Radiomic analysis of lung cancer for the assessment of patient prognosis and intratumor heterogeneity

Detalhes bibliográficos
Autor(a) principal: Ferreira Junior,José Raniery
Data de Publicação: 2021
Outros Autores: Koenigkam-Santos,Marcel, Machado,Camila Vilas Boas, Faleiros,Matheus Calil, Correia,Natália Santana Chiari, Cipriano,Federico Enrique Garcia, Fabro,Alexandre Todorovic, Azevedo-Marques,Paulo Mazzoncini de
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Radiologia Brasileira (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-39842021000200087
Resumo: Abstract Objective: To determine whether the radiomic features of lung lesions on computed tomography correlate with overall survival in lung cancer patients. Materials and Methods: This was a retrospective study involving 101 consecutive patients with malignant neoplasms confirmed by biopsy or surgery. On computed tomography images, the lesions were submitted to semi-automated segmentation and were characterized on the basis of 2,465 radiomic variables. The prognostic assessment was based on Kaplan-Meier analysis and log-rank tests, according to the median value of the radiomic variables. Results: Of the 101 patients evaluated, 28 died (16 dying from lung cancer), and 73 were censored, with a mean overall survival time of 1,819.4 days (95% confidence interval [95% CI]: 1,481.2-2,157.5). One radiomic feature (the mean of the Fourier transform) presented a difference on Kaplan-Meier curves (p < 0.05). A high-risk group of patients was identified on the basis of high values for the mean of the Fourier transform. In that group, the mean survival time was 1,465.4 days (95% CI: 985.2-1,945.6), with a hazard ratio of 2.12 (95% CI: 1.01-4.48). We also identified a low-risk group, in which the mean of the Fourier transform was low (mean survival time of 2,164.8 days; 95% CI: 1,745.4-2,584.1). Conclusion: A radiomic signature based on the Fourier transform correlates with overall survival, representing a prognostic biomarker for risk stratification in patients with lung cancer.
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spelling Radiomic analysis of lung cancer for the assessment of patient prognosis and intratumor heterogeneityTomography, X-ray computedRadiographic image interpretation, computer-assistedLung neoplasmsPrognosisAbstract Objective: To determine whether the radiomic features of lung lesions on computed tomography correlate with overall survival in lung cancer patients. Materials and Methods: This was a retrospective study involving 101 consecutive patients with malignant neoplasms confirmed by biopsy or surgery. On computed tomography images, the lesions were submitted to semi-automated segmentation and were characterized on the basis of 2,465 radiomic variables. The prognostic assessment was based on Kaplan-Meier analysis and log-rank tests, according to the median value of the radiomic variables. Results: Of the 101 patients evaluated, 28 died (16 dying from lung cancer), and 73 were censored, with a mean overall survival time of 1,819.4 days (95% confidence interval [95% CI]: 1,481.2-2,157.5). One radiomic feature (the mean of the Fourier transform) presented a difference on Kaplan-Meier curves (p < 0.05). A high-risk group of patients was identified on the basis of high values for the mean of the Fourier transform. In that group, the mean survival time was 1,465.4 days (95% CI: 985.2-1,945.6), with a hazard ratio of 2.12 (95% CI: 1.01-4.48). We also identified a low-risk group, in which the mean of the Fourier transform was low (mean survival time of 2,164.8 days; 95% CI: 1,745.4-2,584.1). Conclusion: A radiomic signature based on the Fourier transform correlates with overall survival, representing a prognostic biomarker for risk stratification in patients with lung cancer.Publicação do Colégio Brasileiro de Radiologia e Diagnóstico por Imagem2021-04-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-39842021000200087Radiologia Brasileira v.54 n.2 2021reponame:Radiologia Brasileira (Online)instname:Colégio Brasileiro de Radiologia e Diagnóstico por Imagem (CBR)instacron:CBR10.1590/0100-3984.2019.0135info:eu-repo/semantics/openAccessFerreira Junior,José RanieryKoenigkam-Santos,MarcelMachado,Camila Vilas BoasFaleiros,Matheus CalilCorreia,Natália Santana ChiariCipriano,Federico Enrique GarciaFabro,Alexandre TodorovicAzevedo-Marques,Paulo Mazzoncini deeng2021-03-24T00:00:00Zoai:scielo:S0100-39842021000200087Revistahttps://www.scielo.br/j/rb/https://old.scielo.br/oai/scielo-oai.phpradiologiabrasileira@cbr.org.br1678-70990100-3984opendoar:2021-03-24T00:00Radiologia Brasileira (Online) - Colégio Brasileiro de Radiologia e Diagnóstico por Imagem (CBR)false
dc.title.none.fl_str_mv Radiomic analysis of lung cancer for the assessment of patient prognosis and intratumor heterogeneity
title Radiomic analysis of lung cancer for the assessment of patient prognosis and intratumor heterogeneity
spellingShingle Radiomic analysis of lung cancer for the assessment of patient prognosis and intratumor heterogeneity
Ferreira Junior,José Raniery
Tomography, X-ray computed
Radiographic image interpretation, computer-assisted
Lung neoplasms
Prognosis
title_short Radiomic analysis of lung cancer for the assessment of patient prognosis and intratumor heterogeneity
title_full Radiomic analysis of lung cancer for the assessment of patient prognosis and intratumor heterogeneity
title_fullStr Radiomic analysis of lung cancer for the assessment of patient prognosis and intratumor heterogeneity
title_full_unstemmed Radiomic analysis of lung cancer for the assessment of patient prognosis and intratumor heterogeneity
title_sort Radiomic analysis of lung cancer for the assessment of patient prognosis and intratumor heterogeneity
author Ferreira Junior,José Raniery
author_facet Ferreira Junior,José Raniery
Koenigkam-Santos,Marcel
Machado,Camila Vilas Boas
Faleiros,Matheus Calil
Correia,Natália Santana Chiari
Cipriano,Federico Enrique Garcia
Fabro,Alexandre Todorovic
Azevedo-Marques,Paulo Mazzoncini de
author_role author
author2 Koenigkam-Santos,Marcel
Machado,Camila Vilas Boas
Faleiros,Matheus Calil
Correia,Natália Santana Chiari
Cipriano,Federico Enrique Garcia
Fabro,Alexandre Todorovic
Azevedo-Marques,Paulo Mazzoncini de
author2_role author
author
author
author
author
author
author
dc.contributor.author.fl_str_mv Ferreira Junior,José Raniery
Koenigkam-Santos,Marcel
Machado,Camila Vilas Boas
Faleiros,Matheus Calil
Correia,Natália Santana Chiari
Cipriano,Federico Enrique Garcia
Fabro,Alexandre Todorovic
Azevedo-Marques,Paulo Mazzoncini de
dc.subject.por.fl_str_mv Tomography, X-ray computed
Radiographic image interpretation, computer-assisted
Lung neoplasms
Prognosis
topic Tomography, X-ray computed
Radiographic image interpretation, computer-assisted
Lung neoplasms
Prognosis
description Abstract Objective: To determine whether the radiomic features of lung lesions on computed tomography correlate with overall survival in lung cancer patients. Materials and Methods: This was a retrospective study involving 101 consecutive patients with malignant neoplasms confirmed by biopsy or surgery. On computed tomography images, the lesions were submitted to semi-automated segmentation and were characterized on the basis of 2,465 radiomic variables. The prognostic assessment was based on Kaplan-Meier analysis and log-rank tests, according to the median value of the radiomic variables. Results: Of the 101 patients evaluated, 28 died (16 dying from lung cancer), and 73 were censored, with a mean overall survival time of 1,819.4 days (95% confidence interval [95% CI]: 1,481.2-2,157.5). One radiomic feature (the mean of the Fourier transform) presented a difference on Kaplan-Meier curves (p < 0.05). A high-risk group of patients was identified on the basis of high values for the mean of the Fourier transform. In that group, the mean survival time was 1,465.4 days (95% CI: 985.2-1,945.6), with a hazard ratio of 2.12 (95% CI: 1.01-4.48). We also identified a low-risk group, in which the mean of the Fourier transform was low (mean survival time of 2,164.8 days; 95% CI: 1,745.4-2,584.1). Conclusion: A radiomic signature based on the Fourier transform correlates with overall survival, representing a prognostic biomarker for risk stratification in patients with lung cancer.
publishDate 2021
dc.date.none.fl_str_mv 2021-04-01
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=S0100-39842021000200087
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-39842021000200087
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/0100-3984.2019.0135
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.54 n.2 2021
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
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