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©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Oncol. Dec 15, 2024; 16(12): 4548-4552
Published online Dec 15, 2024. doi: 10.4251/wjgo.v16.i12.4548
Published online Dec 15, 2024. doi: 10.4251/wjgo.v16.i12.4548
Estimating prognosis of gastric neuroendocrine neoplasms using machine learning: A step towards precision medicine
Hong-Niu Wang, Jia-Hao An, 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
Co-first authors: Hong-Niu Wang and Jia-Hao An.
Author contributions: Wang HN and An JH drafted the initial manuscript; Zong L reviewed the manuscript; all three authors contributed to this editorial work.
Conflict-of-interest statement: The authors declare that they have no conflict of interest to disclose.
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: Liang Zong, PhD, Doctor, Department of Gastrointestinal Surgery, Changzhi People’s Hospital, No. 502 Changxing Middle Road, Changzhi 046000, Shanxi Province, China. 250537471@qq.com
Received: July 17, 2024
Revised: August 5, 2024
Accepted: August 12, 2024
Published online: December 15, 2024
Processing time: 118 Days and 10.8 Hours
Revised: August 5, 2024
Accepted: August 12, 2024
Published online: December 15, 2024
Processing time: 118 Days and 10.8 Hours
Core Tip
Core Tip: Liu et al’s study addresses a critical issue in determining the postoperative prognosis of gastric neuroendocrine tumors by identifying the significance of lymph node ratio. Moreover, the random survival forest model, a machine-learning approach, surpasses traditional Cox proportional hazards models by enhancing predictive accuracy, clinical utility, and overall performance. This model’s ability to stratify patient risks and personalize survival predictions can aid in formulating targeted postoperative strategies, thus realizing an important aspect of personalized “precision medicine”.