Quantitative MRI for Pediatric Optic Pathway Glioma Treatment Response

定量 MRI 评估儿童视神经胶质瘤治疗反应

• 批准号: 9927849
• 负责人: Robert Andrew Avery
• 金额: $ 47.74万
• 依托单位: CHILDREN'S HOSP OF PHILADELPHIA
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-17 至 2022-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9927849
• 关键词: 3-Dimensional; 8 year old; Address; Affect; Anterior; Biological Markers; Birth; Blindness; Brain; Brain Neoplasms; Categories; Child; Childhood; Childhood Brain Neoplasm; Clinical; Clinical Trials; Complete Blindness; Data; Disease Progression; Enrollment; Excision; Future; Glioma; Goals; Growth; Heterogeneity; Human; Image; Imaging Device; Incidence; Magnetic Resonance Imaging; Malignant Neoplasms; Manufacturer Name; Measurement; Measures; Methods; Modeling; Monitor; Neuraxis; Neurofibromatoses; Neurofibromatosis 1; Operative Surgical Procedures; Optic Chiasm; Optic Nerve; Optic Nerve Glioma; Optics; Outcome; Pathway interactions; Pattern; Pediatric Neoplasm; Phase III Clinical Trials; Physicians; Prediction of Response to Therapy; Predisposition; Protocols documentation; Resolution; Severity of illness; Shapes; Standardization; Structure; Syndrome; Time; Treatment Failure; Tumor Volume; Vision; Visual; Visual Acuity; Visual Pathways; automated segmentation; base; clinical care; clinically relevant; cohort; data harmonization; experience; functional outcomes; graphical user interface; image processing; imaging modality; imaging software; improved; machine learning algorithm; machine learning method; novel; precision medicine; quantitative imaging; response; standard of care; success; treatment response; tumor; two-dimensional; user-friendly

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.

抽象的 低度神经胶质瘤是儿童中最常见的脑肿瘤，通常涉及一个或多个结构 前视觉途径（即视神经，chiasm和tracts）。近20％的儿童 1型神经纤维瘤病（NF1）将形成前视觉途径的低级神经胶质瘤 光学途径神经胶质瘤（OPG）。 NF1-OPG不适合手术切除，可能会导致永久性 视力丧失范围从视力轻度下降到完全失明。 NF1-opgs的孩子通常 经历1至8岁之间的视力丧失，并通过大脑磁共振进行监测 成像（MRI）以评估疾病进展。但是，肿瘤的传统二维（2D）度量 大小不适合评估随着时间的变化以及NF1-OPG对治疗的反应。 我们的建议解决了缺乏健壮和标准化的定量成像（QI）工具和方法 NF1-OPG临床试验所需。我们将开发和验证一种新型的三维（3D）基于MRI的Qi 对这些独特的小儿肿瘤的自动化和全面量化的应用。我们将使用 机器学习算法可容纳来自不同制造商和协议的MRI序列。我们 假设新型的QI应用将准确评估临床试验中的治疗反应。在这个 项目，我们将验证我们的QI软件和机器学习方法以使其准确且自动化 使用NF1-OPG的3阶段临床试验的数据进行肿瘤体积和形状的测量。从这些 措施，我们将创建评估对治疗反应的方法，使医生能够使知情 和客观的治疗决策。 我们的具体目的是：1）制定全面的QI应用程序以执行准确的自动化 NF1-OPG的定量； 2）使用我们的3D QI测量肿瘤确定并预测治疗反应 体积; 3）使用视力结果验证我们的3D QI度量。 研究完成后，我们的QI应用可以通过识别NF1-OPG的临床护理来改变 确定有利与不利的治疗反应的最早时间。 Qi应用程序的能力 准确测量治疗反应，并在MRI制造商中统一数据，并 协议将标准化NF1-OPG临床试验必不可少的成像评估。