Yuan YQ, Chen QQ. Review on article of preoperative prediction in chronic hepatitis B virus patients using spectral computed tomography and machine learning. World J Gastroenterol 2024; 30(38): 4239-4241 [PMID: 39493332 DOI: 10.3748/wjg.v30.i38.4239]
Corresponding Author of This Article
Qian-Qian Chen, MD, Associate Chief Physician, Associate Professor, Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, No. 28 Fuxing Road, Haidian District, Beijing 100853, China. qian_qian_chen@163.com
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Letter to the Editor
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 Gastroenterol. Oct 14, 2024; 30(38): 4239-4241 Published online Oct 14, 2024. doi: 10.3748/wjg.v30.i38.4239
Review on article of preoperative prediction in chronic hepatitis B virus patients using spectral computed tomography and machine learning
Yao-Qian Yuan, Qian-Qian Chen
Yao-Qian Yuan, Qian-Qian Chen, Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing 100853, China
Author contributions: Yuan YQ drafted the article; Chen QQ made critical revisions related to the important intellectual content of the manuscript; all of the authors read and approved the final version of the manuscript to be published.
Conflict-of-interest statement: All authors declare no conflict of interest in publishing the manuscript.
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: Qian-Qian Chen, MD, Associate Chief Physician, Associate Professor, Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, No. 28 Fuxing Road, Haidian District, Beijing 100853, China. qian_qian_chen@163.com
Received: March 11, 2024 Revised: September 5, 2024 Accepted: September 18, 2024 Published online: October 14, 2024 Processing time: 202 Days and 1.6 Hours
Abstract
This letter comments on the article that developed and tested a machine learning model that predicts lymphovascular invasion/perineural invasion status by combining clinical indications and spectral computed tomography characteristics accurately. We review the research content, methodology, conclusions, strengths and weaknesses of the study, and introduce follow-up research to this work.
Core Tip: Accurate preoperative assessment of gastric cancer staging and tumor aggressiveness is critical for the development of individualized treatment. Previous studies have shown that lymphovascular invasion (LVI) and perineural invasion (PNI) can predict tumor invasion and patient prognosis; therefore, preoperative LVI and PNI assessment can help oncologists identify high-risk categories of gastric cancer patients preoperatively and predict outcomes. This letter comments on a published study that showed that the accurate preoperative identification of LVI/PNI in gastric cancer can be achieved by merging clinical markers with portal venous and equilibrium phase spectral computed tomography characteristics.
