Fu YY, Jiao Y, Liu YH, Dong SS. Application and challenges of artificial intelligence in predicting perioperative complications of colorectal cancer. World J Gastrointest Surg 2025; 17(7): 106340 [DOI: 10.4240/wjgs.v17.i7.106340]
Corresponding Author of This Article
Ya-Hui Liu, Department of Hepatobiliary and Pancreatic Surgery, General Surgery Center, The First Hospital of Jilin University, No. 1 Xinmin Street, Changchun 130021, Jilin Province, China. yahui@jlu.edu.cn
Research Domain of This Article
Gastroenterology & Hepatology
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/
World J Gastrointest Surg. Jul 27, 2025; 17(7): 106340 Published online Jul 27, 2025. doi: 10.4240/wjgs.v17.i7.106340
Application and challenges of artificial intelligence in predicting perioperative complications of colorectal cancer
Yang-Yang Fu, Yan Jiao, Ya-Hui Liu, Shan-Shan Dong
Yang-Yang Fu, Department of the First Operation Room, The First Hospital of Jilin University, Changchun 130000, Jilin Province, China
Yan Jiao, Ya-Hui Liu, Department of Hepatobiliary and Pancreatic Surgery, General Surgery Center, The First Hospital of Jilin University, Changchun 130021, Jilin Province, China
Shan-Shan Dong, Department of Anesthesiology, The Second Hospital of Jilin University, Changchun 130022, Jilin Province, China
Co-corresponding authors: Ya-Hui Liu and Shan-Shan Dong.
Author contributions: Dong SS contributed to the writing, editing of the manuscript, and literature search; Fu YY and Jiao Y contributed to the discussion, design of the manuscript and literature search; Liu YH designed the overall concept and outline of the manuscript. All authors have read and approve the final manuscript. In our manuscript, we have designated two co-corresponding authors due to their pivotal roles in the conceptualization, design, and execution of this review. Both authors have contributed significantly to the manuscript’s development, from the initial idea through to the final edits. Dr. Dong SS, from the Department of Anesthesiology, has been instrumental in the writing and editing process, as well as conducting the literature review. Dr. Liu YH, from the Department of Hepatobiliary and Pancreatic Surgery, provided the overall structure and direction of the manuscript, ensuring the integration of clinical insights. Together, their combined contributions reflect their shared responsibility and leadership in this work, justifying their co-corresponding authorship designation.
Conflict-of-interest statement: All the authors report having 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: Ya-Hui Liu, Department of Hepatobiliary and Pancreatic Surgery, General Surgery Center, The First Hospital of Jilin University, No. 1 Xinmin Street, Changchun 130021, Jilin Province, China. yahui@jlu.edu.cn
Received: February 24, 2025 Revised: March 13, 2025 Accepted: March 19, 2025 Published online: July 27, 2025 Processing time: 150 Days and 21.9 Hours
Abstract
Colorectal cancer (CRC) is a prevalent malignancy, with surgery playing a key role in its treatment. However, perioperative complications, such as anastomotic leaks, infections, and mortality, can significantly affect surgical outcomes, extend hospital stays, and increase healthcare costs. Traditional risk prediction models often lack precision, leading to increased interest in artificial intelligence (AI) for improving risk stratification. This review examines the application of AI, particularly machine learning and deep learning, in predicting perioperative complications in CRC surgery. AI models have been employed to predict a variety of postoperative complications, including readmissions, surgical-site infections, anastomotic leakage, and mortality, by analyzing diverse data sources such as electronic health records, medical imaging, and preoperative markers. Despite the promising results, several challenges remain, including data quality, model generalizability, the complexity of clinical data, and ethical and regulatory concerns. The review emphasizes the need for multicenter, diverse datasets and the integration of AI into clinical workflows to improve model performance and adoption. Future efforts should focus on enhancing the transparency and interpretability of AI models to ensure their successful implementation in clinical practice, ultimately improving patient outcomes and surgical decision-making in CRC surgery.
Core Tip: Artificial intelligence (AI), including machine learning and deep learning, is increasingly applied to predict perioperative complications in colorectal cancer surgery. By analyzing diverse data sources such as electronic health records, medical imaging, and preoperative markers, AI models can improve risk stratification, predict complications like anastomotic leakage and mortality, and enhance clinical decision-making. However, challenges such as data quality, model generalizability, and ethical concerns must be addressed. Future efforts should focus on developing interpretable models, utilizing multicenter datasets, and integrating AI into clinical workflows to optimize patient outcomes and ensure successful clinical adoption.
