逻辑回归
接收机工作特性
单变量
医学
单变量分析
转移
计算器
内科学
曲线下面积
癌症
机器学习
肿瘤科
人工智能
多元分析
外科
多元统计
计算机科学
操作系统
作者
Jin‐Qi Sun,Shi‐Nan Wu,Zheng‐Lin Mou,Jia-mei Wen,Hong Wei,Jie Zou,Qingjian Li,Zhaolin Liu,San Hua Xu,Min Kang,Qian Ling,Hui Huang,Xu Chen,Yixin Wang,Xu‐Lin Liao,Gang Tan,Yi Shao
摘要
Abstract Background Ocular metastasis (OM) is a rare metastatic site of primary liver cancer (PLC). The purpose of this study was to establish a clinical predictive model of OM in PLC patients based on machine learning (ML). Methods We retrospectively collected the clinical data of 1540 PLC patients and divided it into a training set and an internal test set in a 7:3 proportion. PLC patients were divided into OM and non‐ocular metastasis (NOM) groups, and univariate logistic regression analysis was performed between the two groups. The variables with univariate logistic analysis p < 0.05 were selected for the ML model. We constructed six ML models, which were internally verified by 10‐fold cross‐validation. The prediction performance of each ML model was evaluated by receiver operating characteristic curves (ROCs). We also constructed a web calculator based on the optimal performance ML model to personalize the risk probability for OM. Results Six variables were selected for the ML model. The extreme gradient boost (XGB) ML model achieved the optimal differential diagnosis ability, with an area under the curve (AUC) = 0.993, accuracy = 0.992, sensitivity = 0.998, and specificity = 0.984. Based on these results, an online web calculator was constructed by using the XGB ML model to help clinicians diagnose and treat the risk probability of OM in PLC patients. Finally, the Shapley additive explanations (SHAP) library was used to obtain the six most important risk factors for OM in PLC patients: CA125, ALP, AFP, TG, CA199, and CEA. Conclusion We used the XGB model to establish a risk prediction model of OM in PLC patients. The predictive model can help identify PLC patients with a high risk of OM, provide early and personalized diagnosis and treatment, reduce the poor prognosis of OM patients, and improve the quality of life of PLC patients.
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