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: 7982613
• 负责人: Eugene Demidenko
• 金额: $ 7.93万
• 依托单位: DARTMOUTH COLLEGE
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7982613
• 关键词: Algorithms; Analysis of Variance; Animal Experiments; Animals; Arts; Biometry; Biostatistics Core; CCNE1 gene; Cells; Clinic; Color; Computer Simulation; Data; Data Analyses; Environment; Evaluation; Fever; Funding; Gray unit of radiation dose; Heating; Image; Image Analysis; Induced Hyperthermia; Injection of therapeutic agent; Magnetism; Measurement; Methods; Modeling; Outcome Assessment; Particle Size; Play; Process; Production; Property; Role; Services; Survival Analysis; Techniques; Temperature; Testing; Time; Tissues; Toxic effect; Translations; Treatment outcome; Tumor Volume; Uncertainty; absorption; base; chemotherapy; design; hyperthermia treatment; imaging modality; in vivo; magnetic field; 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, biodistribufion, etc., using traditional numeric values data as well as innovafive statistical image analyses, (2) statistical analysis of mNP-induced hyperthermia treatment outcomes including toxicity, tumor volume, and survival analysis, (S) modeling and computer simulafion of mNP interacfion with fissue and cells in vivo under an alternating magnefic field (AMF) and predicfion ofthe induced temperature rise in tumors. Model-based stafisfical 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 characterizafion data, to be derived in the DCCNE will come in the form of images. Methods of Mulfivariate ANalysis Of VAriance (MANOVA) will be used for modeling and statisfical comparison of gray scale and color images. To comply with the normal/Gaussian assumption and to eliminate the differences in images illuminafion and contrast, the logit transformafion 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 ofthe mNP treatment of tumors in the DCCNE Projects through the stafistical 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 equafion 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）磁性纳米甲状腺素（MNP）表征测量的技术和临床前数据分析，例如大小，加热评估，生物分布等，使用传统的数值数据，以及使用传统的数字统计数据分析（2）高温治疗结果包括毒性，肿瘤体积和生存分析，（S）在交替的磁虫场（AMF）下与Fissue和细胞体内的MNP InterAcfion建模和计算机模拟，以及肿瘤诱导的温度升高的预测。 基于模型的应答技术将用于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）。 将在DCCNE中得出的大多数MNP特征数据将以图像的形式出现。 Mulfivariate方差分析（MANOVA）的方法将用于建模和探索方法 比较灰度和颜色图像。符合正常/高斯假设和 消除图像光照明和对比度的差异，将使用Logit Transformafion（图像级别强度相对于背景的log）。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定义分析来评估肿瘤对DCCNE项目中MNP治疗的功效。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纳米量表（项目1、3和纳米粒子核心）上的equafion。