Radiomics and deep learning in lung cancer.
Strahlenther Onkol
; 196(10): 879-887, 2020 Oct.
Article
em En
| MEDLINE
| ID: mdl-32367456
ABSTRACT
Lung malignancies have been extensively characterized through radiomics and deep learning. By providing a three-dimensional characterization of the lesion, models based on radiomic features from computed tomography (CT) and positron-emission tomography (PET) have been developed to detect nodules, distinguish malignant from benign lesions, characterize their histology, stage, and genotype. Deep learning models have been applied to automatically segment organs at risk in lung cancer radiotherapy, stratify patients according to the risk for local and distant recurrence, and identify patients candidate for molecular targeted therapy and immunotherapy. Moreover, radiomics has also been applied successfully to predict side effects such as radiation- and immunotherapy-induced pneumonitis and differentiate lung injury from recurrence. Radiomics could also untap the potential for further use of the cone beam CT acquired for treatment image guidance, four-dimensional CT, and dose-volume data from radiotherapy treatment plans. Radiomics is expected to increasingly affect the clinical practice of treatment of lung tumors, optimizing the end-to-end diagnosis-treatment-follow-up chain. The main goal of this article is to provide an update on the current status of lung cancer radiomics.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Processamento de Imagem Assistida por Computador
/
Biologia Computacional
/
Aprendizado Profundo
/
Neoplasias Pulmonares
Tipo de estudo:
Etiology_studies
/
Guideline
/
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Strahlenther Onkol
Assunto da revista:
NEOPLASIAS
/
RADIOTERAPIA
Ano de publicação:
2020
Tipo de documento:
Article
País de afiliação:
Itália