Predictive Value of CT Morphological Features For Metastatic Risk Stratification of Gastrointestinal stromal tumors: Independent Dual- Reader Analysis - Mitrovic Jovanovic, Milica; Jankovic, Aleksandra; Kovac, Jelena; Saponjski, Dusan; Stosic, Katarina; Sljukic, Vladimir; Ebrahimi, Keramatollah; Skrobic, Ognjan; Sabljak, Predrag; Djuric-Stefanovic, Aleksandra (Medical Research, 2026)
Background: Gastrointestinal stromal tumors (GISTs) exhibit variable biological behavior, from indolent lesions to aggressive tumors with metastatic potential. Preoperative risk assessment is important for treatment planning. Therefore, this study evaluated the predictive value and reproducibility of CT morphological features in identifying high-risk GISTs using independent dual-reader analysis. Materials and Methods: This retrospective study included 26 patients with histopathologically confirmed gastric GISTs who underwent biphasic contrast-enhanced MDCT. Two radiologists independently evaluated CT morphological features, including tumor size, mucosal integrity, internal structure, shape, growth pattern, and enlarged feeding or draining vessels (EFDV). Based on disrupted mucosa and EFDV, tumors were stratified into high-risk (HR) and low-risk (LR) groups and correlated with histopathological findings. Results: Histopathological analysis confirmed HR GISTs in 10 and LR GISTs in 16 patients. Excellent interobserver agreement was observed for mucosal integrity (κ=0.843) and tumor structure (κ=0.923), with substantial agreement for EFDV, shape, growth pattern, and predicted risk. Both radiologists demonstrated high sensitivity (90%) for identifying HR GISTs. Larger tumor size, disrupted mucosa, heterogeneous structure, irregular shape, endophytic growth, and EFDV were associated with HR tumors, although specificity remained limited due to overlapping imaging features. Conclusion: CT-derived morphological features are valuable imaging biomarkers for preoperative risk stratification of gastric GISTs. Disrupted mucosa and enlarged peri-/intratumoral vessels were the strongest predictors of high-risk tumors. Despite overlapping imaging features between low- and high-risk lesions, conventional CT analysis may aid in identifying aggressive tumors and guiding treatment planning.