A novel prediction model of prognosis after gastrectomy for gastric carcinoma

Yanghee Woo, Taeil Son, Kijun Song, Naoki Okumura, Yanfeng Hu, Gyu Seok Cho, Jong Won Kim, Seung Ho Choi, Sung Hoon Noh, Woo Jin Hyung

Research output: Contribution to journalArticle

17 Citations (Scopus)

Abstract

Objective: The prognoses of gastric cancer patients vary greatly among countries. Meanwhile, tumor-node-metastasis (TNM) staging system shows limited accuracy in predicting patient-specific survival for gastric cancer. The objective of this study was to create a simple, yet universally applicable survival prediction model for surgically treated gastric cancer patients. Summary Background Data: A prediction model of 5-year overall survival for surgically treated gastric cancer patients regardless of curability was developed using a test data set of 11,851 consecutive patients. Methods: The model's coefficients were selected based on univariate and multivariate analysis of patient, tumor, and surgical factors shown to significantly impact survival using a Cox proportional hazards model. For internal validation, discrimination was calculated with the concordance index (C-statistic) using the bootstrap method and calibration assessed. The model was externally validated using 4 data sets from 3 countries. Results: Our model's C-statistic (0.824) showed better discrimination power than current tumor-node-metastasis staging (0.788) (P < 0.0001). Bootstrap internal validation demonstrated that coefficients remained largely unchanged between iterations, with an average C-statistic of 0.822. The model calibration was accurate in predicting 5-year survival. In the external validation, C-statistics showed good discrimination (range: 0.798-0.868) in patient data sets from 4 participating institutions in 3 different countries. Conclusions: Utilizing clinically practical patient, tumor, and surgical information, we developed a universally applicable prediction model for accurately determining the 5-year overall survival of gastric cancer patients after gastrectomy. Our predictive model was also valid in patients who underwent noncurative resection or inadequate lymphadenectomy.

Original languageEnglish
Pages (from-to)114-120
Number of pages7
JournalAnnals of surgery
Volume264
Issue number1
DOIs
Publication statusPublished - 2016 Jul 1

Fingerprint

Gastrectomy
Stomach
Carcinoma
Stomach Neoplasms
Survival
Calibration
Neoplasms
Neoplasm Metastasis
Lymph Node Excision
Proportional Hazards Models
Multivariate Analysis

All Science Journal Classification (ASJC) codes

  • Surgery

Cite this

Woo, Yanghee ; Son, Taeil ; Song, Kijun ; Okumura, Naoki ; Hu, Yanfeng ; Cho, Gyu Seok ; Kim, Jong Won ; Choi, Seung Ho ; Noh, Sung Hoon ; Hyung, Woo Jin. / A novel prediction model of prognosis after gastrectomy for gastric carcinoma. In: Annals of surgery. 2016 ; Vol. 264, No. 1. pp. 114-120.
@article{b79344289c114562af2e91279a7b4c1d,
title = "A novel prediction model of prognosis after gastrectomy for gastric carcinoma",
abstract = "Objective: The prognoses of gastric cancer patients vary greatly among countries. Meanwhile, tumor-node-metastasis (TNM) staging system shows limited accuracy in predicting patient-specific survival for gastric cancer. The objective of this study was to create a simple, yet universally applicable survival prediction model for surgically treated gastric cancer patients. Summary Background Data: A prediction model of 5-year overall survival for surgically treated gastric cancer patients regardless of curability was developed using a test data set of 11,851 consecutive patients. Methods: The model's coefficients were selected based on univariate and multivariate analysis of patient, tumor, and surgical factors shown to significantly impact survival using a Cox proportional hazards model. For internal validation, discrimination was calculated with the concordance index (C-statistic) using the bootstrap method and calibration assessed. The model was externally validated using 4 data sets from 3 countries. Results: Our model's C-statistic (0.824) showed better discrimination power than current tumor-node-metastasis staging (0.788) (P < 0.0001). Bootstrap internal validation demonstrated that coefficients remained largely unchanged between iterations, with an average C-statistic of 0.822. The model calibration was accurate in predicting 5-year survival. In the external validation, C-statistics showed good discrimination (range: 0.798-0.868) in patient data sets from 4 participating institutions in 3 different countries. Conclusions: Utilizing clinically practical patient, tumor, and surgical information, we developed a universally applicable prediction model for accurately determining the 5-year overall survival of gastric cancer patients after gastrectomy. Our predictive model was also valid in patients who underwent noncurative resection or inadequate lymphadenectomy.",
author = "Yanghee Woo and Taeil Son and Kijun Song and Naoki Okumura and Yanfeng Hu and Cho, {Gyu Seok} and Kim, {Jong Won} and Choi, {Seung Ho} and Noh, {Sung Hoon} and Hyung, {Woo Jin}",
year = "2016",
month = "7",
day = "1",
doi = "10.1097/SLA.0000000000001523",
language = "English",
volume = "264",
pages = "114--120",
journal = "Annals of Surgery",
issn = "0003-4932",
publisher = "Lippincott Williams and Wilkins",
number = "1",

}

Woo, Y, Son, T, Song, K, Okumura, N, Hu, Y, Cho, GS, Kim, JW, Choi, SH, Noh, SH & Hyung, WJ 2016, 'A novel prediction model of prognosis after gastrectomy for gastric carcinoma', Annals of surgery, vol. 264, no. 1, pp. 114-120. https://doi.org/10.1097/SLA.0000000000001523

A novel prediction model of prognosis after gastrectomy for gastric carcinoma. / Woo, Yanghee; Son, Taeil; Song, Kijun; Okumura, Naoki; Hu, Yanfeng; Cho, Gyu Seok; Kim, Jong Won; Choi, Seung Ho; Noh, Sung Hoon; Hyung, Woo Jin.

In: Annals of surgery, Vol. 264, No. 1, 01.07.2016, p. 114-120.

Research output: Contribution to journalArticle

TY - JOUR

T1 - A novel prediction model of prognosis after gastrectomy for gastric carcinoma

AU - Woo, Yanghee

AU - Son, Taeil

AU - Song, Kijun

AU - Okumura, Naoki

AU - Hu, Yanfeng

AU - Cho, Gyu Seok

AU - Kim, Jong Won

AU - Choi, Seung Ho

AU - Noh, Sung Hoon

AU - Hyung, Woo Jin

PY - 2016/7/1

Y1 - 2016/7/1

N2 - Objective: The prognoses of gastric cancer patients vary greatly among countries. Meanwhile, tumor-node-metastasis (TNM) staging system shows limited accuracy in predicting patient-specific survival for gastric cancer. The objective of this study was to create a simple, yet universally applicable survival prediction model for surgically treated gastric cancer patients. Summary Background Data: A prediction model of 5-year overall survival for surgically treated gastric cancer patients regardless of curability was developed using a test data set of 11,851 consecutive patients. Methods: The model's coefficients were selected based on univariate and multivariate analysis of patient, tumor, and surgical factors shown to significantly impact survival using a Cox proportional hazards model. For internal validation, discrimination was calculated with the concordance index (C-statistic) using the bootstrap method and calibration assessed. The model was externally validated using 4 data sets from 3 countries. Results: Our model's C-statistic (0.824) showed better discrimination power than current tumor-node-metastasis staging (0.788) (P < 0.0001). Bootstrap internal validation demonstrated that coefficients remained largely unchanged between iterations, with an average C-statistic of 0.822. The model calibration was accurate in predicting 5-year survival. In the external validation, C-statistics showed good discrimination (range: 0.798-0.868) in patient data sets from 4 participating institutions in 3 different countries. Conclusions: Utilizing clinically practical patient, tumor, and surgical information, we developed a universally applicable prediction model for accurately determining the 5-year overall survival of gastric cancer patients after gastrectomy. Our predictive model was also valid in patients who underwent noncurative resection or inadequate lymphadenectomy.

AB - Objective: The prognoses of gastric cancer patients vary greatly among countries. Meanwhile, tumor-node-metastasis (TNM) staging system shows limited accuracy in predicting patient-specific survival for gastric cancer. The objective of this study was to create a simple, yet universally applicable survival prediction model for surgically treated gastric cancer patients. Summary Background Data: A prediction model of 5-year overall survival for surgically treated gastric cancer patients regardless of curability was developed using a test data set of 11,851 consecutive patients. Methods: The model's coefficients were selected based on univariate and multivariate analysis of patient, tumor, and surgical factors shown to significantly impact survival using a Cox proportional hazards model. For internal validation, discrimination was calculated with the concordance index (C-statistic) using the bootstrap method and calibration assessed. The model was externally validated using 4 data sets from 3 countries. Results: Our model's C-statistic (0.824) showed better discrimination power than current tumor-node-metastasis staging (0.788) (P < 0.0001). Bootstrap internal validation demonstrated that coefficients remained largely unchanged between iterations, with an average C-statistic of 0.822. The model calibration was accurate in predicting 5-year survival. In the external validation, C-statistics showed good discrimination (range: 0.798-0.868) in patient data sets from 4 participating institutions in 3 different countries. Conclusions: Utilizing clinically practical patient, tumor, and surgical information, we developed a universally applicable prediction model for accurately determining the 5-year overall survival of gastric cancer patients after gastrectomy. Our predictive model was also valid in patients who underwent noncurative resection or inadequate lymphadenectomy.

UR - http://www.scopus.com/inward/record.url?scp=84960157536&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84960157536&partnerID=8YFLogxK

U2 - 10.1097/SLA.0000000000001523

DO - 10.1097/SLA.0000000000001523

M3 - Article

C2 - 26945155

AN - SCOPUS:84960157536

VL - 264

SP - 114

EP - 120

JO - Annals of Surgery

JF - Annals of Surgery

SN - 0003-4932

IS - 1

ER -