背景:虽然准确预测颌下腺癌(SGC)患者的总生存期(OS)对于明智的治疗计划至关重要,SGC病例的稀有性阻碍了可靠生存预测模型的发展。这项研究的目的是使用大型数据库确定SGC患者OS的关键预后因素,并构建决策树模型以帮助预测12、24、60和120个月的生存概率。
方法:我们进行了一项回顾性队列研究,流行病学和最终结果(SEER)计划。确定了人口统计学和围手术期预测变量。结果变量12-,24-,60和120个月。利用C5.0算法建立二分决策树模型,树的深度限制在4层以内。为了评估新颖模型的性能,生成受试者操作特征(ROC)曲线,以及准确率等指标,计算ROC曲线下面积(AUC)。
结果:从SEER数据库中确定了1,705、1,666、1,543和1,413例SGC患者的随访时间为12、24、60和120个月,确切的生存状态。预测变量的年龄,性别,手术,辐射,化疗,肿瘤组织学,总结阶段,远处淋巴结转移,婚姻状况对总体生存率有重大影响。然后开发了决策树模型,纳入这些重要的预后指标。预测和实际生存状态之间呈现良好的一致性。对于训练数据集,12-的准确率,24-,60个月和120个月的生存模型分别为0.866、0.767、0.737和0.797。相应地,相同时间点的AUC值分别为0.841,0.756,0.725和0.774.
结论:基于使用大,SEER数据库,建立了预测SGC患者OS的决策树模型。这些模型提供了更详尽的死亡风险评估,并可能导致更个性化的治疗策略。
BACKGROUND: While the accurate prediction of the overall survival (OS) in patients with submandibular gland cancer (SGC) is paramount for informed therapeutic planning, the development of reliable survival prediction models has been hindered by the rarity of SGC cases. The purpose of this study is to identify key prognostic factors for OS in SGC patients using a large database and construct decision tree models to aid the prediction of survival probabilities in 12, 24, 60 and 120 months.
METHODS: We performed a retrospective cohort study using the Surveillance, Epidemiology and End Result (SEER) program. Demographic and peri-operative predictor variables were identified. The outcome variables overall survival at 12-, 24-, 60, and 120 months. The C5.0 algorithm was utilized to establish the dichotomous decision tree models, with the depth of tree limited within 4 layers. To evaluate the performances of the novel models, the receiver operator characteristic (ROC) curves were generated, and the metrics such as accuracy rate, and area under ROC curve (AUC) were calculated.
RESULTS: A total of 1,705, 1,666, 1,543, and 1,413 SGC patients with a follow up of 12, 24, 60 and 120 months and exact survival status were identified from the SEER database. Predictor variables of age, sex, surgery, radiation, chemotherapy, tumor histology, summary stage, metastasis to distant lymph node, and marital status exerted substantial influence on overall survival. Decision tree models were then developed, incorporating these vital prognostic indicators. Favorable consistency was presented between the predicted and actual survival statuses. For the training dataset, the accuracy rates for the 12-, 24-, 60- and 120-month survival models were 0.866, 0.767, 0.737 and 0.797. Correspondingly, the AUC values were 0.841, 0.756, 0.725, and 0.774 for the same time points.
CONCLUSIONS: Based on the most important predictor variables identified using the large, SEER database, decision tree models were established that predict OS of SGC patients. The models offer a more exhaustive evaluation of mortality risk and may lead to more personalized treatment strategies.