ArticleLa Radiologia medica2020
Computed tomography-based radiomics model for discriminating the risk stratification of gastrointestinal stromal tumors.
Article in La Radiologia medica, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers, 1 of them a synthesis that pooled it.
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Who cites it
58 citing papers in PubMed, 1 synthesis or guideline pooled it, 72 citations in OpenAlex.
- Delta radiomics: a systematic review.La Radiologia medica · 2021Pooled it
- Prediction of the mitotic index and preoperative risk stratification of gastrointestinal stromal tumors with CT radiomic features.La Radiologia medica · 2023Trial
- Risk stratification for 1- to 2-cm gastric gastrointestinal stromal tumors: visual assessment of CT and EUS high-risk features versus CT radiomics analysis.European radiology · 2023Trial
- Prediction of recurrence-free survival and adjuvant therapy benefit in patients with gastrointestinal stromal tumors based on radiomics features.La Radiologia medica · 2022Trial
- Different radiomics models in predicting the malignant potential of small intestinal stromal tumors.European journal of radiology open · 2024Article
- Prediction of high Ki-67 proliferation index of gastrointestinal stromal tumors based on CT at non-contrast-enhanced and different contrast-enhanced phases.European radiology · 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
- 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
- Personalized radiomics signature to screen for KIT-11 mutation genotypes among patients with gastrointestinal stromal tumors: a retrospective multicenter study.Journal of translational medicine · 2023Article
- Imaging of human papilloma virus (HPV) related oropharynx tumour: what we know to date.Infectious agents and cancer · 2023Review
- Radiomics in gastrointestinal stromal tumours: an up-to-date review.Japanese journal of radiology · 2023Review
- Development and Validation of Artificial-Intelligence-Based Radiomics Model Using Computed Tomography Features for Preoperative Risk Stratification of Gastrointestinal Stromal Tumors.Journal of personalized medicine · 2023Article
- Clinicopathological, immunohistochemical, molecular-genetic and risk profiles of gastrointestinal stromal tumors in a cohort of Sudanese patients.African health sciences · 2023Article
- Reproducibility of CT radiomic features in lung neuroendocrine tumours (NETs) patients: analysis in a heterogeneous population.La Radiologia medica · 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
- The predictive potential of contrast-enhanced computed tomography based radiomics in the preoperative staging of cT4 gastric cancer.Quantitative imaging in medicine and surgery · 2022Article
- Integration between Novel Imaging Technologies and Modern Radiotherapy Techniques: How the Eye Drove the Chisel.Cancers · 2022Review
- Radiomics in medical imaging: pitfalls and challenges in clinical management.Japanese journal of radiology · 2022Review
- Structured Reporting in Radiological Settings: Pitfalls and Perspectives.Journal of personalized medicine · 2022Review
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe pathological risk degree of gastrointestinal stromal tumors (GISTs) has become an issue of great concern. Computed tomography (CT) is beneficial for showing adjacent tissues in detail and determining metastasis or recurrence of GISTs, but its function is still limited. Radiomics has recently shown a great potential in aiding clinical decision-making. The purpose of our study is to develop and validate CT-based radiomics models for GIST risk stratification.
methodsThree hundred and sixty-six patients clinically suspected of primary GISTs from January 2013 to February 2018 were retrospectively enrolled, among which data from 140 patients were eventually analyzed after exclusion. Data from patient CT images were partitioned based on the National Institutes of Health Consensus Classification, including tumor segmentation, radiomics feature extraction and selection. A radiomics model was then proposed and validated.
resultsThe radiomics signature demonstrated discriminative performance for advanced and nonadvanced GISTs with an area under the curve (AUC) of 0.935 [95% confidence interval (CI) 0.870-1.000] and an accuracy of 90.2% for validation cohort. The radiomics signature demonstrated favorable performance for the risk stratification of GISTs with an AUC of 0.809 (95% CI 0.777-0.841) and an accuracy of 67.5% for the validation cohort. Radiomics analysis could capture features of the four risk categories of GISTs. Meanwhile, this CT-based radiomics signature showed good diagnostic accuracy to distinguish between nonadvanced and advanced GISTs, as well as the four risk stratifications of GISTs.
conclusionOur findings highlight the potential of a quantitative radiomics analysis as a complementary tool to achieve an accurate diagnosis for GISTs.
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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.