Copyright ©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.
Artif Intell Gastroenterol. Aug 28, 2020; 1(2): 37-50
Published online Aug 28, 2020. doi: 10.35712/aig.v1.i2.37
Diagnostic advances of artificial intelligence and radiomics in gastroenterology
Pei Feng, Zhen-Dong Wang, Wei Fan, Heng Liu, Jing-Jing Pan
Pei Feng, Wei Fan, Heng Liu, Jing-Jing Pan, Department of Radiology, PLA Rocket Force Characteristic Medical Center, Beijing 100088, China
Zhen-Dong Wang, Department of Ultrasound, Beijing Sihui Hospital of Traditional Chinese Medicine, Beijing 100022, China
Author contributions: Feng P, Wang ZD, and Pan JJ guaranteed the integrity of entire study; Liu H and Pan JJ designed the research study; Feng P, Wang ZD, and Pan JJ drafted the manuscript; Feng P, Liu H, and Pan JJ revised the manuscript; all authors acquired and interpreted the data, studied the cited literature, agree to ensure that any questions related to the work were appropriately resolved, and have read and approved the final manuscript.
Conflict-of-interest statement: All authors have no conflicts of interest of 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:
Corresponding author: Jing-Jing Pan, MD, Associate Chief Physician, Department of Radiology, PLA Rocket Force Characteristic Medical Center, No. 16, Xinwai Street, Beijing 100088, China.
Received: May 27, 2020
Peer-review started: May 27, 2020
First decision: August 9, 2020
Revised: August 22, 2020
Accepted: August 27, 2020
Article in press: August 27, 2020
Published online: August 28, 2020

Traditional medical imaging, including ultrasound, computed tomography, magnetic resonance imaging, or positron emission tomography, remains widely used diagnostic modalities for gastrointestinal diseases at present. These modalities are used to assess changes in morphology, attenuation, signal intensity, and enhancement characteristics. Gastrointestinal tumors, especially malignant tumors, are commonly seen in clinical practice with an increasing number of deaths each year. Because the imaging manifestations of different diseases usually overlap, accurate early diagnosis of tumor lesions, noninvasive and effective evaluation of tumor staging, and prediction of prognosis remain challenging. Fortunately, traditional medical images contain a great deal of important information that cannot be recognized by human eyes but can be extracted by artificial intelligence (AI) technology, which can quantitatively assess the heterogeneity of lesions and provide valuable information, including therapeutic effects and patient prognosis. With the development of computer technology, the combination of medical imaging and AI technology is considered to represent a promising field in medical image analysis. This new emerging field is called “radiomics”, which makes big data mining and extraction from medical imagery possible and can help clinicians make effective decisions and develop personalized treatment plans. Recently, AI and radiomics have been gradually applied to lesion detection, qualitative and quantitative diagnosis, histopathological grading and staging of tumors, therapeutic efficacy assessment, and prognosis evaluation. In this minireview, we briefly introduce the basic principles and technology of radiomics. Then, we review the research and application of AI and radiomics in gastrointestinal diseases, especially diagnostic advancements of radiomics in the differential diagnosis, treatment option, assessment of therapeutic efficacy, and prognosis evaluation of esophageal, gastric, hepatic, pancreatic, and colorectal diseases.

Keywords: Artificial intelligence, Radiomics, Texture analysis, Gastroenterology, Esophageal disease, Gastric diseases, Hepatic disease

Core Tip: This minireview summarizes the research and application of artificial intelligence (AI) technology, radiomics, and texture analysis in gastrointestinal diseases in detail and focuses on the diagnostic advances of AI and radiomics in lesion detection, differential diagnosis, decision of treatment plans, assessment of therapeutic efficacy and tumor response to treatment, and prognosis prediction of gastrointestinal diseases. This technology can provide more valuable information to allow clinicians and radiologists to understand and perform AI and radiomics in their clinical practice.