The contribution of risk prediction models to early detection of lung cancer

John K. Field; Ying Chen; Michael W. Marcus; Fiona E. McRonald; Olaide Y. Raji; Stephen W. Duffy

doi:10.1002/jso.23384

The contribution of risk prediction models to early detection of lung cancer

John K. Field^*, Ying Chen, Michael W. Marcus, Fiona E. McRonald, Olaide Y. Raji, Stephen W. Duffy

^*Corresponding author for this work

Research output: Contribution to journal › Review article › peer-review

19 Citations (Scopus)

Abstract

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.

Original language	English
Pages (from-to)	304-311
Number of pages	8
Journal	Journal of Surgical Oncology
Volume	108
Issue number	5
DOIs	https://doi.org/10.1002/jso.23384
Publication status	Published - Oct 2013
Externally published	Yes

Keywords

early diagnosis
lung neoplasms
risk assessment
screening
statistical model

UN SDGs

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

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10.1002/jso.23384

Cite this

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title = "The contribution of risk prediction models to early detection of lung cancer",

abstract = "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.",

keywords = "early diagnosis, lung neoplasms, risk assessment, screening, statistical model",

author = "Field, {John K.} and Ying Chen and Marcus, {Michael W.} and McRonald, {Fiona E.} and Raji, {Olaide Y.} and Duffy, {Stephen W.}",

year = "2013",

month = oct,

doi = "10.1002/jso.23384",

language = "English",

volume = "108",

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AU - Chen, Ying

AU - Marcus, Michael W.

AU - McRonald, Fiona E.

AU - Raji, Olaide Y.

AU - Duffy, Stephen W.

PY - 2013/10

Y1 - 2013/10

N2 - 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.

AB - 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.

KW - early diagnosis

KW - lung neoplasms

KW - risk assessment

KW - screening

KW - statistical model

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M3 - Review article

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EP - 311

JO - Journal of Surgical Oncology

JF - Journal of Surgical Oncology

IS - 5

ER -

The contribution of risk prediction models to early detection of lung cancer

Abstract

Keywords

UN SDGs

Access to Document

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