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World J Gastrointest Oncol. Jul 15, 2026; 18(7): 119986
Published online Jul 15, 2026. doi: 10.4251/wjgo.v18.i7.119986
Published online Jul 15, 2026. doi: 10.4251/wjgo.v18.i7.119986
Artificial intelligence in quantitative imaging of esophageal cancer: A review on radiomics, sarcopenia, and survival modeling
Sivan Sathish, Department of Oral Medicine and Radiology, Teerthanker Mahaveer Dental College and Research Centre, Teerthanker Mahaveer University, Moradabad 244001, Uttar Pradesh, India
Ankita Jain, Department of Public Health Dentistry, Teerthanker Mahaveer Dental College and Research Centre, Teerthanker Mahaveer University, Moradabad 244001, Uttar Pradesh, India
Kratee Sharma, Department of Periodontology, Teerthanker Mahaveer Dental College and Research Centre, Teerthanker Mahaveer University, Moradabad 244001, Uttar Pradesh, India
Karthika B, Department of Dental Surgery, Bhaarath Medical College and Hospital, Chennai 600073, Tamil Nādu, India
Author contributions: Sathish S designed the outline of the review, coordinated the writing, performed the majority of the writing, and prepared the figures and tables; Jain A contributed to literature evaluation and provided critical input in writing the manuscript; Sharma K assisted in literature search, organization of references, and manuscript writing; Karthika B contributed to clinical interpretation and provided critical revisions to the manuscript; and all authors thoroughly reviewed and endorsed the final manuscript.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Corresponding author: Sivan Sathish, Head, Professor, Department of Oral Medicine and Radiology, Teerthanker Mahaveer Dental College and Research Centre, Teerthanker Mahaveer University, Delhi Road, Moradabad 244001, Uttar Pradesh, India.
Received: February 12, 2026
Revised: March 9, 2026
Accepted: April 16, 2026
Published online: July 15, 2026
Processing time: 145 Days and 10.1 Hours
Revised: March 9, 2026
Accepted: April 16, 2026
Published online: July 15, 2026
Processing time: 145 Days and 10.1 Hours
Core Tip
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.