Study on the Prediction of HER-2 Expression Status in Gastric Cancer Patients using 18 F-FDG PET/CT Radiomics
10.11969/j.issn.1673-548X.2024.10.018
- VernacularTitle:18F-FDG PET/CT影像组学预测胃癌患者HER-2表达状态研究
- Author:
Chen HE
1
;
Fan YANG
;
Hanlin ZHANG
Author Information
1. 730000 兰州大学第二医院
- Keywords:
Gastric cancer;
PET/CT;
Radiomic;
HER-2
- From:
Journal of Medical Research
2024;53(10):99-104,109
- CountryChina
- Language:Chinese
-
Abstract:
Objective To assess the predictive value of the model combined conventional metabolic parameters and radiomics fea-tures from 18F-FDG PET/CT for predicting HER-2 expression status in gastric cancer.Methods A retrospective analysis included 110 gastric cancer patients from The Second Hospital Lanzhou University and The Gansu Provincial People's Hospital.All patients under-went 18 F-FDG PET/CT before treatment.The Lanzhou cases were randomly divided into a 7∶3 training set(n=61)and an internal vali-dation set(n=26),while data from Gansu Provincial People's Hospital(n=23)served as an external validation set.LASSO regression and 10-fold cross-validation were employed to select PET parameters and radiomics features in the training set.Logistic regression was used to create PET,radiomics,and combined model.Evaluation included ROC curves and the DeLong test for model comparison.Clinical utility was assessed using decision curve analysis,and model consistency was observed through calibration curves.Results Models based on 3,2 and 5 selected features for the PET,radiomics,and combined model.ROC curves demonstrated strong predictive performance for all models.The DeLong test showed a significant difference between the combined model and radiomics model in the training set(P＜0.05),with no statistical difference between the PET model and radiomics model(P＞0.05).Internal and external validation sets showed no statistical differences among the three models(P＞0.05).Calibration curves indicated good fitting effects for each model,and decision curve analysis revealed higher clinical utility for the combined model compared to the other two models.Conclusion The combined model provides a robust predictive tool for determining HER-2 expression status in gastric cancer patients.
