Microvascular invasion in hepatocellular carcinoma: is it predictable with quantitative computed tomography parameters?
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Radiologia Brasileira (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-39842019000500287 |
Resumo: | Abstract Objective: To investigate whether quantitative computed tomography (CT) measurements can predict microvascular invasion (MVI) in hepatocellular carcinoma (HCC). Materials and Methods: This was a retrospective analysis of 200 cases of surgically proven HCCs in 125 consecutive patients evaluated between March 2010 and November 2017. We quantitatively measured regions of interest in lesions and adjacent areas of the liver on unenhanced CT scans, as well as in the arterial, portal venous, and equilibrium phases on contrast-enhanced CT scans. Enhancement profiles were analyzed and compared with histopathological references of MVI. Univariate and multivariate logistic regression analyses were used in order to evaluate CT parameters as potential predictors of MVI. Results: Of the 200 HCCs, 77 (38.5%) showed evidence of MVI on histopathological analysis. There was no statistical difference between HCCs with MVI and those without, in terms of the percentage attenuation ratio in the portal venous phase (114.7 vs. 115.8) and equilibrium phase (126.7 vs. 128.2), as well as in terms of the relative washout ratio, also in the portal venous and equilibrium phases (15.0 vs. 8.2 and 31.4 vs. 26.3, respectively). Conclusion: Quantitative dynamic CT parameters measured in the preoperative period do not appear to correlate with MVI in HCC. |
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Radiologia Brasileira (Online) |
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Microvascular invasion in hepatocellular carcinoma: is it predictable with quantitative computed tomography parameters?Carcinoma, hepatocellularTomography, X-ray computedLiver neoplasms/surgeryLiver transplantationAbstract Objective: To investigate whether quantitative computed tomography (CT) measurements can predict microvascular invasion (MVI) in hepatocellular carcinoma (HCC). Materials and Methods: This was a retrospective analysis of 200 cases of surgically proven HCCs in 125 consecutive patients evaluated between March 2010 and November 2017. We quantitatively measured regions of interest in lesions and adjacent areas of the liver on unenhanced CT scans, as well as in the arterial, portal venous, and equilibrium phases on contrast-enhanced CT scans. Enhancement profiles were analyzed and compared with histopathological references of MVI. Univariate and multivariate logistic regression analyses were used in order to evaluate CT parameters as potential predictors of MVI. Results: Of the 200 HCCs, 77 (38.5%) showed evidence of MVI on histopathological analysis. There was no statistical difference between HCCs with MVI and those without, in terms of the percentage attenuation ratio in the portal venous phase (114.7 vs. 115.8) and equilibrium phase (126.7 vs. 128.2), as well as in terms of the relative washout ratio, also in the portal venous and equilibrium phases (15.0 vs. 8.2 and 31.4 vs. 26.3, respectively). Conclusion: Quantitative dynamic CT parameters measured in the preoperative period do not appear to correlate with MVI in HCC.Publicação do Colégio Brasileiro de Radiologia e Diagnóstico por Imagem2019-10-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-39842019000500287Radiologia Brasileira v.52 n.5 2019reponame:Radiologia Brasileira (Online)instname:Colégio Brasileiro de Radiologia e Diagnóstico por Imagem (CBR)instacron:CBR10.1590/0100-3984.2018.0123info:eu-repo/semantics/openAccessLahan-Martins,DanielPerales,Simone RegesGallani,Stephanie KilarisCosta,Larissa Bastos Eloy daLago,Eduardo Andreazza DalBoin,Ilka de Fátima Santana FerreiraCaserta,Nelson Marcio GomesAtaide,Elaine Cristina deeng2019-10-21T00:00:00Zoai:scielo:S0100-39842019000500287Revistahttps://www.scielo.br/j/rb/https://old.scielo.br/oai/scielo-oai.phpradiologiabrasileira@cbr.org.br1678-70990100-3984opendoar:2019-10-21T00:00Radiologia Brasileira (Online) - Colégio Brasileiro de Radiologia e Diagnóstico por Imagem (CBR)false |
dc.title.none.fl_str_mv |
Microvascular invasion in hepatocellular carcinoma: is it predictable with quantitative computed tomography parameters? |
title |
Microvascular invasion in hepatocellular carcinoma: is it predictable with quantitative computed tomography parameters? |
spellingShingle |
Microvascular invasion in hepatocellular carcinoma: is it predictable with quantitative computed tomography parameters? Lahan-Martins,Daniel Carcinoma, hepatocellular Tomography, X-ray computed Liver neoplasms/surgery Liver transplantation |
title_short |
Microvascular invasion in hepatocellular carcinoma: is it predictable with quantitative computed tomography parameters? |
title_full |
Microvascular invasion in hepatocellular carcinoma: is it predictable with quantitative computed tomography parameters? |
title_fullStr |
Microvascular invasion in hepatocellular carcinoma: is it predictable with quantitative computed tomography parameters? |
title_full_unstemmed |
Microvascular invasion in hepatocellular carcinoma: is it predictable with quantitative computed tomography parameters? |
title_sort |
Microvascular invasion in hepatocellular carcinoma: is it predictable with quantitative computed tomography parameters? |
author |
Lahan-Martins,Daniel |
author_facet |
Lahan-Martins,Daniel Perales,Simone Reges Gallani,Stephanie Kilaris Costa,Larissa Bastos Eloy da Lago,Eduardo Andreazza Dal Boin,Ilka de Fátima Santana Ferreira Caserta,Nelson Marcio Gomes Ataide,Elaine Cristina de |
author_role |
author |
author2 |
Perales,Simone Reges Gallani,Stephanie Kilaris Costa,Larissa Bastos Eloy da Lago,Eduardo Andreazza Dal Boin,Ilka de Fátima Santana Ferreira Caserta,Nelson Marcio Gomes Ataide,Elaine Cristina de |
author2_role |
author author author author author author author |
dc.contributor.author.fl_str_mv |
Lahan-Martins,Daniel Perales,Simone Reges Gallani,Stephanie Kilaris Costa,Larissa Bastos Eloy da Lago,Eduardo Andreazza Dal Boin,Ilka de Fátima Santana Ferreira Caserta,Nelson Marcio Gomes Ataide,Elaine Cristina de |
dc.subject.por.fl_str_mv |
Carcinoma, hepatocellular Tomography, X-ray computed Liver neoplasms/surgery Liver transplantation |
topic |
Carcinoma, hepatocellular Tomography, X-ray computed Liver neoplasms/surgery Liver transplantation |
description |
Abstract Objective: To investigate whether quantitative computed tomography (CT) measurements can predict microvascular invasion (MVI) in hepatocellular carcinoma (HCC). Materials and Methods: This was a retrospective analysis of 200 cases of surgically proven HCCs in 125 consecutive patients evaluated between March 2010 and November 2017. We quantitatively measured regions of interest in lesions and adjacent areas of the liver on unenhanced CT scans, as well as in the arterial, portal venous, and equilibrium phases on contrast-enhanced CT scans. Enhancement profiles were analyzed and compared with histopathological references of MVI. Univariate and multivariate logistic regression analyses were used in order to evaluate CT parameters as potential predictors of MVI. Results: Of the 200 HCCs, 77 (38.5%) showed evidence of MVI on histopathological analysis. There was no statistical difference between HCCs with MVI and those without, in terms of the percentage attenuation ratio in the portal venous phase (114.7 vs. 115.8) and equilibrium phase (126.7 vs. 128.2), as well as in terms of the relative washout ratio, also in the portal venous and equilibrium phases (15.0 vs. 8.2 and 31.4 vs. 26.3, respectively). Conclusion: Quantitative dynamic CT parameters measured in the preoperative period do not appear to correlate with MVI in HCC. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-10-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-39842019000500287 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-39842019000500287 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/0100-3984.2018.0123 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Publicação do Colégio Brasileiro de Radiologia e Diagnóstico por Imagem |
publisher.none.fl_str_mv |
Publicação do Colégio Brasileiro de Radiologia e Diagnóstico por Imagem |
dc.source.none.fl_str_mv |
Radiologia Brasileira v.52 n.5 2019 reponame:Radiologia Brasileira (Online) instname:Colégio Brasileiro de Radiologia e Diagnóstico por Imagem (CBR) instacron:CBR |
instname_str |
Colégio Brasileiro de Radiologia e Diagnóstico por Imagem (CBR) |
instacron_str |
CBR |
institution |
CBR |
reponame_str |
Radiologia Brasileira (Online) |
collection |
Radiologia Brasileira (Online) |
repository.name.fl_str_mv |
Radiologia Brasileira (Online) - Colégio Brasileiro de Radiologia e Diagnóstico por Imagem (CBR) |
repository.mail.fl_str_mv |
radiologiabrasileira@cbr.org.br |
_version_ |
1754208940253511680 |