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World J Hepatol. May 27, 2025; 17(5): 105446
Published online May 27, 2025. doi: 10.4254/wjh.v17.i5.105446
Progress in the application of fludeoxyglucose positron emission tomography computed tomography in biliary tract cancer
Jia-Xin Yin, Xin Fan, Qiao-Liang Chen, Jing Chen, Jian He
Jia-Xin Yin, Xin Fan, Qiao-Liang Chen, Jing Chen, Jian He, Department of Nuclear Medicine, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, Jiangsu Province, China
Author contributions: Yin JX reviewed the literature and drafted the manuscript; Yin JX and He J conceived the idea for the manuscript; Fan X, Chen QL and Chen J provided comprehensive perspectives; He J revised and finalized the manuscript; all authors have read and approved the final version of the manuscript.
Conflict-of-interest statement: All 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: Jian He, MD, PhD, Associate Professor, Department of Nuclear Medicine, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, No. 321 Zhongshan Road, Nanjing 210008, Jiangsu Province, China. hjxueren@126.com
Received: January 23, 2025
Revised: April 10, 2025
Accepted: May 7, 2025
Published online: May 27, 2025
Processing time: 125 Days and 1.4 Hours
Abstract

Biliary tract cancer (BTC) is a group of heterogeneous sporadic diseases, including intrahepatic, hilar, and distal cholangiocarcinoma, as well as gallbladder cancer. BTC is characterized by high invasiveness and extremely poor prognosis, with a global increased incidence due to intrahepatic cholangiocarcinoma (ICC). The 18F-fludeoxyglucose positron emission tomography (PET) computed tomography (18F-FDG PET/CT) combines glucose metabolic information (reflecting the glycolytic activity of tumor cells) with anatomical structure to assess tumor metabolic heterogeneity, systemic metastasis, and molecular characteristics noninvasively, overcoming the limitations of traditional imaging in the detection of micrometastases and recurrent lesions. 18F-FDG PET/CT offers critical insights in clinical staging, therapeutic evaluation, and prognostic prediction of BTC. This article reviews research progress in this field over the past decade, with a particular focus on the advances made in the last 3 years, which have not been adequately summarized and recognized. The research paradigm in this field is shifting from qualitative to quantitative studies, and there have been significant breakthroughs in using 18F-FDG PET/CT metabolic information to predict gene expression in ICC. Radiomics and deep learning techniques have been applied to ICC for prognostic prediction and differential diagnosis. Additionally, PET/magnetic resonance imaging is increasingly demonstrating its value in this field.

Keywords: Biliary tract cancer; Positron radiopharmaceuticals; Fluorodeoxyglucose positron emission tomography; Radiomics; Positron emission tomography computed tomography; Positron emission tomography magnetic resonance imaging

Core Tip: Biliary tract cancer constitutes a heterogeneous group of sporadic diseases characterized by high invasiveness and dismal prognosis. The 18F-fludeoxyglucose positron emission tomography (PET) computed tomography simultaneously provides data on glucose metabolism and anatomical structures. This paper examines the research advances in this domain over the past decade. The research approach in this area is transitioning from qualitative to quantitative analyses, and radiomics along with deep learning techniques have been extensively utilized. The utility of PET magnetic resonance imaging in this field is progressively becoming more apparent.