ArticleFrontiers in oncology2021
CT Radiomics for the Preoperative Prediction of Ki67 Index in Gastrointestinal Stromal Tumors: A Multi-Center Study.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Prediction of the mitotic index and preoperative risk stratification of gastrointestinal stromal tumors with CT radiomic features.La Radiologia medica · 2023Trial
- Prediction of recurrence-free survival and adjuvant therapy benefit in patients with gastrointestinal stromal tumors based on radiomics features.La Radiologia medica · 2022Trial
- Preoperatively Predicting Risk Stratification for GISTs ≤2 cm by Radiomics Model: A Dual-center StudyCurrent medical imaging · 2026Article
- Can CT Radiomics Predict the Ki-67 Index of Gastrointestinal Stromal Tumors (GISTs)? A Systematic Review and Meta-Analysis.Cancers · 2025Review
- Interpretable machine learning model based on CT semantic features and radiomics features to preoperatively predict Ki-67 expression in gastrointestinal stromal tumors.Scientific reports · 2024Article
- Computed tomography radiogenomics: A potential tool for prediction of molecular subtypes in gastric stromal tumor.World journal of gastrointestinal oncology · 2024Article
- Predicting the progression-free survival of gastrointestinal stromal tumors after imatinib therapy through multi-sequence magnetic resonance imaging.Abdominal radiology (New York) · 2024Article
- Prediction of Ki-67 expression in gastrointestinal stromal tumors using radiomics of plain and multiphase contrast-enhanced CT.European radiology · 2023Article
- A radiomics-clinical combined nomogram-based on non-enhanced CT for discriminating the risk stratification in GISTs.Journal of cancer research and clinical oncology · 2023Article
- A novel radiomics based on multi-parametric magnetic resonance imaging for predicting Ki-67 expression in rectal cancer: a multicenter study.BMC medical imaging · 2023Article
- Virtual biopsy in abdominal pathology: where do we stand?BJR open · 2023Review
- Early Prediction and Monitoring of Treatment Response in Gastrointestinal Stromal Tumors by Means of Imaging: A Systematic Review.Diagnostics (Basel, Switzerland) · 2022Review
- Predictive Value of a Radiomics Nomogram Model Based on Contrast-Enhanced Computed Tomography for KIT Exon 9 Gene Mutation in Gastrointestinal Stromal Tumors.Technology in cancer research & treatmentArticle
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6 authors.
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Abstract
purposeThis study established and verified a radiomics model for the preoperative prediction of the Ki67 index of gastrointestinal stromal tumors (GISTs). MATERIALS AND
methodsA total of 344 patients with GISTs from three hospitals were divided into a training set and an external validation set. The tumor region of interest was delineated based on enhanced computed-tomography (CT) images to extract radiomic features. The Boruta algorithm was used for dimensionality reduction of the features, and the random forest algorithm was used to construct the model for radiomics prediction of the Ki67 index. The receiver operating characteristic (ROC) curve was used to evaluate the model's performance and generalization ability.
resultsAfter dimensionality reduction, a feature subset having 21 radiomics features was generated. The generated radiomics model had an the area under curve (AUC) value of 0.835 (95% confidence interval(CI): 0.761-0.908) in the training set and 0.784 (95% CI: 0.691-0.874) in the external validation cohort.
conclusionThe radiomics model of this study had the potential to predict the Ki67 index of GISTs preoperatively.
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