Can visual assessment of blood flow patterns by dynamic contrast-enhanced computed tomography distinguish between malignant and benign lung tumors?
Background Dynamic contrast-enhanced computed tomography (DCE-CT) is a tool, which, in theory, can quantify the blood flow and blood volume of tissues. In structured qualitative analysis, parametric color maps yield a visual impression of the blood flow and blood volume within the tissue being studi...
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| Veröffentlicht in: | Acta radiologica open 2017-05, Vol.6 (5), p.2058460117710053-2058460117710053 |
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| creator | Harders, Stefan Walbom Madsen, Hans Henrik Nellemann, Hanne Marie Rasmussen, Torben Riis Thygesen, Jesper Hager, Henrik Andersen, Niels Trolle Rasmussen, Finn |
| description | Background
Dynamic contrast-enhanced computed tomography (DCE-CT) is a tool, which, in theory, can quantify the blood flow and blood volume of tissues. In structured qualitative analysis, parametric color maps yield a visual impression of the blood flow and blood volume within the tissue being studied, allowing for quick identification of the areas with the highest or lowest blood flow and blood volume.
Purpose
To examine whether DCE-CT could be used to distinguish between malignant and benign lung tumors in patients with suspected lung cancer.
Material and Methods
Fifty-nine patients with suspected lung cancer and a lung tumor on their chest radiograph were included for DCE-CT. The tumors were categorized using structured qualitative analysis of tumor blood flow patterns. Histopathology was used as reference standard.
Results
Using structured qualitative analysis of tumor blood flow patterns, it was possible to distinguish between malignant and benign lung tumors (Fisher–Freeman–Halton exact test, P = 0.022). The inter-reader agreement of this method of analysis was slight to moderate (kappa = 0.30; 95% confidence interval [CI] = 0.13–0.46).
Conclusion
DCE-CT in suspected lung cancer using structured qualitative analysis of tumor blood flow patterns is accurate as well as somewhat reproducible. However, there are significant limitations to DCE-CT. |
| doi_str_mv | 10.1177/2058460117710053 |
| format | Article |
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Dynamic contrast-enhanced computed tomography (DCE-CT) is a tool, which, in theory, can quantify the blood flow and blood volume of tissues. In structured qualitative analysis, parametric color maps yield a visual impression of the blood flow and blood volume within the tissue being studied, allowing for quick identification of the areas with the highest or lowest blood flow and blood volume.
Purpose
To examine whether DCE-CT could be used to distinguish between malignant and benign lung tumors in patients with suspected lung cancer.
Material and Methods
Fifty-nine patients with suspected lung cancer and a lung tumor on their chest radiograph were included for DCE-CT. The tumors were categorized using structured qualitative analysis of tumor blood flow patterns. Histopathology was used as reference standard.
Results
Using structured qualitative analysis of tumor blood flow patterns, it was possible to distinguish between malignant and benign lung tumors (Fisher–Freeman–Halton exact test, P = 0.022). The inter-reader agreement of this method of analysis was slight to moderate (kappa = 0.30; 95% confidence interval [CI] = 0.13–0.46).
Conclusion
DCE-CT in suspected lung cancer using structured qualitative analysis of tumor blood flow patterns is accurate as well as somewhat reproducible. However, there are significant limitations to DCE-CT.</description><identifier>ISSN: 2058-4601</identifier><identifier>EISSN: 2058-4601</identifier><identifier>DOI: 10.1177/2058460117710053</identifier><identifier>PMID: 28607762</identifier><language>eng</language><publisher>London, England: SAGE Publications</publisher><subject>Blood ; Blood flow ; Blood volume ; Computed tomography ; Confidence intervals ; Flow distribution ; Histopathology ; Lung cancer ; Qualitative analysis ; Radiographs ; Tomography ; Tumors</subject><ispartof>Acta radiologica open, 2017-05, Vol.6 (5), p.2058460117710053-2058460117710053</ispartof><rights>The Foundation Acta Radiologica 2017</rights><rights>The Foundation Acta Radiologica 2017. This work is licensed under the Creative Commons Attribution – Non-Commercial License http://creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>The Foundation Acta Radiologica 2017 2017 The Foundation Acta Radiologica</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c345t-9a0fc15ec7bde2142308e6072114213b719eebf63a12c0c12b387fb9b9a64dc3</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/PMC5453405/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5453405/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,21965,27852,27923,27924,44944,45332,53790,53792</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28607762$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Harders, Stefan Walbom</creatorcontrib><creatorcontrib>Madsen, Hans Henrik</creatorcontrib><creatorcontrib>Nellemann, Hanne Marie</creatorcontrib><creatorcontrib>Rasmussen, Torben Riis</creatorcontrib><creatorcontrib>Thygesen, Jesper</creatorcontrib><creatorcontrib>Hager, Henrik</creatorcontrib><creatorcontrib>Andersen, Niels Trolle</creatorcontrib><creatorcontrib>Rasmussen, Finn</creatorcontrib><title>Can visual assessment of blood flow patterns by dynamic contrast-enhanced computed tomography distinguish between malignant and benign lung tumors?</title><title>Acta radiologica open</title><addtitle>Acta Radiol Open</addtitle><description>Background
Dynamic contrast-enhanced computed tomography (DCE-CT) is a tool, which, in theory, can quantify the blood flow and blood volume of tissues. In structured qualitative analysis, parametric color maps yield a visual impression of the blood flow and blood volume within the tissue being studied, allowing for quick identification of the areas with the highest or lowest blood flow and blood volume.
Purpose
To examine whether DCE-CT could be used to distinguish between malignant and benign lung tumors in patients with suspected lung cancer.
Material and Methods
Fifty-nine patients with suspected lung cancer and a lung tumor on their chest radiograph were included for DCE-CT. The tumors were categorized using structured qualitative analysis of tumor blood flow patterns. Histopathology was used as reference standard.
Results
Using structured qualitative analysis of tumor blood flow patterns, it was possible to distinguish between malignant and benign lung tumors (Fisher–Freeman–Halton exact test, P = 0.022). The inter-reader agreement of this method of analysis was slight to moderate (kappa = 0.30; 95% confidence interval [CI] = 0.13–0.46).
Conclusion
DCE-CT in suspected lung cancer using structured qualitative analysis of tumor blood flow patterns is accurate as well as somewhat reproducible. However, there are significant limitations to DCE-CT.</description><subject>Blood</subject><subject>Blood flow</subject><subject>Blood volume</subject><subject>Computed tomography</subject><subject>Confidence intervals</subject><subject>Flow distribution</subject><subject>Histopathology</subject><subject>Lung cancer</subject><subject>Qualitative analysis</subject><subject>Radiographs</subject><subject>Tomography</subject><subject>Tumors</subject><issn>2058-4601</issn><issn>2058-4601</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>AFRWT</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNp1UU1rHSEUldLShNfsuypCN91Mq873pqU8-gWBbrKXq3NnnmHUqToJ73fkD9fhpWkaKAgej-ee6_ES8pqz95y37QfB6q5q2IY5Y3X5jJxvVLFxzx_hM3IR4zVjjFd12dT8JTkTXcPathHn5G4Pjt6YuMJMIUaM0aJL1I9Uzd4PdJz9LV0gJQwuUnWkw9GBNZpq71KAmAp0B3Aah8zYZU0ZJG_9FGA5ZLWJybhpNfFAFaZbREctzGZykLuAGzLr8onOq5toWq0P8dMr8mKEOeLF_b4jV1-_XO2_F5c_v_3Yf74sdFnVqeiBjZrXqFs1oOCVKFmHOZfgGfNStbxHVGNTAheaaS5U2bWj6lUPTTXockc-nmyXVVkcNG6BZrkEYyEcpQcj_71x5iAnfyPr_I9V_vAdeXdvEPyvFWOS1kSN8wwO_Rol71kvRF5tlr59Ir32a3A5nRSVqNqai24zZCeVDj7GgOPDYziT25zl05nnkjePQzwU_JlwFhQnQYQJ_3b9r-Fvs3W3ZA</recordid><startdate>201705</startdate><enddate>201705</enddate><creator>Harders, Stefan Walbom</creator><creator>Madsen, Hans Henrik</creator><creator>Nellemann, Hanne Marie</creator><creator>Rasmussen, Torben Riis</creator><creator>Thygesen, Jesper</creator><creator>Hager, Henrik</creator><creator>Andersen, Niels Trolle</creator><creator>Rasmussen, Finn</creator><general>SAGE Publications</general><general>Sage Publications Ltd</general><scope>AFRWT</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7RV</scope><scope>7U7</scope><scope>7XB</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>KB0</scope><scope>NAPCQ</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>201705</creationdate><title>Can visual assessment of blood flow patterns by dynamic contrast-enhanced computed tomography distinguish between malignant and benign lung tumors?</title><author>Harders, Stefan Walbom ; Madsen, Hans Henrik ; Nellemann, Hanne Marie ; Rasmussen, Torben Riis ; Thygesen, Jesper ; Hager, Henrik ; Andersen, Niels Trolle ; Rasmussen, Finn</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c345t-9a0fc15ec7bde2142308e6072114213b719eebf63a12c0c12b387fb9b9a64dc3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Blood</topic><topic>Blood flow</topic><topic>Blood volume</topic><topic>Computed tomography</topic><topic>Confidence intervals</topic><topic>Flow distribution</topic><topic>Histopathology</topic><topic>Lung cancer</topic><topic>Qualitative analysis</topic><topic>Radiographs</topic><topic>Tomography</topic><topic>Tumors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Harders, Stefan Walbom</creatorcontrib><creatorcontrib>Madsen, Hans Henrik</creatorcontrib><creatorcontrib>Nellemann, Hanne Marie</creatorcontrib><creatorcontrib>Rasmussen, Torben Riis</creatorcontrib><creatorcontrib>Thygesen, Jesper</creatorcontrib><creatorcontrib>Hager, Henrik</creatorcontrib><creatorcontrib>Andersen, Niels Trolle</creatorcontrib><creatorcontrib>Rasmussen, Finn</creatorcontrib><collection>Sage Journals GOLD Open Access 2024</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Nursing & Allied Health Database</collection><collection>Toxicology Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Nursing & Allied Health Premium</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Acta radiologica open</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Harders, Stefan Walbom</au><au>Madsen, Hans Henrik</au><au>Nellemann, Hanne Marie</au><au>Rasmussen, Torben Riis</au><au>Thygesen, Jesper</au><au>Hager, Henrik</au><au>Andersen, Niels Trolle</au><au>Rasmussen, Finn</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Can visual assessment of blood flow patterns by dynamic contrast-enhanced computed tomography distinguish between malignant and benign lung tumors?</atitle><jtitle>Acta radiologica open</jtitle><addtitle>Acta Radiol Open</addtitle><date>2017-05</date><risdate>2017</risdate><volume>6</volume><issue>5</issue><spage>2058460117710053</spage><epage>2058460117710053</epage><pages>2058460117710053-2058460117710053</pages><issn>2058-4601</issn><eissn>2058-4601</eissn><abstract>Background
Dynamic contrast-enhanced computed tomography (DCE-CT) is a tool, which, in theory, can quantify the blood flow and blood volume of tissues. In structured qualitative analysis, parametric color maps yield a visual impression of the blood flow and blood volume within the tissue being studied, allowing for quick identification of the areas with the highest or lowest blood flow and blood volume.
Purpose
To examine whether DCE-CT could be used to distinguish between malignant and benign lung tumors in patients with suspected lung cancer.
Material and Methods
Fifty-nine patients with suspected lung cancer and a lung tumor on their chest radiograph were included for DCE-CT. The tumors were categorized using structured qualitative analysis of tumor blood flow patterns. Histopathology was used as reference standard.
Results
Using structured qualitative analysis of tumor blood flow patterns, it was possible to distinguish between malignant and benign lung tumors (Fisher–Freeman–Halton exact test, P = 0.022). The inter-reader agreement of this method of analysis was slight to moderate (kappa = 0.30; 95% confidence interval [CI] = 0.13–0.46).
Conclusion
DCE-CT in suspected lung cancer using structured qualitative analysis of tumor blood flow patterns is accurate as well as somewhat reproducible. However, there are significant limitations to DCE-CT.</abstract><cop>London, England</cop><pub>SAGE Publications</pub><pmid>28607762</pmid><doi>10.1177/2058460117710053</doi><oa>free_for_read</oa></addata></record> |
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| ispartof | Acta radiologica open, 2017-05, Vol.6 (5), p.2058460117710053-2058460117710053 |
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| source | DOAJ Directory of Open Access Journals; Sage Journals GOLD Open Access 2024; PubMed Central Open Access; EZB-FREE-00999 freely available EZB journals; PubMed Central |
| subjects | Blood Blood flow Blood volume Computed tomography Confidence intervals Flow distribution Histopathology Lung cancer Qualitative analysis Radiographs Tomography Tumors |
| title | Can visual assessment of blood flow patterns by dynamic contrast-enhanced computed tomography distinguish between malignant and benign lung tumors? |
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