Predictive value of dual-energy CT multi-quantitative parameters for risk stratification of gastrointestinal stromal tumors

LI Xiangyang; LI Siyuan; ZHANG Yehua; XU Chijie; DENG Xiaoyi

doi:10.19300/j.2026.L22378

Abstract：Objective To evaluate the predictive value of multiple quantitative parameters derived from dual-energy computed tomography(DECT)for risk stratification of gastrointestinal stromal tumors(GISTs).Methods This retrospective study included 36 patients with surgically and pathologically confirmed GISTs,with a mean age of 62.2±10.8 years.Patients were categorized into a high-risk group(n=15)and a low-risk group(n=21)based on the number of mitotic nuclei.Clinical characteristics were assessed,and multi-quantitative parameters from DECT were measured,including iodine concentration(IC),normalized iodine concentration(NIC),fat fraction(FF),electron density(Rho),effective atomic number(Zeff),and dual-energy index(DEI)on venous-phase fusion images.The Chi-square test or t-test was employed to compare differences in parameters between the two groups.Univariate and multivariate Logistic regression analyses were performed to identify independent predictors for high risk GIST,and a combined logistic regression model integrating multiple factors was constructed.Receiver operating characteristic(ROC)curves were used to assess the predictive performance of the model,and the area under the curve(AUC)was calculated.The DeLong's test was used to compare difference in AUC values among models.Calibration curve and decision curve analysis(DCA)were used to evaluate model calibration and clinical applicability.Results Univariate Logistic regression identified tumor location,heterogeneous enhancement,necrosis/cystic degeneration,maximum diameter,venous-phase IC,and NIC as predictors of high-risk GIST(all P<0.05).Multivariate analysis revealed that maximal tumor diameter[odds ratio(OR)=1.59,P=0.012]and NIC(OR=1.08,P=0.014)were independent risk factors for high-risk GIST.The combined model constructed based on these two independent predictors achieved an AUC of 0.95,with a sensitivity of 93.8%and a specificity of 85.0%,showing significantly better predictive performance than each individual factor(all P<0.05).The calibration curve demonstrated good agreement between predicted probabilities and observed outcomes.DCA showed that the combined model yielded a higher net benefit within a threshold probability range of 0.05-0.73.Conclusion Multiple quantitative parameters derived from DECT can non-invasively predict the stratification of GISTs preoperatively,providing a reference for clinical treatment decision-making.