Machine Learning-Based Identification of Cardiomyopathy Risk in Childhood Cancer Survivors
基于机器学习的儿童癌症幸存者心肌病风险识别
基本信息
- 批准号:10730177
- 负责人:
- 金额:$ 22.75万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2023
- 资助国家:美国
- 起止时间:2023-07-01 至 2025-06-30
- 项目状态:未结题
- 来源:
- 关键词:3-DimensionalAddressAdultAgeAnthracyclineArchitectureArtificial IntelligenceBiomedical EngineeringCalibrationCancer PatientCancer SurvivorCardiacCardiologyCardiomyopathiesCardiotoxicityChemotherapy and/or radiationChestChildClinicalCommunity Clinical Oncology ProgramComputersConsensusDataData SetDependenceDetectionDevelopmentDiagnosisDisease OutcomeEarly DiagnosisEarly InterventionEchocardiographyEquilibriumExposure toFunctional disorderGeneral PopulationGeometryGuidelinesHeartHeart DiseasesHeart failureImageIndividualInterdisciplinary StudyInternationalInterobserver VariabilityIonizing radiationLeftLeft Ventricular DysfunctionLeft Ventricular Ejection FractionLong-Term SurvivorsMachine LearningMalignant Childhood NeoplasmMeasurementMeasuresMedical ImagingMedicineMethodsMonitorMorbidity - disease rateNational Clinical Trials NetworkNetwork-basedOncologyOnset of illnessPatientsPatternPediatric Oncology GroupProcessRadiation therapyRecommendationResearchRiskSedation procedureShortening FractionStandardizationSurvivorsTestingTherapeuticTimeTrainingTreatment-Related CancerVentricularcancer imagingcardiac magnetic resonance imagingchildhood cancer survivorcohortconvolutional neural networkcostdeep learningdisease diagnosisefficacy evaluationheart functionhigh riskhigh risk populationimage archival systemimaging biomarkerimaging modalityimprovedimproved outcomeinsightinterestoutcome predictionprematureprogramsscreeningstandard of caretwo-dimensionalultrasoundunstructured data
项目摘要
PROJECT SUMMARY / ABSTRACT
Treatment-related cardiomyopathy/heart failure (CHF) is a leading cause of premature morbidity in childhood
cancer survivors. Given the widespread use of anthracycline and related cardiotoxic chemotherapeutics, and in
combination with radiotherapy exposure to the chest, over half of long-term survivors of childhood cancer are at
significantly increased risk of early CHF compared with an age-matched general population. Currently, national
and international consensus guidelines recommend the routine use of 2-dimensional (2D) echocardiography to
screen this high-risk population for early signs of CHF, in particular, left ventricular (LV) systolic dysfunction and
changes in LV geometry. At present, 2D echocardiography represents the standard of care across the US given
its widespread availability, relatively lower cost, and avoidance of ionizing radiation or sedation. Nevertheless,
limitations of 2D echocardiography include greater intra-patient and inter-observer variability. As a result, current
echocardiography-based surveillance continues to have limited sensitivity and often requires serial studies
before a patient is identified as having a potential abnormality. Although there is insufficient evidence to guide
CHF management specific to pediatric cancer survivors, the evidence for non-cancer-related cardiomyopathy in
both children and adults suggests that earlier intervention can mitigate or delay CHF progression. Therefore,
methods that improve the detection of early CHF in childhood cancer survivors may have important clinical
implications. Deep learning (DL), a subfield of machine learning, can automatically extract patterns from large
unstructured datasets, such as medical images, and is increasingly being utilized in medicine for disease
diagnosis as well as disease onset and outcome prediction. We propose to leverage a unique imaging dataset
we have assembled from the Children’s Oncology Group (COG), a part of the NCI-sponsored National Clinical
Trials Network and Community Oncology Research Program, to explore the potential of DL for enhanced
detection of CHF. We have longitudinal echocardiographic data on over 100 survivors of childhood cancer who
developed CHF and over 350 who did not, all defined using standardized criteria, representing an imaging
repository of >3000 individual echocardiograms (and growing). Using this extant and clinically annotated dataset,
we propose to: 1) Using a deep convolutional neural network (DCNN), identify the optimal process for a DL-
based assessment of CHF in pediatric cancer survivors; and 2) Assess the feasibility and preliminary efficacy of
DCNN-based prediction of cardiomyopathy onset from pre-CHF diagnosis echocardiograms. Expected results
include the development of a DCNN that will differentiate between abnormal and normal echocardiograms from
pediatric cancer survivors with and without CHF, respectively. After optimization, we will conduct a preliminary
efficacy analysis to determine how many years in advance a survivor's transition to CHF can be predicted using
an optimized DCNN.
项目摘要/摘要
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
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Patrick M Boyle其他文献
Natural strategies for the spatial optimization of metabolism in synthetic biology
合成生物学中代谢空间优化的自然策略
- DOI:
10.1038/nchembio.975 - 发表时间:
2012-05-17 - 期刊:
- 影响因子:13.700
- 作者:
Christina M Agapakis;Patrick M Boyle;Pamela A Silver - 通讯作者:
Pamela A Silver
Patrick M Boyle的其他文献
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{{ truncateString('Patrick M Boyle', 18)}}的其他基金
Mechanistic Relationships Between Fibrosis, Fibrillation, and Stroke: Multi-Scale, Multi-Physics Simulations
纤维化、颤动和中风之间的机制关系:多尺度、多物理场模拟
- 批准号:
10441932 - 财政年份:2022
- 资助金额:
$ 22.75万 - 项目类别:
Mechanistic Relationships Between Fibrosis, Fibrillation, and Stroke: Multi-Scale, Multi-Physics Simulations
纤维化、颤动和中风之间的机制关系:多尺度、多物理场模拟
- 批准号:
10617841 - 财政年份:2022
- 资助金额:
$ 22.75万 - 项目类别:
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