Western Validation of a Novel Gastric Cancer Prognosis Prediction Model in US Gastric Cancer Patients

Yanghee Woo, Bryan Goldner, Taeil Son, Kijun Song, Sung Hoon Noh, Yuman Fong, Woo Jin Hyung

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6 Citations (Scopus)

Abstract

Background: A novel prediction model for accurate determination of 5-year overall survival of gastric cancer patients was developed by an international collaborative group (G6+). This prediction model was created using a single institution's database of 11,851 Korean patients and included readily available and clinically relevant factors. Already validated using external East Asian cohorts, its applicability in the American population was yet to be determined. Study Design: Using the Surveillance, Epidemiology, and End Results (SEER) dataset, 2014 release, all patients diagnosed with gastric adenocarcinoma who underwent surgical resection between 2002 and 2012, were selected. Characteristics for analysis included: age, sex, depth of tumor invasion, number of positive lymph nodes, total lymph nodes retrieved, presence of distant metastasis, extent of resection, and histology. Concordance index (C-statistic) was assessed using the novel prediction model and compared with the prognostic index, the seventh edition of the TNM staging system. Results: Of the 26,019 gastric cancer patients identified from the SEER database, 15,483 had complete datasets. Validation of the novel prediction tool revealed a C-statistic of 0.762 (95% CI 0.754 to 0.769) compared with the seventh TNM staging model, C-statistic 0.683 (95% CI 0.677 to 0.689), (p < 0.001). Conclusions: Our study validates a novel prediction model for gastric cancer in the American patient population. Its superior prediction of the 5-year survival of gastric cancer patients in a large Western cohort strongly supports its global applicability. Importantly, this model allows for accurate prognosis for an increasing number of gastric cancer patients worldwide, including those who received inadequate lymphadenectomy or underwent a noncurative resection.

Original languageEnglish
Pages (from-to)252-258
Number of pages7
JournalJournal of the American College of Surgeons
Volume226
Issue number3
DOIs
Publication statusPublished - 2018 Mar

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All Science Journal Classification (ASJC) codes

  • Surgery

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