CT radiomics based on the peritumoral adipose region of gastric adenocarcinoma for preoperative prediction of lymph node metastasis

Xue-Mei Ding; Hai-Ying Zhou; Yue-Su Wang; Jin-Ming Cao; Jing Ou; Xiao-Ming Zhang; Tian-Wu Chen

doi:10.1016/j.ejrad.2024.111479

CT radiomics based on the peritumoral adipose region of gastric adenocarcinoma for preoperative prediction of lymph node metastasis

Eur J Radiol. 2024 Apr 22:175:111479. doi: 10.1016/j.ejrad.2024.111479. Online ahead of print.

Authors

Xue-Mei Ding¹, Hai-Ying Zhou², Yue-Su Wang², Jin-Ming Cao³, Jing Ou², Xiao-Ming Zhang⁴, Tian-Wu Chen⁵

Affiliations

¹ The First Clinical College of Jinan University, Guangzhou 510630, China; Department of Radiology, The Second Clinical Medical School of North Sichuan Medical College, Nanchong Central Hospital, Nanchong, Sichuan 637000, China.
² Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan 637000, China.
³ Department of Radiology, The Second Clinical Medical School of North Sichuan Medical College, Nanchong Central Hospital, Nanchong, Sichuan 637000, China.
⁴ The First Clinical College of Jinan University, Guangzhou 510630, China; Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan 637000, China. Electronic address: zhangxm@nsmc.edu.cn.
⁵ Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China. Electronic address: tianwuchen_nsmc@163.com.

PMID: 38663124
DOI: 10.1016/j.ejrad.2024.111479

Abstract

Purpose: To construct and validate CT radiomics model based on the peritumoral adipose region of gastric adenocarcinoma to preoperatively predict lymph node metastasis (LNM).

Methods and methods: 293 consecutive gastric adenocarcinoma patients receiving radical gastrectomy with lymph node dissection in two medical institutions were stratified into a development set (from Institution A, n = 237), and an external validation set (from Institution B, n = 56). Volume of interest of peritumoral adipose region was segmented on preoperative portal-phase CT images. The least absolute shrinkage and selection operator method and stepwise logistic regression were used to select features and build radiomics models. Manual classification was performed according to routine CT characteristics. A classifier incorporating the radiomics score and CT characteristics was developed for predicting LNM. Area under the receiver operating characteristic curve (AUC) was used to show discrimination between tumors with and without LNM, and the calibration curves and Brier score were used to evaluate the predictive accuracy. Violin plots were used to show the distribution of radiomics score.

Results: AUC values of radiomics model to predict LNM were 0.938, 0.905, and 0.872 in the training, internal test, and external validation sets, respectively, higher than that of manual classification (0.674, all P values < 0.01). The radiomics score of the positive LNM group were higher than that of the negative group in all sets (both P-values < 0.001). The classifier showed no improved predictive power compared with the radiomics signature alone with AUC values of 0.916 and 0.872 in the development and external validation sets, respectively. Multivariate analysis showed that radiomics score was an independent predictor.

Conclusions: Radiomics model based on peritumoral adipose region could be a useful approach for preoperative LNM prediction in gastric adenocarcinoma.

Keywords: Gastric adenocarcinoma; Lymph node metastasis; Peritumoral analysis; Radiomics.