ArticleChinese journal of cancer research = Chung-kuo yen cheng yen chiu2020
A CT-based radiomics nomogram for prediction of human epidermal growth factor receptor 2 status in patients with gastric cancer.
Article in Chinese journal of cancer research = Chung-kuo yen cheng yen chiu, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed, 32 citations in OpenAlex.
- Radiomics analysis of multiphase contrast-enhanced CT imaging for differentiating primary gastric lymphoma and Borrmann type IV gastric cancer.European journal of radiology open · 2026Article
- Preoperative prediction of HER2 expression in gastric cancer based on an enhanced CT radiomics model.Translational cancer research · 2026Article
- Radiogenomic landscape of the hallmarks of cancer.Biomarker research · 2026Review
- Comparison of CT-based radiomics models for the prediction of human epidermal growth factor receptor 2 status in gastric cancer.BMC medical imaging · 2026Article
- Evolving Paradigms in Gastric Cancer Staging: From Conventional Imaging to Advanced MRI and Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2026Review
- Interpretable deep learning-based hierarchical multi-modal fusion model for predicting HER2 expression in gastric cancer.Frontiers in oncology · 2026Article
- From Images to Genes: Radiogenomics Based on Artificial Intelligence to Achieve Non-Invasive Precision Medicine in Cancer Patients.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Prediction of HER2 status via random forest in 3257 Chinese patients with gastric cancer.Clinical and experimental medicine · 2023Article
- A virtual biopsy study of microsatellite instability in gastric cancer based on deep learning radiomics.Insights into imaging · 2023Article
- PreoperativeQuantitative imaging in medicine and surgery · 2023Article
- Development and external validation of a radiomics model for assessment of HER2 positivity in men and women presenting with gastric cancer.Insights into imaging · 2023Article
- Development and validation of multivariate models integrating preoperative clinicopathological and radiographic findings to predict HER2 status in gastric cancer.Scientific reports · 2022Article
- [Research progress in CT-based radiomics constructing hypopharyngeal cancer and multisystem tumor prediction model].Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery · 2022Review
- CT-based radiomic nomogram for preoperative prediction of DNA mismatch repair deficiency in gastric cancer.Frontiers in oncology · 2022Article
- Accurate preoperative staging and HER2 status prediction of gastric cancer by the deep learning system based on enhanced computed tomography.Frontiers in oncology · 2022Article
- Radiomics Model for Predicting TP53 Status Using CT and Machine Learning Approach in Laryngeal Squamous Cell Carcinoma.Frontiers in oncology · 2022Article
- Review
- Current status and future perspectives on neoadjuvant therapy in gastric cancer.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2021Article
- The Value of Predicting Human Epidermal Growth Factor Receptor 2 Status in Adenocarcinoma of the Esophagogastric Junction on CT-Based Radiomics Nomogram.Frontiers in oncology · 2021Article
- Texture Analysis Using Semiquantitative Kinetic Parameter Maps from DCE-MRI: Preoperative Prediction of HER2 Status in Breast Cancer.Frontiers in oncology · 2021Article
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Authors and funding
15 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo develop and validate a computed tomography (CT)-based radiomics nomogram for predicting human epidermal growth factor receptor 2 (HER2) status in patients with gastric cancer.
methodsThis retrospective study included 134 patients with gastric cancer (HER2-negative: n=87; HER2-positive: n=47) from April 2013 to March 2018, who were then randomly divided into training (n=94) and validation (n=40) cohorts. Radiomics features were obtained from the CT images showing gastric cancer. Least absolute shrinkage and selection operator (LASSO) regression analysis was utilized for building the radiomics signature. A multivariable logistic regression method was applied to develop a prediction model incorporating the radiomics signature and independent clinicopathologic risk predictors, which were then visualized as a radiomics nomogram. The predictive performance of the nomogram was assessed in the training and validation cohorts.
resultsThe radiomics signature was significantly associated with HER2 status in both training (P<0.001) and validation (P=0.023) cohorts. The prediction model that incorporated the radiomics signature and carcinoembryonic antigen (CEA) level demonstrated good discriminative performance for HER2 status prediction, with an area under the curve (AUC) of 0.799 [95% confidence interval (95% CI): 0.704-0.894] in the training cohort and 0.771 (95% CI: 0.607-0.934) in the validation cohort. The calibration curve of the radiomics nomogram also showed good calibration. Decision curve analysis showed that the radiomics nomogram was useful.
conclusionsWe built and validated a radiomics nomogram with good performance for HER2 status prediction in gastric cancer. This radiomics nomogram could serve as a non-invasive tool to predict HER2 status and guide clinical treatment.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.