Intratumoral and peritumoral CT-based radiomics for predicting the microsatellite instability in gastric cancer

Xingchi Chen; Zijian Zhuang; Lin Pen; Jing Xue; Haitao Zhu; Lirong Zhang; Dongqing Wang

doi:10.1007/s00261-023-04165-9

Intratumoral and peritumoral CT-based radiomics for predicting the microsatellite instability in gastric cancer

Abdom Radiol (NY). 2024 Feb 2. doi: 10.1007/s00261-023-04165-9. Online ahead of print.

Authors

Xingchi Chen^#¹, Zijian Zhuang^#¹, Lin Pen², Jing Xue², Haitao Zhu¹, Lirong Zhang^{3

4}, Dongqing Wang^{5

6}

Affiliations

¹ Department of Medical Imaging, The Affiliated Hospital of Jiangsu University, Zhenjiang, 212001, Jiangsu Province, China.
² School of Medicine, Jiangsu University, Zhenjiang, 212001, Jiangsu Province, China.
³ Department of Medical Imaging, The Affiliated Hospital of Jiangsu University, Zhenjiang, 212001, Jiangsu Province, China. tianchen861@ujs.edu.cn.
⁴ Institute of Imaging and Artificial Intelligence, Jiangsu University, Zhenjiang, 212000, Jiangsu Province, China. tianchen861@ujs.edu.cn.
⁵ Department of Medical Imaging, The Affiliated Hospital of Jiangsu University, Zhenjiang, 212001, Jiangsu Province, China. wangdongqing71@163.com.
⁶ Institute of Imaging and Artificial Intelligence, Jiangsu University, Zhenjiang, 212000, Jiangsu Province, China. wangdongqing71@163.com.

^# Contributed equally.

PMID: 38305796
DOI: 10.1007/s00261-023-04165-9

Abstract

Purpose: To investigate the value of intratumoral and peritumoral radiomics based on contrast-enhanced computer tomography (CECT) to preoperatively predict microsatellite instability (MSI) status in gastric cancer (GC) patients.

Methods: A total of 189 GC patients, including 63 patients with MSI-high (MSI-H) and 126 patients with MSI-low/stable (MSI-L/S), were randomly divided into the training cohort and validation cohort. Intratumoral and 5-mm peritumoral regions' radiomics features were extracted from CECT images. The features were standardized by Z-score, and the Inter- and intraclass correlation coefficient, univariate logistic regression analysis, and least absolute shrinkage and selection operator (LASSO) were applied to select the optimal radiomics features. Radiomics scores (Rad-score) based on intratumoral regions, peritumoral regions, and intratumoral + 5-mm peritumoral regions were calculated by weighting the linear combination of the selected features with their respective coefficients to construct the intratumoral model, peritumoral model, and intratumoral + peritumoral model. Logistic regression was used to establish a combined model by combining clinical characteristics, CT semantic features, and Rad-score of intratumoral and peritumoral regions.

Results: Eleven radiomics features were selected to establish a radiomics intratumoral + peritumoral model. CT-measured tumor length and tumor location were independent risk factors for MSI status. The established combined model obtained the highest area under the receiver operating characteristic (ROC) curve (AUC) of 0.830 (95% CI, 0.727-0.906) in the validation cohort. The calibration curve and decision curve demonstrated its good model fitness and clinical application value.

Conclusion: The combined model based on intratumoral and peritumoral CECT radiomics features and clinical factors can predict the MSI status of GS with moderate accuracy before surgery, which helps formulate personalized treatment strategies.

Keywords: Computed tomography; Gastric cancer; Microsatellite instability; Radiomics.

Abstract

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