Radiomics signature based on computed tomography images for the preoperative prediction of lymph node metastasis at individual stations in gastric cancer: A multicenter study. (December 2021)

• Record Type: Journal Article
• Authors: Sun, Zepang; Jiang, Yuming; Chen, Chuanli; Zheng, Huan; Huang, Weicai; Xu, Benjamin; Tang, Weijing; Yuan, Qingyu; Zhou, Kangneng; Liang, Xiaokun; Chen, Hao; Han, Zhen; Feng, Hao; Yu, Shitong; Hu, Yanfeng; Yu, Jiang; Zhou, Zhiwei; Wang, Wei; Xu, Yikai; Li, Guoxin
• Is Part Of: Radiotherapy and oncology. Volume 165(2021)
• Page Start: 179
• Page End: 190
• Publication Date: 2021-12
• Subjects: Radiomics -- Gastric cancer -- Lymph node metastasis -- Individual stations prediction -- Computed tomography; Oncology -- Periodicals; Radiotherapy -- Periodicals; Tumors -- Periodicals; Medical Oncology -- Periodicals; Neoplasms -- radiotherapy -- Periodicals; Radiotherapy -- Periodicals; Radiothérapie -- Périodiques; Cancérologie -- Périodiques; Tumeurs -- Périodiques; Electronic journals; 616.9940642
• DOI: 10.1016/j.radonc.2021.11.003
• Languages: English
• ISSNs: 0167-8140

Abstract

Highlight: Preoperative individualized lymph node metastasis prediction can guide surgical treatment for gastric cancer. Radiomics using CT images can effectively predict lymph node metastasis at individual stations. CT images contain substantial information on lymph node metastases. Abstract: Background: Specific diagnosis and treatment of gastric cancer (GC) require accurate preoperative predictions of lymph node metastasis (LNM) at individual stations, such as estimating the extent of lymph node dissection. This study aimed to develop a radiomics signature based on preoperative computed tomography (CT) images, for predicting the LNM status at each individual station. Methods: We enrolled 1506 GC patients retrospectively from two centers as training (531) and external (975) validation cohorts, and recruited 112 patients prospectively from a single center as prospective validation cohort. Radiomics features were extracted from preoperative CT images and integrated with clinical characteristics to construct nomograms for LNM prediction at individual lymph node stations. Performance of the nomograms was assessed through calibration, discrimination and clinical usefulness. Results: In training, external and prospective validation cohorts, radiomics signature was significantly associated with LNM status. Moreover, radiomics signature was an independent predictor of LNM status in the multivariable logistic regression analysis. The radiomics nomograms revealed good prediction performances, with AUCs of 0.716–0.871 in the training cohort, 0.678–0.768 in the external validation cohort and 0.700–0.841 in the prospective validation cohort for 12 nodal stations. The nomograms demonstrated a significant agreement between the actual probability and predictive probability in calibration curves. Decision curve analysis showed that nomograms had better net benefit than clinicopathologic characteristics. Conclusion: Radiomics nomograms for individual lymph node stations presented good prediction accuracy, which could provide important information for individual diagnosis and treatment of gastric cancer.