Virtual elastography ultrasound via generative adversarial network for breast cancer diagnosis.
Nat Commun
; 14(1): 788, 2023 02 11.
Article
en En
| MEDLINE
| ID: mdl-36774357
ABSTRACT
Elastography ultrasound (EUS) imaging is a vital ultrasound imaging modality. The current use of EUS faces many challenges, such as vulnerability to subjective manipulation, echo signal attenuation, and unknown risks of elastic pressure in certain delicate tissues. The hardware requirement of EUS also hinders the trend of miniaturization of ultrasound equipment. Here we show a cost-efficient solution by designing a deep neural network to synthesize virtual EUS (V-EUS) from conventional B-mode images. A total of 4580 breast tumor cases were collected from 15 medical centers, including a main cohort with 2501 cases for model establishment, an external dataset with 1730 cases and a portable dataset with 349 cases for testing. In the task of differentiating benign and malignant breast tumors, there is no significant difference between V-EUS and real EUS on high-end ultrasound, while the diagnostic performance of pocket-sized ultrasound can be improved by about 5% after V-EUS is equipped.
Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
Neoplasias de la Mama
/
Diagnóstico por Imagen de Elasticidad
Tipo de estudio:
Diagnostic_studies
Idioma:
En
Revista:
Nat Commun
Asunto de la revista:
BIOLOGIA
/
CIENCIA
Año:
2023
Tipo del documento:
Article