Letter to the Editor
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Radiol. Nov 28, 2024; 16(11): 703-707
Published online Nov 28, 2024. doi: 10.4329/wjr.v16.i11.703
Relationship between pancreatic morphological changes and diabetes in autoimmune pancreatitis: Multimodal medical imaging assessment has important potential
Qing-Biao Zhang, Dan Liu, Jun-Bang Feng, Chun-Qi Du, Chuan-Ming Li
Qing-Biao Zhang, Jun-Bang Feng, Chun-Qi Du, Chuan-Ming Li, Department of Medical Imaging, Chongqing Emergency Medical Center, Chongqing University Central Hospital, School of Medicine, Chongqing University, Chongqing 400014, China
Dan Liu, Department of Cardiology, Chongqing Traditional Chinese Medicine Hospital, Chongqing 400000, China
Co-first authors: Qing-Biao Zhang and Dan Liu.
Co-corresponding authors: Chun-Qi Du and Chuan-Ming Li.
Author contributions: Zhang QB and Liu D contribute equally to this study as co-first authors; Du CQ and Li CM contribute equally to this study as co-first authors; Zhang QB, Liu D and Feng JB designed the research; Zhang QB performed the research; Zhang QB, Du CQ and Feng JB analysed the data; Zhang QB, Liu D, Du CQ, Feng JB and Li CM wrote the letter; and Feng JB and Li CM revised the letter.
Conflict-of-interest statement: All of the authors report 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: Chuan-Ming Li, Associate Professor, MD, Department of Medical Imaging, Chongqing Emergency Medical Center, Chongqing University Central Hospital, School of Medicine, Chongqing University, No. 1 Jiankang Road, Yuzhong District, Chongqing 400014, China. lichuanming@hospital.cqmu.edu.cn
Received: October 3, 2024
Revised: November 7, 2024
Accepted: November 19, 2024
Published online: November 28, 2024
Processing time: 54 Days and 18.1 Hours
Abstract

Autoimmune pancreatitis (AIP) is a special type of chronic pancreatitis with clinical symptoms of obstructive jaundice and abdominal discomfort; this condition is caused by autoimmunity and marked by pancreatic fibrosis and dysfunction. Previous studies have revealed a close relationship between early pancreatic atrophy and the incidence rate of diabetes in type 1 AIP patients receiving steroid treatment. Shimada et al performed a long-term follow-up study and reported that the pancreatic volume (PV) of these patients initially exponentially decreased but then slowly decreased, which was considered to be an important factor related to diabetes; moreover, serum IgG4 levels were positively correlated with PV during follow-up. In this letter, regarding the original study presented by Shimada et al, we present our insights and discuss how multimodal medical imaging and artificial intelligence can be used to better assess the relationship between pancreatic morphological changes and diabetes in patients with AIP.

Keywords: Autoimmune pancreatitis; Diabetes; Pancreatic morphological changes; Multimodal medical imaging; Artificial intelligence

Core Tip: Early pancreatic atrophy is associated with a greater incidence of diabetes in patients with type 1 autoimmune pancreatitis who are treated with steroids. Shimada et al reported that the pancreatic volume of these patients initially exponentially decreased, but then slowly decreased, which considered to be an important factor related to diabetes. However, we identified several potential shortcomings of this study, such as its small sample size and complex measurement process. In future research, multimodal medical MRI images and artificial intelligence algorithms should be used, and large research samples should be included to increase the universality and reliability of the research results.