World J Gastrointest Surg. Feb 27, 2024; 16(2): 345-356 Published online Feb 27, 2024. doi: 10.4240/wjgs.v16.i2.345
Machine learning-based radiomics score improves prognostic prediction accuracy of stage II/III gastric cancer: A multi-cohort study
Ying-Hao Xiang, Huan Mou, Bo Qu, Hui-Rong Sun
Ying-Hao Xiang, Huan Mou, Bo Qu, Hui-Rong Sun, Department of General Surgery, Lichuan People's Hospital, Enshi 445400, Hubei Province, China
Author contributions: Xiang YH and Sun HR contributed to study conceptualization and design; Xiang YH, Mou H, and Qu B contributed to data acquisition; Xiang YH, Mou H, and Sun HR contributed to the methodology and formal analyses; Qu B contributed to the software; all authors contributed to writing, reviewing, editing, and final approval of the manuscript.
Institutional review board statement: This study was approved by the medical ethics committee of Lichuan People's Hospital (approval No. LCPH-IRB-20231018).
Informed consent statement: Patients were not required to give informed consent to the study as the analysis used anonymous clinical data that were obtained after each patient agreed to treatment by written consent.
Conflict-of-interest statement: The authors declare that they have no conflicts of interest.
Data sharing statement: The data associated with this study can be obtained from the first and corresponding author upon reasonable request.
STROBE statement: The authors have read the STROBE Statement—checklist of items, and the manuscript was prepared and revised according to the STROBE Statement—checklist of items.
Corresponding author: Hui-Rong Sun, Doctor, Surgical Oncologist, Department of General Surgery, Lichuan People's Hospital, No. 12 Longchuan Avenue, Enshi 445400, Hubei Province, China. shr0339@163.com
Received: December 4, 2023 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 Processing time: 83 Days and 1 Hours
ARTICLE HIGHLIGHTS
Research background
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.
Research motivation
To develop an OS-associated computed tomography image radiomics score (OACRS) based on 141 patients from two cohorts using machine learning and radiomics.
Research objectives
To investigate the association between radiomics and OS of gastric cancer to develop a robust and non-invasive biomarker for predicting OS.
Research methods
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.
Research results
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.
Research conclusions
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.
Research perspectives
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.
