Parikh KS, Kumar A. Nomographic predictive models for complications after minimally invasive esophagectomy: Current status and future perspectives. World J Gastrointest Surg 2025; 17(12): 113586 [DOI: 10.4240/wjgs.v17.i12.113586]
Corresponding Author of This Article
Ashok Kumar, FACS, FASCRS, FRCS, Full Professor, Department of Surgical Gastroenterology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Raebareli Road, Lucknow 226014, Uttar Pradesh, India. doc.ashokgupta@gmail.com
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Transplantation
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Minireviews
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This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Dec 27, 2025 (publication date) through Dec 25, 2025
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Publication Name
World Journal of Gastrointestinal Surgery
ISSN
1948-9366
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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
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Parikh KS, Kumar A. Nomographic predictive models for complications after minimally invasive esophagectomy: Current status and future perspectives. World J Gastrointest Surg 2025; 17(12): 113586 [DOI: 10.4240/wjgs.v17.i12.113586]
World J Gastrointest Surg. Dec 27, 2025; 17(12): 113586 Published online Dec 27, 2025. doi: 10.4240/wjgs.v17.i12.113586
Nomographic predictive models for complications after minimally invasive esophagectomy: Current status and future perspectives
Kush S Parikh, Ashok Kumar
Kush S Parikh, Ashok Kumar, Department of Surgical Gastroenterology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow 226014, Uttar Pradesh, India
Co-first authors: Kush S Parikh and Ashok Kumar.
Author contributions: Parikh KS did the literature search and wrote the manuscript; Kumar A designed the concept, revised and edited the manuscript; Parikh KS and Kumar A contributed equally to this article, they are the co-first authors of this 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.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Ashok Kumar, FACS, FASCRS, FRCS, Full Professor, Department of Surgical Gastroenterology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Raebareli Road, Lucknow 226014, Uttar Pradesh, India. doc.ashokgupta@gmail.com
Received: August 29, 2025 Revised: September 15, 2025 Accepted: November 6, 2025 Published online: December 27, 2025 Processing time: 118 Days and 12.2 Hours
Core Tip
Core Tip: Minimally invasive esophagectomy is associated with reduced pulmonary morbidity, less blood loss, faster recovery and equivalent oncological outcomes compared to open esophagectomy, however the overall procedure related morbidity continues to be high. This review highlights the key procedure related morbidity and associated risk factors for the same in patients undergoing minimally invasive esophagectomy. Various risk prediction nomograms have been proposed for anticipating and reducing these complications to improve treatment outcomes, but they have certain limitations which hinder their generalized utility. The growing use of artificial intelligence and machine learning promises to create more sophisticated models that enhance the risk predictive accuracy and help personalize treatment plans to minimize complications and achieve better treatment outcomes.
