Lymph Node Quantification System for Multisite Clinical Trials

用于多站点临床试验的淋巴结定量系统

• 批准号: 10687096
• 负责人: GORDON J HARRIS
• 金额: $ 59.58万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10687096
• 关键词: Address; Artificial Intelligence; Back; Basic Science; Cancer Center; Cancer Patient; Clinical; Clinical Management; Clinical Oncology; Clinical Research; Clinical Trials; Clinical Trials Database; Clinical assessments; Data; Data Set; Databases; Diagnostic; Disease; Disease Progression; Disease model; Evaluation; Feedback; Functional Imaging; Gene Expression Profiling; Goals; Hodgkin Disease; Human; Image; Image Analysis; Imaging Device; Informatics; Investments; Laboratories; Lesion; Lymphoma; Machine Learning; Malignant Neoplasms; Manuals; Maps; Measurement; Measures; Metabolic; Metabolism; Morphology; Multi-Institutional Clinical Trial; NCI-Designated Cancer Center; Nodal; Pathology; Pathway interactions; Patients; Performance; Phenotype; Positron-Emission Tomography; Quantitative Evaluations; Relapse; Reliability of Results; Reporting; Research Personnel; Risk; Scanning; Scientific Advances and Accomplishments; Services; Solid Neoplasm; Source; Staging; Standardization; Structure; Surrogate Endpoint; System; Technology; Time; Training; Translating; Treatment Protocols; Tumor Burden; Work; X-Ray Computed Tomography; anatomic imaging; automated segmentation; burden of illness; cancer clinical trial; cancer imaging; cancer therapy; clinical practice; cloud based; cohort; commercialization; design; experience; glucose metabolism; imaging Segmentation; improved; industry partner; innovation; lymph nodes; multidisciplinary; new technology; novel; novel therapeutics; participant enrollment; precision medicine; prognostic indicator; quantitative imaging; radiologist; success; task analysis; tool; treatment effect; treatment response; treatment strategy; tumor; usability

Project Summary / Abstract In patients with lymphomas and other cancers, quantitative evaluation of the extent of tumor burden is im- portant for staging, restaging, and assessment of therapeutic response or relapse; yet measurement of overall tumor burden is challenging with current tools, particularly when lymph nodes are confluent or difficult to fully differentiate from surrounding structures. Precision medicine and novel therapeutics are emphasizing the need to introduce a risk-adapted approach to tailor appropriate treatment strategies for cancer patients. The ability to quantitatively assess cancer phenotypes with functional and anatomical imaging that could efficiently and ac- curately map patients to gene expression profiling, clinical information, matching cohorts, and novel treatment regimens could potentially result in more optimal management of patients with cancer. This Academic-Industry Partnership aims to translate recently developed technologies for semi- automated image segmentation and quantification of lymph nodes into robust tools and integrate them into an existing cloud-based system for management of multicenter oncology clinical trials. The ability to semi- automatically segment lymph node pathology with computed tomography (CT), as well as quantify nodal me- tabolism with positron emission tomography (PET) will enable comprehensive tracking of morphological and functional changes related to disease progression and treatment response. Since 2004, the Dana-Farber/Harvard Cancer Center's (DF/HCC) Tumor Imaging Metrics Core (TIMC) has developed the Precision Imaging Metrics, LLC (PIM) platform to manage clinical trial image assessment workflows. Currently, there are nearly 50,000 consistently measured lymph node measurements in the TIMC database. The PIM system is used to make over 20,000 time point imaging assessments per year at eight NCI- designated Cancer Centers and aims to grow quickly by transitioning to a fully cloud-hosted system. Given sufficient training data, state-of-the-art machine learning and artificial intelligence (AI) technolo- gies can meet or even exceed human performance on specific imaging analysis tasks. Recent studies have indicated that AI-based lymph node segmentation from CT scans is nearing human performance levels, and we will extend and translate this work into a commercial tool. Specifically, our aim is to translate recent ad- vancements in AI-based segmentation into deployable services, and integrate these services into the clinical trial workflow. The proposed system will be designed to incorporate expert feedback provided by image ana- lysts and radiologists back into the ground truth dataset, allowing for continuous improvement in accuracy and clinical acceptance. We will extend our semi-automatic CT segmentation technologies to quantify lymph node metabolism in PET/CT, using lymphoma as the model disease. Integration of these technologies with PIM will provide an ongoing source of consistently measured quantitative data across a network of cancer centers.

项目摘要 /摘要 在淋巴瘤和其他癌症患者中，对肿瘤负担程度的定量评估是不可能的 用于对治疗反应或复发的分期，重新陈述和评估；但是总体测量 肿瘤负担在当前工具方面具有挑战性，尤其是当淋巴结汇合或难以完全 与周围结构区分开。精密医学和新型治疗学强调了需求 引入一种适应风险的方法来为癌症患者量身定制适当的治疗策略。能力 定量评估具有功能和解剖成像的癌症表型 巧妙地将患者绘制为基因表达分析，临床信息，匹配队列和新型治疗 方案可能会导致对癌症患者的最佳治疗。 这种学术行业伙伴关系旨在翻译最近开发的半半技术技术 自动化图像分割和将淋巴结定量到鲁棒工具中，并将其集成到一个 现有的基于云的系统用于管理多中心肿瘤学临床试验。半半的能力 使用计算机断层扫描（CT）自动分段淋巴结病理学，并量化节点me- 带有正电子发射断层扫描（PET）的Tabolism将能够全面跟踪形态学和 功能变化与疾病进展和治疗反应有关。 自2004年以来，Dana-Farber/Harvard癌症中心（DF/HCC）肿瘤成像指标核心（TIMC） 已经开发了Precision Imaging Metrics，LLC（PIM）平台来管理临床试验图像评估 工作流程。目前，TIMC中有近50,000个持续测量的淋巴结测量值 数据库。 PIM系统每年在八个NCI上每年进行超过20,000个时间成像评估 指定的癌症中心，旨在通过过渡到完全云托管系统来迅速增长。 鉴于足够的培训数据，最先进的机器学习和人工智能（AI）技术 GIE在特定的成像分析任务上可以达到甚至超过人类的绩效。最近的研究 表明来自CT扫描的基于AI的淋巴结分割正在接近人类绩效水平，并且 我们将把这项工作扩展并将其转化为商业工具。具体而言，我们的目的是翻译最近的广告 基于AI的分段中的Vantions将这些服务集成到临床上，并将这些服务集成 试用工作流程。拟议的系统将旨在结合图像和图像提供的专家反馈 LYST和放射科医生重新回到地面真相数据集中，可以持续提高准确性和 临床接受。我们将扩展半自动CT分割技术以量化淋巴结 PET/CT中的代谢，使用淋巴瘤作为模型疾病。这些技术与PIM的集成将 在癌症中心网络中提供持续测量的定量数据的持续来源。