Tumor-location-guided CNNs for Pediatric Low-grade Glioma Molecular Biomarker Classification Using MRI
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Pediatric low-grade glioma (pLGG) is the most common type of brain cancer among children, and the identification of molecular markers for pLGG is crucial for successful treatment planning. Current standard care is biopsy, which is invasive. Thus, the non-invasive imaging-based approaches, where Machine Learning (ML) has a high potential, are impactful. Recently, we developed a tumor-location-based algorithm and demonstrated its potential to differentiate pLGG molecular subtypes. In this work, we first reevaluated the performance of the location-based algorithm on a larger pLGG dataset, which includes 214 patients and achieved an area under the receiver operating characteristic curve (AUROC) of 77.90. A Convolutional Neural Network (CNN) based algorithm increased the average AUROC to 86.11. Ultimately, we designed and implemented a tumor-location-guided CNN algorithm and achieved average AUROC of 88.64. Using a repeated experiment approach with 100 runs, we ensured the results were reproducible and the improvement was statistically significant.
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