CT Characteristics of Emphysema for Evaluation of Pulmonary Nodules
肺气肿CT特征评价肺结节
基本信息
- 批准号:9163261
- 负责人:
- 金额:$ 17.87万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2016
- 资助国家:美国
- 起止时间:2016-09-01 至 2021-07-31
- 项目状态:已结题
- 来源:
- 关键词:AddressAdvisory CommitteesAffectBenignBiological AssayBiological MarkersBiometryBlood VesselsBlood VolumeCaliberCancer CenterCancer Death RatesCancer DetectionCancer ModelCancerousCessation of lifeCharacteristicsClinicalClinical TrialsComplementComputer SimulationCoupledDataData SetDetectionDevelopmentDiagnosisDiagnosticEnrollmentEnvironmentEpidemiologyEpigenetic ProcessEvaluationGoalsGrowthImageImage AnalysisIndividualK-Series Research Career ProgramsKnowledgeLeadLobularLocationLungLung noduleMalignant - descriptorMalignant NeoplasmsMalignant neoplasm of lungMalignant neoplasm of thoraxMeasuresMentorsModelingMolecularMorbidity - disease rateMorphologyNodulePathologicPerformancePhysiciansPhysiologyPositioning AttributePrincipal InvestigatorProceduresProcessPulmonary EmphysemaRadialRadiology SpecialtyResearchResearch PersonnelResourcesRiskRisk FactorsScanningScientistSmokerSpecificityStructure of parenchyma of lungSystemTestingTissuesTrainingUniversitiesVermontWorkX-Ray Computed Tomographyarmbiomarker developmentcancer biomarkerscancer diagnosiscareer developmentclinical practicecohortcostdensitydesignexperienceimaging biomarkerimprovedinnovationinvestigator traininglung acinus structurelung cancer screeningmedical specialtiesmortalitynovelnovel strategiesprogramsquantitative imagingscreeningskillstumorvirtual
项目摘要
PROJECT SUMMARY/ABSTRACT
Research: The National Lung Screening Trial (NLST) demonstrated a 20% reduction in mortality with the use
of annual CT screening but with significant morbidity due to procedures performed for evaluation of the 94% of
nodules that were not cancerous. The goal of this proposal is to improve the specificity for distinguishing which
of these “indeterminate” nodules is lung cancer by using quantitative features associated with emphysema
available from a single CT scan. Our preliminary data indicate that moderate centrilobular emphysema (MCE),
and its associated vascular features, provide a niche for the growth of nascent lung. An innovative approach of
this proposal is to use features of MCE in the sphere of lung around a nodule to improve specificity for
detecting cancer. However, increasing specificity results in decreasing sensitivity. In the case of lung cancer
screening more “missed” cancers may increase lung cancer deaths. To understand the effect of decreasing
sensitivity on mortality we will use the novel UVM Lung Cancer Model, which accurately recapitulates lung
cancer death and CT screening rates, to identify an optimal set of biomarkers for identifying cancer among the
many screen detected nodules.
Candidate: Dr. Kinsey’s clinical practice is in Interventional Pulmonary, a specialty that focuses on dealing
with the problem of indeterminate nodules. He has applied his MPH epidemiology and biostatistical training,
coupled with experience in quantitative CT imaging, to begin to address the problem of indeterminate nodules
in the COPDGene data set and as the Principal Investigator of the UVM Molecular, Epigenetic, and Radiologic
Biomarkers for Thoracic Cancers (MEROPTIC) study. However, he needs further training in the process of
developing CT features as imaging biomarkers and in computer modeling, which will allow him to understand
the effect of deployment of these biomarkers on lung cancer mortality. To address these critical gaps in
knowledge his primary mentor, Dr. Jason Bates, has designed a program of didactic training and supervised
research that will propel Dr. Kinsey to an independent investigator in the field of lung cancer diagnosis.
Environment: Dr. Kinsey’s primary mentor, Dr. Bates, is internationally recognized for his work in pulmonary
physiology and computer modeling and has a proven track record of training investigators and physician
scientists. The co-mentor, Dr. George Washko, is an expert in quantitative imaging analysis and has an
established mentoring relationship with Dr. Kinsey. A scientific advisory committee with established expertise
in radiology, biostatistics, lung cancer, and biomarker development complements this outstanding mentoring
team. Dr. Kinsey is further supported by a network of individuals and resources, under the auspices of the
Vermont Lung Center and University of Vermont Cancer Center, that is committed to the development of junior
investigators.
项目摘要/摘要:
研究:美国国家肺部疾病筛查和试验中心(NLST)的研究表明,随着药物的使用,死亡率降低了约20%。
每年进行一次CT筛查,但因手术而出现明显并发症的患者中,有294%的患者进行了评估。
结节并不是完全癌变的。这项提案的主要目标是进一步提高区分哪些结节的特异性。
通过使用与肺气肿相关的定量诊断特征,这些不确定的结节可能是肺癌和癌症。
从单次CT扫描可以获得。我们的初步数据表明,中度的小叶中心型肺气肿(MCE),。
以及与其相关的血管和血管功能,为促进新生肺的快速增长提供了一个新的利基市场。
这项建议旨在利用MCE在肺部周围结节周围的范围内的特征,以进一步提高诊断的特异性。
检测癌症。然而,提高特异性会导致敏感性降低。尤其是在首例肺癌患者中。
筛查更多“漏掉”的癌症,可能会增加肺癌和死亡。为了更好地了解癌症减少的影响。
对死亡率的敏感性,我们将继续使用最新的UVM-肺癌模型,该模型可以准确地概括肺部。
癌症死亡率和CT筛查率,以帮助确定一套最佳的生物标记物,用于在这些指标中识别癌症。
许多电子屏幕都检测到了结节。
候选人:Kinsey博士的主要临床医学实践专业是介入肺,这是一个专注于医学交易的医学专科专业。
随着不确定的结节的严重问题的解决,他已经申请了他的公共卫生、流行病学和生物统计学的培训,他说。
再加上他们在定量CT成像方面的经验,我们将开始努力解决不确定的结节的主要问题。
在这项研究中,COPDgene的数据被设置为美国国立卫生研究院分子、表观遗传学、癌症和放射学的首席研究员。
胸癌(MEROPTIC)的生物标记物研究。然而,他还需要在治疗的过程中进行进一步的培训。
开发CT技术的特点是在计算机辅助建模中使用成像和生物标记物,这将使他更好地理解。
这些关键生物标记物的应用效果与肺癌和死亡率有关。我们需要解决这些关键的技术差距。
他的主要导师杰森·贝茨博士的知识,已经设计了一套完整的教学培训计划和监督计划。
研究表明,这将推动金赛博士成为一名独立的医学调查员,致力于肺癌和癌症诊断领域的研究。
环境:他是金赛医生的主要导师,也是贝茨医生,他因在肺部疾病方面的第一份工作而获得国际公认的荣誉。
生理学和计算机建模技术已经取得了良好的成绩,为研究人员和医生提供了良好的培训。
科学家。他的共同导师,美国医生乔治·沃什科,是一位在定量医学成像和分析方面的著名专家,他已经完成了一项研究。
与Kinsey博士建立了良好的指导关系。他是一个拥有成熟专业知识的科学研究咨询委员会。
在放射学、生物统计学、肺癌、癌症、癌症和其他生物标记物的开发方面,这些都是对这一杰出的教育指导的补充。
K.Kinsey博士团队得到了一个由许多个人和资源组成的网络的进一步支持,该网络是在世界银行的赞助下进行的。
佛蒙特州癌症研究中心主任和佛蒙特州癌症研究中心主任表示,他们致力于促进青少年的健康发展。
调查人员。
项目成果
期刊论文数量(0)
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Charles Matthew Kinsey其他文献
Charles Matthew Kinsey的其他文献
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{{ truncateString('Charles Matthew Kinsey', 18)}}的其他基金
CT Characteristics of Emphysema for Evaluation of Pulmonary Nodules
肺气肿CT特征评价肺结节
- 批准号:
10405873 - 财政年份:2021
- 资助金额:
$ 17.87万 - 项目类别:
CT Characteristics of Emphysema for Evaluation of Pulmonary Nodules
肺气肿CT特征评价肺结节
- 批准号:
9753352 - 财政年份:2016
- 资助金额:
$ 17.87万 - 项目类别:
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$ 17.87万 - 项目类别:
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