Purpose: In meningiomas, TERT promotor mutations are rare but qualify the diagnosis of anaplasia, directly impacting adjuvant therapy. Effective screening for patients at risk for promotor mutations could enable more targeted molecular analyses and improve diagnosis and treatment. Methods: Semiautomatic segmentation of intracranial grade 2/3 meningiomas was performed on preoperative magnetic resonance imaging. Discriminatory power to predict TERT promoter mutations was analyzed using a random forest algorithm with an increasing number of radiomic features. Two final models with five and eight features with both fixed and differing radiomics features were developed and adjusted to eliminate random effects and to avoid overfitting. Results: A total of 117 image sets including training (N= 94) and test data (N= 23) were analyzed. To eliminate random effects and demonstrate the robustness of our approach, data partitioning and subsequent model development and testing were repeated a total of 100 times (each time with repartitioned training and independent test data). The established five- and eight-feature models with both fixed and different radiomics features enabled the prediction of TERT with similar but excellent performance. The five-feature (different/fixed) model predicted TERT promotor mutation status with a mean AUC of 91.8%/94.3%, mean accuracy of 85.5%/88.9%, mean sensitivity of 88.6%/91.4%, mean specificity of 83.2%/87.0%, and a mean Cohen’s Kappa of 71.0%/77.7%. The eight-feature (different/fixed) model predicted TERT promotor mutation status with a mean AUC of 92.7%/94.6%, mean accuracy of 87.3%/88.9%, mean sensitivity of 89.6%/90.6%, mean specificity of 85.5%/87.5%, and a mean Cohen’s Kappa of 74.4%/77.6%. Of note, the addition of further features of up toN= 8 only slightly increased the performance. Conclusions: Radiomics-based machine learning enables prediction of TERT promotor mutation status in meningiomas with excellent discriminatory performance. Future analyses in larger cohorts should include grade 1 lesions as well as additional molecular alterations.
目的：在脑膜瘤中，TERT启动子突变虽罕见，但可作为间变型诊断依据，直接影响辅助治疗决策。对存在启动子突变风险的患者进行有效筛查，可实现更具针对性的分子分析，从而优化诊疗策略。方法：基于术前磁共振成像对颅内2/3级脑膜瘤进行半自动分割。采用随机森林算法分析影像组学特征对TERT启动子突变的预测效能，并逐步增加特征数量。最终构建了包含五个和八个特征的两组模型，其中特征组合分为固定组与差异组，并通过调整消除随机效应并避免过拟合。结果：共分析117例影像数据集，包括训练集（94例）和测试集（23例）。为消除随机效应并验证方法的稳健性，进行了100次数据重划分及模型构建测试（每次均重新划分训练集与独立测试集）。所建立的五特征与八特征模型（含固定与差异特征组合）对TERT启动子突变的预测性能相近且表现优异。五特征模型（差异/固定组）预测TERT启动子突变状态的平均曲线下面积（AUC）为91.8%/94.3%，平均准确率85.5%/88.9%，平均灵敏度88.6%/91.4%，平均特异度83.2%/87.0%，平均Cohen's Kappa系数71.0%/77.7%。八特征模型（差异/固定组）的平均AUC为92.7%/94.6%，平均准确率87.3%/88.9%，平均灵敏度89.6%/90.6%，平均特异度85.5%/87.5%，平均Cohen's Kappa系数74.4%/77.6%。值得注意的是，特征数量增加至八个仅使性能轻微提升。结论：基于影像组学的机器学习模型能以优异判别效能预测脑膜瘤TERT启动子突变状态。未来应在更大规模队列中开展研究，并纳入1级病变及其他分子变异类型。
Radiomics-Based Prediction of TERT Promotor Mutations in Intracranial High-Grade Meningiomas
肿瘤(癌症)患者之家