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
生物统计、数据分析和计算(BDAC)核心将为达特茅斯CCNE的项目提供以下服务:(1)利用传统的数值数据和创新的统计图像分析,对磁性纳米颗粒(MNP)的表征测量进行技术分析和临床前数据分析,如尺寸、热评估、生物分布等;(2)对MNP诱导的热疗结果进行统计分析,包括毒性、肿瘤体积和生存分析,(S)对交变磁场下MNP与体内组织和细胞的相互作用进行建模和计算机模拟,并预测肿瘤的诱导温度升高。将使用以模型为基础的统计技术来描述和评价国家行动方案。与方法驱动的算法不同,基于模型的方法允许评估方法的不确定度(例如,通过标准误差),因此能够进行统计意义测试(项目1、3,纳米颗粒核心)。
将在DCCNE中得出的大多数mNP表征数据将以图像的形式出现。多变量方差分析方法(MANOVA)将用于灰度和彩色图像的建模和统计比较。为了符合正常/高斯假设并消除图像照度和对比度的差异,将使用Logit变换(图像级别强度相对于背景的对数)。项目1、2、3、NDPC和TPB核心。BDAC Core将通过对肿瘤再生长数据的统计分析和生存分析,评估DCCNE项目中mNP治疗肿瘤的疗效。将特别强调对mNP热疗和化疗等治疗协同作用的统计意义评估(项目1、2和4)。对mNPs的散射场和吸收场的建模和计算机模拟将在选择适合动物实验的生物学条件方面发挥重要作用,如AMF的强度、注入浓度、磁场暴露时间、颗粒大小等。mNP诱导的热疗的数值评估将先于动物实验,通过估计肿瘤内的比吸收率(SAR)和求解纳米尺度的生物热方程(项目1、3和纳米颗粒核心)。
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:
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资助金额:$61.94万
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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: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:
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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:
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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:
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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