Li Zhemin, Guan Guangmin, Liu Zining, Li Jiazheng, Ying Xiangji, Shan Fei, Li Ziyu
Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Gastrointestinal Cancer Center, Peking University Cancer Hospital & Institute, Beijing, China.
Front Surg. 2022 Jul 21;9:916001. doi: 10.3389/fsurg.2022.916001. eCollection 2022.
Peritoneal carcinomatosis (PC) of gastric cancer indicates a poor outcome and is mainly diagnosed by staging laparoscopy (SL). This study was designed to develop a risk stratification model based on the number of risk factors to exempt low-risk patients from unnecessary SL.
This was a retrospective cohort study based on a single institution between January 2015 and December 2019. SL is indicated for patients of advanced locoregional stage, and clinicopathologic characteristics of 535 consecutive patients were included. PC-associated variables were identified by logistic regression analysis. A risk stratification model based on the number of risk factors was constructed, and we defined its predictive value with a receiver operating characteristic (ROC) curve and negative predictive value.
In total, 15.9% of included patients were found to have PC during SL. Borrmann type IV, elevated CA125, and tumour diameter ≥5 cm were independent risk factors of PC. These three factors combined with cT4 were selected as predictive factors, and the number of predictive variables was significantly related to the possibility of PC (2.0%, 12.8%, 20.0%, 54.2%, and 100%, respectively). When the cutoff value is more than one predictive factor, the negative predictive value is 98.0%, with an area under the curve of 0.780. This model could exempt 29.8% of unnecessary SL compared to the indication of the current NCCN guideline.
We constructed a simple model to predict the probability of PC using the number of predictive factors. It is recommended that patients without any of these factors should be exempt from SL.
胃癌腹膜种植转移(PC)提示预后不良,主要通过分期腹腔镜检查(SL)进行诊断。本研究旨在基于危险因素数量建立一个风险分层模型,以使低风险患者免于不必要的SL。
这是一项基于单一机构在2015年1月至2019年12月期间进行的回顾性队列研究。SL适用于局部晚期患者,纳入了535例连续患者的临床病理特征。通过逻辑回归分析确定与PC相关的变量。构建了一个基于危险因素数量的风险分层模型,并通过受试者工作特征(ROC)曲线和阴性预测值定义其预测价值。
总共15.9%的纳入患者在SL期间被发现有PC。Borrmann IV型、CA125升高和肿瘤直径≥5 cm是PC的独立危险因素。这三个因素与cT4相结合被选为预测因素,预测变量的数量与PC的可能性显著相关(分别为2.0%、12.8%、20.0%、54.2%和100%)。当临界值超过一个预测因素时,阴性预测值为98.0%,曲线下面积为0.780。与当前NCCN指南的适应证相比,该模型可使29.8%的不必要SL得以豁免。
我们构建了一个简单的模型,通过预测因素的数量来预测PC的可能性。建议没有这些因素的患者免于SL。