Principles of Tracer Kinetic Analysis in Oncology, Part I Principles and Overview of Methodology

被引:16
|
作者
Pantel, Austin R. [1 ]
Viswanath, Varsha [1 ]
Muzi, Mark [2 ]
Doot, Robert K. [1 ]
Mankoff, David A. [1 ]
机构
[1] Univ Penn, Dept Radiol, Philadelphia, PA 19104 USA
[2] Univ Washington, Dept Radiol, Seattle, WA 98195 USA
关键词
kinetic analysis; dynamic imaging; PET/CT; POSITRON-EMISSION-TOMOGRAPHY; CEREBRAL GLUCOSE-UTILIZATION; TUMOR BLOOD-FLOW; DYNAMIC PET DATA; BREAST-CANCER; F-18; FLUORODEOXYGLUCOSE; TRANSFER CONSTANTS; GRAPHICAL ANALYSIS; METABOLIC-RATE; MODEL;
D O I
10.2967/jnumed.121.263518
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
PET enables noninvasive imaging of regional in vivo cancer biology. By engineering a radiotracer to target specific biologic processes of relevance to cancer (e.g., cancer metabolism, blood flow, proliferation, and tumor receptor expression or ligand binding), PET can detect cancer spread, characterize the cancer phenotype, and assess its response to treatment. For example, imaging of glucose metabolism using the radiolabeled glucose analog F-18-FDG has widespread applications to all 3 of these tasks and plays an important role in cancer care. However, the current clinical practice of imaging at a single time point remote from tracer injection (i.e., static imaging) does not use all the information that PET cancer imaging can provide, especially to address questions beyond cancer detection. Reliance on tracer measures obtained only from static imaging may also lead to misleading results. In this 2-part continuing education paper, we describe the principles of tracer kinetic analysis for oncologic PET (part 1), followed by examples of specific implementations of kinetic analysis for cancer PET imaging that highlight the added benefits over static imaging (part 2). This review is designed to introduce nuclear medicine clinicians to basic concepts of kinetic analysis in oncologic imaging, with a goal of illustrating how kinetic analysis can augment our understanding of in vivo cancer biology, improve our approach to clinical decision making, and guide the interpretation of quantitative measures derived from static images.
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页码:342 / 352
页数:11
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