To develop and validate multivariate models integrating endoscopic biopsy, tumor markers, and CT findings based on late arterial phase (LAP) to predict serosal invasion in gastric cancer (GC). The preoperative differentiation degree, tumor markers, CT morphological characteristics, and CT value-related and texture parameters of 154 patients with GC were analyzed retrospectively. Multivariate models based on regression analysis and machine learning algorithms were performed to improve the diagnostic efficacy. The differentiation degree, carbohydrate antigen (CA) 199, CA724, CA242, and multiple CT findings based on LAP differed significantly between T1–3 and T4 GCs in the primary cohort (all P < 0.05). Multivariate models based on regression analysis and random forest achieved AUCs of 0.849 and 0.865 in the primary cohort, respectively. We developed and validated multivariate models integrating endoscopic biopsy, tumor markers, CT morphological characteristics, and CT value-related and texture parameters to predict serosal invasion in GCs and achieved favorable performance.

中文翻译：

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胃癌浆膜浸润的预测：基于晚期动脉期 CT 图像整合术前临床病理特征和影像学发现的多变量模型的开发和验证

开发和验证基于动脉晚期 (LAP) 的内窥镜活检、肿瘤标志物和 CT 结果的多变量模型，以预测胃癌 (GC) 的浆膜浸润。回顾性分析154例GC患者术前分化程度、肿瘤标志物、CT形态特征、CT值相关和纹理参数。执行基于回归分析和机器学习算法的多变量模型以提高诊断效率。初级队列中 T1-3 和 T4 GCs 的分化程度、碳水化合物抗原 (CA) 199、CA724、CA242 和基于 LAP 的多次 CT 结果差异显着（均 P < 0.05）。基于回归分析和随机森林的多变量模型在主要队列中分别实现了 0.849 和 0.865 的 AUC。