Abstract：
Although rare overall, salivary gland carcinomas (SGCs) are among the most common oral and maxillofacial malignancies. The aim of this study was to develop a machine learning-based model to predict the survival of patients with SGC. Patients in whom SGC was confirmed by histological testing and who underwent primary extirpation at the authors\' institution between 1963 and 2014 were identified. Demographic and clinicopathological data with complete follow-up information were collected for analysis. Feature selection methods were used to determine the correlation between prognosis-related factors and survival in the collected patient data. The collected clinicopathological data and multiple machine learning algorithms were used to develop a survival prediction model. Three machine learning algorithms were applied to construct the prediction models. The area under the receiver operating characteristic curve (AUC) and accuracy were used to measure model performance. The best classification performance was achieved with a LightGBM algorithm (AUC = 0.83, accuracy = 0.91). This model enabled prognostic prediction of patient survival. The model may be useful in developing personalized diagnostic and treatment strategies and formulating individualized follow-up plans, as well as assisting in the communication between doctors and patients, facilitating a better understanding of and compliance with treatment.
摘要：
虽然总体上很少见，唾液腺癌（SGC）是最常见的口腔和颌面部恶性肿瘤之一。这项研究的目的是开发一种基于机器学习的模型来预测SGC患者的生存率。确定了在1963年至2014年期间通过组织学测试确认SGC并在作者机构进行了初次摘除的患者。收集具有完整随访信息的人口统计学和临床病理数据进行分析。在收集的患者数据中，使用特征选择方法确定预后相关因素与生存之间的相关性。收集的临床病理数据和多种机器学习算法用于建立生存预测模型。应用三种机器学习算法来构建预测模型。接收器工作特征曲线下面积（AUC）和准确性用于测量模型性能。用LightGBM算法实现了最佳分类性能(AUC=0.83,准确度=0.91)。该模型能够预测患者生存的预后。该模型可能有助于制定个性化的诊断和治疗策略以及制定个性化的随访计划，以及协助医生和病人之间的沟通，有助于更好地理解和遵守治疗。
