Karmakar R, Gade P, Wang HC, Mukundan A. Very early recurrence in pancreatic cancer: Redefining prognostic markers and surveillance strategies. World J Gastrointest Surg 2026; 18(1): 114574 [DOI: 10.4240/wjgs.v18.i1.114574]
Corresponding Author of This Article
Arvind Mukundan, PhD, Assistant Professor, Postdoctoral Fellow, Department of Mechanical Engineering, National Chung Cheng University, No. 168 University Road, Min Hsiung, Chiayi 62102, Taiwan. arvindmukund96@gmail.com
Research Domain of This Article
Surgery
Article-Type of This Article
Editorial
Open-Access Policy of This Article
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/
Jan 27, 2026 (publication date) through Jan 29, 2026
Times Cited of This Article
Times Cited (0)
Journal Information of This Article
Publication Name
World Journal of Gastrointestinal Surgery
ISSN
1948-9366
Publisher of This Article
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
Share the Article
Karmakar R, Gade P, Wang HC, Mukundan A. Very early recurrence in pancreatic cancer: Redefining prognostic markers and surveillance strategies. World J Gastrointest Surg 2026; 18(1): 114574 [DOI: 10.4240/wjgs.v18.i1.114574]
World J Gastrointest Surg. Jan 27, 2026; 18(1): 114574 Published online Jan 27, 2026. doi: 10.4240/wjgs.v18.i1.114574
Very early recurrence in pancreatic cancer: Redefining prognostic markers and surveillance strategies
Riya Karmakar, Pratham Gade, Hsiang-Chen Wang, Arvind Mukundan
Riya Karmakar, Hsiang-Chen Wang, Arvind Mukundan, Department of Mechanical Engineering, National Chung Cheng University, Chiayi 62102, Taiwan
Riya Karmakar, Arvind Mukundan, School of Engineering and Technology, Sanjivani University, Sanjivani Factory, Kopargaon 423603, Mahārāshtra, India
Pratham Gade, Department of Information Technology, Sanjivani College of Engineering, Kopargaon 423603, Mahārāshtra, India
Co-first authors: Riya Karmakar and Pratham Gade.
Co-corresponding authors: Hsiang-Chen Wang and Arvind Mukundan.
Author contributions: Karmakar R and Gade P contributed to investigation and software; Karmakar R, Gade P, Wang HC, and Mukundan A contributed to conceptualization, review and editing; Gade P wrote the original draft; Wang HC and Mukundan A contributed to formal analysis and project administration; Wang HC contributed to supervision; Karmakar R and Wang HC contributed equally to the manuscript as co-first g authors; Mukundan A and Gade P made equal contributions as co-correspond in authors. All authors have read and agreed to the published version of the 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: Arvind Mukundan, PhD, Assistant Professor, Postdoctoral Fellow, Department of Mechanical Engineering, National Chung Cheng University, No. 168 University Road, Min Hsiung, Chiayi 62102, Taiwan. arvindmukund96@gmail.com
Received: September 23, 2025 Revised: October 6, 2025 Accepted: October 24, 2025 Published online: January 27, 2026 Processing time: 120 Days and 16.5 Hours
Abstract
This editorial discusses the difficulties and challenges of managing pancreatic ductal adenocarcinoma. Approximately 50% of patients experience recurrence within one year after the curative-intent surgery as its highly lethal malignancy, the most sever outcome is very early recurrence (VER), which occurs within 12 weeks which reflects an aggressive tumor. The limitation of standards after surgery management in preventing VER, which significantly causes death in six-months (32.44% vs 14.77%) which highlights the urgent need of better predictive models to categorize patients. The study by Martlı et al which combines traditional statistical methods with machine learning to improve prediction accuracy. The findings from study highlights poorly differentiated (grade 3) tumor as the stronger predictor of VER. The research also presents an innovative association between VER and tumor present in pancreatic head. An abnormal red cell distribution width, a marker of systemic inflammation was also identified as a risk factor. The predictive accuracy has been increased by using random forest modelling which identifies nonlinear pattern in the data, reinforcing the importance of these factors. The clinical importance for these finding is significant; identifying patients with grade 3 tumors, pancreatic head tumors, or elevated red cell distribution width allows for tailored, intensive surveillance and consideration for the early initiation of adjuvant therapy. This editorial supports the combination of predictors with clinical practice to optimize treatment outcomes, and reduce the risk of VER, improve quality of life for patient population, while underscoring the need for multi-institutional validation and future molecular studies to further elucidate the mechanisms driving this aggressive disease.
Core Tip: Very early recurrence within 12 weeks of pancreatic cancer surgery represents surgical failure with outcome similar to palliative care. poorly separated (grade 3) tumors, pancreatic head location, and elevated red cell distribution width are emerged as key indicators. Machine learning optimized models integrated carbohydrate antigen 19-9 and S100 calcium binding protein A2 biomarkers offer precision medicine possibility, but implementation faces continuous socioeconomic obstacles requiring healthcare system transformation and collaborative international efforts.
