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

Abstract

Learning Objectives: On successful completion of this activity, participants should be able to describe (1) describe principles of PET tracer kinetic analysis for oncologic applications; (2) list methods used for PET kinetic analysis for oncology; and (3) discuss application of kinetic modeling for cancer-specific diagnostic needs.Financial Disclosure: This work was supported by KL2 TR001879, R01 CA211337, R01 CA113941, R33 CA225310, Komen SAC130060, R50 CA211270, and K01 DA040023. Dr. Pantel is a consultant or advisor for Progenics and Blue Earth Diagnostics and is a meeting participant or lecturer for Blue Earth Diagnostics. Dr. Mankoff is on the scientific advisory boards of GE Healthcare, Philips Healthcare, Reflexion, and ImaginAb and is the owner of Trevarx; his wife is the chief executive officer of Trevarx. The authors of this article have indicated no other relevant relationships that could be perceived as a real or apparent conflict of interest.CME Credit: SNMMI is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to sponsor continuing education for physicians. SNMMI designates each JNM continuing education article for a maximum of 2.0 AMA PRA Category 1 Credits. Physicians should claim only credit commensurate with the extent of their participation in the activity. For CE credit, SAM, and other credit types, participants can access this activity through the SNMMI website (http://www.snmmilearningcenter.org) through March 2025PET 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 ¹⁸F-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.

Keywords: PET/CT; dynamic imaging; kinetic analysis.

FIGURE 1.

(Top) Depiction of movement of tracer (green diamond) from vasculature to its intracellular tumor target (maroon V). Tracer is delivered to tumor via regional blood flow; travels from capillary, through interstitial space, and into cell, where it interacts with its target in this example; and may also follow reverse path. Static PET image represents summation of tracer in each of these steps in tissue. For kinetic analysis, compartments are constructed that represent key states of tracer (e.g., reversible compartment representing tracer in interstitial space and in cell, and bound compartment representing tracer engaging with target. Rate constants (K₁, k₂, k₃, and k₄) represent transfers between compartments (defined in Table 1), which may depict multiple tracers moving through multiple barriers.

FIGURE 2.

(Top) Data are acquired at single bed position (left) and reconstructed into multiframe dynamic images (middle), on which regions of interest are drawn to create time–activity curves (right). Blood time–activity curve peaks early in scan (red arrow, corresponding to early dynamic frames), whereas tumor activity peaks later (blue arrow, corresponding to later dynamic frames). (Middle) In this schematic overview of the approach to kinetic analysis, raw whole-blood time–activity curve must be partitioned into plasma vs. red blood cell activity and corrected for metabolites to obtain plasma time–activity curve, which is then used as input to kinetic modeling process. Dynamic tissue time–activity curves act as standard of truth against which model estimates are compared in iterative process of kinetic parameter estimation. (Bottom) Kinetic parameter estimation (model-generated tissue curve) improves through model optimization as iterations increase.

FIGURE 3.

Kinetic model for generic 1-tissue-compartment model (top), generic 2-tissue-compartment model (middle), and 2-tissue-compartment model for ¹⁸F-FDG (bottom). Barriers encountered by radiotracer as it moves between compartments are also noted.

FIGURE 4.

(A) MRI demonstrates contrast enhancement in recurrent right frontal glioma, with viable tissue predominately seen posteriorly. (B) Summed 20- to 60-min 2-[¹¹C-11]thymidine PET image demonstrates relatively high background uptake of tracer throughout brain, with mildly increased tracer uptake in enhancing portion of recurrent glioma. (C) 2-[¹¹C-11]thymidine K_i parametric image from mixture analysis demonstrates increased contrast of tumor compared with normal brain, underscoring benefits of kinetic analysis. (Modified with permission of (57).)

FIGURE 5.

Representative low-flux and high-flux lesions in plots of individual components of model curve. Radiotracer is freely exchanged in first compartment but is trapped in second compartment. Major contributor of uptake in low-flux lesion is reversible (first) compartment, whereas trapped (second) compartment is major contributor of uptake in high-flux lesion.

FIGURE 6.

Graphical methods of data analysis, including Patlak (A) and Logan (B) plots, where C_Plasma is blood time–activity curve and C_Tissue is tissue time–activity curve. t = time.