Principles of Tracer Kinetic Analysis in Oncology, Part II: Examples and Future Directions

Abstract

Learning Objectives: On successful completion of this activity, participants should be able to (1) describe examples of the application of PET tracer kinetic analysis to oncology; (2) list applications research and possible clinical applications in oncology where kinetic analysis is helpful; and (3) discuss future applications of kinetic modeling to cancer research and possible clinical cancer imaging practice.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 April 2025.Kinetic analysis of dynamic PET imaging enables the estimation of biologic processes relevant to disease. Through mathematic analysis of the interactions of a radiotracer with tissue, information can be gleaned from PET imaging beyond static uptake measures. Part I of this 2-part continuing education paper reviewed the underlying principles and methodology of kinetic modeling. In this second part, the benefits of kinetic modeling for oncologic imaging are illustrated through representative case examples that demonstrate the principles and benefits of kinetic analysis in oncology. Examples of the model types discussed in part I are reviewed here: a 1-tissue-compartment model (¹⁵O-water), an irreversible 2-tissue-compartment model (¹⁸F-FDG), and a reversible 2-tissue-compartment model (3'-deoxy-3'-¹⁸F-fluorothymidine). Kinetic approaches are contrasted with static uptake measures typically used in the clinic. Overall, this 2-part review provides the reader with background in kinetic analysis to understand related research and improve the interpretation of clinical nuclear medicine studies with a focus on oncologic imaging.

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

FIGURE 1.

(A) Thick sagittal PET images of ¹⁸F-FDG (top) and ¹⁵O-water (bottom) demonstrate ¹⁸F-FDG uptake throughout breast cancer (open arrow), with relatively decreased blood flow centrally; solid arrow denotes heart. This regional metabolism–blood flow mismatch centrally suggests region of hypoxia. After chemotherapy, residual viable tumor was seen in center of tumor, suggesting chemotherapy resistance. (Reprinted from (7), noting that analysis in this publication used ROIs that did not account for tumoral heterogeneity.) (B–D) Changes in kinetic parameters (MR_FDG [B]; blood flow estimated by ¹⁵O-water [C]; ¹⁸F-FDG K_i [D]) from baseline to mid therapy in study of patients with LABC demonstrate associations with tumor response. (E) Changes in SUV, however, were not significant. pCR = pathologic complete response. (Reprinted from (11).)

FIGURE 2.

In study quantifying response to chemotherapy in breast cancer, percentage change in SUV is compared with percentage change in MR_FDG. For patient in lowest tertile of baseline SUV uptake (A), only 65% of maximum detectable percentage change (solid arrow) in SUV (change in SUV when change in MR_FDG = −100%) is able to be theoretically achieved. This is compared with 86% of maximum detectable percentage change in SUV in patients with greater baseline uptake (open arrow) (B), underscoring impact of nonspecific uptake on static ¹⁸F-FDG uptake measures. (Adapted from (36).)

FIGURE 3.

(A) Compartmental model of ¹⁸F-FLT with 2 reversible tissue compartments. (B) Representative time–activity curves for tumor, muscle, and marrow. (C) ¹⁸F-FLT-PET image demonstrating left lung cancer and normal marrow uptake. (D) Correlation of K_FLT from 3-parameter model using 60 min of data compared with 4-parameter model with 120 min of data shows underestimate of K_FLT with 3-parameter model using more data, as expected from preliminary mathematic studies. (Adapted from (54).) C_met = concentration of metabolites in arterial plasma; C_pFLT = concentration of ¹⁸F-FLT in arterial plasma; FLT-gluc = ¹⁸F-FLT-glucuronide; FLTDP = ¹⁸F-FLT-diphosphate; FLTMP = ¹⁸F-FLT-monophosphate; FLTTP = ¹⁸F-FLT-triphosphate; Q_e = exchangeable tissue compartment; Q_m = compartment of trapped ¹⁸F-FLT phosphorylated nucleotides.

FIGURE 4.

(Left) Schematic illustrating benefit of extended AFOV total-body (TB) PET scanners (blue rectangles represent AFOV of each scanner). Extended AFOV TB PET scanners enable simultaneous kinetic analysis of all major body organs. Images (middle) and time–activity curves (right) from dynamic ¹⁸F-FDG dataset of healthy human subject imaged on the extended AFOV scanner at the University of Pennsylvania demonstrate ability to capture relatively noise-free time–activity curves. (Adapted from (62).) Univ of Penn = University of Pennsylvania.