Prevent Cancer Foundation quantitative CT imaging workshop XVI: lung cancer, COPD and cardiovascular disease - on the cusp of transformation, Arlington, VA

Abstract Throughout the course of this Workshop, members of the advocacy community, imaging researchers, computer and measurement scientists, clinicians and policy-focused workshop attendees engaged in cross-cutting discussions from innovative technical aspects of thoracic imaging to policy approach...

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Main Authors: James L. Mulshine, Riccardo S. Avila, Daniel C. Sullivan, David F. Yankelevitz, Raúl San José Estépar, Laurie Fenton Ambrose, Bruce Pyenson, Carolyn R. Aldigé
Format: Article
Language:English
Published: BMC 2020-10-01
Series:Translational Medicine Communications
Subjects:
Online Access:http://link.springer.com/article/10.1186/s41231-020-00069-8
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spelling doaj-8c16afbd99e94d38a67998b0bddb58f42020-11-25T03:44:32ZengBMCTranslational Medicine Communications2396-832X2020-10-015111010.1186/s41231-020-00069-8Prevent Cancer Foundation quantitative CT imaging workshop XVI: lung cancer, COPD and cardiovascular disease - on the cusp of transformation, Arlington, VAJames L. Mulshine0Riccardo S. Avila1Daniel C. Sullivan2David F. Yankelevitz3Raúl San José Estépar4Laurie Fenton Ambrose5Bruce Pyenson6Carolyn R. Aldigé7Rush UniversityAccumetra, LLCDuke UniversityIcahn School of Medicine, The Mount Sinai Health SystemBrigham and Women’s Hospital – Harvard Medical SchoolGO2 Foundation for Lung CancerMilliman, Inc.Prevent Cancer FoundationAbstract Throughout the course of this Workshop, members of the advocacy community, imaging researchers, computer and measurement scientists, clinicians and policy-focused workshop attendees engaged in cross-cutting discussions from innovative technical aspects of thoracic imaging to policy approaches to ensure equitable access to all at-risk individuals when implementing lung cancer screening services. A major aspect of these implementation discussions was how to efficiently collect routine thoracic CT-based screening with de-identified clinical outcomes data to support the development of robust imaging tools, including responsible AI development, to better detect and manage early lung cancer as well as other major tobacco-related thoracic diseases. A future vision involves routinely collecting a substantial fraction of every thoracic screening CT image to establish a large, curated collection of de-identified thoracic CT images with clinical outcome data to support open research for building better computational imaging tools for early thoracic disease management. Imaging researchers are positioned to develop much better workflow software tools to promote more efficient, outpatient management of the screening process for populations at-risk for lung cancer, especially with the rapid development of promising AI tools. Efficient and effective management tools for the large numbers of at-risk ever smokers could allow the primary care community to discuss lung cancer screening despite their heavy existing clinical demands. Supporting the primary care community in this fashion may significantly improve the current slow uptake of lung cancer screening and save many lives in the process.http://link.springer.com/article/10.1186/s41231-020-00069-8Lung cancer screeningTobacco-related diseasesQuantitative imagingArtificial intelligenceImage quality conformance measuresImage archive
collection DOAJ
language English
format Article
sources DOAJ
author James L. Mulshine
Riccardo S. Avila
Daniel C. Sullivan
David F. Yankelevitz
Raúl San José Estépar
Laurie Fenton Ambrose
Bruce Pyenson
Carolyn R. Aldigé
spellingShingle James L. Mulshine
Riccardo S. Avila
Daniel C. Sullivan
David F. Yankelevitz
Raúl San José Estépar
Laurie Fenton Ambrose
Bruce Pyenson
Carolyn R. Aldigé
Prevent Cancer Foundation quantitative CT imaging workshop XVI: lung cancer, COPD and cardiovascular disease - on the cusp of transformation, Arlington, VA
Translational Medicine Communications
Lung cancer screening
Tobacco-related diseases
Quantitative imaging
Artificial intelligence
Image quality conformance measures
Image archive
author_facet James L. Mulshine
Riccardo S. Avila
Daniel C. Sullivan
David F. Yankelevitz
Raúl San José Estépar
Laurie Fenton Ambrose
Bruce Pyenson
Carolyn R. Aldigé
author_sort James L. Mulshine
title Prevent Cancer Foundation quantitative CT imaging workshop XVI: lung cancer, COPD and cardiovascular disease - on the cusp of transformation, Arlington, VA
title_short Prevent Cancer Foundation quantitative CT imaging workshop XVI: lung cancer, COPD and cardiovascular disease - on the cusp of transformation, Arlington, VA
title_full Prevent Cancer Foundation quantitative CT imaging workshop XVI: lung cancer, COPD and cardiovascular disease - on the cusp of transformation, Arlington, VA
title_fullStr Prevent Cancer Foundation quantitative CT imaging workshop XVI: lung cancer, COPD and cardiovascular disease - on the cusp of transformation, Arlington, VA
title_full_unstemmed Prevent Cancer Foundation quantitative CT imaging workshop XVI: lung cancer, COPD and cardiovascular disease - on the cusp of transformation, Arlington, VA
title_sort prevent cancer foundation quantitative ct imaging workshop xvi: lung cancer, copd and cardiovascular disease - on the cusp of transformation, arlington, va
publisher BMC
series Translational Medicine Communications
issn 2396-832X
publishDate 2020-10-01
description Abstract Throughout the course of this Workshop, members of the advocacy community, imaging researchers, computer and measurement scientists, clinicians and policy-focused workshop attendees engaged in cross-cutting discussions from innovative technical aspects of thoracic imaging to policy approaches to ensure equitable access to all at-risk individuals when implementing lung cancer screening services. A major aspect of these implementation discussions was how to efficiently collect routine thoracic CT-based screening with de-identified clinical outcomes data to support the development of robust imaging tools, including responsible AI development, to better detect and manage early lung cancer as well as other major tobacco-related thoracic diseases. A future vision involves routinely collecting a substantial fraction of every thoracic screening CT image to establish a large, curated collection of de-identified thoracic CT images with clinical outcome data to support open research for building better computational imaging tools for early thoracic disease management. Imaging researchers are positioned to develop much better workflow software tools to promote more efficient, outpatient management of the screening process for populations at-risk for lung cancer, especially with the rapid development of promising AI tools. Efficient and effective management tools for the large numbers of at-risk ever smokers could allow the primary care community to discuss lung cancer screening despite their heavy existing clinical demands. Supporting the primary care community in this fashion may significantly improve the current slow uptake of lung cancer screening and save many lives in the process.
topic Lung cancer screening
Tobacco-related diseases
Quantitative imaging
Artificial intelligence
Image quality conformance measures
Image archive
url http://link.springer.com/article/10.1186/s41231-020-00069-8
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