ArticleClinical Medicine Insights. Oncology2024
Prediction of High-Risk Gastrointestinal Stromal Tumor Recurrence Based on Delta-CT Radiomics Modeling: A 3-Year Follow-up Study After Surgery.
Article in Clinical Medicine Insights. Oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Comparison of nomograms and predictive tools in gastrointestinal stromal tumor.Journal of gastrointestinal oncology · 2026Article
- Delta radiomics-based nomogram for preoperative prediction vessels encapsulating tumor clusters (VETC) and prognosis in hepatocellular carcinoma using dynamic contrast-enhanced CT.BMC medical imaging · 2026Article
- Can CT Radiomics Predict the Ki-67 Index of Gastrointestinal Stromal Tumors (GISTs)? A Systematic Review and Meta-Analysis.Cancers · 2025Review
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Medium- to high-risk classification-gastrointestinal stromal tumors (MH-GIST) have a high recurrence rate and are difficult to treat. This study aims to predict the recurrence of MH-GIST within 3 years after surgery based on clinical data and preoperative Delta-CT Radiomics modeling. Methods: A retrospective analysis was conducted on clinical imaging data of 242 cases confirmed to have MH-GIST after surgery, including 92 cases of recurrence and 150 cases of normal. The training set and test set were established using a 7:3 ratio and time cutoff point. In the training set, multiple prediction models were established based on clinical data of MH-GIST and the changes in radiomics texture of enhanced computed tomography (CT) at different time periods (Delta-CT radiomics). The area under curve (AUC) values of each model were compared using the Delong test, and the clinical net benefit of the model was tested using decision curve analysis (DCA). Then, the model was externally validated in the test set, and a novel nomogram predicting the recurrence of MH-GIST was finally created. Results: Univariate analysis confirmed that tumor volume, tumor location, neutrophil-lymphocyte ratio (NLR), platelet lymphocyte ratio (PLR), diabetes, spicy hot pot, CT enhancement mode, and Radscore 1/2 were predictive factors for MH-GIST recurrence ( Conclusions: The nomogram of clinical and Delta-CT radiomics has important clinical value in predicting the recurrence of MH-GIST, providing reliable data reference for its diagnosis, treatment, and clinical decision-making.
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