Xing Yuncan, Yu Guanhua, Jiang Zheng, Wang Zheng
Department of Colorectal Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Transl Cancer Res. 2024 Nov 30;13(11):5943-5952. doi: 10.21037/tcr-24-1194. Epub 2024 Nov 18.
Liver metastasis (LM) is of vital importance in making treatment-related decisions in patients with colorectal cancer (CRC). The aim of our study was to develop and validate prediction models for LM in CRC by making use of machine learning.
We selected patients diagnosed with CRC from 2010 to 2015 from the Surveillance, Epidemiology, and End Results (SEER) database. Four machine-learning methods, eXtreme gradient boost (XGB), decision tree (DT), random forest (RF), and support vector machine (SVM), were employed to develop a predictive model. The receiver operating characteristic (ROC) curves, decision curve analysis (DCA) curves and calibration curves were adopted to evaluate the model performance. The SHapley Additive exPlanation (SHAP) technique was chosen for visual analysis to enhance the interpretation of the outcomes of models.
A total of 51,632 patients suffering from CRC were selected from the SEER database. Excellent accuracy of machine learning models was showed from ROC curves. In both the training and validation cohorts, calibration curves for the likelihood of LM demonstrated a high degree of concordance between model prediction and actual observation. The DCA indicated that each machine learning model can yield net benefits for both treat-none and treat-all strategies. Carcinoembryonic antigen (CEA) and N stage were identified as the most significant risk factors for LM based on the SHAP summary plot of the RF and XGB models.
The XGB and RF were the best machine learning models among the four algorithms, of which CEA and N stage were identified as the most important risk factors related to LM.
肝转移（LM）在结直肠癌（CRC）患者治疗相关决策中至关重要。我们研究的目的是利用机器学习开发并验证CRC中LM的预测模型。
我们从监测、流行病学和最终结果（SEER）数据库中选取了2010年至2015年诊断为CRC的患者。采用四种机器学习方法，即极端梯度提升（XGB）、决策树（DT）、随机森林（RF）和支持向量机（SVM）来开发预测模型。采用受试者工作特征（ROC）曲线、决策曲线分析（DCA）曲线和校准曲线来评估模型性能。选择SHapley加性解释（SHAP）技术进行可视化分析，以增强对模型结果的解释。
从SEER数据库中总共选取了51632例CRC患者。ROC曲线显示机器学习模型具有出色的准确性。在训练和验证队列中，LM可能性的校准曲线表明模型预测与实际观察之间具有高度一致性。DCA表明，每个机器学习模型对于不治疗和全部治疗策略都能产生净效益。基于RF和XGB模型的SHAP汇总图，癌胚抗原（CEA）和N分期被确定为LM最重要的危险因素。
XGB和RF是四种算法中最佳的机器学习模型，其中CEA和N分期被确定为与LM相关的最重要危险因素。
