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The contribution of risk prediction models to early detection of lung cancer
Author(s) -
Field John K.,
Chen Ying,
Marcus Michael W.,
Mcronald Fiona E.,
Raji Olaide Y.,
Duffy Stephen W.
Publication year - 2013
Publication title -
journal of surgical oncology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.201
H-Index - 111
eISSN - 1096-9098
pISSN - 0022-4790
DOI - 10.1002/jso.23384
Subject(s) - medicine , lung cancer , lung cancer screening , intensive care medicine , computed tomography , harm , predictive modelling , risk assessment , cancer , risk analysis (engineering) , oncology , surgery , machine learning , computer science , computer security , political science , law
Low‐dose computed tomography screening is a strategy for early diagnosis of lung cancer. The success of such screening will be dependent upon identifying populations at sufficient risk in order to maximise the benefit‐to‐harm ratio of the intervention. To facilitate this, the lung cancer risk prediction community has established several risk models with good predictive performance. This review focuses on current progress in risk modelling for lung cancer prediction, with some views on future development. J. Surg. Oncol. 2013 108:304–311 . © 2013 Wiley Periodicals, Inc.