Comparison of three-parameter kinetic model analysis to standard Patlak’s analysis in 18F-FDG PET imaging of lung cancer patients

Abstract

BackgroundPatlak’s graphical analysis can provide tracer net influx constant (Ki) with limitation of assuming irreversible tracer trapping, that is, release rate constant (kb) set to zero. We compared linear Patlak’s analysis to non-linear three-compartment three-parameter kinetic model analysis (3P-KMA) providing Ki, kb, and fraction of free 18F-FDG in blood and interstitial volume (Vb).MethodsDynamic PET data of 21 lung cancer patients were retrospectively analyzed, yielding for each patient an 18F-FDG input function (IF) and a tissue time-activity curve. The former was fitted with a three-exponentially decreasing function, and the latter was fitted with an analytical formula involving the fitted IF data (11 data points, ranging 7.5–57.5 min post-injection). Bland-Altman analysis was used for Ki comparison between Patlak’s analysis and 3P-KMA. Additionally, a three-compartment five-parameter KMA (5P-KMA) was implemented for comparison with Patlak’s analysis and 3P-KMA.ResultsWe found that 3P-KMA Ki was significantly greater than Patlak’s Ki over the whole patient series, + 6.0% on average, with limits of agreement of ± 17.1% (95% confidence). Excluding 8 out of 21 patients with kb > 0 deleted this difference. A strong correlation was found between Ki ratio (=3P-KMA/Patlak) and kb (R = 0.801; P < 0.001). No significant difference in Ki was found between 3P-KMA versus 5P-KMA, and between 5P-KMA versus Patlak’s analysis, with limits of agreement of ± 23.0 and ± 31.7% (95% confidence), respectively.ConclusionsComparison between 3P-KMA and Patlak’s analysis significantly showed that the latter underestimates Ki because it arbitrarily set kb to zero: the greater the kb value, the greater the Ki underestimation. This underestimation was not revealed when comparing 5P-KMA and Patlak’s analysis. We suggest that further studies are warranted to investigate the 3P-KMA efficiency in various tissues showing greater 18F-FDG trapping reversibility than lung cancer lesions.

Is Patlak y-intercept a relevant metrics?

“…Third, the possible occurrence of tracer-trapping reversibility, even of low amplitude (low k 4value; Fig. 1), may generate an unpredictable slope-value underestimation, and, hence, an unpredictable Y 0 overestimation (thus associated to an unpredictable K i underestimation) [13]. Finally, combining all the above-described origins of measurement uncertainty contributes to a substantial measurement uncertainty in the graphical assessment of Y 0 .…”

An abbreviated therapy-dosimetric equation for the companion diagnostic/therapeutic [64/67Cu]Cu-SARTATE

“…Peak time of decay-uncorrected activity concentration of trapped [ 64 Cu]Cu-SARTATE was assessed from an analytical solution of the nonlinear Patlak’s equation, involving the above-reported mono-exponentially decaying IF [ 6 ]: …”

“…4 was obtained by adding the term “F D × IF(t peak-uncorr )” to Eq. 5 right-hand side and by dividing the whole by IF(t peak-uncorr ), since the tumor-to-blood SUV ratio equals the tumor-to-blood activity–concentration ratio [ 6 ].…”

Distribution Volume of 18F-BMS‐986192 in NSCLC Patients

“…We would like to further analyze the latter lower correlation since, under post-injectiontime conditions we address in this letter: (i) VT may be assessed by the ratio of tissue/plasma tracer concentration, i.e., Ctissue/Cplasma ; and (ii) the SUVBW/Cplasma ratio may be also proportional to VT since SUVBW is proportional to Ctissue. To clarify this issue, we have fitted the 18 F-BMS-tumor TAC by using a three-compartment three-parameter kinetic model (data extracted from Figures 3-4 by Huisman with the Web-Plot-Digitizer software; R 2 =0.996 and 0.998, respectively) (1,3). Estimates of Ki and kb were provided, leading to compute VT as Ki/kb = 4.7 mL.cm -3 .…”