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The contribution of risk prediction models to early detection of lung cancer
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 predic...
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Published in: | Journal of surgical oncology 2013-10, Vol.108 (5), p.304-311 |
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container_title | Journal of surgical oncology |
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creator | Field, John K. Chen, Ying Marcus, Michael W. Mcronald, Fiona E. Raji, Olaide Y. Duffy, Stephen W. |
description | 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. |
doi_str_mv | 10.1002/jso.23384 |
format | article |
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Oncol. 2013 108:304–311. © 2013 Wiley Periodicals, Inc.</description><identifier>ISSN: 0022-4790</identifier><identifier>EISSN: 1096-9098</identifier><identifier>DOI: 10.1002/jso.23384</identifier><identifier>PMID: 23996507</identifier><language>eng</language><publisher>United States: Blackwell Publishing Ltd</publisher><subject>Early Detection of Cancer ; early diagnosis ; Female ; Humans ; Lung cancer ; lung neoplasms ; Lung Neoplasms - diagnosis ; Male ; Models, Statistical ; Risk ; risk assessment ; Risk Reduction Behavior ; screening ; statistical model ; Tomography, X-Ray Computed</subject><ispartof>Journal of surgical oncology, 2013-10, Vol.108 (5), p.304-311</ispartof><rights>2013 Wiley Periodicals, Inc.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3914-7a426127ee35789337e5816bae88a22dff0b7f0f2f0a27a901c7e0778171a4b03</citedby><cites>FETCH-LOGICAL-c3914-7a426127ee35789337e5816bae88a22dff0b7f0f2f0a27a901c7e0778171a4b03</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/23996507$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Field, John K.</creatorcontrib><creatorcontrib>Chen, Ying</creatorcontrib><creatorcontrib>Marcus, Michael W.</creatorcontrib><creatorcontrib>Mcronald, Fiona E.</creatorcontrib><creatorcontrib>Raji, Olaide Y.</creatorcontrib><creatorcontrib>Duffy, Stephen W.</creatorcontrib><title>The contribution of risk prediction models to early detection of lung cancer</title><title>Journal of surgical oncology</title><addtitle>J. Surg. Oncol</addtitle><description>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.</description><subject>Early Detection of Cancer</subject><subject>early diagnosis</subject><subject>Female</subject><subject>Humans</subject><subject>Lung cancer</subject><subject>lung neoplasms</subject><subject>Lung Neoplasms - diagnosis</subject><subject>Male</subject><subject>Models, Statistical</subject><subject>Risk</subject><subject>risk assessment</subject><subject>Risk Reduction Behavior</subject><subject>screening</subject><subject>statistical model</subject><subject>Tomography, X-Ray Computed</subject><issn>0022-4790</issn><issn>1096-9098</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><recordid>eNp10EFP2zAUB3BrAo3CdtgXmCJxGYfAs53E9hF10AJVOawIiYvlOC8jJY2LnQj67UlpywGJky379_56-hPyi8IpBWBn8-BOGecy-UYGFFQWK1Byjwz6PxYnQsEBOQxhDgBKZcl3csB4f0lBDMhk9oiRdU3rq7xrK9dErox8FZ6ipceisu9PC1dgHaLWRWh8vYoKbNHucN01_yNrGov-B9kvTR3w5_Y8IneXF7PhOJ7cjq6G55PYckWTWJiEZZQJRJ4KqTgXmEqa5QalNIwVZQm5KKFkJRgmjAJqBYIQkgpqkhz4EfmzyV1699xhaPWiChbr2jTouqBpwmUq0ixb0-NPdO463_TbrZVgKZUy6dXJRlnvQvBY6qWvFsavNAW9rlj3Fev3inv7e5vY5QssPuSu0x6cbcBLVePq6yR9_e92FxlvJqrQ4uvHhPFPOhNcpPp-OtIPfx8uxzfDqZ7yNw9ekz8</recordid><startdate>201310</startdate><enddate>201310</enddate><creator>Field, John K.</creator><creator>Chen, Ying</creator><creator>Marcus, Michael W.</creator><creator>Mcronald, Fiona E.</creator><creator>Raji, Olaide Y.</creator><creator>Duffy, Stephen W.</creator><general>Blackwell Publishing Ltd</general><general>Wiley Subscription Services, Inc</general><scope>BSCLL</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>K9.</scope><scope>7X8</scope></search><sort><creationdate>201310</creationdate><title>The contribution of risk prediction models to early detection of lung cancer</title><author>Field, John K. ; Chen, Ying ; Marcus, Michael W. ; Mcronald, Fiona E. ; Raji, Olaide Y. ; Duffy, Stephen W.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3914-7a426127ee35789337e5816bae88a22dff0b7f0f2f0a27a901c7e0778171a4b03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Early Detection of Cancer</topic><topic>early diagnosis</topic><topic>Female</topic><topic>Humans</topic><topic>Lung cancer</topic><topic>lung neoplasms</topic><topic>Lung Neoplasms - diagnosis</topic><topic>Male</topic><topic>Models, Statistical</topic><topic>Risk</topic><topic>risk assessment</topic><topic>Risk Reduction Behavior</topic><topic>screening</topic><topic>statistical model</topic><topic>Tomography, X-Ray Computed</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Field, John K.</creatorcontrib><creatorcontrib>Chen, Ying</creatorcontrib><creatorcontrib>Marcus, Michael W.</creatorcontrib><creatorcontrib>Mcronald, Fiona E.</creatorcontrib><creatorcontrib>Raji, Olaide Y.</creatorcontrib><creatorcontrib>Duffy, Stephen W.</creatorcontrib><collection>Istex</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of surgical oncology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Field, John K.</au><au>Chen, Ying</au><au>Marcus, Michael W.</au><au>Mcronald, Fiona E.</au><au>Raji, Olaide Y.</au><au>Duffy, Stephen W.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>The contribution of risk prediction models to early detection of lung cancer</atitle><jtitle>Journal of surgical oncology</jtitle><addtitle>J. Surg. Oncol</addtitle><date>2013-10</date><risdate>2013</risdate><volume>108</volume><issue>5</issue><spage>304</spage><epage>311</epage><pages>304-311</pages><issn>0022-4790</issn><eissn>1096-9098</eissn><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. © 2013 Wiley Periodicals, Inc.</abstract><cop>United States</cop><pub>Blackwell Publishing Ltd</pub><pmid>23996507</pmid><doi>10.1002/jso.23384</doi><tpages>8</tpages></addata></record> |
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subjects | Early Detection of Cancer early diagnosis Female Humans Lung cancer lung neoplasms Lung Neoplasms - diagnosis Male Models, Statistical Risk risk assessment Risk Reduction Behavior screening statistical model Tomography, X-Ray Computed |
title | The contribution of risk prediction models to early detection of lung cancer |
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