Quantitative Imaging Core

定量成像核心

• 批准号: 10333178
• 负责人: NATARAJAN RAGHUNAND
• 金额: $ 16.22万
• 依托单位: H. LEE MOFFITT CANCER CTR & RES INST
• 依托单位国家: 美国
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-02-18 至 2027-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10333178
• 关键词: Advocate; Algorithms; Bioinformatics; Biological Markers; Biometry; Calibration; Cancer Center Support Grant; Characteristics; Clinical Protocols; Clinical Research; Cluster Analysis; Color; Computer software; Custom; Data; Diagnosis; Diagnostic Imaging; Discipline of Nuclear Medicine; Doctor of Philosophy; Documentation; Elements; Evolution; Experimental Designs; Funding; Future; Goals; Image; Image Analysis; Immunohistochemistry; Instruction; Interventional radiology; Journals; Lead; Machine Learning; Malignant Neoplasms; Manuals; Maps; Measurement; Medical Imaging; Microscopy; Modality; Molecular Target; Mus; Normal Cell; Normal tissue morphology; Outcome; Output; Pathologic; Patients; Peer Review; Pharmaceutical Preparations; Pharmacodynamics; Physician Executives; Preparation; Principal Investigator; Protocols documentation; Publications; Qi; Radiology Specialty; Reporting; Reproducibility; Research; Research Personnel; Research Project Grants; Scientist; Series; Services; Slide; Source; Standardization; Therapeutic Trials; Time; Tissues; Training; Validation; algorithm development; animal imaging; base; biomarker development; clinical imaging; cloud based; cost effective; data integrity; data management; deep learning algorithm; design; digital pathology; image processing; imaging biomarker; improved; interest; investigator-initiated trial; live cell microscopy; lung lesion; member; neoplastic cell; novel therapeutics; oncology program; operation; pathology imaging; physical science; pre-clinical research; preclinical study; predictive modeling; programs; quantitative imaging; radiological imaging; radiologist; radiomics; reconstruction; research study; response; symposium; tool; translational study; treatment response; tumor; tumor progression

PROJECT SUMMARY QUANTITATIVE IMAGING CORE The overarching goal of the Quantitative Imaging Core (QIC), formerly the Image Response Assessment Team (IRAT), is to provide one-stop expertise in standardized medical imaging and reproducible image biomarker development from the analysis of radiologic and digital pathology images collected in clinical and preclinical studies. QIC provides expertise and tools for radiologic tumor response assessment measurement and reporting, imaging data management, study coordination, and standard and advanced image processing operations, including multispectral analysis, radiomics and pathomics. QIC also provides expertise in custom quantitative image algorithm development and in design of radiologic imaging protocols for clinical studies. Spanning multiple imaging scales and modalities, QIC offers quantitative analysis of image data acquired by live-cell microscopy, digital pathology, small animal imaging, and clinical imaging. Data extracted from images are provided to Members in formats suitable for downstream bioinformatics, biostatistics, and machine learning analyses. QIC activities towards its goals are organized along three Specific Aims: Aim 1: To provide high reliability and fast turnaround times for standard radiologic tumor response assessment metrics. Aim 2: To improve clinical and preclinical research studies at Moffitt, by providing turnkey imaging biomarker services from quantitative imaging, radiomics and pathomics analyses. Aim 3: To educate scientists and clinicians on experimental design elements required for the reproducible acquisition and analysis of QI data in clinical and preclinical studies. Customizable algorithms developed by QIC allow Members to pursue unique hypotheses characterizing and quantifying cancer progression, evolution and response to therapy. QIC services enable investigators to unlock information contained in radiologic and digital pathology images collected in clinical, translational and pre-clinical studies. Since 2016, QIC usage has increased by 29% and has contributed to 87 publications (25 high impact), and 600 clinical protocols, representing 4,668 unique patients. QIC increased staffing since 2016 and currently operates at 68% usage capacity. In FY20, QIC supported 60 Members across all five Programs (CBE 14%, CE 5%, MM 66%, HOB 1%, IO 14%), with 64% of those Members holding peer review funding. This represented 81% of all QIC usage. Future priorities of the QIC are to leverage Moffitt’s enterprise-scale, cloud-based analytics platforms and to implement newly developed digital pathology and multiplex immunohistochemistry image processing modules to support analyses of tumor sections. Using commercial image processing tools, QIC will also enhance training for Members, trainees, and staff.

项目总结 定量成像岩心 定量成像核心(QIC)(前身为图像响应评估团队)的首要目标 (IRAT)，是提供标准化医学成像和可复制图像生物标记物的一站式专业知识 从临床和临床前收集的放射学和数字病理图像分析的进展 学习。QIC为放射肿瘤反应评估、测量和报告提供专业知识和工具， 影像数据管理、研究协调以及标准和高级图像处理操作， 包括多光谱分析、放射组学和病理组学。QIC还提供定制量化方面的专业知识 图像算法开发和临床研究放射成像方案的设计。跨越多个 成像尺度和模式，QIC提供对活细胞显微镜获取的图像数据的定量分析， 数字病理学、小动物影像和临床影像。从图像中提取的数据被提供给 适用于下游生物信息学、生物统计学和机器学习分析的成员。QIC 实现其目标的活动是按照三个具体目标组织的： 目的1：为标准放射学肿瘤反应评估提供高可靠性和快速周转时间 指标。 目标2：通过提供交钥匙成像生物标记物，改善莫菲特的临床和临床前研究 来自定量成像、放射组学和病理组学分析的服务。 目标3：对科学家和临床医生进行有关可重复性实验设计要素的教育 临床和临床前研究中QI数据的获取和分析。 QIC开发的可定制算法允许成员追求独特的假设，以表征和 量化癌症进展、演变和对治疗的反应。QIC服务使调查人员能够解锁 临床、翻译和临床前收集的放射和数字病理图像中包含的信息 学习。自2016年以来，QIC的使用增加了29%，并为87种出版物(25种高影响力)做出了贡献， 和600个临床方案，代表4668名独特的患者。QIC自2016年以来增加了人员编制，目前 以68%的使用能力运行。在2010财年，QIC支持所有五个计划中的60名成员(CBE 14%、CE 5%，MM 66%，HOB1%，IO 14%)，其中%的成员持有同行评议资金。这代表了 占所有QIC使用量的81%。QIC未来的优先事项是利用Moffitt的企业级、基于云的分析 平台，并实施新开发的数字病理和多路免疫组织化学图像 支持肿瘤切片分析的处理模块。使用商业图像处理工具，QIC将 还加强对成员、学员和工作人员的培训。