Published online Feb 27, 2024. doi: 10.4240/wjgs.v16.i2.345
Peer-review started: December 4, 2023
First decision: December 17, 2023
Revised: January 1, 2024
Accepted: January 29, 2024
Article in press: January 29, 2024
Published online: February 27, 2024
Accurately evaluating the overall survival (OS) of gastric cancer patients remains difficult. Compelling evidence showed that radiomics was related to tumor stroma, heterogeneity, antitumor immunity and tumor microenvironment.
To develop an OS-associated computed tomography image radiomics score (OACRS) based on 141 patients from two cohorts using machine learning and radiomics.
To investigate the association between radiomics and OS of gastric cancer to develop a robust and non-invasive bio
A retrospective multi-cohort study was conducted. Approximately 1700 radiomics features were extracted from primary tumor and 36 important features were selected as predictors to calculated OACRS.
OACRS was a risk factor and was independent of skeletal muscle index (SMI), skeletal muscle density (SMD), and pathological features. Importantly, OACRS outperformed SMI and SMD and could improve OS prediction.
A novel biomarker based on machine learning and radiomics was developed that exhibited exceptional OS discrimination potential. Gastric cancer patients who have a higher OACSR might have a poor OS.
Considering the nature of retrospective studies, prospective studies with large sample sizes are recommended to further validate the correlation between radiomics and stage II/III gastric cancer OS.