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: 8310104
• 负责人: Eugene Demidenko
• 金额: $ 7.7万
• 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
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/8310104
• 关键词: Algorithms; Animal Experiments; Animals; Biometry; CCNE1 gene; Cancer Center; 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; Nanotechnology; 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; in vivo; 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, 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、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）表征测量的技术和临床前数据进行分析，如大小、加热评价、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；(5)建立交变磁场（AMF）下mNP与体内组织和细胞相互作用的模型和计算机模拟，并预测肿瘤中诱导的温升。