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.

项目概要/摘要 在患有淋巴瘤和其他癌症的患者中，对肿瘤负荷程度的定量评估非常重要。 对于分期、再分期以及治疗反应或复发的评估很重要；但总体测量 使用当前的工具，肿瘤负荷具有挑战性，特别是当淋巴结汇合或难以完全清除时 与周围的结构区分开来。精准医学和新型疗法强调了这一需求 引入风险适应方法，为癌症患者制定适当的治疗策略。有能力 通过功能和解剖成像定量评估癌症表型，可以有效且有效地评估癌症表型。 精心地将患者映射到基因表达谱、临床信息、匹配队列和新治疗方法 治疗方案可能会给癌症患者带来更优化的治疗。 这种学术-工业合作伙伴关系旨在将最近开发的技术转化为半 将淋巴结的自动图像分割和量化集成到强大的工具中，并将其集成到 现有的基于云的系统用于管理多中心肿瘤学临床试验。半的能力 通过计算机断层扫描 (CT) 自动分割淋巴结病理，并量化淋巴结状况 正电子发射断层扫描 (PET) 的代谢将能够全面跟踪形态学和 与疾病进展和治疗反应相关的功能变化。 自 2004 年以来，丹娜法伯/哈佛大学癌症中心 (DF/HCC) 肿瘤成像指标核心 (TIMC) 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 个一致测量的淋巴结测量结果 数据库。 PIM 系统每年在 8 个 NCI 中心进行超过 20,000 次时间点成像评估 指定的癌症中心，旨在通过过渡到完全云托管的系统来快速发展。 有了足够的训练数据、最先进的机器学习和人工智能（AI）技术， 在特定的成像分析任务上，人工智能可以达到甚至超过人类的表现。最近的研究有 表明基于人工智能的 CT 扫描淋巴结分割已接近人类表现水平，并且 我们将扩展这项工作并将其转化为商业工具。具体来说，我们的目标是翻译最近的广告- 基于人工智能的细分可部署服务的进步，并将这些服务集成到临床中 试验工作流程。拟议系统的设计将纳入图像分析提供的专家反馈 lysts和放射科医生回到地面真实数据集，从而不断提高准确性和 临床接受。我们将扩展我们的半自动 CT 分割技术来量化淋巴结 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源。