Medical School of Chinese PLA, Beijing, China.
Department of Stomatology, Chinese PLA General Hospital, 28 Fuxing Road, Haidian District, Beijing, 100853, China.
BMC Oral Health. 2023 Aug 13;23(1):567. doi: 10.1186/s12903-023-03255-w.
We aimed to develop a 5-year overall survival prediction model for patients with oral tongue squamous cell carcinoma based on machine learning methods.
The data were obtained from electronic medical records of 224 OTSCC patients at the PLA General Hospital. A five-year overall survival prediction model was constructed using logistic regression, Support Vector Machines, Decision Tree, Random Forest, Extreme Gradient Boosting, and Light Gradient Boosting Machine. Model performance was evaluated according to the area under the curve (AUC) of the receiver operating characteristic curve. The output of the optimal model was explained using the Python package (SHapley Additive exPlanations, SHAP).
After passing through the grid search and secondary modeling, the Light Gradient Boosting Machine was the best prediction model (AUC = 0.860). As explained by SHapley Additive exPlanations, N-stage, age, systemic inflammation response index, positive lymph nodes, plasma fibrinogen, lymphocyte-to-monocyte ratio, neutrophil percentage, and T-stage could perform a 5-year overall survival prediction for OTSCC. The 5-year survival rate was 42%.
The Light Gradient Boosting Machine prediction model predicted 5-year overall survival in OTSCC patients, and this predictive tool has potential prognostic implications for patients with OTSCC.
我们旨在基于机器学习方法为口腔舌鳞状细胞癌患者建立一个 5 年总体生存率预测模型。
数据来自于解放军总医院 224 例 OTSCC 患者的电子病历。使用逻辑回归、支持向量机、决策树、随机森林、极端梯度提升和轻梯度提升机构建了 5 年总体生存率预测模型。根据受试者工作特征曲线下的面积（AUC）评估模型性能。使用 Python 包（SHapley Additive exPlanations，SHAP）解释最佳模型的输出。
经过网格搜索和二次建模，轻梯度提升机是最佳预测模型（AUC=0.860）。根据 SHapley Additive exPlanations 的解释，N 期、年龄、全身炎症反应指数、阳性淋巴结、血浆纤维蛋白原、淋巴细胞与单核细胞比值、中性粒细胞百分比和 T 期可以对 OTSCC 患者进行 5 年总体生存率预测。5 年生存率为 42%。
Light Gradient Boosting Machine 预测模型预测了 OTSCC 患者的 5 年总体生存率，该预测工具对 OTSCC 患者具有潜在的预后意义。
