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Abstract：
The (re)hemorrhage in patients with sporadic cerebral cavernous malformations (CCM) was the primary aim for CCM management. However, accurately identifying the potential (re)hemorrhage among sporadic CCM patients in advance remains a challenge. This study aims to develop machine learning models to detect potential (re)hemorrhage in sporadic CCM patients. This study was based on a dataset of 731 sporadic CCM patients in open data platform Dryad. Sporadic CCM patients were followed up 5 years from January 2003 to December 2018. Support vector machine (SVM), stacked generalization, and extreme gradient boosting (XGBoost) were used to construct models. The performance of models was evaluated by area under receiver operating characteristic curves (AUROC), area under the precision-recall curve (PR-AUC) and other metrics. A total of 517 patients with sporadic CCM were included (330 female [63.8%], mean [SD] age at diagnosis, 42.1 [15.5] years). 76 (re)hemorrhage (14.7%) occurred during follow-up. Among 3 machine learning models, XGBoost model yielded the highest mean (SD) AUROC (0.87 [0.06]) in cross-validation. The top 4 features of XGBoost model were ranked with SHAP (SHapley Additive exPlanations). All-Elements XGBoost model achieved an AUROCs of 0.84 and PR-AUC of 0.49 in testing set, with a sensitivity of 0.86 and a specificity of 0.76. Importantly, 4-Elements XGBoost model developed using top 4 features got a AUROCs of 0.83 and PR-AUC of 0.40, a sensitivity of 0.79, and a specificity of 0.72 in testing set. Two machine learning-based models achieved accurate performance in identifying potential (re)hemorrhages within 5 years in sporadic CCM patients. These models may provide insights for clinical decision-making.
摘要：
散发性脑海绵状畸形（CCM）患者的（再）出血是CCM管理的主要目的。然而,提前准确识别散发性CCM患者的潜在（再）出血仍然是一个挑战。这项研究旨在开发机器学习模型，以检测散发性CCM患者的潜在（再）出血。本研究基于开放数据平台Dryad中731名零星CCM患者的数据集。2003年1月至2018年12月对散发性CCM患者进行5年随访。支持向量机(SVM)堆叠概括，和极端梯度提升(XGBoost)用于构建模型。通过受试者工作特征曲线下面积(AUROC)评估模型的性能，精确率-召回率曲线下面积(PR-AUC)和其他指标。共纳入517例散发性CCM患者(330例女性[63.8%],诊断时的平均[SD]年龄，42.1[15.5]年)。随访期间发生76例(再)出血(14.7%)。在3种机器学习模型中，XGBoost模型在交叉验证中产生最高平均值(SD)AUROC(0.87[0.06])。XGBoost模型的前4个特征以SHAP（Shapley添加剂扩张）排名。All-ElementsXGBoost模型在测试集中实现了0.84的AUROC和0.49的PR-AUC，灵敏度为0.86，特异性为0.76。重要的是,使用前4个特征开发的4元素XGBoost模型在测试集中获得0.83的AUROC和0.40的PR-AUC，0.79的灵敏度和0.72的特异性。两个基于机器学习的模型在识别散发性CCM患者5年内的潜在（再）出血方面实现了准确的性能。这些模型可以为临床决策提供见解。
