Fang Ying, Wan Jun, Zeng Yukai
Department of Joint Surgery, Hangzhou Xiaoshan Hospital of Traditional Chinese Medicine, Hangzhou, Zhejiang, China.
Department of Emergency surgery, Yangtze University Jingzhou Hospital, No.26, Chuyuan Road, Jingzhou, Hubei, China.
J Cancer Res Clin Oncol. 2024 Sep 16;150(9):420. doi: 10.1007/s00432-024-05937-6.
This study aims to establish a predictive model for assessing the risk of esophageal cancer lung metastasis using machine learning techniques.
Data on esophageal cancer patients from 2010 to 2020 were extracted from the surveillance, epidemiology, and end results (SEER) database. Through univariate and multivariate logistic regression analyses, eight indicators related to the risk of lung metastasis were selected. These indicators were incorporated into six machine learning classifiers to develop corresponding predictive models. The performance of these models was evaluated and compared using metrics such as The area under curve (AUC), accuracy, sensitivity, specificity, and F1 score.
A total of 20,249 confirmed cases of esophageal cancer were included in this study. Among them, 14,174 cases (70%) were assigned to the training set while 6075 cases (30%) constituted the internal test set. Primary site location, tumor histology, tumor grade classification system T staging criteria N staging criteria brain metastasis bone metastasis liver metastasis emerged as independent risk factors for esophageal cancer with lung metastasis. Amongst the six constructed models, the GBM algorithm-based machine learning model demonstrated superior performance during internal dataset validation. AUC, accuracy, sensitivity, and specificity values achieved by this model stood at respectively at 0.803, 0.849, 0.604, and 0.867.
We have developed an online calculator based on the GBM model ( https://lvgrkyxcgdvo7ugoyxyywe.streamlit.app/)to aid clinical decision-making and treatment planning.
本研究旨在利用机器学习技术建立一个评估食管癌肺转移风险的预测模型。
从监测、流行病学和最终结果（SEER）数据库中提取2010年至2020年食管癌患者的数据。通过单因素和多因素逻辑回归分析，选择了八个与肺转移风险相关的指标。将这些指标纳入六个机器学习分类器中，以开发相应的预测模型。使用曲线下面积（AUC）、准确率、灵敏度、特异度和F1分数等指标对这些模型的性能进行评估和比较。
本研究共纳入20249例确诊食管癌病例。其中，14174例（70%）被分配到训练集，6075例（30%）构成内部测试集。原发部位、肿瘤组织学、肿瘤分级分类系统、T分期标准、N分期标准、脑转移、骨转移、肝转移是食管癌发生肺转移的独立危险因素。在构建的六个模型中，基于梯度提升机（GBM）算法的机器学习模型在内部数据集验证中表现出卓越的性能。该模型的AUC、准确率、灵敏度和特异度值分别为0.803、0.849、0.604和0.867。
我们基于GBM模型开发了一个在线计算器（https://lvgrkyxcgdvo7ugoyxyywe.streamlit.app/），以辅助临床决策和治疗规划。
