State Key Laboratory of Oncology in South China, Guangzhou, China.
Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
Cancer Med. 2023 May;12(9):10899-10907. doi: 10.1002/cam4.5801. Epub 2023 Mar 19.
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.
唾液腺癌较为罕见，且预后通常较差。因此，识别具有危险因素的人群并及时进行干预以避免疾病进展具有重要意义。本研究提供了一种有效的预测模型来筛选目标患者，并有助于构建具有成本效益的随访策略。我们纳入了 249 名诊断为唾液腺癌的患者，使用 Cox 比例风险单变量和多变量回归模型分析了预后危险因素。将患者数据按照 7:3 的比例分为训练集和验证集，并使用训练集建立随机生存森林（RSF）模型，使用验证集进行验证。最大选择秩统计方法用于确定与生存最显著相关的切点值。单变量 Cox 回归提示年龄、吸烟、饮酒、未经治疗、神经侵犯、包膜侵犯、皮肤侵犯、肿瘤大于 4cm、T 和 N 期晚期、远处转移和非黏液细胞癌是预后不良的危险因素，多变量分析提示女性、年龄增长、吸烟、未经治疗和非黏液细胞癌是危险因素。时间依赖性 ROC 曲线显示，在验证集中，RSF 预测模型在 1、2 和 3 年生存率的 AUC 分别为 0.696、0.779 和 0.765。对数秩检验提示，从 RSF 计算得出的切点风险评分 7.42 最能有效划分具有显著不同预后的患者。基于 RSF 的预测模型能够有效筛选预后不良的患者。
