Machine learning applications in the detection and treatment of esophageal cancer.
Authors
Affiliations (4)
Affiliations (4)
- Department of Thoracic Surgery, Affiliated Hospital of Jining Medical University, No. 89, Guhuai Road, Shandong, 272000, Jining, China.
- Shandong First Medical University, Jinan, 250117, Shandong, China.
- Jining Medical University, Jining, 272000, Shandong, China.
- Department of Thoracic Surgery, Affiliated Hospital of Jining Medical University, No. 89, Guhuai Road, Shandong, 272000, Jining, China. [email protected].
Abstract
Esophageal cancer (EC) is a gastrointestinal malignancy associated with a poor prognosis worldwide. It is characterized by an insidious onset, marked tumor heterogeneity, and complex diagnostic and therapeutic pathways. Consequently, clinical management continues to face challenges in early screening, accurate diagnosis, and the assessment of treatment response and prognosis. In recent years, the rapid advancement of artificial intelligence (AI) and machine learning (ML) techniques in medical image analysis and multidimensional data modeling has provided novel technological approaches for the precision diagnosis and treatment of EC. This narrative review summarizes recent advances in the application of AI in EC, with a particular focus on endoscopic and imaging-assisted diagnosis, prediction of treatment response and prognostic assessment. Existing studies have reported promising diagnostic performance of deep learning-based endoscopic image analysis models. In some controlled or retrospective settings, their performance has approached that of expert readers in the detection of EC and precancerous lesions, although prospective validation remains limited. ML models integrating clinical information, radiomic features, and selected biomarkers have also shown promising performance in predicting treatment response and supporting risk stratification. However, most existing studies are based on retrospective, single-center, or regionally confined datasets and are subject to several limitations, including population heterogeneity, insufficient data standardization, limited external validation, and poor model interpretability, which collectively hinder clinical translation. Overall, AI holds great promise for the precision management of EC; however, its clinical translation still depends on multicenter collaboration, the establishment of high-quality datasets, prospective validation studies, and the parallel development of ethical and regulatory frameworks.