Improving Deep Learning Models for Pediatric Low-Grade Glioma Tumors Molecular Subtype Identification Using 3D Probability Distributions of Tumor Location

• URL: http://arxiv.org/abs/2210.07287v2
• Date: Tue, 24 Oct 2023 18:47:41 GMT
• Title: Improving Deep Learning Models for Pediatric Low-Grade Glioma Tumors Molecular Subtype Identification Using 3D Probability Distributions of Tumor Location
• Authors: Khashayar Namdar, Matthias W. Wagner, Kareem Kudus, Cynthia Hawkins, Uri Tabori, Brigit Ertl-Wagner, Farzad Khalvati
• Abstract summary: CNN models for pLGG subtype identification rely on tumor segmentation. We propose to augment the CNN models using tumor location probability in MRI data. We achieved statistically significant improvements by incorporating tumor location into the CNN models.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Background and Purpose: Pediatric low-grade glioma (pLGG) is the most common type of brain tumor in children, and identification of molecular markers for pLGG is crucial for successful treatment planning. Convolutional Neural Network (CNN) models for pLGG subtype identification rely on tumor segmentation. We hypothesize tumor segmentations are suboptimal and thus, we propose to augment the CNN models using tumor location probability in MRI data. Materials and Methods: Our REB-approved retrospective study included MRI Fluid-Attenuated Inversion Recovery (FLAIR) sequences of 143 BRAF fused and 71 BRAF V600E mutated tumors. Tumor segmentations (regions of interest (ROIs)) were provided by a pediatric neuroradiology fellow and verified by a senior pediatric neuroradiologist. In each experiment, we randomly split the data into development and test with an 80/20 ratio. We combined the 3D binary ROI masks for each class in the development dataset to derive the probability density functions (PDF) of tumor location, and developed three pipelines: location-based, CNN-based, and hybrid. Results: We repeated the experiment with different model initializations and data splits 100 times and calculated the Area Under Receiver Operating Characteristic Curve (AUC). The location-based classifier achieved an AUC of 77.90, 95% confidence interval (CI) (76.76, 79.03). CNN-based classifiers achieved AUC of 86.11, CI (84.96, 87.25), while the tumor-location-guided CNNs outperformed the formers with an average AUC of 88.64 CI (87.57, 89.72), which was statistically significant (Student's t-test p-value 0.0018). Conclusion: We achieved statistically significant improvements by incorporating tumor location into the CNN models. Our results suggest that manually segmented ROIs may not be optimal.

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