Literature DB >> 26271201

Validation of a gastric cancer nomogram using a cancer registry.

Awais Ashfaq1, John T Kidwell1, Lee J McGhan2, Amylou C Dueck3, Barbara A Pockaj1, Richard J Gray1, Sanjay P Bagaria4, Nabil Wasif1.   

Abstract

BACKGROUND: A Memorial Sloan Kettering (MSKCC) nomogram predicts disease specific survival (DSS) for gastric adenocarcinoma. The goal of this study is to use a cancer registry to compare nomogram predicted survival with actual survival in the general population.
METHODS: All patients undergoing surgery for gastric adenocarcinoma from the Surveillance, Epidemiology, and End Results (SEER) database (1988-2012) were studied.
RESULTS: 6954 patients were identified. Majority of cancers were in the antrum (30.2%), and had intestinal histology (73.7%). Median follow-up was 8.2 years. Five year DSS for nomogram risk groups (0-25%, 26-50%, 51-75%, and 76-100%) was 23%, 48%, 57%, and 81% respectively. Actual DSS was 7-15% lower than nomogram predicted DSS. Relative to patients in the 76-100% 5-year DSS risk group, patients in the 0-25%, 26-50%, and 51-75% groups had significantly higher risks of death with hazard ratios of 6.84 (95%CI 6.12-7.65), 3.30 (95%CI 2.83-3.86), and 2.64 (95%CI 2.30-3.03), respectively (all P < 0.001). The concordance index for 5-year nomogram predicted DSS was 0.68 (95%CI 0.67-0.69).
CONCLUSIONS: The MSKCC gastric cancer nomogram over-estimates DSS from gastric cancer in the general population and has a moderate concordance index. Predictive tools generated at specialized institutions may not perform as well in the general population.
© 2015 Wiley Periodicals, Inc.

Entities:  

Keywords:  SEER program; gastric cancer; nomograms; prognosis; registries; validation studies

Mesh:

Year:  2015        PMID: 26271201     DOI: 10.1002/jso.23999

Source DB:  PubMed          Journal:  J Surg Oncol        ISSN: 0022-4790            Impact factor:   3.454


  6 in total

1.  Evaluating TNM stage prognostic ability in a population-based cohort of gastric adenocarcinoma patients in a low-incidence country.

Authors:  Alyson L Mahar; Brandon Zagorski; Daniel Kagedan; Matthew Dixon; Abraham El-Sedfy; Jovanka Vasilevska-Ristovska; Daniela Cortinovis; Corwyn Rowsell; Calvin Law; Lucy Helyer; Lawrence Paszat; Natalie Coburn
Journal:  Can J Public Health       Date:  2018-08-08

2.  Establishment and validation of prognostic nomograms in first-line metastatic gastric cancer patients.

Authors:  Yukiya Narita; Shigenori Kadowaki; Isao Oze; Yosuke Kito; Takeshi Kawakami; Nozomu Machida; Hiroya Taniguchi; Takashi Ura; Masashi Ando; Seiji Ito; Masahiro Tajika; Yasushi Yatabe; Hirofumi Yasui; Kei Muro
Journal:  J Gastrointest Oncol       Date:  2018-02

Review 3.  Prediction models for patients with esophageal or gastric cancer: A systematic review and meta-analysis.

Authors:  H G van den Boorn; E G Engelhardt; J van Kleef; M A G Sprangers; M G H van Oijen; A Abu-Hanna; A H Zwinderman; V M H Coupé; H W M van Laarhoven
Journal:  PLoS One       Date:  2018-02-08       Impact factor: 3.240

4.  A nomogram to predict prognosis for gastric cancer with peritoneal dissemination.

Authors:  Shi Chen; Xijie Chen; Runcong Nie; Liying Ou Yang; Aihong Liu; Yuanfang Li; Zhiwei Zhou; Yingbo Chen; Junsheng Peng
Journal:  Chin J Cancer Res       Date:  2018-08       Impact factor: 5.087

5.  A NADE nomogram to predict the probability of 6-month unfavorable outcome in Chinese patients with ischemic stroke.

Authors:  Chao Sun; Xiang Li; Baili Song; Xiangliang Chen; Linda Nyame; Yukai Liu; Dan Tang; Mako Ibrahim; Zheng Zhao; Chao Liu; Miao Yan; Xiding Pan; Jie Yang; Junshan Zhou; Jianjun Zou
Journal:  BMC Neurol       Date:  2019-11-07       Impact factor: 2.474

6.  Development and validation of an artificial neural network prognostic model after gastrectomy for gastric carcinoma: An international multicenter cohort study.

Authors:  Ziyu Li; Xiaolong Wu; Xiangyu Gao; Fei Shan; Xiangji Ying; Yan Zhang; Jiafu Ji
Journal:  Cancer Med       Date:  2020-07-15       Impact factor: 4.452

  6 in total

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