Retrospective Study
Copyright ©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Oncol. Apr 15, 2025; 17(4): 100089
Published online Apr 15, 2025. doi: 10.4251/wjgo.v17.i4.100089
Machine learning for modeling and identifying risk factors of pancreatic fistula
Mikhail B Potievskiy, Leonid O Petrov, Sergei A Ivanov, Pavel V Sokolov, Vladimir S Trifanov, Nikolai A Grishin, Ruslan I Moshurov, Peter V Shegai, Andrei D Kaprin
Mikhail B Potievskiy, Center for Clinical Trials, Center for Innovative Radiological and Regenerative Technologies, FSBI “National Medical Research Radiological Center” of the Ministry of Health of the Russian Federation, Obninsk 249036, Kaluzhskaya Oblast, Russia
Leonid O Petrov, Department of Radiation and Surgical Treatment of Abdominal Diseases, A. Tsyb Medical Radiological Center, FSBI “National Medical Research Radiological Center” of the Ministry of Health of the Russian Federation, Obninsk 249036, Kaluzhskaya Oblast, Russia
Sergei A Ivanov, Andrei D Kaprin, Department of Administration, FSBI “National Medical Research Radiological Center” of the Ministry of Health of the Russian Federation, Obninsk 249036, Kaluzhskaya Oblast, Russia
Pavel V Sokolov, Department of Operation Unit, FSBI “National Medical Research Radiological Center” of the Ministry of Health of the Russian Federation, Obninsk 249036, Kaluzhskaya Oblast, Russia
Vladimir S Trifanov, Nikolai A Grishin, Ruslan I Moshurov, Department of Abdominal Oncology, P. Herzen Moscow Oncological Institute, FSBI “National Medical Research Radiological Center” of the Ministry of Health of the Russian Federation, Obninsk 249036, Kaluzhskaya Oblast, Russia
Peter V Shegai, Center for Innovative Radiological and Regenerative Technologies, FSBI “National Medical Research Radiological Center” of the Ministry of Health of the Russian Federation, Obninsk 249036, Kaluzhskaya Oblast, Russia
Andrei D Kaprin, Department of Urology and Operative Nephrology with Course of Oncology, Medical Faculty, Medical Institute, Peoples’ Friendship University of Russia, Moscow 117198, Moskva, Russia
Author contributions: Potievskiy MB, Petrov LO, Sokolov PV, Grishin NA, and Moshurov RI designed the study and collected the data; Trifanov VS, Ivanov SA, Shegai PV, and Kaprin AD contributed to study design and revised the manuscript; Potievskiy MB developed the machine learning tools and wrote the manuscript draft.
Institutional review board statement: The aforementioned project has been reviewed and approved by the Review Board, based on the Declaration of Helsinki, the manuscript was reviewed by members of the Review Board and recommended for publication in a peer-reviewed journal.
Informed consent statement: This is a retrospective study. The FSBI NMRRC Research Ethics Committee has confirmed that no ethical approval is required.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
Data sharing statement: The raw data supporting the conclusions of this article will be made available by the authors on request. The code is publicly available at GitHub: https://github.com/MikhailPot/POPF.
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: Mikhail B Potievskiy, MD, MSc, Research Scientist, Center for Clinical Trials, Center for Innovative Radiological and Regenerative Technologies, FSBI “National Medical Research Radiological Center” of the Ministry of Health of the Russian Federation, Koroleva 4, Obninsk 249036, Kaluzhskaya Oblast, Russia. potievskiymikhail@gmail.com
Received: August 7, 2024
Revised: December 5, 2024
Accepted: February 5, 2025
Published online: April 15, 2025
Processing time: 230 Days and 4.7 Hours
Core Tip

Core Tip: The study provides a machine-learning approach with a focus on medical data evaluation, based on algorithm selection. The best algorithm was CatBoost. Young age and large tumor size were associated with fistula B/C and biochemical leak development. Blood and drain amylase level increases and biochemical leak were the major risk factors of subsequent fistula B/C development.