Predicting the Mortality Benefit of CT Screening for Second Lung Cancer in a High-Risk Population
Patients who survive an index lung cancer (ILC) after surgical resection continue to be at significant risk for a metachronous lung cancer (MLC). Indeed, this risk is much higher than the risk of developing an ILC in heavy smokers. There is currently little evidence upon which to base guidelines for...
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description | Patients who survive an index lung cancer (ILC) after surgical resection continue to be at significant risk for a metachronous lung cancer (MLC). Indeed, this risk is much higher than the risk of developing an ILC in heavy smokers. There is currently little evidence upon which to base guidelines for screening at-risk patients for MLC, and the risk-reward tradeoffs for screening this patient population are unknown. The goal of this investigation was to estimate the maximum mortality benefit of CT screening for MLC. We developed a computational model to estimate the maximum rates of CT detection of MLC and surgical resection to be expected in a given population as a function of time after resection of an ILC. Applying the model to a hypothetical high-risk population suggests that screening for MLC within 5 years after resection of an ILC may identify only a very small number of treatable cancers. The risk of death from a potentially resectable MLC increases dramatically past this point, however, suggesting that screening after 5 years is imperative. The model also predicts a substantial detection gap for MLC that demonstrates the benefit to be gained as more sensitive screening methods are developed. |
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Indeed, this risk is much higher than the risk of developing an ILC in heavy smokers. There is currently little evidence upon which to base guidelines for screening at-risk patients for MLC, and the risk-reward tradeoffs for screening this patient population are unknown. The goal of this investigation was to estimate the maximum mortality benefit of CT screening for MLC. We developed a computational model to estimate the maximum rates of CT detection of MLC and surgical resection to be expected in a given population as a function of time after resection of an ILC. Applying the model to a hypothetical high-risk population suggests that screening for MLC within 5 years after resection of an ILC may identify only a very small number of treatable cancers. The risk of death from a potentially resectable MLC increases dramatically past this point, however, suggesting that screening after 5 years is imperative. The model also predicts a substantial detection gap for MLC that demonstrates the benefit to be gained as more sensitive screening methods are developed.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0165471</identifier><identifier>PMID: 27806080</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Age ; Aged ; Aged, 80 and over ; Biology and Life Sciences ; Cancer ; Cancer screening ; Computer applications ; Early Detection of Cancer ; Female ; Health aspects ; Health risks ; Histology ; Humans ; Lung cancer ; Lung diseases ; Lung Neoplasms - diagnostic imaging ; Lung Neoplasms - mortality ; Lung Neoplasms - surgery ; Male ; Mathematical models ; Medical imaging ; Medical research ; Medical screening ; Medicine and Health Sciences ; Middle Aged ; Models, Theoretical ; Mortality ; Neoplasms, Second Primary - diagnostic imaging ; Neoplasms, Second Primary - mortality ; Patients ; Population ; Prognosis ; Reinforcement ; Risk ; Screening ; Smoking ; Smoking - adverse effects ; Surgery ; Surveillance ; Tomography ; Tomography, X-Ray Computed - methods</subject><ispartof>PloS one, 2016-11, Vol.11 (11), p.e0165471-e0165471</ispartof><rights>COPYRIGHT 2016 Public Library of Science</rights><rights>2016 Kinsey et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2016 Kinsey et al 2016 Kinsey et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c725t-3d1a5ceda1eb2d347ba74e5dd03175f82b6ae357d53e73e007a2267be84471883</citedby><cites>FETCH-LOGICAL-c725t-3d1a5ceda1eb2d347ba74e5dd03175f82b6ae357d53e73e007a2267be84471883</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5091818/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5091818/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,724,777,781,861,882,2096,2915,23847,27905,27906,53772,53774,79349,79350</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/27806080$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Yang, Fan</contributor><creatorcontrib>Kinsey, C Matthew</creatorcontrib><creatorcontrib>Hamlington, Katharine L</creatorcontrib><creatorcontrib>O'Toole, Jacqueline</creatorcontrib><creatorcontrib>Stapleton, Renee</creatorcontrib><creatorcontrib>Bates, Jason H T</creatorcontrib><title>Predicting the Mortality Benefit of CT Screening for Second Lung Cancer in a High-Risk Population</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Patients who survive an index lung cancer (ILC) after surgical resection continue to be at significant risk for a metachronous lung cancer (MLC). Indeed, this risk is much higher than the risk of developing an ILC in heavy smokers. There is currently little evidence upon which to base guidelines for screening at-risk patients for MLC, and the risk-reward tradeoffs for screening this patient population are unknown. The goal of this investigation was to estimate the maximum mortality benefit of CT screening for MLC. We developed a computational model to estimate the maximum rates of CT detection of MLC and surgical resection to be expected in a given population as a function of time after resection of an ILC. Applying the model to a hypothetical high-risk population suggests that screening for MLC within 5 years after resection of an ILC may identify only a very small number of treatable cancers. The risk of death from a potentially resectable MLC increases dramatically past this point, however, suggesting that screening after 5 years is imperative. The model also predicts a substantial detection gap for MLC that demonstrates the benefit to be gained as more sensitive screening methods are developed.</description><subject>Age</subject><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Biology and Life Sciences</subject><subject>Cancer</subject><subject>Cancer screening</subject><subject>Computer applications</subject><subject>Early Detection of Cancer</subject><subject>Female</subject><subject>Health aspects</subject><subject>Health risks</subject><subject>Histology</subject><subject>Humans</subject><subject>Lung cancer</subject><subject>Lung diseases</subject><subject>Lung Neoplasms - diagnostic imaging</subject><subject>Lung Neoplasms - mortality</subject><subject>Lung Neoplasms - surgery</subject><subject>Male</subject><subject>Mathematical models</subject><subject>Medical imaging</subject><subject>Medical research</subject><subject>Medical screening</subject><subject>Medicine and Health Sciences</subject><subject>Middle Aged</subject><subject>Models, Theoretical</subject><subject>Mortality</subject><subject>Neoplasms, Second Primary - diagnostic imaging</subject><subject>Neoplasms, Second Primary - mortality</subject><subject>Patients</subject><subject>Population</subject><subject>Prognosis</subject><subject>Reinforcement</subject><subject>Risk</subject><subject>Screening</subject><subject>Smoking</subject><subject>Smoking - adverse effects</subject><subject>Surgery</subject><subject>Surveillance</subject><subject>Tomography</subject><subject>Tomography, X-Ray Computed - methods</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNqNk99v0zAQxyMEYmPwHyCIhITgocWOYyd5QRoVsEpFm9bBq-XYl9QjtYvtIPbf49BsatAkkB_863PfO9_5kuQ5RnNMCvzu2vbOiG6-swbmCDOaF_hBcowrks1YhsjDg_VR8sT7a4QoKRl7nBxlRYkYKtFxIi4cKC2DNm0aNpB-sS6IToeb9AMYaHRIbZMurtK1dABmoBrr0jVIa1S66uN-IYwEl2qTivRMt5vZpfbf0wu76zsRtDVPk0eN6Dw8G-eT5Ounj1eLs9nq_PNycbqaySKjYUYUFlSCEhjqTJG8qEWRA1UKEVzQpsxqJoDQQlECBQGECpFlrKihzOPDy5KcJC_3urvOej5mx3NcEspKXFV5JJZ7QllxzXdOb4W74VZo_ufAupYLF7TsgLNM5bKoKlIjlDNVVqIGkKqWFQOhchy13o_e-noLSoIJTnQT0emN0Rve2p-cogqXeAj3zSjg7I8efOBb7SV0nTBg-yHunOWIoBjDv1FCaUaqKovoq7_Q-xMxUq2Ib9WmsTFEOYjy0yGZKHoe3M7voeJQsNWx_vF3xPOJwduJQWQC_Aqt6L3ny_Xl_7Pn36bs6wN2A6ILG2-7fvhdfgrme1A6672D5q4eGPGhaW6zwYem4WPTRLMXh7W8M7rtEvIbNRQQMg</recordid><startdate>20161102</startdate><enddate>20161102</enddate><creator>Kinsey, C Matthew</creator><creator>Hamlington, Katharine L</creator><creator>O'Toole, Jacqueline</creator><creator>Stapleton, Renee</creator><creator>Bates, Jason H T</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><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>IOV</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20161102</creationdate><title>Predicting the Mortality Benefit of CT Screening for Second Lung Cancer in a High-Risk Population</title><author>Kinsey, C Matthew ; Hamlington, Katharine L ; O'Toole, Jacqueline ; Stapleton, Renee ; Bates, Jason H T</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c725t-3d1a5ceda1eb2d347ba74e5dd03175f82b6ae357d53e73e007a2267be84471883</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Age</topic><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Biology and Life Sciences</topic><topic>Cancer</topic><topic>Cancer screening</topic><topic>Computer applications</topic><topic>Early Detection of Cancer</topic><topic>Female</topic><topic>Health aspects</topic><topic>Health risks</topic><topic>Histology</topic><topic>Humans</topic><topic>Lung cancer</topic><topic>Lung diseases</topic><topic>Lung Neoplasms - diagnostic imaging</topic><topic>Lung Neoplasms - mortality</topic><topic>Lung Neoplasms - surgery</topic><topic>Male</topic><topic>Mathematical models</topic><topic>Medical imaging</topic><topic>Medical research</topic><topic>Medical screening</topic><topic>Medicine and Health Sciences</topic><topic>Middle Aged</topic><topic>Models, Theoretical</topic><topic>Mortality</topic><topic>Neoplasms, Second Primary - diagnostic imaging</topic><topic>Neoplasms, Second Primary - mortality</topic><topic>Patients</topic><topic>Population</topic><topic>Prognosis</topic><topic>Reinforcement</topic><topic>Risk</topic><topic>Screening</topic><topic>Smoking</topic><topic>Smoking - adverse effects</topic><topic>Surgery</topic><topic>Surveillance</topic><topic>Tomography</topic><topic>Tomography, X-Ray Computed - methods</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kinsey, C Matthew</creatorcontrib><creatorcontrib>Hamlington, Katharine L</creatorcontrib><creatorcontrib>O'Toole, Jacqueline</creatorcontrib><creatorcontrib>Stapleton, Renee</creatorcontrib><creatorcontrib>Bates, Jason H T</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Opposing Viewpoints</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Meteorological & Geoastrophysical Abstracts - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kinsey, C Matthew</au><au>Hamlington, Katharine L</au><au>O'Toole, Jacqueline</au><au>Stapleton, Renee</au><au>Bates, Jason H T</au><au>Yang, Fan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Predicting the Mortality Benefit of CT Screening for Second Lung Cancer in a High-Risk Population</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2016-11-02</date><risdate>2016</risdate><volume>11</volume><issue>11</issue><spage>e0165471</spage><epage>e0165471</epage><pages>e0165471-e0165471</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Patients who survive an index lung cancer (ILC) after surgical resection continue to be at significant risk for a metachronous lung cancer (MLC). Indeed, this risk is much higher than the risk of developing an ILC in heavy smokers. There is currently little evidence upon which to base guidelines for screening at-risk patients for MLC, and the risk-reward tradeoffs for screening this patient population are unknown. The goal of this investigation was to estimate the maximum mortality benefit of CT screening for MLC. We developed a computational model to estimate the maximum rates of CT detection of MLC and surgical resection to be expected in a given population as a function of time after resection of an ILC. Applying the model to a hypothetical high-risk population suggests that screening for MLC within 5 years after resection of an ILC may identify only a very small number of treatable cancers. The risk of death from a potentially resectable MLC increases dramatically past this point, however, suggesting that screening after 5 years is imperative. The model also predicts a substantial detection gap for MLC that demonstrates the benefit to be gained as more sensitive screening methods are developed.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>27806080</pmid><doi>10.1371/journal.pone.0165471</doi><tpages>e0165471</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Age Aged Aged, 80 and over Biology and Life Sciences Cancer Cancer screening Computer applications Early Detection of Cancer Female Health aspects Health risks Histology Humans Lung cancer Lung diseases Lung Neoplasms - diagnostic imaging Lung Neoplasms - mortality Lung Neoplasms - surgery Male Mathematical models Medical imaging Medical research Medical screening Medicine and Health Sciences Middle Aged Models, Theoretical Mortality Neoplasms, Second Primary - diagnostic imaging Neoplasms, Second Primary - mortality Patients Population Prognosis Reinforcement Risk Screening Smoking Smoking - adverse effects Surgery Surveillance Tomography Tomography, X-Ray Computed - methods |
title | Predicting the Mortality Benefit of CT Screening for Second Lung Cancer in a High-Risk Population |
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