Radiomics in liver diseases: Current progress and future opportunities.
Liver Int
; 40(9): 2050-2063, 2020 09.
Article
em En
| MEDLINE
| ID: mdl-32515148
ABSTRACT
Liver diseases, a wide spectrum of pathologies from inflammation to neoplasm, have become an increasingly significant health problem worldwide. Noninvasive imaging plays a critical role in the clinical workflow of liver diseases, but conventional imaging assessment may provide limited information. Accurate detection, characterization and monitoring remain challenging. With progress in quantitative imaging analysis techniques, radiomics emerged as an efficient tool that shows promise to aid in personalized diagnosis and treatment decision-making. Radiomics could reflect the heterogeneity of liver lesions via extracting high-throughput and high-dimensional features from multi-modality imaging. Machine learning algorithms are then used to construct clinical target-oriented imaging biomarkers to assist disease management. Here, we review the methodological process in liver disease radiomics studies in a stepwise fashion from data acquisition and curation, region of interest segmentation, liver-specific feature extraction, to task-oriented modelling. Furthermore, the applications of radiomics in liver diseases are outlined in aspects of diagnosis and staging, evaluation of liver tumour biological behaviours, and prognosis according to different disease type. Finally, we discuss the current limitations of radiomics in liver disease studies and explore its future opportunities.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Aprendizado de Máquina
/
Neoplasias Hepáticas
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2020
Tipo de documento:
Article