  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.
