A novel prognosis prediction model after completion gastrectomy for remnant gastric cancer

Development and validation using international multicenter databases

Jun Lu, Zhi Fang Zheng, Jun Feng Zhou, Bin Bin Xu, Chao Hui Zheng, Ping Li, Jian Wei Xie, Jia Bin Wang, Jian Xian Lin, Qi Yue Chen, Mark Truty, Qing Liang He, Chang Ming Huang

Research output: Contribution to journalArticle

Abstract

Background: Examined lymph node counts of remnant gastric cancer patients are often insufficient, and the prognostic ability of tumor-node-metastasis staging is therefore limited. This study aimed to create a simple and universally applicable prediction model for RGC patients after completion of gastrectomy. Methods: A 5-year overall survival prediction model for remnant gastric cancer patients was developed using a test dataset of 148 consecutive patients. Model coefficients were obtained based on the Cox analysis of clinicopathological factors. Prognostic performance was assessed with the concordance index (C-index) and decision curve analysis. For internal validation, the bootstrap method and calibration assessment were used. The model was validated using 2 external cohorts from China (First Affiliated Hospital of Fujian Medical University, n = 46) and the United States (Mayo Clinic, n = 20). Results: Depth of tumor invasion, number of metastatic lymph nodes, distant metastasis, and operative time were independent prognostic factors. Our model's C-index (0.761) showed better discriminatory power than that of the eighth tumor-node-metastasis staging system (0.714, P =.001). The model calibration was accurate at predicting 5-year survival. Decision curve analysis showed that the model had a greater benefit, and the results were also confirmed by bootstrap internal validation. In external validation, the C-index and decision curve analysis showed good prognostic performances in patient datasets from 2 participating institutions. Moreover, we verified the reliability of the model in an analysis of patients with different examined lymph node counts (>15 or ≤15). Conclusion: Utilizing clinically practical information, we developed a universally applicable prediction model for accurately determining the 5-year overall survival of remnant gastric cancer patients after completion of gastrectomy. Our predictive model outperformed tumor-node-metastasis staging in diverse international datasets regardless of examined lymph node counts.

Original languageEnglish (US)
JournalSurgery (United States)
DOIs
StatePublished - Jan 1 2019

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Gastric Stump
Gastrectomy
Stomach Neoplasms
Databases
Decision Support Techniques
Lymph Nodes
Neoplasm Metastasis
Calibration
Neoplasms
Survival
Operative Time
Statistical Factor Analysis
China

ASJC Scopus subject areas

  • Surgery

Cite this

A novel prognosis prediction model after completion gastrectomy for remnant gastric cancer : Development and validation using international multicenter databases. / Lu, Jun; Zheng, Zhi Fang; Zhou, Jun Feng; Xu, Bin Bin; Zheng, Chao Hui; Li, Ping; Xie, Jian Wei; Wang, Jia Bin; Lin, Jian Xian; Chen, Qi Yue; Truty, Mark; He, Qing Liang; Huang, Chang Ming.

In: Surgery (United States), 01.01.2019.

Research output: Contribution to journalArticle

Lu, Jun ; Zheng, Zhi Fang ; Zhou, Jun Feng ; Xu, Bin Bin ; Zheng, Chao Hui ; Li, Ping ; Xie, Jian Wei ; Wang, Jia Bin ; Lin, Jian Xian ; Chen, Qi Yue ; Truty, Mark ; He, Qing Liang ; Huang, Chang Ming. / A novel prognosis prediction model after completion gastrectomy for remnant gastric cancer : Development and validation using international multicenter databases. In: Surgery (United States). 2019.
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abstract = "Background: Examined lymph node counts of remnant gastric cancer patients are often insufficient, and the prognostic ability of tumor-node-metastasis staging is therefore limited. This study aimed to create a simple and universally applicable prediction model for RGC patients after completion of gastrectomy. Methods: A 5-year overall survival prediction model for remnant gastric cancer patients was developed using a test dataset of 148 consecutive patients. Model coefficients were obtained based on the Cox analysis of clinicopathological factors. Prognostic performance was assessed with the concordance index (C-index) and decision curve analysis. For internal validation, the bootstrap method and calibration assessment were used. The model was validated using 2 external cohorts from China (First Affiliated Hospital of Fujian Medical University, n = 46) and the United States (Mayo Clinic, n = 20). Results: Depth of tumor invasion, number of metastatic lymph nodes, distant metastasis, and operative time were independent prognostic factors. Our model's C-index (0.761) showed better discriminatory power than that of the eighth tumor-node-metastasis staging system (0.714, P =.001). The model calibration was accurate at predicting 5-year survival. Decision curve analysis showed that the model had a greater benefit, and the results were also confirmed by bootstrap internal validation. In external validation, the C-index and decision curve analysis showed good prognostic performances in patient datasets from 2 participating institutions. Moreover, we verified the reliability of the model in an analysis of patients with different examined lymph node counts (>15 or ≤15). Conclusion: Utilizing clinically practical information, we developed a universally applicable prediction model for accurately determining the 5-year overall survival of remnant gastric cancer patients after completion of gastrectomy. Our predictive model outperformed tumor-node-metastasis staging in diverse international datasets regardless of examined lymph node counts.",
author = "Jun Lu and Zheng, {Zhi Fang} and Zhou, {Jun Feng} and Xu, {Bin Bin} and Zheng, {Chao Hui} and Ping Li and Xie, {Jian Wei} and Wang, {Jia Bin} and Lin, {Jian Xian} and Chen, {Qi Yue} and Mark Truty and He, {Qing Liang} and Huang, {Chang Ming}",
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T2 - Development and validation using international multicenter databases

AU - Lu, Jun

AU - Zheng, Zhi Fang

AU - Zhou, Jun Feng

AU - Xu, Bin Bin

AU - Zheng, Chao Hui

AU - Li, Ping

AU - Xie, Jian Wei

AU - Wang, Jia Bin

AU - Lin, Jian Xian

AU - Chen, Qi Yue

AU - Truty, Mark

AU - He, Qing Liang

AU - Huang, Chang Ming

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Y1 - 2019/1/1

N2 - Background: Examined lymph node counts of remnant gastric cancer patients are often insufficient, and the prognostic ability of tumor-node-metastasis staging is therefore limited. This study aimed to create a simple and universally applicable prediction model for RGC patients after completion of gastrectomy. Methods: A 5-year overall survival prediction model for remnant gastric cancer patients was developed using a test dataset of 148 consecutive patients. Model coefficients were obtained based on the Cox analysis of clinicopathological factors. Prognostic performance was assessed with the concordance index (C-index) and decision curve analysis. For internal validation, the bootstrap method and calibration assessment were used. The model was validated using 2 external cohorts from China (First Affiliated Hospital of Fujian Medical University, n = 46) and the United States (Mayo Clinic, n = 20). Results: Depth of tumor invasion, number of metastatic lymph nodes, distant metastasis, and operative time were independent prognostic factors. Our model's C-index (0.761) showed better discriminatory power than that of the eighth tumor-node-metastasis staging system (0.714, P =.001). The model calibration was accurate at predicting 5-year survival. Decision curve analysis showed that the model had a greater benefit, and the results were also confirmed by bootstrap internal validation. In external validation, the C-index and decision curve analysis showed good prognostic performances in patient datasets from 2 participating institutions. Moreover, we verified the reliability of the model in an analysis of patients with different examined lymph node counts (>15 or ≤15). Conclusion: Utilizing clinically practical information, we developed a universally applicable prediction model for accurately determining the 5-year overall survival of remnant gastric cancer patients after completion of gastrectomy. Our predictive model outperformed tumor-node-metastasis staging in diverse international datasets regardless of examined lymph node counts.

AB - Background: Examined lymph node counts of remnant gastric cancer patients are often insufficient, and the prognostic ability of tumor-node-metastasis staging is therefore limited. This study aimed to create a simple and universally applicable prediction model for RGC patients after completion of gastrectomy. Methods: A 5-year overall survival prediction model for remnant gastric cancer patients was developed using a test dataset of 148 consecutive patients. Model coefficients were obtained based on the Cox analysis of clinicopathological factors. Prognostic performance was assessed with the concordance index (C-index) and decision curve analysis. For internal validation, the bootstrap method and calibration assessment were used. The model was validated using 2 external cohorts from China (First Affiliated Hospital of Fujian Medical University, n = 46) and the United States (Mayo Clinic, n = 20). Results: Depth of tumor invasion, number of metastatic lymph nodes, distant metastasis, and operative time were independent prognostic factors. Our model's C-index (0.761) showed better discriminatory power than that of the eighth tumor-node-metastasis staging system (0.714, P =.001). The model calibration was accurate at predicting 5-year survival. Decision curve analysis showed that the model had a greater benefit, and the results were also confirmed by bootstrap internal validation. In external validation, the C-index and decision curve analysis showed good prognostic performances in patient datasets from 2 participating institutions. Moreover, we verified the reliability of the model in an analysis of patients with different examined lymph node counts (>15 or ≤15). Conclusion: Utilizing clinically practical information, we developed a universally applicable prediction model for accurately determining the 5-year overall survival of remnant gastric cancer patients after completion of gastrectomy. Our predictive model outperformed tumor-node-metastasis staging in diverse international datasets regardless of examined lymph node counts.

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