ArticleFrontiers in oncology2026
A CT-based radiomics model for preoperative risk stratification of gastrointestinal stromal tumors.
Article in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Gastrointestinal stromal tumors (GISTs) are common mesenchymal tumors with variable malignancy potential, making accurate preoperative risk stratification crucial for treatment planning. Traditional methods rely on pathological and clinical features but often overlook tumor heterogeneity. This study aims to develop and validate a CT-based radiomics model for GISTs risk stratification to improve clinical decision-making. Methods: This retrospective, multi-center study developed and validated a radiomics-based risk prediction model in accordance with the TRIPOD-ML statement. It included 123 patients with GISTs from two hospitals, divided into training (n=68), testing (n=30), and external validation (n=25) cohorts. Tumor delineation was performed using 3D segmentation on venous-phase contrast-enhanced CT scans. Radiomics features (n=1784) were extracted and refined using feature selection methods, including LASSO and ANOVA. Six machine learning algorithms were evaluated, and the support vector machine (SVM) model demonstrated optimal performance. Model evaluation included metrics such as AUC, calibration curves, and decision curve analysis (DCA). Results: The SVM-based radiomics model achieved robust performance, with AUC values of 0.906 (95% CI: 0.812-0.964) in the testing cohort and 0.867 (95% CI: 0.724-0.956) in the external validation cohort. Calibration curves indicated strong agreement between predicted and observed outcomes, while DCA highlighted significant clinical utility across different thresholds. Key radiomics features provided accurate differentiation between Lower Risk and Elevated Risk groups, aligning with clinical stratification needs. Conclusions: The developed CT-based radiomics model offers a reliable, externally validated tool for GISTs risk stratification, addressing limitations of traditional methods by incorporating tumor heterogeneity and enhancing predictive accuracy. This model has the potential to guide personalized treatment strategies, particularly in distinguishing patients with GISTs requiring adjuvant therapy from those suitable for surgical resection alone. This study was approved by the appropriate ethics committee with a waiver of informed consent.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.