Multi-center application of a convolutional neural network for preoperative detection of cavernous sinus invasion in pituitary adenomas.
Neuroradiology
; 66(3): 353-360, 2024 Mar.
Article
em En
| MEDLINE
| ID: mdl-38236424
ABSTRACT
OBJECTIVE:
Cavernous sinus invasion (CSI) plays a pivotal role in determining management in pituitary adenomas. The study aimed to develop a Convolutional Neural Network (CNN) model to diagnose CSI in multiple centers.METHODS:
A total of 729 cases were retrospectively obtained in five medical centers with (n = 543) or without CSI (n = 186) from January 2011 to December 2021. The CNN model was trained using T1-enhanced MRI from two pituitary centers of excellence (n = 647). The other three municipal centers (n = 82) as the external testing set were imported to evaluate the model performance. The area-under-the-receiver-operating-characteristic-curve values (AUC-ROC) analyses were employed to evaluate predicted performance. Gradient-weighted class activation mapping (Grad-CAM) was used to determine models' regions of interest.RESULTS:
The CNN model achieved high diagnostic accuracy (0.89) in identifying CSI in the external testing set, with an AUC-ROC value of 0.92 (95% CI, 0.88-0.97), better than CSI clinical predictor of diameter (AUC-ROC 0.75), length (AUC-ROC 0.80), and the three kinds of dichotomizations of the Knosp grading system (AUC-ROC 0.70-0.82). In cases with Knosp grade 3A (n = 24, CSI rate, 0.35), the accuracy the model accounted for 0.78, with sensitivity and specificity values of 0.72 and 0.78, respectively. According to the Grad-CAM results, the views of the model were confirmed around the sellar region with CSI.CONCLUSIONS:
The deep learning model is capable of accurately identifying CSI and satisfactorily able to localize CSI in multicenters.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Neoplasias Hipofisárias
/
Adenoma
/
Seio Cavernoso
Tipo de estudo:
Diagnostic_studies
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
Neuroradiology
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
China