ArticleFrontiers in oncology2022
Deep learning and radiomics to predict the mitotic index of gastrointestinal stromal tumors based on multiparametric MRI.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed, 10 citations in OpenAlex.
- A deep learning and radiomics fusion model enhances endoscopic ultrasonography diagnosis of gastric tumors.Frontiers in oncology · 2026Article
- Radiomics models with baseline MRI and clinical data to predict target therapy response and high-risk mortality in metastatic GIST.Scientific reports · 2025Article
- Artificial Intelligence Tools for Supporting Histopathologic and Molecular Characterization of Gynecological Cancers: A Review.Journal of clinical medicine · 2025Review
- Can CT Radiomics Predict the Ki-67 Index of Gastrointestinal Stromal Tumors (GISTs)? A Systematic Review and Meta-Analysis.Cancers · 2025Review
- Ultrasound elastography for the differential diagnosis of benign and malignant gastrointestinal stromal tumors.World journal of gastrointestinal surgery · 2025Article
- Development and validation of a computed tomography-based radiomics signature to predict "highest-risk" from patients with high-risk gastrointestinal stromal tumor.Journal of gastrointestinal oncology · 2024Article
- Prediction of High-Risk Gastrointestinal Stromal Tumor Recurrence Based on Delta-CT Radiomics Modeling: A 3-Year Follow-up Study After Surgery.Clinical Medicine Insights. Oncology · 2024Article
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Authors and funding
10 authors at 2 institutions in 2 countries.
Funding
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Abstract
Introduction: Preoperative evaluation of the mitotic index (MI) of gastrointestinal stromal tumors (GISTs) represents the basis of individualized treatment of patients. However, the accuracy of conventional preoperative imaging methods is limited. The aim of this study was to develop a predictive model based on multiparametric MRI for preoperative MI prediction. Methods: A total of 112 patients who were pathologically diagnosed with GIST were enrolled in this study. The dataset was subdivided into the development ( Results: The hybrid model showed good MI prediction ability at the image level; the area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), and accuracy in the test set were 0.947 (95% confidence interval [CI]: 0.927-0.968), 0.964 (95% CI: 0.930-0.978), and 90.8 (95% CI: 88.0-93.0), respectively. With the average probabilities from multiple samples per patient, good performance was also achieved at the patient level, with AUROC, AUPRC, and accuracy of 0.930 (95% CI: 0.828-1.000), 0.941 (95% CI: 0.792-1.000), and 93.6% (95% CI: 79.3-98.2) in the test set, respectively. Discussion: The deep learning-based hybrid model demonstrated the potential to be a good tool for the operative and non-invasive prediction of MI in GIST patients.
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