唾液腺恶性肿瘤很少见，通常伴随着不良预后。因此，识别有风险因素的人群并及时干预以避免疾病进展非常重要。该研究提供了一个有效的预测模型，可以筛选目标患者，并有助于构建一种具有成本效益的随访策略。我们纳入了249名被诊断为唾液腺肿瘤的患者，并使用Cox比例风险单变量和多变量回归模型分析预测危险因素。将患者的数据分成培训和验证集，比例为7:3，并使用培训集建立随机生存森林(RSF)模型并在验证集上进行验证。最大选取排名统计方法用于确定与生存率最相关的断点值。单变量Cox回归分析表明，年龄、吸烟、饮酒、未治疗、神经侵犯、包膜侵犯、皮肤侵犯、大于4cm的肿瘤、晚期T和N分期、远处转移以及非粘液性癌症是不良预后的危险因素，而多变量分析表明，女性、衰老、吸烟、未治疗和非粘液性癌症是危险因素。时间依赖ROC曲线显示，在验证集中，RSF预测模型在1年、2年和3年的生存中的AUC分别为0.696、0.779和0.765。Log-rank检验表明，从RSF计算出的7.42风险评分的切点最有效地区分了患者的显著不同的预后。基于RSF的预测模型可以有效地筛选出有不良预后的患者。 ©2023 The Authors. Cancer Medicine由John Wiley＆Sons Ltd.出版。

Salivary gland malignancies are rare and are often acompanied by poor prognoses. So, identifying the populations with risk factors and timely intervention to avoid disease progression is significant. This study provides an effective prediction model to screen the target patients and is helpful to construct a cost-effective follow-up strategy. We enrolled 249 patients diagnosed with salivary gland tumors and analyzed prognostic risk factors using Cox proportional hazard univariable and multivariable regression models. The patients' data were split into training and validation sets on a 7:3 ratio, and the random survival forest (RSF) model was established using the training sets and validated using the validation sets. The maximally selected rank statistics method was used to determine a cut point value corresponding to the most significant relation with survival. Univariable Cox regression suggested age, smoking, alcohol consumption, untreated, neural invasion, capsular invasion, skin invasion, tumors larger than 4 cm, advanced T and N stage, distant metastasis, and non-mucous cell carcinoma were risk factors for poor prognosis, and multivariable analysis suggested that female, aging, smoking, untreated, and non-mucous cell carcinoma were risk factors. The time-dependent ROC curve showed the AUC of the RSF prediction model on 1-, 2-, and 3-year survival were 0.696, 0.779, and 0.765 respectively in the validation sets. Log-rank tests suggested that the cut point 7.42 risk score calculated from the RSF was most effective in dividing patients with significantly different prognoses. The prediction model based on the RSF could effectively screen patients with poor prognoses.© 2023 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.