Machine learning‑based prediction of survival prognosis in esophageal squamous cell carcinoma

Abstract

The current prognostic tools for esophageal squamous cell carcinoma (ESCC) lack the necessary accuracy to facilitate individualized patient management strategies. To address this issue, this study was conducted to develop a machine learning (ML) prediction model for ESCC patients' survival management. Six ML approaches, including Rpart, Elastic Net, GBM, Random Forest, GLMboost, and the machine learning-extended CoxPH method, were employed to develop risk prediction models. The model was trained on a dataset of 1954 ESCC patients with 27 clinical features and validated on a dataset of 487 ESCC patients. The discriminative performance of the models was assessed using the concordance index (C-index). The best performing model was used for risk stratification and clinical evaluation. The study found that N stage, T stage, surgical margin, tumor grade, tumor length, sex, MPV, AST, FIB, and Mg are the important feature for ESCC patients’ survival. The machine learning-extended CoxPH model, Elastic Net, and Random Forest had similar performance in predicting the mortality risk of ESCC patients, and outperformed GBM, GLMboost, and Rpart. The risk scores derived from the CoxPH model effectively stratified ESCC patients into low-, intermediate-, and high-risk groups with distinctly different 3-year overall survival (OS) probabilities of 80.8%, 58.2%, and 29.5%, respectively. This risk stratification was also observed in the validation cohort. Furthermore, the risk model demonstrated greater discriminative ability and net benefit than the AJCC8th stage, suggesting its potential as a prognostic tool for predicting survival events and guiding clinical decision-making. The classical algorithm of the CoxPH method was also found to be sufficiently good for interpretive studies.

Prediction of five-year survival among esophageal cancer patients using machine learning

“… Xu et al [ 21 ] Five-year 16 features Yes (Univariate and multivariate regression analysis) clinicopathological characteristics and follow-up data of ESCC patients at the Department of Thoracic Surgery in Northern Jiangsu People's Hospital Gender, age, type of surgery, hypertension, diabetes, smoking, drinking, tumor size, tumor center location, histological grade, PT stage, pN stage, vascular invasion, nerve invasion, pathological types, surgical margins, 810 patients with ESCC Decision tree, RF, SVM, GBM, XG-Boost No The XG-Boost model with (AUC = 0.855; 95% CI, 0.808–0.902) was considered optimal. Zhang et al [ 35 ] Three-year and five-year survival 27 features Yes (LASSO regularization and univariable Cox regression analysis) One single-center database of Sichuan Cancer Hospital Age, sex, Karnofsky performance scale score, tumor length, tumor grade, tumor location, vascular invasion, surgical margin, dissected lymph nodes number, nerve invasion, T stage, N stage, AJCC8th stage, surgical intervention alone, hematocrit, mean platelet volume, neutrophil to lymphocyte ratio, monocytes, eosinophil, direct bilirubin, albumin, aspartate aminotransferase, alkaline phosphatase, sodium, magnesium, fibrinogen, lymphocyte -to- monocytes ratio 2441 ESCC patients R-part, Elastic Net, GBM, RF, GLMboost, and ML-extended CoxPH method No ML-extended CoxPH has a 75.4%, 45.8%, and 26.9% prediction capability for stratifying the low, medium, and high-risk groups for three-year survival. Also, it gained 65.3%, 29.7%, and 11% for 5-year survival.…”

Creation of a machine learning-based prognostic prediction model for various subtypes of laryngeal cancer

“…A classic model for survival analysis, the Cox proportional hazards (CoxPH) model has been the most commonly applied multifactor analysis technique in survival analysis to date 18 , 19 .…”

Research on Adaptive Intelligent Decision Algorithm in Industry 4.0 Digital Economy and Management Transformation Based on Clustering Algorithm

“…The whole process of network technology application refers to the application of information technology and network technology to the whole life cycle process of product design, production, processing, operation, sales and after-sales. This requires full-dimensional integration of partners with the Internet economy, including major suppliers and technical solution suppliers of manufacturing enterprises, important customers of manufacturing enterprises and other thirdparty partners [28]. The strategy of Industry 4.0 is to use the information physical system to develop flexible manufacturing to a new height, and through the deep integration of information technology and communication technology to industrialization, the production mode of manufacturing enterprises will be transformed to socialization, visualization, lean and digitalization [29].…”