including age， sex， tumor location， pathological classification， extent of surgical resection， postoperative radiotherapy， and chemotherapy. The methods for statistical analysis were used to determine the risk factors for postoperative survival of patients. Based on these factors， machine learning algorithms were used to establish a prognostic model， and internal and external validations were performed for comprehensive evaluation.Results The comprehensive analysis showed that age， pathological classification， extent of surgical resection， postoperative radiotherapy， and chemotherapy were associated with the postoperative survival time of patients. The multivariate analysis further revealed that pathological classification， extent of surgical resection， and postoperative radiotherapy were risk factors for postoperative survival of patients. The prognostic model established had excellent predictive performance and could accurately evaluate the prognosis of patients.Conclusions Pathological classification， extent of surgical resection， and postoperative radiotherapy are important influencing factors for postoperative survival of patients with glioma. The prognostic model established in this study provides a scientific prognostic assessment tool for clinicians.