AccScience Publishing / ARNM / Online First / DOI: 10.36922/arnm.8350
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REVIEW ARTICLE

Current status and future potential of radiomics in the management of patients with gastric cancer

Qiao Zheng1 ,  Haoze Zheng1 ,  Ziyang Liu1 ,  Wenhao Guo2 ,  Sunjun Li3 ,  Junhao Ma1 ,  Yuxin Lou1 ,  Lijing Liu4 ,  Congying Xie1* ,  Xiance Jin1,2*
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1 Department of Radiation and Medical Oncology, 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
2 Department of Education, School of Basic Medical Science, Wenzhou Medical University, Wenzhou, Zhejiang, China
3 Department of Education, Alberta Institute, Wenzhou Medical University, Wenzhou, Zhejiang, China
4 Department of Radiation and Medical Oncology, Changxing County People’s Hospital, Zhejiang University School of Medicine Second Affiliated Hospital, Hangzhou, Zhejiang, China
ARNM, 8350 https://doi.org/10.36922/arnm.8350
Received: 31 December 2024 | Revised: 4 March 2025 | Accepted: 19 March 2025 | Published online: 8 May 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Gastric cancer (GC) is one of the most common malignant tumors in the world and has attracted much attention due to its high incidence and mortality. Early detection and accurate diagnosis of GC is very important to improve the survival rate of patients. Traditional imaging techniques, such as computed tomography (CT), have played an important role in the diagnosis and staging of GC, but their accuracy and predictive power are still limited. Radiomics, as an emerging multidisciplinary field, combines medical imaging, computer technology, and bioinformatics to reveal potential biological information in image data through quantitative extraction of image features. To review the current status and future potential of radiomics in the diagnosis, prognosis, and outcome prediction for patients with GC, related studies with keywords such as “radiomics,” “gastric cancer,” and “predict” were retrieved from PubMed (until October 2022). The clinical value of radiomics in GC research, which encompasses diagnosis, clinical staging, and prognostic prediction, was analyzed and summarized. A total of 52 original articles in radiomics were selected for the prediction of diagnostic classification (n = 9), tumor-node-metastasis stages (n = 19), prognosis and response prediction to treatment (n = 20), and the use of deep learning algorithm (n = 4). Among these, 39 (75%) were single-center studies and 13 (25%) were multicenter studies, with sample sizes ranging from 47 to 1,778. In terms of imaging modalities, 44 studies utilized CT-based radiomics, six used positron emission tomography-CT, and a few employed magnetic resonance imaging. With the continuous improvement and development in radiomics, it has great potential in the diagnosis, post-treatment follow-up, and treatment decision for patients with GC.

Keywords
Gastric cancer
Radiomics
Assessment
Predict
Prognosis
Funding
This study is financially supported by the Joint Laboratory of Basic Medicine and Intelligent Medicine.
Conflict of interest
Congying Xie is an Editorial Board Member of this journal but was not in any way involved in the editorial and peer-review process conducted for this paper, directly or indirectly. Separately, other authors declared that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.
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