Application of deep learning and novel survival models to predict MCI-to-AD dementia progression
Application of deep learning and novel survival models to predict MCI-to-AD dementia progression
批准号:
10725359
负责人:
Guogen Shan
金额:
$8.61万
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
项目状态:
未结题
起止时间:
2023-09-01 至 2025-05-31
关键词:
Alzheimer&aposs DiseaseAlzheimer&aposs disease patientAlzheimer’s disease biomarkerAmyloid beta-ProteinBiological MarkersCerebrospinal FluidClinicalClinical DataClinical ResearchClinical TrialsDataDementiaDiagnosisDiseaseEventFundingFutureGlial Fibrillary Acidic ProteinImpaired cognitionIndividualMagnetic Resonance ImagingMeasuresMemory LossMethodsModelingModernizationNerve DegenerationNeurodegenerative DisordersOutcomeParentsParticipantPatient CarePatientsPerformancePharmaceutical PreparationsPhosphorylationPlacebosPlasmaPlayPositron-Emission TomographyProcessPropertyReportingResearch Project GrantsRisk FactorsSamplingSpeedSubgroupSurvival RateTechniquesTestingTimeUnited States National Institutes of HealthValidationVisitcohortcostdeep learningdrug developmentdrug testingfallsfollow-uphazardhigh riskimprovedindexinginterestmild cognitive impairmentneuroimagingnonalzheimer dementianovelpotential biomarkerpre-clinicalpredictive modelingprogramsresearch studytau Proteinstau-1theoriestime intervaltooltranslational neuroscienceweb site
中文摘要
项目总结/文摘
英文摘要
Project summary/Abstract
Alzheimer's disease (AD) is a common and costly neurodegenerative disease that is characterized by a long
pre-clinical stage, including a prodromal stage of AD also referred to as mild cognitive impairment (MCI).
Many, but not all, MCI patients progress to AD dementia at varying rates. Among MCI patients, late stage
MCI patients progress to AD faster than early stage MCI patients: a faster annual cognitive decline with
loss of memory. As potential disease modifying drugs are tested for their ability to delay AD dementia, it
becomes critical to have tools that can better accurately predict MCI-to-AD dementia conversion. This would
allow selection of cohorts most likely to decline during the study period, maximizing the ability to detect a
drug/placebo difference. The proposed project will respond to PA-20-200: NIH Small Research Grant Program
(Parent R03 Clinical Trial Not Allowed). In Aim 1, we will develop new deep survival models to predict MCI-to-
AD dementia conversion using baseline measures, by using data from the AD Neuroimaging Initiative (ADNI)
study. We will use data from the NIH funded Center for Neurodegeneration and Translational Neuroscience
(CNTN) as the test data. The majority of the existing deep survival models were developed for right censored
data, but MCI-to-AD dementia conversion is interval censored. When interval censored data are analyzed by
using the methods developed for right censored data, the survival rates are always over-estimated that leads
to the delay in AD dementia diagnosis. We will develop separate prediction models for early stage MCI and
late stage MCI with biomarkers from cerebrospinal fluid (CSF), positron emission tomography (PET), magnetic
resonance imaging (MRI), and clinical measures. Recently, several new biomarkers have been discovered for
AD that are of interest to this study. These include plasma phosphorylated-tau181 (p-tau181), p-tau217, and
the ratio of amyloid-β 42 and amyloid-β 40, and glial fibrillary acidic protein (GFAP). In progressive disorders
like AD, most clinical events are very strongly correlated with the dynamics of the disease. In Aim 2, we will
develop novel survival models for interval-censored data with time-varying longitudinal biomarker data. Built
on our developed penalized survival model for interval censored data using baseline measures, we propose to
extend that model to leverage longitudinal biomarker data to produce more accurate predictions about future
conversion. Biomarkers along with clinical and demographic features were shown to improve the model
performances for right censored data. We expect that the new survival models will be able to improve model
prediction for interval censored data as compared to state-of-the-art models. This project will develop optimal
deep survival models to predict MCI-to-AD dementia conversion for each MCI subgroup. The results of this
project will provide important understanding of how each feature contributes to prediction of MCI-to-AD
dementia conversion.
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
Alzheimer's Disease: New Trial Designs for Emerging Challenges
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批准号:10586025
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项目类别:
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资助金额:$29.04万
-
财政年份:2021
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负责人:Guogen Shan
-
依托单位:
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项目类别:
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依托单位:
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依托单位:
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资助金额:$30.14万
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依托单位:
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资助金额:$7.63万
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依托单位:
国内基金
海外基金
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依托单位: