Endoscopy Center, The Affiliated Hospital (Group) of Putian University, Putian, 351100 Fujian, China.
School of Basic Medicine, Medical College of Putian University, Putian, 351100 Fujian, China.
Comput Math Methods Med. 2022 Sep 13;2022:4862376. doi: 10.1155/2022/4862376. eCollection 2022.
Accurate prediction is essential for the survival of patients with nonmetastatic gastric signet ring cell carcinoma (GSRC) and medical decision-making. Current models rely on prespecified variables, limiting their performance and not being suitable for individual patients. Our study is aimed at developing a more precise model for predicting 1-, 3-, and 5-year overall survival (OS) in patients with nonmetastatic GSRC based on a machine learning approach.
We selected 2127 GSRC patients diagnosed from 2004 to 2014 from the Surveillance, Epidemiology, and End Results (SEER) database and then randomly partitioned them into a training and validation cohort. We compared the performance of several machine learning-based models and finally chose the eXtreme gradient boosting (XGBoost) model as the optimal method to predict the OS in patients with nonmetastatic GSRC. The model was assessed using the receiver operating characteristic curve (ROC).
In the training cohort, for predicting OS rates at 1-, 3-, and 5-year, the AUCs of the XGBoost model were 0.842, 0.831, and 0.838, respectively, while in the testing cohort, the AUCs of 1-, 3-, and 5-year OS rates were 0.749, 0.823, and 0.829, respectively. Besides, the XGBoost model also performed better when compared with the American Joint Committee on Cancer (AJCC) stage. The performance for this model was stably maintained when stratified by age and ethnicity.
The XGBoost-based model accurately predicts the 1-, 3-, and 5-year OS in patients with nonmetastatic GSRC. Machine learning is a promising way to predict the survival outcomes of tumor patients.
准确的预测对于非转移性胃印戒细胞癌（GSRC）患者的生存和医疗决策至关重要。目前的模型依赖于预设变量，限制了它们的性能，并且不适合个别患者。我们的研究旨在基于机器学习方法为非转移性 GSRC 患者建立更精确的 1 年、3 年和 5 年总生存（OS）预测模型。
我们从 SEER 数据库中选择了 2004 年至 2014 年期间诊断的 2127 例 GSRC 患者，并将其随机分为训练和验证队列。我们比较了几种基于机器学习的模型的性能，最终选择了极端梯度提升（XGBoost）模型作为预测非转移性 GSRC 患者 OS 的最佳方法。该模型使用接收者操作特征曲线（ROC）进行评估。
在训练队列中，XGBoost 模型预测 1 年、3 年和 5 年 OS 率的 AUC 分别为 0.842、0.831 和 0.838，而在测试队列中，1 年、3 年和 5 年 OS 率的 AUC 分别为 0.749、0.823 和 0.829。此外，与美国癌症联合委员会（AJCC）分期相比，XGBoost 模型表现更好。该模型的性能在按年龄和种族分层时也保持稳定。
XGBoost 模型可准确预测非转移性 GSRC 患者的 1 年、3 年和 5 年 OS。机器学习是预测肿瘤患者生存结果的一种很有前途的方法。
