Meningiomas are known to have relatively lower aggressiveness and better outcomes than other central nervous system (CNS) tumors. However, there is considerable overlap between clinical and radiological features characterizing benign, atypical, and malignant tumors. In this study, we developed methods and a practical app designed to assist with the diagnosis and prognosis of meningiomas. Statistical learning models were trained and validated on 62,844 patients from the Surveillance, Epidemiology, and End Results database. We used balanced logistic regression-random forest ensemble classifiers and proportional hazards models to learn multivariate patterns of association between malignancy, survival, and a series of basic clinical variables-such as tumor size, location, and surgical procedure. We demonstrate that our models are capable of predicting meaningful individual-specific clinical outcome variables and show good generalizability across 16 SEER registries. A free smartphone and web application is provided for readers to access and test the predictive models (www.meningioma.app). Future model improvements and prospective replication will be necessary to demonstrate true clinical utility. Rather than being used in isolation, we expect that the proposed models will be integrated into larger and more comprehensive models that integrate imaging and molecular biomarkers. Whether for meningiomas or other tumors of the CNS, the power of these methods to make individual-patient predictions could lead to improved diagnosis, patient counseling, and outcomes.

中文翻译：

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使用监测、流行病学和最终结果数据库对脑膜瘤恶性程度和生存率进行个体患者预测。

众所周知，脑膜瘤比其他中枢神经系统 (CNS) 肿瘤具有相对较低的侵袭性和更好的结果。然而，良性、非典型和恶性肿瘤的临床和放射学特征之间存在相当大的重叠。在这项研究中，我们开发了旨在协助脑膜瘤的诊断和预后的方法和实用应用程序。统计学习模型在来自监测、流行病学和最终结果数据库的 62,844 名患者中进行了训练和验证。我们使用平衡逻辑回归-随机森林集成分类器和比例风险模型来学习恶性肿瘤、生存率和一系列基本临床变量（例如肿瘤大小、位置和手术程序）之间的多变量关联模式。我们证明，我们的模型能够预测有意义的个体特异性临床结果变量，并在 16 个 SEER 注册中心中表现出良好的普遍性。为读者提供免费的智能手机和网络应用程序来访问和测试预测模型（www.meningioma.app）。未来的模型改进和前瞻性复制对于证明真正的临床实用性是必要的。我们期望所提出的模型将被集成到更大、更全面的模型中，从而集成成像和分子生物标志物，而不是单独使用。无论是脑膜瘤还是其他中枢神经系统肿瘤，这些方法对个体患者进行预测的能力都可以改善诊断、患者咨询和结果。