CT-based identification of pediatric non-Wilms tumors using convolutional neural networks at a single center.
Pediatr Res
; 94(3): 1104-1110, 2023 09.
Article
en En
| MEDLINE
| ID: mdl-36959318
ABSTRACT
BACKGROUND:
Deep learning (DL) is more and more widely used in children's medical treatment. In this study, we have developed a computed tomography (CT)-based DL model for identifying undiagnosed non-Wilms tumors (nWTs) from pediatric renal tumors.METHODS:
This study collected and analyzed the preoperative clinical data and CT images of pediatric renal tumor patients diagnosed by our center from 2008 to 2020, and established a DL model to identify nWTs noninvasively.RESULTS:
A total of 364 children who had been confirmed by histopathology with renal tumors from our center were enrolled, including 269 Wilms tumors (WTs) and 95 nWTs. For DL model development, all cases were randomly allocated to training set (218 cases), validation set (73 cases), and test set (73 cases). In the test set, the DL model achieved area under the curve of 0.831 (95% CI 0.712-0.951) in discriminating WTs from nWTs, with the accuracy, sensitivity, and specificity of 0.781, 0.563, and 0.842, respectively. The sensitivity of our model was higher than a radiologist with 15 years of experience.CONCLUSIONS:
We presented a DL model for identifying undiagnosed nWTs from pediatric renal tumors, with the potential to improve the image-based diagnosis. IMPACT Deep learning model was used for the first time to identify pediatric renal tumors in this study. Deep learning model can identify non-Wilms tumors from pediatric renal tumors. Deep learning model based on computed tomography images can improve tumor diagnosis rate.
Texto completo:
1
Colección:
01-internacional
Asunto principal:
Tumor de Wilms
/
Neoplasias Renales
Tipo de estudio:
Diagnostic_studies
/
Observational_studies
/
Prognostic_studies
Límite:
Child
/
Humans
Idioma:
En
Revista:
Pediatr Res
Año:
2023
Tipo del documento:
Article
País de afiliación:
China