为了建立并验证一个基于双层探测器光谱CT（DLCT）和临床放射学特征的预测模型，以预测胃癌（GC）的微卫星不稳定性（MSI）状态，并探索预测结果与患者预后之间的关系。共计264名接受术前DLCT检查的GC患者被随机分配到训练组（n=187）和验证组（n=80）。通过多元逻辑回归分析，使用临床放射学特征和DLCT参数构建临床和DLCT模型。构建组合的DLCT参数（CDLCT）来预测MSI。通过整合显著的临床放射学特征和CDLCT，使用多元逻辑回归分析构建组合预测模型。使用Kaplan-Meier生存分析来探索组合模型预测结果的预后显著性。 在本研究中，有70名（26.52%）MSI高（MSI-H）的GC患者。肿瘤位置和CT_N分期是MSI-H的独立危险因素。在验证组中，临床模型和DLCT模型预测MSI状态的曲线下面积（AUC）分别为0.721和0.837。组合模型在验证组中实现了高预测效力，AUC为0.879，敏感性为78.95％，特异性为75.4％。生存分析表明，组合模型能够按照无复发生存期（p=0.010）将GC患者分层。 组合模型为无创预测GC的MSI状态和手术后肿瘤复发风险分层提供了有效工具。MSI是GC中一个重要的分子亚型。但是，只能通过活检或术后肿瘤组织评估MSI状态。我们的研究开发了一种基于DLCT的组合模型，可以在术前有效地预测MSI。我们的结果还显示，组合模型可以根据无复发生存期分层患者。这对临床医生选择适当的治疗策略以避免肿瘤复发和预测临床预后可能是有价值的。•肿瘤位置和CT_N分期是GC中MSI-H的独立预测因子。 •定量的DLCT参数在预测GC的MSI状态方面显示出潜力。 •组合模型整合了临床放射学特征和CDLCT，可以改善预测性能。 •预测结果可以分层手术后肿瘤复发的风险。© 2023. 欧洲放射学会（ESR）。

To construct and validate a prediction model based on dual-layer detector spectral CT (DLCT) and clinico-radiologic features to predict the microsatellite instability (MSI) status of gastric cancer (GC) and to explore the relationship between the prediction results and patient prognosis.A total of 264 GC patients who underwent preoperative DLCT examination were randomly allocated into the training set (n = 187) and validation set (n = 80). Clinico-radiologic features and DLCT parameters were used to build the clinical and DLCT model through multivariate logistic regression analysis. A combined DLCT parameter (CDLCT) was constructed to predict MSI. A combined prediction model was constructed using multivariate logistic regression analysis by integrating the significant clinico-radiologic features and CDLCT. The Kaplan-Meier survival analysis was used to explore the prognostic significant of the prediction results of the combined model.In this study, there were 70 (26.52%) MSI-high (MSI-H) GC patients. Tumor location and CT_N staging were independent risk factors for MSI-H. In the validation set, the area under the curve (AUC) of the clinical model and DLCT model for predicting MSI status was 0.721 and 0.837, respectively. The combined model achieved a high prediction efficacy in the validation set, with AUC, sensitivity, and specificity of 0.879, 78.95%, and 75.4%, respectively. Survival analysis demonstrated that the combined model could stratify GC patients according to recurrence-free survival (p = 0.010).The combined model provides an efficient tool for predicting the MSI status of GC noninvasively and tumor recurrence risk stratification after surgery.MSI is an important molecular subtype in gastric cancer (GC). But MSI can only be evaluated using biopsy or postoperative tumor tissues. Our study developed a combined model based on DLCT which could effectively predict MSI preoperatively. Our result also showed that the combined model could stratify patients according to recurrence-free survival. It may be valuable for clinicians in choosing appropriate treatment strategies to avoid tumor recurrence and predicting clinical prognosis in GC.• Tumor location and CT_N staging were independent predictors for MSI-H in GC. • Quantitative DLCT parameters showed potential in predicting MSI status in GC. • The combined model integrating clinico-radiologic features and CDLCT could improve the predictive performance. • The prediction results could stratify the risk of tumor recurrence after surgery.© 2023. European Society of Radiology (ESR).