Artificial intelligence in quantitative imaging of esophageal cancer: A review on radiomics, sarcopenia, and survival modeling

Abstract

Esophageal cancer remains one of the most lethal malignancies worldwide, with survival outcomes varying widely even among patients with similar clinical stages. Recent advances in artificial intelligence (AI) have enabled the extraction of quantitative imaging features, known as radiomics, from routine computed tomography and positron emission tomography/computed tomography scans, offering new opportunities for precision prognostication. At the same time, body composition metrics such as sarcopenia and visceral adiposity have emerged as important predictors of treatment tolerance and overall survival. This article summarizes current evidence on artificial intelligence-based approaches that integrate tumor radiomics and host body composition for survival modeling in esophageal cancer. It outlines methodological frameworks, model performance, and key predictors identified across studies, and discusses challenges related to data harmonization, feature reproducibility, and clinical translation. The combined use of radiomics and body composition analysis through machine learning offers a promising path toward individualized, image-based survival prediction beyond conventional staging systems.

Keywords: Esophageal cancer; Artificial intelligence; Radiomics; Deep learning; Sarcopenia; Survival prediction; Quantitative imaging

Core Tip: This article highlights how artificial intelligence (AI) enables a shift from anatomy-based staging to quantitative, image-driven prognostication in esophageal cancer. By integrating tumor radiomics with AI-derived body composition markers such as sarcopenia, survival models can capture both tumor aggressiveness and host vulnerability from routine imaging modalities. These multimodal approaches consistently outperform conventional staging in survival prediction and risk stratification. Despite challenges in standardization and validation, AI-based quantitative imaging offers a clinically scalable pathway toward personalized survival modeling and precision treatment planning in esophageal cancer.