Artificial intelligence in lung cancer: current applications and perspectives.
Jpn J Radiol
; 41(3): 235-244, 2023 Mar.
Article
en En
| MEDLINE
| ID: mdl-36350524
ABSTRACT
Artificial intelligence (AI) has been a very active research topic over the last years and thoracic imaging has particularly benefited from the development of AI and in particular deep learning. We have now entered a phase of adopting AI into clinical practice. The objective of this article was to review the current applications and perspectives of AI in thoracic oncology. For pulmonary nodule detection, computer-aided detection (CADe) tools have been commercially available since the early 2000s. The more recent rise of deep learning and the availability of large annotated lung nodule datasets have allowed the development of new CADe tools with fewer false-positive results per examination. Classical machine learning and deep-learning methods were also used for pulmonary nodule segmentation allowing nodule volumetry and pulmonary nodule characterization. For pulmonary nodule characterization, radiomics and deep-learning approaches were used. Data from the National Lung Cancer Screening Trial (NLST) allowed the development of several computer-aided diagnostic (CADx) tools for diagnosing lung cancer on chest computed tomography. Finally, AI has been used as a means to perform virtual biopsies and to predict response to treatment or survival. Thus, many detection, characterization and stratification tools have been proposed, some of which are commercially available.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Aprendizaje Profundo
/
Neoplasias Pulmonares
Límite:
Humans
Idioma:
En
Revista:
Jpn J Radiol
Asunto de la revista:
DIAGNOSTICO POR IMAGEM
/
RADIOLOGIA
/
RADIOTERAPIA
Año:
2023
Tipo del documento:
Article
País de afiliación:
Francia