医学成像从定性到定量的演变:机遇、挑战和方法(会议报告)

E. Jackson
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引用次数: 0

摘要

在过去的十年中,人们越来越关注定量成像生物标志物(qib),它被定义为“从体内图像中获得的客观测量特征,作为正常生物过程、致病过程或对治疗干预反应的指标”1。为了将定性成像评估发展到使用qib,需要开发和标准化数据采集、数据分析和数据显示技术,以及适当的报告结构。因此,QIB应用程序的成功实现在很大程度上依赖于医学物理学、放射学、统计学和信息学领域的专业知识,以及来自成像采集、分析和报告系统供应商的协作。成功实施后,qib将提供具有已知偏差和方差的图像衍生指标,可通过解剖学和生理学相关措施进行验证,包括治疗反应(以及该反应的异质性)和结果。这样的非侵入性定量测量可以有效地用于临床和转化研究,并将为精准医学的目标做出重大贡献。本次演讲将重点关注:1)概述QIB应用的机会,并举例说明在研究和患者护理中的应用;2)讨论QIB应用实施中的关键挑战;3)概述联邦、科学和专业组织(包括但不限于RSNA、NCI、FDA和NIST)为应对这些挑战所做的努力。1Sullivan, Obuchowski, Kessler,等。放射学,epub, 2015年8月。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The evolution of medical imaging from qualitative to quantitative: opportunities, challenges, and approaches (Conference Presentation)
Over the past decade, there has been an increasing focus on quantitative imaging biomarkers (QIBs), which are defined as “objectively measured characteristics derived from in vivo images as indicators of normal biological processes, pathogenic processes, or response to a therapeutic intervention”1. To evolve qualitative imaging assessments to the use of QIBs requires the development and standardization of data acquisition, data analysis, and data display techniques, as well as appropriate reporting structures. As such, successful implementation of QIB applications relies heavily on expertise from the fields of medical physics, radiology, statistics, and informatics as well as collaboration from vendors of imaging acquisition, analysis, and reporting systems. When successfully implemented, QIBs will provide image-derived metrics with known bias and variance that can be validated with anatomically and physiologically relevant measures, including treatment response (and the heterogeneity of that response) and outcome. Such non-invasive quantitative measures can then be used effectively in clinical and translational research and will contribute significantly to the goals of precision medicine. This presentation will focus on 1) outlining the opportunities for QIB applications, with examples to demonstrate applications in both research and patient care, 2) discussing key challenges in the implementation of QIB applications, and 3) providing overviews of efforts to address such challenges from federal, scientific, and professional organizations, including, but not limited to, the RSNA, NCI, FDA, and NIST. 1Sullivan, Obuchowski, Kessler, et al. Radiology, epub August 2015.
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