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
基本信息
- 批准号:10687096
- 负责人:
- 金额:$ 59.58万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 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:
- 关键词:AddressArtificial IntelligenceBackBasic ScienceCancer CenterCancer PatientClinicalClinical ManagementClinical OncologyClinical ResearchClinical TrialsClinical Trials DatabaseClinical assessmentsDataData SetDatabasesDiagnosticDiseaseDisease ProgressionDisease modelEvaluationFeedbackFunctional ImagingGene Expression ProfilingGoalsHodgkin DiseaseHumanImageImage AnalysisImaging DeviceInformaticsInvestmentsLaboratoriesLesionLymphomaMachine LearningMalignant NeoplasmsManualsMapsMeasurementMeasuresMetabolicMetabolismMorphologyMulti-Institutional Clinical TrialNCI-Designated Cancer CenterNodalPathologyPathway interactionsPatientsPerformancePhenotypePositron-Emission TomographyQuantitative EvaluationsRelapseReliability of ResultsReportingResearch PersonnelRiskScanningScientific Advances and AccomplishmentsServicesSolid NeoplasmSourceStagingStandardizationStructureSurrogate EndpointSystemTechnologyTimeTrainingTranslatingTreatment ProtocolsTumor BurdenWorkX-Ray Computed Tomographyanatomic imagingautomated segmentationburden of illnesscancer clinical trialcancer imagingcancer therapyclinical practicecloud basedcohortcommercializationdesignexperienceglucose metabolismimaging Segmentationimprovedindustry partnerinnovationlymph nodesmultidisciplinarynew technologynovelnovel therapeuticsparticipant enrollmentprecision medicineprognostic indicatorquantitative imagingradiologistsuccesstask analysistooltreatment effecttreatment responsetreatment strategytumorusability
项目摘要
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.
项目总结/摘要
在淋巴瘤和其他癌症患者中,肿瘤负荷程度的定量评估是不确定的。
对于分期、再分期和治疗反应或复发的评估很重要;但总体
肿瘤负荷对于当前的工具是具有挑战性的,特别是当淋巴结融合或难以完全转移时,
与周围的结构不同。精准医学和新型疗法强调了
引入风险适应方法,为癌症患者量身定制适当的治疗策略。的能力
通过功能和解剖成像定量评估癌症表型,
将患者精确地映射到基因表达谱、临床信息、匹配队列和新治疗
治疗方案可能会对癌症患者产生更优化的管理。
这一学术-工业合作伙伴关系旨在将最近开发的技术转化为半导体,
淋巴结的自动图像分割和量化到强大的工具,并将它们集成到一个
用于管理多中心肿瘤临床试验的现有云系统。半-
自动分割淋巴结病理与计算机断层扫描(CT),以及量化的淋巴结转移,
正电子发射断层扫描(PET)的Tabolism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,Dana-Farber/哈佛癌症中心(DF/HCC)的肿瘤成像核心(TIMC)
开发了Precision Imaging LLC(PIM)平台,用于管理临床试验图像评估
工作流程。目前,在TIMC中有近50,000个一致测量的淋巴结测量值
数据库PIM系统用于每年在8个NCI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增长。
如果有足够的训练数据、最先进的机器学习和人工智能(AI)技术,
GIES在特定的成像分析任务上可以达到甚至超过人类的表现。最近的研究
表明基于AI的CT扫描淋巴结分割接近人类性能水平,
我们将把这项工作扩展并转化为商业工具。具体来说,我们的目标是翻译最近的广告-
在基于AI的细分为可部署的服务,并将这些服务集成到临床
审判工作流程。拟议的系统将被设计为结合图像分析提供的专家反馈,
分析师和放射科医生返回到地面实况数据集,允许不断提高准确性,
临床验收。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
提供跨癌症中心网络的持续测量的定量数据源。
项目成果
期刊论文数量(1)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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{{ truncateString('GORDON J HARRIS', 18)}}的其他基金
Extensible Open Source Zero-Footprint Web Viewer for Cancer Imaging Research
用于癌症成像研究的可扩展开源零足迹 Web 查看器
- 批准号:
10644112 - 财政年份:2023
- 资助金额:
$ 59.58万 - 项目类别:
Extensible open-source zero-footprint web viewer for oncologic imaging research
用于肿瘤成像研究的可扩展开源零足迹 Web 查看器
- 批准号:
9324177 - 财政年份:2015
- 资助金额:
$ 59.58万 - 项目类别:
NEUROIMAGING IN PERSONS AT RISK FOR HUNTINGTON'S DISEASE
亨廷顿氏病高危人群的神经影像学检查
- 批准号:
2333004 - 财政年份:1994
- 资助金额:
$ 59.58万 - 项目类别:
NEUROIMAGING IN PERSONS AT RISK FOR HUNTINGTON'S DISEASE
亨廷顿氏病高危人群的神经影像学检查
- 批准号:
2272196 - 财政年份:1994
- 资助金额:
$ 59.58万 - 项目类别:
NEUROIMAGING IN PERSONS AT RISK FOR HUNTINGTON'S DISEASE
亨廷顿氏病高危人群的神经影像学检查
- 批准号:
2272197 - 财政年份:1994
- 资助金额:
$ 59.58万 - 项目类别:
NEUROIMAGING IN PERSONS AT RISK FOR HUNTINGTON'S DISEASE
亨廷顿氏病高危人群的神经影像学检查
- 批准号:
2272198 - 财政年份:1994
- 资助金额:
$ 59.58万 - 项目类别:
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