NFS-23. ASSOCIATION OF LOCALIZED MAGNETIC RESONANCE IMAGING FEATURES IN ANTERIOR VISUAL PATHWAY WITH VISUAL ACUITY LOSS AMONG CHILDREN WITH NF1-OPG

Abstract

Abstract BACKGROUND Half of children with optic pathway gliomas associated with neurofibromatosis type 1 (NF1-OPG) are at risk for vision acuity (VA) loss. NF1-OPGs manifest along the anterior visual pathway (AVP), where the overall shape and volume from MRI have been recently associated with VA loss. To investigate the added value of localized features within the optic nerves (ONs) and chiasm, we propose an automatic deep-learning framework for VA loss prediction from volumetric and radiomic analysis of ONs, chiasm, and AVP. METHODS We collected MRIs of 60 children with NF1-OPG from Children’s National Hospital (CNH, GE platform) and 75 from Children’s Hospital of Philadelphia (CHOP, Siemens platform). MRI sequences included T1-, T2-weighted and T2-FLAIR. Experts annotated the AVP to establish ground truth. Neuro-ophthalmic evaluation identified VA loss (LogMAR >= 0.2) in 52/135 children. Automatic AVP segmentation was performed using the Swin transformer network. ONs and chiasm were split through template-based registration. Brain volume, tumor location, child age, and 1,172 radiomic features were used to predict VA loss using support vector machines. Sequential feature selection associated with risk VA loss was done using sensitivity and univariate statistical tests (ANOVA). We compared predictions based on new ON and chiasm analysis with results based on AVP alone. RESULTS Balanced accuracy, sensitivity, specificity, and AUROC of VA loss prediction were 0.865, 0.882, 0.857, 0.899, respectively. Significant risk factors were maximal image intensity and gray level run length entropy in T2-FLAIR for ONs, and image intensity range in T2-FLAIR for chiasm. AUROC based on analysis of AVP alone dropped to 0.728 (p-value=0.028). CONCLUSIONS Deep learning-based analysis indicates an association of new localized MRI features in ONs and chiasm with VA loss, which show enhanced capability in predicting VA loss. This automated framework has the potential to accelerate and guide the treatment decisions for children with NF1-OPGs.