Department of Neurosurgery, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, P.R. China.
Department of Head and Neck Tumor Surgery, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, P.R. China.
World Neurosurg. 2023 Aug;176:e644-e650. doi: 10.1016/j.wneu.2023.05.113. Epub 2023 Jun 2.
Here, we aimed to investigate the clinical parameters affecting the recurrence of meningiomas, and to construct a predictive nomogram model, so as to predict the recurrence-free survival (RFS) of meningiomas more accurately.
The Clinical, imaging, and pathological data of 155 primary meningioma patients treated surgically from January 2014 to March 2021 were retrospectively analyzed. Independent prognostic factors affecting postoperative recurrence of meningioma were identified by univariate and multivariate Cox regression analyses. A predictive nomogram was established based on independent influence parameters. Subsequently, time-dependent receiver operating characteristic curve, calibration curve, and Kaplan-Meier method were utilized to evaluate the predictive ability of the model.
The multivariate Cox regression analysis showed that tumor size, Ki-67 index, and resection extent had independent prognostic significance, and these parameters were subsequently used to construct a predictive nomogram. Receiver operating characteristic curves indicated that the model was more accurate in predicting RFS than independent factors. Calibration curves suggested that the predicted RFS were similar to the actual observed RFS. In the Kaplan-Meier analysis, the RFS of high-risk cases was obviously shorter than that of low-risk cases.
The tumor size, Ki-67 index, and extent of resection were independent factors affecting the RFS of meningioma. The predictive nomogram based on these factors can be used as an effective method to stratify the recurrence risk of meningioma and provide a reference for patients to choose personalized treatment.
在这里，我们旨在研究影响脑膜瘤复发的临床参数，并构建预测列线图模型，以便更准确地预测脑膜瘤无复发生存（RFS）。
回顾性分析了 2014 年 1 月至 2021 年 3 月期间 155 例接受手术治疗的原发性脑膜瘤患者的临床、影像和病理资料。通过单因素和多因素 Cox 回归分析确定影响脑膜瘤术后复发的独立预后因素。基于独立影响参数建立预测列线图。随后，采用时间依赖性接受者操作特征曲线、校准曲线和 Kaplan-Meier 方法评估模型的预测能力。
多因素 Cox 回归分析表明，肿瘤大小、Ki-67 指数和切除程度具有独立的预后意义，这些参数随后被用于构建预测列线图。接受者操作特征曲线表明，该模型在预测 RFS 方面比独立因素更准确。校准曲线表明，预测的 RFS 与实际观察到的 RFS 相似。在 Kaplan-Meier 分析中，高危病例的 RFS 明显短于低危病例。
肿瘤大小、Ki-67 指数和切除程度是影响脑膜瘤 RFS 的独立因素。基于这些因素的预测列线图可以作为分层脑膜瘤复发风险的有效方法，并为患者选择个性化治疗提供参考。
