Prognostic Value of Computed Tomography Radiomics Features in Patients with Gastric Cancer Following Curative Resection
Overview
Authors
Affiliations
Objectives: The present study aimed to investigate the clinical prognostic significance of radiomics signature (R-signature) in patients with gastric cancer who had undergone radical resection.
Methods: A total of 181 patients with gastric cancer who had undergone radical resection were enrolled in this retrospective study. The association between the R-signature and overall survival (OS) was assessed in the primary cohort and verified in the validation cohort. Furthermore, the performance of a radiomics nomogram integrating the R-signature and significant clinicopathological risk factors was evaluated.
Results: The R-signature, which consisted of six imaging features, stratified patients with gastric cancer who had undergone radical resection into two prognostic risk groups in both cohorts. The radiomics nomogram incorporating R-signature and significant clinicopathological risk factors (T stage, N stage, and differentiation) exhibited significant prognostic superiority over clinical nomogram and R-signature alone (Harrell concordance index, 0.82 vs 0.71 and 0.82 vs 0.74, respectively, p < 0.001 in both analyses). All calibration curves showed remarkable consistency between predicted and actual survival, and decision curve analysis verified the usefulness of the radiomics nomogram for clinical practice.
Conclusions: The R-signature could be used to stratify patients with gastric cancer following radical resection into high- and low-risk groups. Furthermore, the radiomics nomogram provided better predictive accuracy than other predictive models and might aid clinicians with therapeutic decision-making and patient counseling.
Key Points: • Radiomics can stratify the gastric cancer patients following radical resection into high- and low-risk groups. • Radiomics can improve the prognostic value of TNM staging system. • Radiomics may facilitate personalized treatment of gastric cancer patients.
Zhen S, Wei Y, Song R, Liu X, Li P, Kong X Front Oncol. 2024; 14:1389278.
PMID: 39301548 PMC: 11410566. DOI: 10.3389/fonc.2024.1389278.
The Applications of Artificial Intelligence in Digestive System Neoplasms: A Review.
Zhang S, Mu W, Dong D, Wei J, Fang M, Shao L Health Data Sci. 2024; 3:0005.
PMID: 38487199 PMC: 10877701. DOI: 10.34133/hds.0005.
Zhou L, Wu H, Zhou H J Imaging Inform Med. 2024; 37(4):1261-1272.
PMID: 38429561 PMC: 11300411. DOI: 10.1007/s10278-024-01060-7.
Yang C, Zhou Q, Li M, Xu L, Zeng Y, Liu J BMC Cancer. 2023; 23(1):1089.
PMID: 37950207 PMC: 10636947. DOI: 10.1186/s12885-023-11575-x.
Artificial intelligence applications in computed tomography in gastric cancer: a narrative review.
Ma T, Wang H, Ye Z Transl Cancer Res. 2023; 12(9):2379-2392.
PMID: 37859746 PMC: 10583011. DOI: 10.21037/tcr-23-201.