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Prediction of serosal invasion in gastric cancer: development and validation of multivariate models integrating preoperative clinicopathological features and radiographic findings based on late arterial phase CT images

机译:胃癌血栓侵袭预测:基于晚动阶段CT图像的术前临床病理特征和放射线摄像性研究的多元模型的开发与验证

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摘要

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.
机译:发展和验证基于晚动阶段(LAP)的内窥镜活组织检查,肿瘤标志物和CT结果的多变量模型,以预测胃癌(GC)的血液侵袭。回顾性地分析了154例GC患者的术前分化度,肿瘤标志物,CT形态特征和CT值相关和纹理参数。基于回归分析和机器学习算法的多变量模型进行了提高诊断效能。基于圈子的分化度,碳水化合物抗原(CA)199,Ca724,Ca242和初级队列中的T1-3和T4 GCS之间的多CT结果不同(所有p≤0.05)。基于回归分析和随机森林的多变量模型分别在主要队列中实现了0.849和0.865的AUC。我们开发和验证了多元模型,整合内窥镜活组织检查,肿瘤标志物,CT形态特征,以及CT值相关和纹理参数,以预测GCS中的血液侵袭并取得了有利的性能。

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