Imaging Heterogeneity in Lung Cancer: Techniques, Applications, and Challenges.
AJR Am J Roentgenol
; 207(3): 534-43, 2016 Sep.
Article
em En
| MEDLINE
| ID: mdl-27305342
ABSTRACT
OBJECTIVE:
Texture analysis involves the mathematic processing of medical images to derive sets of numeric quantities that measure heterogeneity. Studies on lung cancer have shown that texture analysis may have a role in characterizing tumors and predicting patient outcome. This article outlines the mathematic basis of and the most recent literature on texture analysis in lung cancer imaging. We also describe the challenges facing the clinical implementation of texture analysis.CONCLUSION:
Texture analysis of lung cancer images has been applied successfully to FDG PET and CT scans. Different texture parameters have been shown to be predictive of the nature of disease and of patient outcome. In general, it appears that more heterogeneous tumors on imaging tend to be more aggressive and to be associated with poorer outcomes and that tumor heterogeneity on imaging decreases with treatment. Despite these promising results, there is a large variation in the reported data and strengths of association.Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Diagnóstico por Imagem
/
Interpretação de Imagem Assistida por Computador
/
Neoplasias Pulmonares
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2016
Tipo de documento:
Article