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J Oncol. 2019 Apr 10;2019:6012826. doi: 10.1155/2019/6012826. eCollection 2019.

Development and External Validation of Web-Based Models to Predict the Prognosis of Remnant Gastric Cancer after Surgery: A Multicenter Study.

Chen QY1,2, Zhong Q1,2, Zhou JF3, Qiu XT4, Dang XY5, Cai LS6, Su GQ7, Xu DB8, Liu ZY1,2, Li P1,2, Guo KQ5, Xie JW1,2, Chen QX6, Wang JB1,2, Li TW7, Lin JX1,2, Lin SM8, Lu J1,2, Cao LL1,2, Lin M1,2, Tu RH1,2, Huang ZN1,2, Lin JL1,2, Lin W4, He QL3, Zheng CH1,2, Huang CM1,2.

Author information

1
Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, China.
2
Key Laboratory of Ministry of Education of Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China.
3
Department of Gastrointestinal Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.
4
Department of Gastrointestinal Surgery and Gastrointestinal Surgery Research Institute, The Affiliated Hospital of Putian University, Putian, China.
5
Department of General Surgery, Shanxi Provincial Cancer Hospital, Shanxi, China.
6
Department of General Surgery Unit 4, Zhangzhou Affiliated Hospital of Fujian Medical University, Zhangzhou, China.
7
Department of Gastrointestinal Surgery, The First Affiliated Hospital of Xiamen University, Xiamen, China.
8
Department of Gastrointestinal Surgery, Longyan First Hospital Affiliated to Fujian Medical University, Longyan, China.

Abstract

Background:

Remnant gastric cancer (RGC) is a rare malignant tumor with poor prognosis. There is no universally accepted prognostic model for RGC.

Methods:

We analyzed data for 253 RGC patients who underwent radical gastrectomy from 6 centers. The prognosis prediction performances of the AJCC7th and AJCC8th TNM staging systems and the TRM staging system for RGC patients were evaluated. Web-based prediction models based on independent prognostic factors were developed to predict the survival of the RGC patients. External validation was performed using a cohort of 49 Chinese patients.

Results:

The predictive abilities of the AJCC8th and TRM staging systems were no better than those of the AJCC7th staging system (c-index: AJCC7th vs. AJCC8th vs. TRM, 0.743 vs. 0.732 vs. 0.744; P>0.05). Within each staging system, the survival of the two adjacent stages was not well discriminated (P>0.05). Multivariate analysis showed that age, tumor size, T stage, and N stage were independent prognostic factors. Based on the above variables, we developed 3 web-based prediction models, which were superior to the AJCC7th staging system in their discriminatory ability (c-index), predictive homogeneity (likelihood ratio chi-square), predictive accuracy (AIC, BIC), and model stability (time-dependent ROC curves). External validation showed predictable accuracies of 0.780, 0.822, and 0.700, respectively, in predicting overall survival, disease-specific survival, and disease-free survival.

Conclusions:

The AJCC TNM staging system and the TRM staging system did not enable good distinction among the RGC patients. We have developed and validated visual web-based prediction models that are superior to these staging systems.

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