Cloud quantitative imaging for whole-body tumor burden in neurofibromatoses

神经纤维瘤全身肿瘤负荷的云定量成像

• 批准号: 9762866
• 负责人: Wenli Cai
• 金额: $ 71.94万
• 依托单位: IQ MEDICAL IMAGING, LLC
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-05-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9762866
• 关键词: Agreement; Algorithms; Cancer Center; Clinic; Clinical; Clinical Research; Clinical Services; Cloud Computing; Communities; Computer software; Data Collection; Development; Disease; Documentation; Evaluation; Evaluation Studies; Funding; General Hospitals; Goals; Hospitals; Image Analysis; Infrastructure; Internet; Intuition; Letters; Licensing; Life; Magnetic Resonance Imaging; Massachusetts; Measurement; Medical Device; Medical Imaging; Methods; Modeling; Monitor; NF1 gene; Names; Nerve Sheath Tumors; Neurofibromatoses; Patients; Performance; Phase; Plexiform Neurofibroma; Predisposition; Preparation; Privatization; Progression-Free Survivals; Recovery; Regulation; Reporting; Reproducibility; Running; Schwannomatosis; Sensitivity and Specificity; Site; Small Business Technology Transfer Research; Software Tools; Structure; System; Techniques; Technology; Three-Dimensional Imaging; Time; Translating; Translations; Tumor Burden; Tumor Volume; Validation; Vendor; base; clinical translation; cloud platform; cost; deep learning; end of life; follow-up; imaging modality; improved; interest; neurogenetics; predict clinical outcome; prospective; prototype; quantitative imaging; research clinical testing; software as a service; software development; treatment response; tumor; tumor progression; virtual; whole body imaging

Project Summary / Abstract The neurofibromatoses (NFs), including NF1, NF2, and schwannomatosis, are a group of autosomal-dominant neurogenetic disorders characterized by a predisposition in virtually 100% of patients to develop multiple nerve sheath tumors. The determination of tumor burden on magnetic resonance imaging (MRI) is indispensable for the longitudinal management of NF patients, which includes life-long follow-up for the monitoring of tumor progression and the assessment of treatment response. However, volumetric quantification of NF tumors is not a clinical routine because of the technical challenges in the accurate and reproducible segmentation of highly-irregular and infiltrating NF tumors, in particular plexiform neurofibromas on MRI. The goal of this STTR project is to develop cloud quantitative imaging (CQI) for NF software, denoted as CQI-NF, which will provide the technical and clinical service for volumetric quantification of NF tumors on whole-body and regional MRI via "virtualization" (cloud computing) technology. The product developed in this STTR will provide access to this technology for the NF clinical community nationwide and worldwide without the excessive cost to maintain on-site advanced volumetric imaging analysis software and hardware. This project will be built upon existing technologies for volumetric imaging analysis developed on the software platform “3DQI” in the 3D Imaging Lab at Massachusetts General Hospital (MGH), and will be evaluated using 200 longitudinal whole-body and regional MRI cases collected from the NF community worldwide. The specific aims of this Phase II project are: (1) Development of CQI-NF system: We will continue to develop the CQI-NF system prototyped in our Phase I project to improve the accuracy and efficiency of segmentation by paralleling dynamic-threshold level set (DT level set) in multi-server platform, combining deep-learning in DT level set for the segmentation of NF tumors, and to translate the CQI-NF system from 3DQI/NF into a private cloud platform (such as TeraRecon's iNtuition CLOUD) for the provision of volumetric quantification of NF using a software-as-a-service (SaaS) model in the NF community. (2) Evaluation of CQI-NF system: We will conduct a retrospective clinical study to evaluate the accuracy and reproducibility of the CQI-NF system in the longitudinal monitoring of 200 NF patients collected at MGH Cancer Center and our clinical collaborators worldwide in the NF community. (3) Preparation of FDA 510(k) clearance submission: We will establish the quality management system for CQI-NF to meet FDA regulation, and prepare the required documentation for FDA 510(k) clearance submission for the long-term project goal of clinical translation of CQI-NF. The successful development and validation of the proposed CQI-NF system will have a high clinical impact in the NF community by providing a cloud-computing infrastructure for the volumetric imaging analysis of NF on MRI, which is not available in current clinical routine, thereby leading to a substantial advance in the longitudinal management of NF patients.

项目概要/摘要 神经纤维瘤病 (NF)，包括 NF1、NF2 和神经鞘瘤病，是一组常染色体显性遗传病 神经遗传性疾病，其特征是几乎 100% 的患者倾向于出现多条神经 磁共振成像（MRI）确定肿瘤负荷对于鞘膜肿瘤是必不可少的。 NF 患者的纵向管理，包括对肿瘤监测的终身随访 然而，NF 肿瘤的体积量化并不是一个有效的方法。 由于准确和可重复的分割方面的技术挑战，临床常规 高度不规则和浸润性神经纤维瘤，特别是 MRI 上的丛状神经纤维瘤。 该 STTR 项目的目标是开发用于 NF 软件的云定量成像（CQI），表示为 CQI-NF，将为NF肿瘤的体积定量提供技术和临床服务 通过“虚拟化”（云计算）技术进行全身和局部 MRI 开发的产品。 STTR 将为全国和全球的 NF 临床社区提供使用该技术的机会，而无需 该项目维护现场先进体积成像分析软件和硬件的成本过高。 将建立在软件平台上开发的现有体积成像分析技术的基础上 马萨诸塞州综合医院 (MGH) 3D 成像实验室的“3DQI”将使用 200 从全球 NF 社区收集的纵向全身和局部 MRI 病例。 二期项目的具体目标是： （1）CQI-NF系统的开发：我们将继续开发 我们在第一阶段项目中原型化的 CQI-NF 系统通过以下方式提高分割的准确性和效率： 多服务器平台中并行动态阈值水平集（DT水平集），结合DT中的深度学习 用于 NF 肿瘤分割的水平集，并将 CQI-NF 系统从 3DQI/NF 转换为私有 云平台（例如 TeraRecon 的 iNtuition CLOUD），用于使用 NF 进行体积量化 NF社区的软件即服务（SaaS）模式（2）CQI-NF系统的评估：我们将进行一个评估。 回顾性临床研究，以纵向评估 CQI-NF 系统的准确性和可重复性 对 MGH 癌症中心和我们在 NF 的全球临床合作者收集的 200 名 NF 患者进行监测 (3) 准备 FDA 510(k) 许可提交：我们将建立质量管理 CQI-NF 系统以满足 FDA 法规的要求，并准备 FDA 510(k) 许可所需的文件 提交CQI-NF临床转化的长期项目目标。 所提出的 CQI-NF 系统的成功开发和验证将对以下领域产生重大临床影响： NF 社区通过为 MRI 上 NF 的体积成像分析提供云计算基础设施， 这在当前的临床常规中是不可用的，从而导致纵向的实质性进步 NF 患者的管理。

项目成果

Ten-Year Follow-up of Internal Neurofibroma Growth Behavior in Adult Patients With Neurofibromatosis Type 1 Using Whole-Body MRI.

使用全身 MRI 对 1 型神经纤维瘤病成年患者的内部神经纤维瘤生长行为进行十年随访。

• DOI: 10.1212/wnl.0000000000201535
• 发表时间: 2023
• 期刊: Neurology
• 影响因子: 9.9
• 作者: Ly,KIna;Merker,VanessaL;Cai,Wenli;Bredella,MiriamA;Muzikansky,Alona;Thalheimer,RaquelD;Da,JenniferLiwei;Orr,ChristinaC;Herr,HamiltonP;Morris,MaryE;Chang,ConnieY;Harris,GordonJ;Plotkin,ScottR;Jordan,JustinT
• 通讯作者: Jordan,JustinT