Non-invasive Assessment of Human Epidermal Growth Factor Receptor 2 Expression in Gastric Cancer Based on Deep Learning: A Computed Tomography-based Multicenter Study.
IF 3.8 2区 医学Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
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引用次数: 0
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.
期刊介绍：
Academic Radiology publishes original reports of clinical and laboratory investigations in diagnostic imaging, the diagnostic use of radioactive isotopes, computed tomography, positron emission tomography, magnetic resonance imaging, ultrasound, digital subtraction angiography, image-guided interventions and related techniques. It also includes brief technical reports describing original observations, techniques, and instrumental developments; state-of-the-art reports on clinical issues, new technology and other topics of current medical importance; meta-analyses; scientific studies and opinions on radiologic education; and letters to the Editor.
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