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SOURCE: A registry-based prediction model for overall survival in patients with metastatic oesophageal or gastric cancer

  • Héctor G. van den Boorn
  • , Ameen Abu-Hanna
  • , Emil Ter Veer
  • , Jessy Joy van Kleef
  • , Florian Lordick
  • , Michael Stahl
  • , Jaffer A. Ajani
  • , Rosine Guimbaud
  • , Se Hoon Park
  • , Susan J. Dutton
  • , Yung Jue Bang
  • , Narikazu Boku
  • , Nadia Haj Mohammad
  • , Mirjam A.G. Sprangers
  • , Rob H.A. Verhoeven
  • , Aeilko H. Zwinderman
  • , Martijn G.H. van Oijen
  • , Hanneke W.M. van Laarhoven

Research output: Contribution to journalArticlepeer-review

Abstract

�Prediction models are only sparsely available for metastatic oesophagogastric cancer. Because treatment in this setting is often preference-based, decision-making with the aid of a prediction model is wanted. The aim of this study is to construct a prediction model, called SOURCE, for the overall survival in patients with metastatic oesophagogastric cancer. Data from patients with metastatic oesophageal (n = 8010) or gastric (n = 4763) cancer diagnosed during 2005– 2015 were retrieved from the nationwide Netherlands cancer registry. A multivariate Cox regression model was created to predict overall survival for various treatments. Predictor selection was performed via the Akaike Information Criterion and a Delphi consensus among experts in palliative oesophagogastric cancer. Validation was performed according to a temporal internal-external scheme. The predictive quality was assessed with the concordance-index (c-index) and calibration. The model c-indices showed consistent discriminative ability during validation: 0.71 for oesophageal cancer and 0.68 for gastric cancer. The calibration showed an average slope of 1.0 and intercept of 0.0 for both tumour locations, indicating a close agreement between predicted and observed survival. With a fair c-index and good calibration, SOURCE provides a solid foundation for further investigation in clinical practice to determine its added value in shared decision making.

Original languageEnglish
Article number187
JournalCancers
Volume11
Issue number2
DOIs
StatePublished - Feb 2019
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Cox regression
  • Delphi consensus
  • Gastric cancer
  • Metastasis
  • Oesophageal cancer
  • Prediction model

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