Wang HN, An JH, Zong L. Advances in artificial intelligence for predicting complication risks post-laparoscopic radical gastrectomy for gastric cancer: A significant leap forward. World J Gastroenterol 2024; 30(43): 4669-4671 [PMID: 39575406 DOI: 10.3748/wjg.v30.i43.4669]
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Wang HN, An JH, Zong L. Advances in artificial intelligence for predicting complication risks post-laparoscopic radical gastrectomy for gastric cancer: A significant leap forward. World J Gastroenterol 2024; 30(43): 4669-4671 [PMID: 39575406 DOI: 10.3748/wjg.v30.i43.4669]
World J Gastroenterol. Nov 21, 2024; 30(43): 4669-4671 Published online Nov 21, 2024. doi: 10.3748/wjg.v30.i43.4669
Advances in artificial intelligence for predicting complication risks post-laparoscopic radical gastrectomy for gastric cancer: A significant leap forward
Hong-Niu Wang, Jia-Hao An, Liang Zong
Hong-Niu Wang, Jia-Hao An, Department of Graduate School of Medicine, Changzhi Medical College, Changzhi 046000, Shanxi Province, China
Liang Zong, Department of Gastrointestinal Surgery, Changzhi People’s Hospital, Changzhi 046000, Shanxi Province, China
Author contributions: Wang HN and An JH drafted the initial manuscript; Zong L reviewed the manuscript; All three authors contributed to the work.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Received: August 3, 2024 Revised: September 25, 2024 Accepted: October 18, 2024 Published online: November 21, 2024 Processing time: 89 Days and 0.7 Hours
Abstract
In a recent paper, Hong et al developed an artificial intelligence (AI)-driven predictive scoring system for potential complications following laparoscopic radical gastrectomy for gastric cancer patients. They demonstrated that integrating AI with random forest models significantly improved the preoperative prediction and patient outcome management accuracy. By incorporating data from multiple centers, their model ensures standardization, reliability, and broad applicability, distinguishing it from the prior models. The present study highlights AI's potential in clinical decision support, aiding in the preoperative and postoperative management of gastric cancer patients. Our findings may pave the way for future prospective studies to further enhance AI-supported diagnoses in clinical practice.
Core Tip: Hong et al developed a predictive scoring system that uses machine learning techniques including LASSO regression, random forests, and artificial neural networks to assess complications following laparoscopic radical gastrectomy for gastric cancer. Their model, which was validated using data from multiple centers, showed high diagnostic accuracy and sensitivity, particularly with the random forest method. This innovative artificial intelligence-driven approach enhances surgical safety, reduces complication risks, and offers a valuable tool for both preoperative and postoperative decision-making, particularly for less-experienced gastroenterologists managing gastric cancer cases.
