Xiang YH, Mou H, Qu B, Sun HR. Machine learning-based radiomics score improves prognostic prediction accuracy of stage II/III gastric cancer: A multi-cohort study. World J Gastrointest Surg 2024; 16(2): 345-356 [PMID: 38463348 DOI: 10.4240/wjgs.v16.i2.345]
Corresponding Author of This Article
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
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Retrospective Cohort Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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: http://creativecommons.org/licenses/by-nc/4.0/
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.
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: https://creativecommons.org/Licenses/by-nc/4.0/
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
Abstract
BACKGROUND
Although accurately evaluating the overall survival (OS) of gastric cancer patients remains difficult, radiomics is considered an important option for studying prognosis.
AIM
To develop a robust and unbiased biomarker for predicting OS using machine learning and computed tomography (CT) image radiomics.
METHODS
This study included 181 stage II/III gastric cancer patients, 141 from Lichuan People's Hospital, and 40 from the Cancer Imaging Archive (TCIA). Primary tumors in the preoperative unenhanced CT images were outlined as regions of interest (ROI), and approximately 1700 radiomics features were extracted from each ROI. The skeletal muscle index (SMI) and skeletal muscle density (SMD) were measured using CT images from the lower margin of the third lumbar vertebra. Using the least absolute shrinkage and selection operator regression with 5-fold cross-validation, 36 radiomics features were identified as important predictors, and the OS-associated CT image radiomics score (OACRS) was calculated for each patient using these important predictors.
RESULTS
Patients with a high OACRS had a poorer prognosis than those with a low OACRS score (P < 0.05) and those in the TCIA cohort. Univariate and multivariate analyses revealed that OACRS was a risk factor [RR = 3.023 (1.896-4.365), P < 0.001] independent of SMI, SMD, and pathological features. Moreover, OACRS outperformed SMI and SMD and could improve OS prediction (P < 0.05).
CONCLUSION
A novel biomarker based on machine learning and radiomics was developed that exhibited exceptional OS discrimination potential.
Core Tip: We investigated 141 patients with locally advanced gastric cancer and developed the overall survival-associated computed tomography image radiomics score (OACRS) using machine learning and radiomics. The data revealed that OACRS was associated with overall survival (OS) in these patients, in addition to OS in the Cancer Imaging Archive cohort. Importantly, OACRS could improve the predicted accuracy of OS.
