ArticleInsights into imaging2023
Development and external validation of a radiomics model for assessment of HER2 positivity in men and women presenting with gastric cancer.
Article in Insights into imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 15 citations in OpenAlex.
- MRI-based radiomics with automated segmentation and manual refinement for predicting treatment response in unresectable hepatocellular carcinoma: a multicenter study.Abdominal radiology (New York) · 2026Observational
- Radiomics in Gastric Cancer: Advancing Precision Medicine.Journal of gastric cancer · 2026Review
- Preoperative prediction of Ki-67 expression in breast cancer using a combined radiomics model based on 18F-FDG PET/CT.Medicine · 2026Article
- Comparison of CT-based radiomics models for the prediction of human epidermal growth factor receptor 2 status in gastric cancer.BMC medical imaging · 2026Article
- Noninvasive evaluation of treatment-related protein expression status in advanced gastric cancer using dynamic nomograms with dual-energy CT: a multicenter study.European radiology · 2026Article
- Interpretable deep learning-based hierarchical multi-modal fusion model for predicting HER2 expression in gastric cancer.Frontiers in oncology · 2026Article
- Overlooked and underpowered: a meta-research addressing sample size in radiomics prediction models for binary outcomes.European radiology · 2025Article
- Hepatoid adenocarcinoma of the stomach: discrimination from conventional gastric adenocarcinoma with a computed tomography-based radiomics nomogram.Journal of gastrointestinal oncology · 2024Article
- A radiomics-based interpretable machine learning model to predict the HER2 status in bladder cancer: a multicenter study.Insights into imaging · 2024Article
- Contrast-enhanced computed tomography-based radiomics nomogram for predicting HER2 status in urothelial bladder carcinoma.Frontiers in oncology · 2024Article
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundTo develop and externally validate a conventional CT-based radiomics model for identifying HER2-positive status in gastric cancer (GC).
methods950 GC patients who underwent pretreatment CT were retrospectively enrolled and assigned into a training cohort (n = 388, conventional CT), an internal validation cohort (n = 325, conventional CT) and an external validation cohort (n = 237, dual-energy CT, DECT). Radiomics features were extracted from venous phase images to construct the "Radscore". On the basis of univariate and multivariate analyses, a conventional CT-based radiomics model was built in the training cohort, combining significant clinical-laboratory characteristics and Radscore. The model was assessed and validated regarding its diagnostic effectiveness and clinical practicability using AUC and decision curve analysis, respectively.
resultsLocation, clinical TNM staging, CEA, CA199, and Radscore were independent predictors of HER2 status (all p < 0.05). Integrating these five indicators, the proposed model exerted a favorable diagnostic performance with AUCs of 0.732 (95%CI 0.683-0.781), 0.703 (95%CI 0.624-0.783), and 0.711 (95%CI 0.625-0.798) observed for the training, internal validation, and external validation cohorts, respectively. Meanwhile, the model would offer more net benefits than the default simple schemes and its performance was not affected by the age, gender, location, immunohistochemistry results, and type of tissue for confirmation (all p > 0.05).
conclusionsThe conventional CT-based radiomics model had a good diagnostic performance of HER2 positivity in GC and the potential to generalize to DECT, which is beneficial to simplify clinical workflow and help clinicians initially identify potential candidates who might benefit from HER2-targeted therapy.
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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.