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、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）使用传统数值数据以及创新的统计图像分析，对磁性纳米粒子（mNP）表征测量进行技术和临床前数据分析，例如尺寸、加热评估、生物分布等，（2）mNP 引起的高热的统计分析 治疗结果包括毒性、肿瘤体积和生存分析、(S) 交变磁场 (AMF) 下 mNP 与体内组织和细胞相互作用的建模和计算机模拟以及肿瘤中诱导温升的预测。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，基于模型的方法允许评估方法的不确定性（例如通过标准误差），因此可以进行统计显着性测试（项目 1、3，纳米粒子核心）。 DCCNE 中导出的大多数 mNP 表征数据将以图像的形式出现。多元方差分析（MANOVA）方法将用于灰度和彩色图像的建模和统计比较。为了符合正态/高斯假设并消除图像照明和对比度的差异，将使用逻辑变换（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核心将通过肿瘤再生数据的统计分析和生存分析来评估DCCNE项目中mNP治疗肿瘤的疗效。将特别强调治疗协同作用的统计显着性评估，例如 mNP 热疗和化疗（项目 1、2 和 4）。 mNP 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合理条件方面发挥重要作用，例如 AMF 强度、注射浓度、磁场暴露时间、颗粒尺寸等。mNP 引起的高温的数值评估将在动物实验之前通过估计肿瘤内的比吸收率 (SAR) 和求解生物热方程 纳米尺度（项目 1、3 和纳米颗粒核心）。