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Updated: May 30, 2025
Gene Regulation and Targeted Therapy in Gastric Cancer Peritoneal Metastasis: Radiological Findings from Dual Energy CT and PET/CT
Published on: January 22, 2018
Non-invasive Assessment of Human Epidermal Growth Factor Receptor 2 Expression in Gastric Cancer Based on Deep
Zhong-Hui Wu1, Xiao-Rong Ren2, Yu-Qi Meng1
1Department 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.).
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.
