基于CT影像组学特征构建胃癌根治术后复发风险预测模型，探索其术前动态评估的临床价值。

回顾性选择2020年1月至2023年12月于康复大学青岛中心医院接受胃癌根治术的215例胃腺癌患者作为研究对象，所有患者术前均完成多期相（平扫、动脉期、静脉期）增强CT检查。使用256层CT进行标准化扫描，通过ITK-SNAP软件手动勾画静脉期肿瘤三维容积（观察者间一致性Kappa>0.85），并基于PyRadiomics提取1152个影像组学特征。经观察者内、观察者间一致性检验（ICC≥0.75）、单因素分析及LASSO回归（λ=0.032，10倍交叉验证）筛选核心特征并构建Logistic回归模型。数据集按7∶3比例分层随机分为训练集（150例）和独立验证集（65例），评估模型的预测效能。

215例胃癌患者中67例患者术后出现复发，其余148例未见术后复发。单因素分析结果显示：复发组和未复发组患者在脉管浸润、神经浸润、肿瘤分化程度、手术切缘状态和接受辅助化疗方面存在明显差异，差异有统计学意义（P<0.05）。经过筛选后，模型最终保留8个核心影像组学特征，包括形态学、一阶统计、纹理分析及小波变换特征等。受试者工作特征曲线分析显示，CT影像组学模型在预测训练集的曲线下面积（AUC）为0.865，敏感度和特异度分别为69.4%、88.1%；在预测验证集的AUC为0.875，敏感度和特异度分别为94.4%、63.8%。

基于CT影像组学特征构建的预测模型在胃癌根治术后复发风险预测中展现出较好的诊断效能，实现了对胃癌患者的术前动态风险评估。该模型能够为高风险患者的强化随访及辅助治疗精准化提供客观决策支持。

To construct a prediction model based on CT radiomics features to predict the recurrence risk of gastric cancer after radical operation, and to explore the clinical value of its preoperative dynamic evaluation.

A total of 215 patients with gastric adenocarcinoma who underwent radical gastrectomy from January 2020 to December 2023 were retrospectively selected as the research objects. All patients completed multi-phase (plain scan, arterial phase and venous phase) enhanced CT examination before operation. Siemens 256-slice CT was used for standardized scanning, and the three-dimensional volume of venous tumor was manually delineated by ITK-SNAP software (the consistency between observers was Kappa>0.85), and 1152 imaging features were extracted based on PyRadiomics. The core features were screened by intra-observer and inter-observer consistency test (ICC≥0.75), single factor analysis and LASSO regression (λ=0.032, 10 times cross-validation) and a Logistic regression model was constructed. The data set was randomly divided into training set (150 cases) and independent verification set (65 cases) according to the ratio of 7∶3, and the prediction efficiency of the model was evaluated.

Among 215 patients with gastric cancer, 67 patients had postoperative recurrence, and the remaining 148 patients did not have postoperative recurrence. The results of univariate analysis showed that there were significant differences in vascular infiltration, nerve infiltration, tumor differentiation, surgical margin status and receiving adjuvant chemotherapy between patients with recurrence and those without recurrence (P<0.05). After a series of screening, the model finally retained eight core image omics features, including morphology, first-order statistics, texture analysis and wavelet transform features. Receiver operating characteristic curve analysis showed that the area under the curve (AUC) of the CT image omics model in predicting the training set was 0.865, and the sensitivity and specificity were 69.4% and 88.1% respectively. The AUC in predicting validation set was 0.875, and the sensitivity and specificity were 94.4% and 63.8% respectively.

The prediction model based on the characteristics of CT imaging shows good diagnostic efficiency in the risk prediction of recurrence after radical gastrectomy, and realizes the preoperative dynamic risk assessment of gastric cancer patients. This model can provide objective decision support for intensive follow-up and accurate adjuvant treatment of high-risk patients.