使用监测、流行病学和最终结果数据库开发和内部验证食管癌症肝转移的预后模型

摘要(中文译文)

晚期食管癌（EC）合并肝转移患者的准确生存预测对临床决策至关重要。然而，现有的列线图往往缺乏足够的精确度。为解决这一局限，本研究旨在开发并验证一种机器学习（ML）模型以提高预测准确性。数据提取自监测、流行病学和最终结果（SEER）数据库，纳入2010年至2020年间诊断为EC合并肝转移的患者。患者按7：3比例随机分配至训练集和内部测试集。使用训练集中的临床变量训练六种ML模型——逻辑回归（LR）、随机森林（RF）、XGBoost（XGB）、支持向量机（SVM）、K近邻（KNN）和迭代二分器3（ID3）。通过DeLong检验进行模型选择。在内部测试集中使用受试者工作特征（ROC）曲线、曲线下面积（AUC）、校准图、混淆矩阵和决策曲线分析（DCA）严格评估模型性能。使用SHapley加法解释（SHAP）方法实现模型可解释性。共纳入1，760例EC合并肝转移患者。在6种模型中，LR模型表现出最佳性能。在内部测试集中，该模型预测6个月生存状态的AUC为0.764[95%置信区间（CI）：0.735-0.791]，灵敏度为0.641（95% CI：0.600-0.680），特异度为0.788（95% CI：0.749-0.820）。预测1年生存时，AUC为0.792（95% CI：0.753-0.830），灵敏度为0.784（95% CI：0.736-0.831），特异度为0.675（95% CI：0.616-0.736）。校准曲线显示良好的一致性（6个月Brier评分为0.240，95% CI：0.238-0.241；1年Brier评分为0.158，95% CI：0.143-0.175）。DCA证实该模型在合理阈值范围内具有净临床获益。SHAP分析显示，接受化疗是影响预后预测的最重要变量。基于LR的模型表现出良好的预测性能，可作为EC合并肝转移患者短期生存评估的参考，接受化疗被确定为关键预测变量。

Abstract

Accurate survival prediction for patients with advanced esophageal cancer (EC) and liver metastases is critical for clinical decision-making. However, existing nomograms often lack sufficient precision. To address this limitation, this study aimed to develop and validate a machine learning (ML) model to improve predictive accuracy. Data were extracted from the Surveillance, Epidemiology, and End Results (SEER) database for patients diagnosed with EC and liver metastases between 2010 and 2020. Patients were randomly assigned to training and internal test sets in a 7:3 ratio. Six ML models-Logistic Regression (LR), Random Forest (RF), XGBoost (XGB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Iterative Dichotomiser 3 (ID3)-were trained using clinical variables from the training set. Model selection was performed via the DeLong test. Performance was rigorously evaluated in the internal test set using receiver operating characteristic (ROC) curves, area under the curve (AUC), calibration plots, confusion matrices, and decision curve analysis (DCA). Model interpretability was achieved using the SHapley Additive exPlanations (SHAP) method. A total of 1,760 patients with EC and liver metastases were included. Among the 6 models, the LR model demonstrated the best performance. In the internal test set, the model achieved an AUC of 0.764 [95% confidence interval (CI): 0.735-0.791] for predicting 6-month survival status, with a sensitivity of 0.641 (95% CI: 0.600-0.680) and a specificity of 0.788 (95% CI: 0.749-0.820). For predicting 1-year survival, the AUC was 0.792 (95% CI: 0.753-0.830), with a sensitivity of 0.784 (95% CI: 0.736-0.831) and a specificity of 0.675 (95% CI: 0.616-0.736). The calibration curve indicated good agreement (the Brier score of 6-month was 0.240, 95% CI: 0.238-0.241; the Brier score of 1-year was 0.158, 95% CI: 0.143-0.175). DCA confirmed the model's net clinical benefit within a reasonable threshold range. SHAP analysis revealed that receipt of chemotherapy was the most important variable influencing prognosis prediction. The LR-based model demonstrates favorable predictive performance and may serve as a reference for short-term survival assessment in patients with EC and liver metastases, with receipt of chemotherapy identified as the key predictive variable.

如何引用

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