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Articles Published Processes
7/13/2026 9:40:26 AM | Browse: 1 | Download: 1
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Received |
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2026-02-12 02:54 |
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Peer-Review Started |
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2026-02-12 02:58 |
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First Decision by Editorial Office Director |
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2026-02-27 08:38 |
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Return for Revision |
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2026-02-27 08:38 |
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Revised |
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2026-03-09 08:32 |
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Publication Fee Transferred |
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Second Decision by Editor |
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2026-04-16 02:37 |
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Second Decision by Editor-in-Chief |
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Final Decision by Editorial Office Director |
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2026-04-16 06:13 |
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Articles in Press |
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2026-04-16 06:13 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2026-05-21 02:40 |
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Publish the Manuscript Online |
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2026-07-13 09:40 |
| ISSN |
1948-5204 (online) |
| Open Access |
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See Permissions. Published by Baishideng Publishing Group Inc. |
| Copyright |
©Author(s) (or their employer(s)) 2026. No commercial re-use. See Permissions. Published by Baishideng Publishing Group Inc. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Gastroenterology & Hepatology |
| Manuscript Type |
Minireviews |
| Article Title |
Artificial intelligence in quantitative imaging of esophageal cancer: A review on radiomics, sarcopenia, and survival modeling
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Sivan Sathish, Ankita Jain, Kratee Sharma and Karthika B |
| ORCID |
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| Funding Agency and Grant Number |
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| 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. sivansathishmfds@yahoo.co.in |
| Key Words |
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. |
| Publish Date |
2026-07-13 09:40 |
| Citation |
Sathish S, Jain A, Sharma K, Karthika B. Artificial intelligence in quantitative imaging of esophageal cancer: A review on radiomics, sarcopenia, and survival modeling. World J Gastrointest Oncol 2026; 18(7): 119986
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| URL |
https://www.wjgnet.com/1948-5204/full/v18/i7/119986.htm |
| DOI |
https://doi.org/10.4251/wjgo.v18.i7.119986 |
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