She Yingxia, Liu Xianwang, Liu Hong, Yang Haiting, Zhang Wenjuan, Han Yinping, Zhou Junlin
Radiology of Department, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, People's Republic of China.
Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China.
Abdom Radiol (NY). 2024 Oct;49(10):3438-3449. doi: 10.1007/s00261-024-04346-0. Epub 2024 May 15.
This study aimed to determine the diagnostic efficacy of various indicators and models for the prediction of gastric cancer with liver metastasis.
Clinical and spectral computed tomography (CT) data from 80 patients with gastric adenocarcinoma who underwent surgical resection were retrospectively analyzed. Patients were divided into metastatic and non-metastatic groups based on whether or not to occur liver metastasis, and the region of interest (ROI) was measured manually on each phase iodine map at the largest level of the tumor. Iodine concentration (IC), normalized iodine concentration (nIC), and clinical data of the primary gastric lesions were analyzed. Logistic regression analysis was used to construct the clinical indicator (CI) and clinical indicator-spectral CT iodine concentration (CI-Spectral CT-IC) Models, which contained all of the parameters with statistically significant differences between the groups. Receiver operating characteristic (ROC) curves were constructed to evaluate the accuracy of the models.
The metastatic group showed significantly higher levels of Cancer antigen125 (CA125), carcinoembryonic antigen (CEA), IC, and nIC in the arterial phase, venous phase, and delayed phase than the non-metastatic group (all p < 0.05). Normalized iodine concentration Venous Phase (nICVP) exhibited a favorable performance among all IC and nIC parameters for forecasting gastric cancer with liver metastasis (area under the curve (AUC), 0.846). The combination model of clinical data with significant differences and nICVP showed the best diagnostic accuracy for predicting liver metastasis from gastric cancer, with an AUC of 0.897.
nICVP showed the best diagnostic efficacy for predicting gastric cancer with liver metastasis. Clinical Indicators-normalized ICVP model can improve the prediction accuracy for this condition.
本研究旨在确定各种指标和模型对预测胃癌肝转移的诊断效能。
回顾性分析80例行手术切除的胃腺癌患者的临床及光谱计算机断层扫描（CT）数据。根据是否发生肝转移将患者分为转移组和非转移组，并在肿瘤最大层面的各期碘图上手动测量感兴趣区（ROI）。分析原发性胃病变的碘浓度（IC）、标准化碘浓度（nIC）及临床数据。采用逻辑回归分析构建临床指标（CI）和临床指标-光谱CT碘浓度（CI-光谱CT-IC）模型，模型包含组间具有统计学显著差异的所有参数。绘制受试者工作特征（ROC）曲线以评估模型的准确性。
转移组在动脉期、静脉期和延迟期的癌胚抗原125（CA125）、癌胚抗原（CEA）、IC及nIC水平均显著高于非转移组（均P<0.05）。在所有IC和nIC参数中，标准化碘浓度静脉期（nICVP）对预测胃癌肝转移表现出良好性能（曲线下面积[AUC]，0.846）。具有显著差异的临床数据与nICVP的联合模型对预测胃癌肝转移显示出最佳诊断准确性，AUC为0.897。
nICVP对预测胃癌肝转移显示出最佳诊断效能。临床指标-标准化ICVP模型可提高对此种情况的预测准确性。
