Non-invasive Assessment of Human Epidermal Growth Factor Receptor 2 Expression in Gastric Cancer Based on Deep Learning: A Computed Tomography-based Multicenter Study

Zhong-Hui Wu; Xiao-Rong Ren; Yu-Qi Meng; Xin-Yun Wang; Ning-Xin Yang; Xiao-Yu Wang; Gang Ren

doi:10.1016/j.acra.2024.12.041

Non-invasive Assessment of Human Epidermal Growth Factor Receptor 2 Expression in Gastric Cancer Based on Deep Learning: A Computed Tomography-based Multicenter Study

Acad Radiol. 2025 Jan 26:S1076-6332(24)01031-6. doi: 10.1016/j.acra.2024.12.041. Online ahead of print.

Authors

Zhong-Hui Wu¹, Xiao-Rong Ren², Yu-Qi Meng¹, Xin-Yun Wang³, Ning-Xin Yang³, Xiao-Yu Wang³, Gang Ren⁴

Affiliations

¹ Department of Radiology, Xinhua Hospital, Shanghai Jiaotong University Medical School, Shanghai 200092, China (Z.H.W., Y.Q.M., X.Y.W., N.X.Y., X.Y.W., G.R.); Shanghai Medical College, Fudan University, Shanghai 200032, China (Z.H.W., X.R.R., Y.Q.M.).
² Shanghai Medical College, Fudan University, Shanghai 200032, China (Z.H.W., X.R.R., Y.Q.M.).
³ Department of Radiology, Xinhua Hospital, Shanghai Jiaotong University Medical School, Shanghai 200092, China (Z.H.W., Y.Q.M., X.Y.W., N.X.Y., X.Y.W., G.R.).
⁴ Department of Radiology, Xinhua Hospital, Shanghai Jiaotong University Medical School, Shanghai 200092, China (Z.H.W., Y.Q.M., X.Y.W., N.X.Y., X.Y.W., G.R.). Electronic address: rengang527@163.com.

PMID: 39870563
DOI: 10.1016/j.acra.2024.12.041

Abstract

Rationale and objectives: The expression of human epidermal growth factor receptor 2 (HER2) in gastric cancer is closely associated with its treatment outcomes and prognosis. This study aims to develop and validate a HER2 prediction model based on computed tomography (CT). Additionally, the study evaluates the robustness of the proposed model.

Materials and methods: This retrospective study included 1059 patients from three hospitals (A, B, and C), where patients from hospitals A and B formed the training set (720 cases), and patients from hospital C served as the external test set (339 cases). Venous-phase CT radiomic features were extracted, normalized using the Z-score method, and simplified via principal component analysis. Feature selection was performed using recursive feature elimination (RFE), analysis of variance, Relief, and the Kruskal-Wallis (KW) test, followed by modeling using Lasso-regularized logistic regression and Support Vector Machine (SVM) methods. The models were evaluated and validated using the area under the curve (AUC) and decision curve analysis to determine the best-performing model.

Results: The positive proportions of HER2 expression were 8.60% (52/658) in the training set and 5.60% (19/320) in the test set. Eight distinct models were developed to predict HER2 expression. Among these, the model utilizing RFE and Lasso-regularized logistic regression (LR-Lasso) exhibited the highest predictive performance, with AUC values of 0.7874 (95% CI: 0.7346-0.8402) in the training set and 0.8033 (95% CI: 0.7288-0.8788) in the test set. Compared to other models, this model provided a greater net benefit on the decision curve analysis. These results suggest that the proposed model can be effectively applied to predict HER2 expression in patients.

Conclusion: The HER2 prediction model demonstrated promising performance in predicting HER2 expression in gastric cancer patients.

Keywords: Computed tomography; Gastric cancer; HER2; Radiomics.