Identification of Prognostic Factors in Esophageal Cancer Using Machine Learning: A Retrospective Study Based on the
Piman Pocasap1, Sarinya Kongpetch1, Auemduan Prawan1
1Department of Pharmacology, Faculty of Medicine, Khon Kaen University, Khon Kaen 40002, Thailand.
None:
Background: Esophageal cancer (EC) is an aggressive malignancy with low survival rates, making accurate prognosis critical for guiding treatment decisions. Traditional prognostic methods, while essential, often lack precision and comprehensive data insights. This study aims to apply machine learning (ML) approaches to investigate EC prognosis by identifying key factors associated with 5-year survival. Methods: Multiple ML algorithms-Random Forest (RF), Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), AdaBoost, and Naïve Bayes-were applied to a dataset from the SEER database. Model development included exploratory data analysis, internal validation, and 5-fold cross-validation. Traditional survival analysis methods, such as Cox regression and Kaplan-Meier (KM) analysis, were integrated to further explore relationships between key predictor and outcome variables. Additionally, time-series analysis was conducted to examine survival trends over time and identify influencing factors. Results: RF demonstrated the highest predictive performance among the models tested. Key prognostic factors identified included surgery, summary stage, tumor size, metastasis, AJCC M stage, and age. An exploratory analysis of temporal trends further showed changes in survival outcomes across diagnosis years. Conclusions: The findings highlight the potential of ML approaches to analyze prognostic patterns in EC. Integrating ML models with traditional statistical analyses helped identify key prognostic factors such as surgery, summary stage, and metastasis, while the exploratory temporal analysis provided additional context regarding survival trends over time. While promising, further external validation and addressing time-series challenges are necessary. Overall, this study demonstrates the potential of ML to support the identification of prognostic factors in EC and may contribute to more informed clinical decision-making.
