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

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 c...

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Autores principales: 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, de Azevedo-Marques, Paulo Mazzoncini
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Publicação do Colégio Brasileiro de Radiologia e Diagnóstico por Imagem 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8029936/
https://www.ncbi.nlm.nih.gov/pubmed/33854262
http://dx.doi.org/10.1590/0100-3984.2019.0135
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author 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
de Azevedo-Marques, Paulo Mazzoncini
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
de Azevedo-Marques, Paulo Mazzoncini
author_sort Ferreira Junior, José Raniery
collection PubMed
description 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 pubmed-80299362021-04-13 Radiomic analysis of lung cancer for the assessment of patient prognosis and intratumor heterogeneity 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 de Azevedo-Marques, Paulo Mazzoncini Radiol Bras Original Article 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 Imagem 2021 /pmc/articles/PMC8029936/ /pubmed/33854262 http://dx.doi.org/10.1590/0100-3984.2019.0135 Text en https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Article
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
de Azevedo-Marques, Paulo Mazzoncini
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
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_short 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
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8029936/
https://www.ncbi.nlm.nih.gov/pubmed/33854262
http://dx.doi.org/10.1590/0100-3984.2019.0135
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