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

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

Acknowledgments

None.

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.

Author contributions

Conceptualization: Xiance Jin

Investigation: Qiao Zheng, Haoze Zheng

Methodology: Ziyang Liu, Wenhao Guo, Sunjun Li, Junhao Ma, Yuxin Lou, Lijing Liu

Formal analysis: Congying Xie

Writing - original draft: Qiao Zheng, Haoze Zheng

Writing - review & editing: Xiance Jin

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data

Not applicable.