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已发表论文
不可切除食管鳞状细胞癌免疫化疗生存预测的可解释机器学习模型的开发与验证
Received 7 April 2025
Accepted for publication 15 September 2025
Published 4 November 2025 Volume 2025:17 Pages 2609—2619
DOI https://doi.org/10.2147/CMAR.S532944
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Chien-Feng Li
Yaru Wang, Qibing Wu
Department of Radiation Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, People’s Republic of China
Correspondence: Qibing Wu, Email Wqb71vip@163.com
Background: To address the heterogeneity in treatment responses and the lack of robust prognostic tools for unresectable esophageal squamous cell carcinoma (ESCC) patients undergoing immunochemotherapy, this study aimed to develop and validate an interpretable machine learning (ML) model for survival prediction and risk stratification.
Methods: A retrospective cohort of 323 unresectable ESCC patients treated with immunochemotherapy (2019– 2025) was analyzed. Using the XGBoost algorithm, we integrated baseline clinical features (age, tumor location, TNM stage) and laboratory parameters (albumin, globulin, blood glucose) to construct a prognostic model. SHapley Additive exPlanations (SHAP) values were employed to quantify feature contributions, and external validation (n=48) was performed to assess generalizability. SHAP (SHapley Additive exPlanations) is a game theory-based framework that enables model interpretability by quantifying the contribution of each feature to predictions. The primary endpoint was overall survival (OS).
Results: The model achieved AUC values of 0.794 (internal test) and 0.689 (external test), with calibration curves demonstrating strong concordance between predicted and observed survival rates. Key prognostic factors included tumor response, age, hypoalbuminemia, hyperglobulinemia and hyperglycemia. Risk stratification using a nomogram-derived cutoff (total score ≥ 50) revealed significantly inferior 2-year OS in high-risk versus low-risk patients (21.3% vs 58.6%, P< 0.001).
Conclusion: This interpretable ML model effectively predicts survival outcomes in unresectable ESCC patients receiving immunochemotherapy, offering a data-driven tool for personalized therapeutic decision-making. Multicenter prospective trials are warranted to validate its clinical utility.
Keywords: esophageal squamous cell carcinoma, immunochemotherapy, machine learning, prognostic model, SHAP
Department of Radiation Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, People’s Republic of China
Correspondence: Qibing Wu, Email Wqb71vip@163.com
Background: To address the heterogeneity in treatment responses and the lack of robust prognostic tools for unresectable esophageal squamous cell carcinoma (ESCC) patients undergoing immunochemotherapy, this study aimed to develop and validate an interpretable machine learning (ML) model for survival prediction and risk stratification.
Methods: A retrospective cohort of 323 unresectable ESCC patients treated with immunochemotherapy (2019– 2025) was analyzed. Using the XGBoost algorithm, we integrated baseline clinical features (age, tumor location, TNM stage) and laboratory parameters (albumin, globulin, blood glucose) to construct a prognostic model. SHapley Additive exPlanations (SHAP) values were employed to quantify feature contributions, and external validation (n=48) was performed to assess generalizability. SHAP (SHapley Additive exPlanations) is a game theory-based framework that enables model interpretability by quantifying the contribution of each feature to predictions. The primary endpoint was overall survival (OS).
Results: The model achieved AUC values of 0.794 (internal test) and 0.689 (external test), with calibration curves demonstrating strong concordance between predicted and observed survival rates. Key prognostic factors included tumor response, age, hypoalbuminemia, hyperglobulinemia and hyperglycemia. Risk stratification using a nomogram-derived cutoff (total score ≥ 50) revealed significantly inferior 2-year OS in high-risk versus low-risk patients (21.3% vs 58.6%, P< 0.001).
Conclusion: This interpretable ML model effectively predicts survival outcomes in unresectable ESCC patients receiving immunochemotherapy, offering a data-driven tool for personalized therapeutic decision-making. Multicenter prospective trials are warranted to validate its clinical utility.
Keywords: esophageal squamous cell carcinoma, immunochemotherapy, machine learning, prognostic model, SHAP