Quantitative MRI for Pediatric Optic Pathway Glioma Treatment Response

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ABSTRACT Low-grade glioma is the most common brain tumor in children and often involves one or more structures of the anterior visual pathway (i.e., optic nerves, chiasm and tracts). Nearly 20% of children with neurofibromatosis type 1 (NF1) will develop a low-grade glioma of the anterior visual pathway, which are called optic pathway gliomas (OPGs). NF1-OPGs are not amenable to surgical resection and can cause permanent vision loss ranging from a mild decline in visual acuity to complete blindness. Children with NF1-OPGs typically experience vision loss between 1 and 8 years of age and are monitored with brain magnetic resonance imaging (MRI) to assess disease progression. However, traditional two-dimensional (2D) measures of tumor size are not appropriate to assess change over time and how NF1-OPGs are responding to treatment. Our proposal addresses the lack of robust and standardized quantitative imaging (QI) tools and methods needed for NF1-OPG clinical trials. We will develop and validate a novel three-dimensional (3D) MRI-based QI application for automated and comprehensive quantification of these unique pediatric tumors. We will use machine learning algorithms to accommodate MRI sequences from different manufacturers and protocols. We hypothesize that the novel QI application will accurately assess treatment response in clinical trials. In this project, we will validate our QI software and machine learning methods to make accurate and automated measures of tumor volume and shape using data from a phase 3 clinical trial of NF1-OPGs. From these measures, we will create methods to assess response to therapy that will enable physicians to make informed and objective treatment decisions. Our specific aims are: 1) Develop a comprehensive QI application to perform accurate automated quantification of NF1-OPGs; 2) Determine and predict treatment response using our 3D QI measures of tumor volume; and 3) Validate our 3D QI measures using visual acuity outcomes. Upon study completion, our QI application could transform clinical care for NF1-OPG by identifying the earliest time to determine a favorable versus unfavorable treatment response. The QI application's ability to accurately measure treatment response, along with harmonizing data across MRI manufacturers and protocols, will standardize imaging assessments essential to NF1-OPG clinical trials.