Dataset Information

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

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.

SUBMITTER: Chen QY

PROVIDER: S-EPMC6481035 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Chen Qi-Yue QY Zhong Qing Q Zhou Jun-Feng JF Qiu Xian-Tu XT Dang Xue-Yi XY Cai Li-Sheng LS Su Guo-Qiang GQ Xu Dong-Bo DB Liu Zhi-Yu ZY Li Ping P Guo Kai-Qing KQ Xie Jian-Wei JW Chen Qiu-Xian QX Wang Jia-Bin JB Li Teng-Wen TW Lin Jian-Xian JX Lin Shuang-Ming SM Lu Jun J Cao Long-Long LL Lin Mi M Tu Ru-Hong RH Huang Ze-Ning ZN Lin Ju-Li JL Lin Wei W He Qing-Liang QL Zheng Chao-Hui CH Huang Chang-Ming CM

Journal of oncology 20190410

<h4>Background</h4>Remnant gastric cancer (RGC) is a rare malignant tumor with poor prognosis. There is no universally accepted prognostic model for RGC.<h4>Methods</h4>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 ...[more]

PMID: 31093283

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Dataset Information

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

Background

Methods

Results

Conclusions

Publications

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

Similar Datasets

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