ArticleBMC medical imaging2025
Dual-phase contrast-enhanced CT-based intratumoral and peritumoral radiomics for preoperative prediction of lymphovascular invasion in gastric cancer.
Article in BMC medical imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Artificial Intelligence in Gastric Cancer: Diagnostic, Prognostic, and Predictive Developments, Evidence Maturity, and Translational Challenges.Cancer reports (Hoboken, N.J.) · 2026Review
- Development and validation of an interpretable CT-based scoring model for gastric cancer aggressiveness.Abdominal radiology (New York) · 2026Article
- Prediction of Lymphovascular Invasion in Early-Stage Lung Adenocarcinoma Using Artificial Intelligence-Based Radiomics.Cancers · 2025Article
- CT-based deep learning radiomics analysis for preoperative Lauren classification in gastric cancer and explore the tumor microenvironment.European journal of radiology open · 2025Article
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5 authors.
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Abstract
backgroundTo develop and validate a dual-phase contrast-enhanced computed tomography (CT)-based intratumoral and peritumoral radiomics for the prediction of lymphovascular invasion (LVI) in patients with gastric cancer.
methodThree hundred and eighty-three patients with gastric cancer (training cohort, 269 patients; test cohort, 114 patients) were retrospectively enrolled between January 2017 and June 2023. Radiomics features were extracted from the intratumoral volume (ITV) and peritumoral volume (PTV) on CT images at arterial phase (AP) and venous phase (VP), and selected by the least absolute shrinkage and selection operator. Radiomics models were constructed by logistic regression. The clinical-radiomics combined model incorporating the most predictive radiomics signature and clinical risk factors were developed with multivariate analysis. Receiver operating characteristic (ROC) curves were used to evaluate the prediction performance of models.
resultsClinical model comprised of three clinical risk factors including tumor differentiation, CT-reported lymph node metastasis status and CT-TNM staging showed good performance with an area under the ROC curve (AUC) of 0.804 and 0.825 in the training and test cohort, respectively. Compared with the other radiomics models, dual-phase (AP + VP) CT-based ITV + PTV radiomics model presented superior AUC of 0.844 and 0.835 in the training and test cohort, respectively. Clinical-radiomics combined model further improved the discriminatory performance (AUC, 0.903) in the training and test cohort (AUC, 0.901). Decision curve analysis confirmed the net benefit of clinical-radiomics combined model. Subgroup analyses showed that the clinical-radiomics nomogram showed the best performance with an AUC of 0.879 and 0.883 for predicting LVI in T1-T2 and T3-T4 gastric cancer compared with the clinical model and the ITV + PTV-AP + VP radiomics model, respectively.
conclusionsClinical-radiomics combined model integrating clinical risk factors and dual-phase contrast-enhanced CT-based intratumoral and peritumoral radiomics signatures provided favorable performance for predicting LVI in gastric cancer.
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