Improving early diagnosis of symptomatic cancer

Nat Rev Clin Oncol. 2016 Dec;13(12):740-749. doi: 10.1038/nrclinonc.2016.109. Epub 2016 Jul 26.

Abstract

Much time, effort and investment goes into the diagnosis of symptomatic cancer, with the expectation that this approach brings clinical benefits. This investment of resources has been particularly noticeable in the UK, which has, for several years, appeared near the bottom of international league tables for cancer survival in economically developed countries. In this Review, we examine expedited diagnosis of cancer from four perspectives. The first relates to the potential for clinical benefits of expedited diagnosis of symptomatic cancer. Limited evidence from clinical trials is available, but the considerable observational evidence suggests benefits can be obtained from this approach. The second perspective considers how expedited diagnosis can be achieved. We concentrate on data from the UK, where extensive awareness campaigns have been conducted, and initiatives in the primary-care setting, including clinical decision support, have all occurred during a period of considerable national policy change. The third section considers the most appropriate patients for cancer investigations, and the possible community settings for identification of such patients; UK national guidance for selection of patients for investigation is discussed. Finally, the health economics of expedited diagnosis are reviewed, although few studies provide definitive evidence on this topic.

Publication types

  • Review

MeSH terms

  • Costs and Cost Analysis
  • Decision Support Systems, Clinical / economics
  • Early Detection of Cancer / economics
  • Early Detection of Cancer / standards*
  • Health Policy
  • Health Promotion / economics
  • Health Promotion / methods
  • Humans
  • Medical Overuse
  • Neoplasms / diagnosis*
  • Neoplasms / economics
  • Neoplasms / mortality
  • Patient Selection
  • Primary Health Care / economics
  • Primary Health Care / methods
  • Risk Assessment
  • Survival Analysis
  • Time Factors