Quantitative MRI for Pediatric Optic Pathway Glioma Treatment Response

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.

Grant Number: 5UH3CA236536-05
NIH Institute/Center: NIH
Principal Investigator: Robert Avery