Deep learning using contrast-enhanced ultrasound images to predict the nuclear grade of clear cell renal cell carcinoma.
World J Urol
; 42(1): 184, 2024 Mar 21.
Article
en En
| MEDLINE
| ID: mdl-38512539
ABSTRACT
PURPOSE:
To assess the effectiveness of a deep learning model using contrastenhanced ultrasound (CEUS) images in distinguishing between low-grade (grade I and II) and high-grade (grade III and IV) clear cell renal cell carcinoma (ccRCC).METHODS:
A retrospective study was conducted using CEUS images of 177 Fuhrmangraded ccRCCs (93 low-grade and 84 high-grade) from May 2017 to December 2020. A total of 6412 CEUS images were captured from the videos and normalized for subsequent analysis. A deep learning model using the RepVGG architecture was proposed to differentiate between low-grade and high-grade ccRCC. The model's performance was evaluated based on sensitivity, specificity, positive predictive value, negative predictive value and area under the receiver operating characteristic curve (AUC). Class activation mapping (CAM) was used to visualize the specific areas that contribute to the model's predictions.RESULTS:
For discriminating high-grade ccRCC from low-grade, the deep learning model achieved a sensitivity of 74.8%, specificity of 79.1%, accuracy of 77.0%, and an AUC of 0.852 in the test set.CONCLUSION:
The deep learning model based on CEUS images can accurately differentiate between low-grade and high-grade ccRCC in a non-invasive manner.Palabras clave
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Carcinoma de Células Renales
/
Aprendizaje Profundo
/
Neoplasias Renales
Límite:
Humans
Idioma:
En
Revista:
World J Urol
Año:
2024
Tipo del documento:
Article
País de afiliación:
China