Quantitative MRI for Pediatric Optic Pathway Glioma Treatment Response

摘要 低级别胶质瘤是儿童中最常见的脑肿瘤，通常涉及一个或多个脑组织结构。 前视觉通路（即，视神经、视交叉和视束）。近20%的儿童 神经纤维瘤病1型（NF 1）将发展为前部视觉通路的低度胶质瘤，称为 视神经胶质瘤（optic pathway glioma，OPG）。NF 1-OPG不适合手术切除，可导致永久性 视力丧失，范围从视力的轻度下降到完全失明。患有NF 1-OPG的儿童通常 在1至8岁之间经历视力丧失，并通过脑磁共振进行监测 MRI检查以评估疾病进展。然而，传统的肿瘤的二维（2D）测量 大小不适合评估随时间的变化以及NF 1-OPG对治疗的反应。 我们的建议解决了缺乏强大和标准化的定量成像（QI）工具和方法的问题 用于NF 1-OPG临床试验。我们将开发并验证一种新的基于三维（3D）MRI的QI 这是一个自动化和全面量化这些独特的儿科肿瘤的应用程序。我们将使用 机器学习算法，以适应来自不同制造商和协议的MRI序列。我们 假设新的QI应用程序将准确评估临床试验中的治疗反应。在这 项目，我们将验证我们的QI软件和机器学习方法，使准确和自动化 使用NF 1-OPG 3期临床试验的数据测量肿瘤体积和形状。从这些 我们将创建方法来评估对治疗的反应，使医生能够了解 客观的治疗决策。 我们的具体目标是：1）开发全面的QI应用程序，以执行准确的自动化 NF 1-OPG的定量; 2）使用我们的肿瘤3D QI测量确定和预测治疗反应 体积;和3）使用视力结果来测量3D QI。 研究完成后，我们的QI应用程序可以通过识别 确定有利与不利治疗反应的最早时间。QI应用程序能够 准确测量治疗反应，沿着协调MRI制造商之间的数据， 方案，将标准化NF 1-OPG临床试验所必需的成像评估。

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.