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: 10681375
• 负责人: Robert Andrew Avery
• 金额: $ 71.15万
• 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 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10681375
• 关键词: 3-Dimensional; 8 year old; Address; Affect; Agreement; Anterior; Biological Markers; Birth; Blindness; Brain Neoplasms; Categories; Central Nervous System; Child; Childhood; Childhood Brain Neoplasm; Clinical; Clinical Trials; Complete Blindness; Data; Devices; Disease Progression; Enrollment; Excision; Friends; Future; Glioma; Goals; Growth; Heterogeneity; Human; Image; Imaging Device; Incidence; Magnetic Resonance Imaging; Manufacturer; Measurement; Measures; Methods; Modeling; Monitor; 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; Protocols documentation; Recommendation; Resolution; Severity of illness; Shapes; Standardization; Structure; Syndrome; Time; Treatment Failure; Tumor Volume; Vision; Visual; Visual Acuity; Visual Pathways; Visualization; automated segmentation; brain magnetic resonance imaging; cancer predisposition; clinical care; clinical predictors; 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 低度恶性胶质瘤是儿童中最常见的脑肿瘤，通常涉及一种或多种结构 前视觉通路（即视神经、视交叉和视束）。近20%的儿童患有 1 型神经纤维瘤病 (NF1) 会形成前视觉通路的低级别神经胶质瘤，称为 视神经胶质瘤（OPG）。 NF1-OPG 不适合手术切除，并且可能导致永久性 视力丧失，范围从视力轻微下降到完全失明。患有 NF1-OPG 的儿童通常 在 1 至 8 岁之间出现视力丧失，并通过脑磁共振进行监测 成像（MRI）来评估疾病进展。然而，传统的肿瘤二维 (2D) 测量 大小不适合评估随时间的变化以及 NF1-OPG 对治疗的反应。 我们的提案解决了缺乏稳健且标准化的定量成像 (QI) 工具和方法的问题 NF1-OPG 临床试验所需。我们将开发并验证一种新颖的基于三维 (3D) MRI 的 QI 应用程序对这些独特的儿科肿瘤进行自动和全面的定量。我们将使用 机器学习算法可适应不同制造商和协议的 MRI 序列。我们 假设新颖的 QI 应用将准确评估临床试验中的治疗反应。在这个 项目中，我们将验证我们的 QI 软件和机器学习方法，以实现准确和自动化 使用 NF1-OPG 3 期临床试验的数据测量肿瘤体积和形状。从这些 措施，我们将创建评估治疗反应的方法，使医生能够做出明智的决定 和客观的治疗决定。 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 NF1-OPG 的定量； 2) 使用我们的肿瘤 3D QI 测量来确定和预测治疗反应 体积; 3) 使用视力结果验证我们的 3D QI 测量。 研究完成后，我们的 QI 应用程序可以通过识别 NF1-OPG 的临床护理来改变 确定有利或不利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，以及协调 MRI 制造商和 协议将标准化 NF1-OPG 临床试验所必需的影像评估。

项目成果

The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs).

2023 年脑肿瘤分割 (BraTS) 挑战：关注儿科 (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PED)。

• 发表时间: 2024
• 期刊: ArXiv
• 作者: Kazerooni,AnahitaFathi;Khalili,Nastaran;Liu,Xinyang;Haldar,Debanjan;Jiang,Zhifan;Anwar,SyedMuhammed;Albrecht,Jake;Adewole,Maruf;Anazodo,Udunna;Anderson,Hannah;Bagheri,Sina;Baid,Ujjwal;Bergquist,Timothy;Borja,AustinJ;Calabres
• 通讯作者: Calabres