Lung cancer screening efficacy enhanced through radiomic and epigenetic biomarkers
通过放射组学和表观遗传生物标志物增强肺癌筛查功效
基本信息
- 批准号:10663383
- 负责人:
- 金额:$ 34.64万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2022
- 资助国家:美国
- 起止时间:2022-07-11 至 2025-06-30
- 项目状态:未结题
- 来源:
- 关键词:AccountingAdultAffectAmericanAmerican College of Radiology Imaging NetworkAmerican Lung AssociationBenignBiological AssayBiological MarkersCYP1A1 geneCause of DeathCenters for Disease Control and Prevention (U.S.)Cessation of lifeChestChronic BronchitisChronic Obstructive Pulmonary DiseaseCitiesClinicClinicalClinical DataComputational algorithmComputersDNADNA MethylationDataDiagnostic ImagingDiagnostic testsDisease ProgressionEarly DiagnosisEarly treatmentEligibility DeterminationEpigenetic ProcessExposure toGrantHeterogeneityHigh Resolution Computed TomographyImageLesionLobarLungLung diseasesLung noduleMachine LearningMalignant - descriptorMalignant NeoplasmsMalignant neoplasm of lungMeasurementMeasuresMethodsMethylationModelingNoduleObstructive Lung DiseasesOutcomePatient Self-ReportPerformancePersonsPopulationPredictive ValuePredispositionPulmonary EmphysemaRadiation exposureRadiology SpecialtyReportingResearchResearch SubjectsRiskRisk FactorsSamplingScreening for cancerShapesSmokingSmoking Cessation InterventionSmoking HistoryStructureStructure of parenchyma of lungTestingTextureThoracic RadiographyTobaccoTobacco useUnited States Department of Veterans AffairsUniversitiesValidationX-Ray Computed Tomographyairway obstructionautomated segmentationcancer epidemiologycancer riskcigarette smokecigarette smokingclinical diagnosiscohortcomputed tomography screeningdigitalepigenetic markerformer smokerimage processingimprovedinterestlow dose computed tomographylung cancer screeningmachine learning classificationmeetingsmethylation testingmortalitypredictive modelingpreventprospectiveradiomicsrepositoryrisk predictionrisk prediction modelscreeningshared decision makingsmoking exposuretobacco controltobacco exposuretrial enrollmenttv watching
项目摘要
ABSTRACT
Smoking is the largest risk factor for both lung cancer and obstructive lung disease. The National Lung
Screening Trial (NLST) enrolled subjects who reported a cigarette smoking history of at least 30 pack years and
showed that annual low-dose computed tomography (LDCT) screening could reduce mortality from lung cancer
by approximately 16%, compared to conventional chest x-ray. However, it remains clinically challenging to
efficiently distinguish the small number of malignant nodules from the many benign lung nodules detected with
screening. In addition, the chest LDCT data captured during screening also has untapped utility in quantitatively
evaluating obstructive lung disease.
LDCT captures a wealth of information that can be automatically and objectively quantified and extracted
from the image data using computer algorithms. We have methods for automated segmentation of structures of
interest from the image data and will extract hundreds of radiological biomarkers focused on pulmonary nodules,
peri-nodular lung parenchyma, the whole lung, and capture lobar heterogeneity. This study will also incorporate
an objective epigenetic biomarker of smoking history via measurement of DNA methylation at cg05575921. Our
epigenetic biomarker has been shown to strongly predict smoking intensity by several studies. We will use the
objective radiological and epigenetic biomarkers and machine learning approaches to predict both (1) the risk of
lung cancer and (2) rapid obstructive lung disease progression in the NLST screening population. We
hypothesize that incorporating DNA methylation at cg05575921 will be a valuable addition to both prediction
models. Determining the outcome of the hypothesis will guide if this epigenetic biomarker should be incorporated
in prospective lung cancer screening studies.
This project will have impact as it will result in an improved automatic risk prediction algorithm to guide
management in subjects with a lung nodule detected by LDCT screening. This approach can facilitate rapid
treatment for those with cancer and prevent complications from invasive diagnostic testing as well as
unnecessary radiation exposure from diagnostic imaging in those with benign lesions. Predicting rapid
obstructive lung disease progression may be beneficial for clinician/subject shared decision-making discussions
and targeted smoking cessation interventions in addition to improving lung cancer prediction.
摘要
项目成果
期刊论文数量(0)
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Jessica C Sieren其他文献
Accelerating dynamic imaging of the lung using blind compressed sensing
- DOI:
10.1186/1532-429x-16-s1-w27 - 发表时间:
2014-01-16 - 期刊:
- 影响因子:
- 作者:
Sajan Goud Lingala;Yasir Mohsin;John D Newell;Jessica C Sieren;Daniel Thedens;Peter Kollasch;Mathews Jacob - 通讯作者:
Mathews Jacob
Jessica C Sieren的其他文献
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{{ truncateString('Jessica C Sieren', 18)}}的其他基金
Lung cancer screening efficacy enhanced through radiomic and epigenetic biomarkers
通过放射组学和表观遗传生物标志物增强肺癌筛查功效
- 批准号:
10518050 - 财政年份:2022
- 资助金额:
$ 34.64万 - 项目类别:
Nodestruction Multi-Scale Micro-CT Imaging System
Nodestruction多尺度显微CT成像系统
- 批准号:
8734578 - 财政年份:2015
- 资助金额:
$ 34.64万 - 项目类别:
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