  Pediatric Low-Grade Neuroepithelial Tumors (PLGNT) are the most common
pediatric cancer type, accounting for 40% of brain tumors in children, and
identifying PLGNT molecular subtype is crucial for treatment planning. However,
the gold standard to determine the PLGNT subtype is biopsy, which can be
impractical or dangerous for patients. This research improves the performance
of Convolutional Neural Networks (CNNs) in classifying PLGNT subtypes through
MRI scans by introducing a loss function that specifically improves the model's
Area Under the Receiver Operating Characteristic (ROC) Curve (AUROC), offering
a non-invasive diagnostic alternative. In this study, a retrospective dataset
of 339 children with PLGNT (143 BRAF fusion, 71 with BRAF V600E mutation, and
125 non-BRAF) was curated. We employed a CNN model with Monte Carlo random data
splitting. The baseline model was trained using binary cross entropy (BCE), and
achieved an AUROC of 86.11% for differentiating BRAF fusion and BRAF V600E
mutations, which was improved to 87.71% using our proposed AUROC loss function
(p-value 0.045). With multiclass classification, the AUROC improved from 74.42%
to 76. 59% (p-value 0.0016).
