MRI-Based End-To-End Pediatric Low-Grade Glioma Segmentation and Classification.
Can Assoc Radiol J
; 75(1): 153-160, 2024 Feb.
Article
em En
| MEDLINE
| ID: mdl-37401906
ABSTRACT
Purpose:
MRI-based radiomics models can predict genetic markers in pediatric low-grade glioma (pLGG). These models usually require tumour segmentation, which is tedious and time consuming if done manually. We propose a deep learning (DL) model to automate tumour segmentation and build an end-to-end radiomics-based pipeline for pLGG classification.Methods:
The proposed architecture is a 2-step U-Net based DL network. The first U-Net is trained on downsampled images to locate the tumour. The second U-Net is trained using image patches centred around the located tumour to produce more refined segmentations. The segmented tumour is then fed into a radiomics-based model to predict the genetic marker of the tumour.Results:
Our segmentation model achieved a correlation value of over 80% for all volume-related radiomic features and an average Dice score of .795 in test cases. Feeding the auto-segmentation results into a radiomics model resulted in a mean area under the ROC curve (AUC) of .843, with 95% confidence interval (CI) [.78-.906] and .730, with 95% CI [.671-.789] on the test set for 2-class (BRAF V600E mutation BRAF fusion) and 3-class (BRAF V600E mutation BRAF fusion and Other) classification, respectively. This result was comparable to the AUC of .874, 95% CI [.829-.919] and .758, 95% CI [.724-.792] for the radiomics model trained and tested on the manual segmentations in 2-class and 3-class classification scenarios, respectively.Conclusion:
The proposed end-to-end pipeline for pLGG segmentation and classification produced results comparable to manual segmentation when it was used for a radiomics-based genetic marker prediction model.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Proteínas Proto-Oncogênicas B-raf
/
Glioma
Tipo de estudo:
Prognostic_studies
Limite:
Child
/
Humans
Idioma:
En
Revista:
Can Assoc Radiol J
Assunto da revista:
RADIOLOGIA
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Canadá