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find Author "LIU Pengdi" 1 results
  • Prognostic Nomogram for gastric adenocarcinoma: a SEER database-based study

    Objective Establishing Nomogram to predict the overall survival (OS) rate of patients with gastric adenocarcinoma by utilizing the database of the Surveillance, Epidemiology, and End Results (SEER) Program. Methods Obtained the data of 3 272 gastric adenocarcinoma patients who were diagnosed between 2004 and 2014 from the SEER database. These patients were randomly divided into training (n=2 182) and validation (n=1 090) cohorts. The Cox proportional hazards regression model was performed to evaluate the prognostic effects of multiple clinicopathologic factors on OS. Significant prognostic factors were combined to build Nomogram. The predictive performance of Nomogram was evaluated via internal (training cohort data) and external validation (validation cohort data) by calculating index of concordance (C-index) and plotting calibration curves. Results In the training cohort, the results of Cox proportional hazards regression model showed that, age at diagnosis, race, grade, 6th American Joint Committee on Cancer (AJCC) stage, histologic type, and surgery were significantly associated with the survival prognosis (P<0.05). These factors were used to establish Nomogram. The Nomograms showed good accuracy in predicting OS rate, with C-index of 0.751 [95%CI was (0.738, 0.764)] in internal validation and C-index of 0.753 [95% CI was (0.734, 0.772)] in external validation. All calibration curves showed excellent consistency between prediction by Nomogram and actual observation. Conclusion Novel Nomogram for patients with gastric adenocarcinoma was established to predict OS in our study has good prognostic significance, it can provide clinicians with more accurate and practical predictive tools which can quickly and accurately assess the patients’ survival prognosis individually, and can better guiding clinicians in the follow-up treatment of patients.

    Release date:2018-10-11 02:52 Export PDF Favorites Scan
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