Analysis and external validation of a nomogram to predict peritoneal dissemination in gastric cancer

对预测胃癌腹膜播散的列线图进行分析和外部验证

阅读:1

Abstract

OBJECTIVE: Peritoneal dissemination is difficult to diagnose by conventional imaging technologies. We aimed to construct a nomogram to predict peritoneal dissemination in gastric cancer (GC) patients. METHODS: We retrospectively analyzed 1,112 GC patients in Sun Yat-sen University Cancer Center between 2001 and 2010 as the development set and 474 patients from The Sixth Affiliated Hospital, Sun Yat-sen University between 2010 and 2016 as the validation set. The clinicopathological variables associated with gastric cancer with peritoneal dissemination (GCPD) were analyzed. We used logistic regression analysis to identify independent risk factors for peritoneal dissemination. Then, we constructed a nomogram for the prediction of GCPD and defined its predictive value with a receiver operating characteristic (ROC) curve. External validation was performed to validate the applicability of the nomogram. RESULTS: In total, 250 patients were histologically identified as having peritoneal dissemination. Logistic regression analysis demonstrated that age, sex, tumor location, tumor size, signet-ring cell carcinoma (SRCC), T stage, N stage and Borrmann classification IV (Borrmann IV) were independent risk factors for peritoneal dissemination. We constructed a nomogram consisting of these eight factors to predict GCPD and found an optimistic predictive capability, with a C-index of 0.791, an area under the curve (AUC) of 0.791, and a 95% confidence interval (95% CI) of 0.762-0.820. The results found in the external validation set were also promising. CONCLUSIONS: We constructed a highly sensitive nomogram that can assist clinicians in the early diagnosis of GCPD and serve as a reference for optimizing clinical management strategies.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。