title: Refining Prognosis in Lung Cancer: A Report on the Quality and Relevance of Clinical Prognostic Tools.
abstract: BACKGROUND
  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.
authors: *Division of Cancer Care and Epidemiology, Cancer Research Institute, Queen's University, ON; Canada; †Arizona State University, Phoenix, Arizona; ‡Laboratory Medicine and Pathology, Mayo Clinic School of Medicine, Rochester, Minnesota; §Biometric Research Branch, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, Maryland; ‖Department of Biostatistics and Bioinformatics, Duke University and Alliance Statistics and Data Center, Durham, North Carolina; ¶Division of Thoracic Surgery, National Cancer Center Hospital, Tokyo, Japan; and #Department of Thoracic Surgery, Hospital Universitari Mutua Terrassa, Barcelona, Spain.
authors: Alyson L
authors: AL
authors: Mahar
authors: Carolyn
authors: C
authors: Compton
authors: Lisa M
authors: LM
authors: McShane
authors: Susan
authors: S
authors: Halabi
authors: Hisao
authors: H
authors: Asamura
authors: Ramon
authors: R
authors: Rami-Porta
authors: Patti A
authors: PA
authors: Groome
mesh_terms: D006801:Humans; D008175:Lung Neoplasms / Q000401:mortality*; D011379:Prognosis; D012107:Research Design; D016019:Survival Analysis