Radiomic analysis of lung cancer for the assessment of patient prognosis and intratumor heterogeneity
Autor(a) principal: | |
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Data de Publicação: | 2021 |
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=S0100-39842021000200087 |
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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Radiologia Brasileira (Online) |
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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 |
_version_ |
1754208941016875008 |