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MRI-Based End-To-End Pediatric Low-Grade Glioma Segmentation and Classification.
- Source :
-
Canadian Association of Radiologists Journal . Feb2024, Vol. 75 Issue 1, p153-160. 8p. - Publication Year :
- 2024
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 08465371
- Volume :
- 75
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Canadian Association of Radiologists Journal
- Publication Type :
- Academic Journal
- Accession number :
- 175299007
- Full Text :
- https://doi.org/10.1177/08465371231184780