This study aimed to investigate the feasibility of predicting NF2 mutation status based on the MR radiomic analysis in patients with intracranial meningioma.

This retrospective study included 105 patients with meningiomas, including 60 NF2-mutant samples and 45 wild-type samples. Radiomic features were extracted from magnetic resonance imaging scans, including T1-weighted, T2-weighted, and contrast T1-weighted images. Student’s t-test and LASSO regression were performed to select the radiomic features. All patients were randomly divided into training and validation cohorts in a 7:3 ratio. Five linear models (RF, SVM, LR, KNN, and xgboost) were trained to predict the NF2 mutational status. Receiver operating characteristic curve and precision-recall analyses were used to evaluate the model performance. Student’s t-tests were then used to compare the posterior probabilities of NF2 mut/loss prediction for patients with different NF2 statuses.

Nine features had nonzero coefficients in the LASSO regression model. No significant differences was observed in the clinical features. Nine features showed significant differences in patients with different NF2 statuses. Among all machine learning algorithms, SVM showed the best performance. The area under curve and accuracy of the predictive model were 0.85; the F1-score of the precision-recall curve was 0.80. The model risk was assessed by plotting calibration curves. The p-value for the H-L goodness of fit test was 0.411 (p> 0.05), which indicated that the difference between the obtained model and the perfect model was statistically insignificant. The AUC of our model in external validation was 0.83.

A combination of radiomic analysis and machine learning showed potential clinical utility in the prediction of preoperative NF2 status. These findings could aid in developing customized neurosurgery plans and meningioma management strategies before postoperative pathology.

本研究旨在探讨预测的可行性NF2基于颅内脑膜瘤患者 MR 放射组学分析的突变状态。

这项回顾性研究包括 105 名脑膜瘤患者，其中 60NF2-突变样品和45个野生型样品。从磁共振成像扫描中提取放射组学特征，包括 T1 加权、T2 加权和对比 T1 加权图像。进行学生 t 检验和 LASSO 回归以选择放射组学特征。所有患者以 7:3 的比例随机分为训练组和验证组。训练了五个线性模型（RF、SVM、LR、KNN 和 xgboost）来预测NF2突变状态。接收器操作特征曲线和精确召回分析用于评估模型性能。然后使用学生 t 检验比较具有不同 NF2 状态的患者的 NF2 突变/丢失预测的后验概率。

九个特征在 LASSO 回归模型中具有非零系数。临床特征未观察到显着差异。九个特征在不同 NF2 状态的患者中显示出显着差异。在所有机器学习算法中，SVM 表现最好。预测模型的曲线下面积和准确度为0.85；精确召回曲线的 F1 分数为 0.80。通过绘制校准曲线来评估模型风险。HL拟合优度检验的p值为0.411（p>0.05），表明所得模型与完美模型之间的差异在统计学上不显着。我们的模型在外部验证中的 AUC 为 0.83。

放射组学分析和机器学习的结合在预测术前 NF2 状态方面显示出潜在的临床效用。这些发现可能有助于在术后病理之前制定定制的神经外科计划和脑膜瘤管理策略。