Biostatistics, Data Analysis and Computation (BDAC Core)
Biostatistics, Data Analysis and Computation (BDAC Core)
批准号:
8710054
负责人:
Eugene Demidenko
金额:
$14.52万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
--
资助国家:
美国
项目状态:
已结题
起止时间:
至 2016-07-31
关键词:
AlgorithmsAnimal ExperimentsAnimalsBiodistributionBiometryCCNE1 geneCancer CenterCellsClinicColorComputer SimulationDataData AnalysesEnvironmentEquationEvaluationFluorescenceFundingGray unit of radiation doseHeatingHyperthermiaImageImage AnalysisInduced HyperthermiaInjection of therapeutic agentLightingMagnetismMeasurementMethodsModelingMultivariate AnalysisNanotechnologyOutcome AssessmentParticle SizePlayProcessProductionPropertyRoleServicesStatistical ModelsSurvival AnalysisTechniquesTemperatureTestingTimeTissuesToxic effectTranslationsTreatment outcomeTumor VolumeUncertaintyabsorptionbasechemotherapydesignhyperthermia treatmentin vivoinnovationmagnetic fieldmodels and simulationnanoparticlenanoscalenanotherapyperformance testspre-clinicalresearch studyresponsestatisticstreatment effecttumor
中文摘要
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英文摘要
The Biostatistics, Data Analysis, and Computation (BDAC) Core will provide the following services to the projects of the Dartmouth CCNE: (1) technological and preclinical data analysis of magnetic NanoPartide (mNP) characterization measurements, such as size, heating evaluation, biodistribution, etc., using traditional numeric values data as well as innovative statistical image analyses, (2) statistical analysis of mNP-induced hyperthermia treatment outcomes including toxicity, tumor volume, and survival analysis, (S) modeling and computer simulation of mNP interaction with tissue and cells in vivo under an alternating magnetic field (AMF) and prediction ofthe induced temperature rise in tumors. Model-based statistical techniques will be used for mNP characterization and evaluation. Unlike method driven algorithms, the model-based approach allows the assessment ofthe uncertainty of methods (e.g. through the standard error) and therefore enables statistical significance testing (Projects 1, 3, Nanoparticle Core).
The majority of the mNP characterization data, to be derived in the DCCNE will come in the form of images. Methods of Multivariate ANalysis Of VAriance (MANOVA) will be used for modeling and statistical comparison of gray scale and color images. To comply with the normal/Gaussian assumption and to eliminate the differences in images illumination and contrast, the logit transformation will be used (log of the image level intensity with respect to the background). Projects 1, 2, 3, NDPC & TPB cores. The BDAC Core will evaluate the efficacy of the mNP treatment of tumors in the DCCNE Projects through the statistical analysis of tumor regrowth data and survival analysis. A particular emphasis will be given to the statistical significance assessment of the synergy of the treatments, such as mNP hyperthermia and chemotherapy (Projects 1, 2 & 4). Modeling and computer simulation of scattering and absorption fields from mNPs will play an important role in choosing the biologically justified conditions for animal experiments, such as the strength of the AMF, injection concentration, magnetic field exposure time, particle size, etc. The numerical assessment of the mNP-induced hyperthermia will precede animal experiments through estimation ofthe specific absorption rate (SAR) inside the tumor and by solving of the bioheat equation on the nanometer scale (Projects 1, 3, and Nanoparticle Core).
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会议论文
Noninvasive bladder cancer diagnostics via machine learning analysis of nanoscale surface images of epithelial cells extracted from voided urine samples
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批准号:10454232
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项目类别:
-
资助金额:$61.94万
-
财政年份:2021
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负责人:Eugene Demidenko
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依托单位:
Noninvasive bladder cancer diagnostics via machine learning analysis of nanoscale surface images of epithelial cells extracted from voided urine samples
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批准号:10669124
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项目类别:
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资助金额:$61.29万
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财政年份:2021
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负责人:Eugene Demidenko
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依托单位:
Noninvasive bladder cancer diagnostics via machine learning analysis of nanoscale surface images of epithelial cells extracted from voided urine samples
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批准号:10276838
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项目类别:
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资助金额:$67.46万
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财政年份:2021
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负责人:Eugene Demidenko
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依托单位:
Biostatistics, Data Analysis and Computation (BDAC Core)
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批准号:7982613
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项目类别:
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资助金额:$7.93万
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财政年份:2010
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负责人:Eugene Demidenko
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依托单位:
Breast Cancer Detection Using Electrical Impedance Measurements
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批准号:7663862
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项目类别:
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资助金额:$20.87万
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财政年份:2008
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负责人:Eugene Demidenko
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依托单位:
Breast Cancer Detection Using Electrical Impedance Measurements
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批准号:7893578
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项目类别:
-
资助金额:$24.34万
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财政年份:2008
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负责人:Eugene Demidenko
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依托单位:
Breast Cancer Detection Using Electrical Impedance Measurements
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批准号:7527236
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项目类别:
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资助金额:$20.87万
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财政年份:2008
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负责人:Eugene Demidenko
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依托单位:
Biostatistics, Data Analysis and Computation (BDAC Core)
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批准号:8310104
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项目类别:
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资助金额:$7.7万
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财政年份:--
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负责人:Eugene Demidenko
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依托单位:
Biostatistics, Data Analysis and Computation (BDAC Core)
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批准号:8379366
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项目类别:
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资助金额:$15.86万
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财政年份:--
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负责人:Eugene Demidenko
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依托单位:
Biostatistics, Data Analysis and Computation (BDAC Core)
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批准号:8545112
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项目类别:
-
资助金额:$14.59万
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财政年份:--
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负责人:Eugene Demidenko
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依托单位:
Core 3: Biostatistics Shared Resource
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批准号:9151820
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项目类别:
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资助金额:$8.47万
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财政年份:--
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负责人:Eugene Demidenko
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依托单位:
海外基金