A CT-based radiomics model for preoperative risk stratification of gastrointestinal stromal tumors
The SVM-based radiomics model achieved robust performance, with AUC values of 0.906 in the testing cohort and 0.867 in the external validation cohort, indicating high accuracy for preoperative risk stratification of gastrointestinal stromal tumors.
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. 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.
Accurate preoperative risk stratification of GISTs can guide treatment decisions, such as selecting patients who need adjuvant therapy versus those who can undergo surgery alone, thereby improving personalized care.
Evidence level: Állatkísérletes. Állatmodellben vizsgálták.
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