Refining prognosis in lung cancer: A report on the quality and relevance of clinical prognostic tools

Alyson L. Mahar, Carolyn Compton, Lisa M. Mcshane, Susan Halabi, Hisao Asamura, Ramon Rami-Porta, Patti A. Groome

Research output: Contribution to journalArticlepeer-review

30 Scopus citations


Introduction: Accurate, individualized prognostication for lung cancer patients requires the integration of standard patient and pathologic factors, biological, genetic, and other molecular characteristics of the tumor. Clinical prognostic tools aim to aggregate information on an individual patient to predict disease outcomes such as overall survival, but little is known about their clinical utility and accuracy in lung cancer. Methods: A systematic search of the scientific literature for clinical prognostic tools in lung cancer published from January 1, 1996 to January 27, 2015 was performed. In addition, web-based resources were searched. A priori criteria determined by the Molecular Modellers Working Group of the American Joint Committee on Cancer were used to investigate the quality and usefulness of tools. Criteria included clinical presentation, model development approaches, validation strategies, and performance metrics. Results: Thirty-two prognostic tools were identified. Patients with metastases were the most frequently considered population in non-small-cell lung cancer. All tools for small-cell lung cancer covered that entire patient population. Included prognostic factors varied considerably across tools. Internal validity was not formally evaluated for most tools and only 11 were evaluated for external validity. Two key considerations were highlighted for tool development: identification of an explicit purpose related to a relevant clinical population and clear decision points and prioritized inclusion of established prognostic factors over emerging factors. Conclusions: Prognostic tools will contribute more meaningfully to the practice of personalized medicine if better study design and analysis approaches are used in their development and validation.

Original languageEnglish (US)
Pages (from-to)1576-1589
Number of pages14
JournalJournal of Thoracic Oncology
Issue number11
StatePublished - Nov 1 2015


  • Clinical prediction tools
  • Lung cancer
  • Prediction models
  • Prognosis
  • Prognostic model

ASJC Scopus subject areas

  • Oncology
  • Pulmonary and Respiratory Medicine


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