Biostatistics, Data Analysis and Computation (BDAC Core)

生物统计学、数据分析和计算（BDAC 核心）

• 批准号: 8710054
• 负责人: Eugene Demidenko
• 金额: $ 14.52万
• 依托单位: DARTMOUTH COLLEGE
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至 2016-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8710054
• 关键词: Algorithms; Animal Experiments; Animals; Biodistribution; Biometry; CCNE1 gene; Cancer Center; Cells; Clinic; Color; Computer Simulation; Data; Data Analyses; Environment; Equation; Evaluation; Fluorescence; Funding; Gray unit of radiation dose; Heating; Hyperthermia; Image; Image Analysis; Induced Hyperthermia; Injection of therapeutic agent; Lighting; Magnetism; Measurement; Methods; Modeling; Multivariate Analysis; Nanotechnology; Outcome Assessment; Particle Size; Play; Process; Production; Property; Role; Services; Statistical Models; Survival Analysis; Techniques; Temperature; Testing; Time; Tissues; Toxic effect; Translations; Treatment outcome; Tumor Volume; Uncertainty; absorption; base; chemotherapy; design; hyperthermia treatment; in vivo; innovation; magnetic field; models and simulation; nanoparticle; nanoscale; nanotherapy; performance tests; pre-clinical; research study; response; statistics; treatment effect; tumor

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).

生物统计学，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）使用传统的数字数据统计数据，以及Nemberative neyoveration-innovation-Innovation-innovation-neyperational-neyperication-2（2）治疗结果包括毒性，肿瘤体积和生存分析，（S）在交替磁场（AMF）（AMF）下与组织和细胞体内的MNP相互作用建模和计算机模拟，以及预测肿瘤诱导的温度升高。基于模型的统计技术将用于MNP表征和评估。与方法驱动的算法不同，基于模型的方法可以评估方法的不确定性（例如，通过标准误差），因此可以实现统计显着性测试（项目1、3，纳米粒子核心）。 在DCCNE中得出的大多数MNP表征数据将以图像的形式出现。多元方差分析方法（MANOVA）将使用灰度和颜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明和对比度的差异，将使用logit变换（相对于背景相对于图像级别强度的对数）。项目1、2、3，NDPC和TPB核心。 BDAC核心将通过对肿瘤再生数据和生存分析的统计分析来评估DCCNE项目中MNP治疗在DCCNE项目中的功效。将特别强调对治疗的协同作用的统计显着性评估，例如MNP高温和化学疗法（项目1、2和4）。来自MNP的散射和吸收场的建模和计算机模拟将在选择动物实验的生物学上有理的条件中起重要作用在纳米量表上（项目1、3和纳米粒子核心）。