Comparison of three-parameter kinetic model analysis to standard Patlak’s analysis in 18F-FDG PET imaging of lung cancer patients
© The Author(s). 2018
Received: 25 January 2018
Accepted: 11 February 2018
Published: 27 March 2018
Patlak’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).
Dynamic 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.
We 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.
Comparison 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.
Positron emission tomography using [18F]fluorodeoxyglucose (18F-FDG PET) imaging in oncology patients allows physicians to quantify the increased glycolysis of cancer cells . In clinical routine, a tracer uptake index is easily available and thus widely used, namely, the standardized uptake value (SUV) [2, 3]. However, many factors can influence the SUV outcome such as the uptake time, as reported for example in lung tumors . This is the reason why, besides the SUV index, different quantitative parameters that may be obtained from kinetic model analyses (KMAs) have been implemented in a number of studies investigating various tissues [5–13]. These kinetic parameters more accurately describe the tracer trapping and may be useful to better characterize different tumor types or assess treatment response . The KMAs both require a dynamic acquisition over the tissue of interest to obtain its time-activity-curve (TAC) and a serial blood sampling to estimate the so-called input function (IF, i.e., 18F-FDG blood TAC). Among these KMAs, Patlak’s analysis is usually considered as a gold standard that provides the 18F-FDG net influx constant (i.e., the uptake rate constant, Ki) from a linear fitting of graphical data . However, it assumes an irreversible tracer trapping, a well-identified drawback since numerous studies have shown trapping reversibility in various tissues, either under physiological or pathological conditions [6, 9–11, 15].
Assuming that there may be a slow loss of the trapped tracer to the blood, i.e., a reversible trapping, Patlak and Blasberg derived a generalized non-linear equation including a release rate constant (kb) in an exponential term . This non-linear equation may be addressed by using an analytical approach, leading to a three-compartment three-parameter KMA (3P-KMA). 3P-KMA has been applied to healthy human lung and liver, allowing assessment of both Ki (in mL min−1 mL−1), kb (in min−1), and fraction of free 18F-FDG in blood and interstitial volume (Vb; no unit; < 1; also called total blood volume distribution) [12, 15]. It relies on an analytical solution of the non-linear Patlak’s equation that requires to use an IF as a sum of exponentially decreasing functions and up to three functions may usually describe the 18F-FDG IF [16, 17]. Then, it leads to a non-linear formula that is used to fit the experimental tissue TAC by simply adjusting the three above-mentioned kinetic parameters, i.e., without any tissue TAC data transform.
To the best of our knowledge, comparison between non-linear fitting by 3P-KMA and linear fitting by standard Patlak’s analysis that assumes an irreversible tracer trapping has not been reported so far, whatever the tissue either under physiological or pathological conditions. Therefore, the primary aim of this study was to make this comparison in a series of lung cancer patients that was previously acquired . Additionally, Ki, kb, and Vb outcomes obtained from 3P-KMA were compared to those obtained from a three-compartment five-parameter KMA (5P-KMA) that is usually considered as a reference model when tracer trapping is reversible. Actually, 5P-KMA provides four kinetic (micro)parameters (and Vb) from which Ki and kb may be computed, whereas the non-linear fitting of the 3P-KMA provides Ki and kb without any additional computing.
Dynamic data of 21 patients (8 females, 13 males, 71 years old on average, range 40–86) with non-small cell lung cancer obtained from a previous prospective study were retrospectively analyzed . All patients who were enrolled in the prospective study provided written informed consent before participating in it, and the further retrospective study received the approval of the ethics committees of our teaching hospitals. The patients’ mean weight and height were 68 kg (range, 50–85) and 169 cm (range, 150–180), respectively. After 6 h of fasting before the tracer injection, the preinjection average plasma glucose concentration was 1.08 g L−1 (range, 0.87–1.28). The lesion mean size was 31.8 mm (range, 14.7–52.2).
PET imaging and data processing
PET imaging procedure has been previously described in details . Briefly, a low dose CT scan was performed (75 mA, 120 kV, pitch 0.938, rotation time 0.5 s) for attenuation correction of PET emission data and for morphologic information. Then, after an intravenous bolus injection of 18F-FDG (mean 237 MBq; range, 134–507) in a cannula previously inserted in the vein of the arm, a 3D thorax dynamic list-mode acquisition protocol was started lasting 60 min (Gemini GXL, Philips Medical System, Cleveland, USA; no respiratory gating). Images were reconstructed using the iterative method RAMLA LOR-3D, with a 144 × 144 matrix and pixel size of 4 × 4 × 4 mm3. In particular, this dynamic acquisition provided 11 frames of 5 min each, leading to 11 data points of the experimental 18F-FDG IF and of the experimental cancer tissue TAC, ranging 7.5–57.5 min post-injection. For determining the experimental 18F-FDG IF, in each patient, a volume of interest (VOI) was drawn over the descending thoracic aorta in each frame of the dynamic acquisition yielding an intermediate 18F-FDG blood TAC (i.e., intermediate IF). Then, the final IF was obtained through a calibration of the intermediate IF with the 18F-FDG plasma value measured in a venous blood sampling performed at 45 min post-injection, that is, when an equilibrium is reached between 18F-FDG concentration in arterial and vein blood [3, 16]. VOIs for IF and tissue TAC were semi-automatically placed over three consecutive slices to include the five hottest voxels within the VOI.
Implementing Patlak’s analysis, 3P-KMA, and 5P-KMA
Assuming that there may be a slow loss of the trapped tracer to the blood and when the analysis remains limited to data collected for the period t > t* after injection, that is, when the reversible compartments are in effective steady state with the blood plasma, Patlak and Blasberg derived a non-linear equation including a release rate constant (kb) :
AT(t) (in kBq mL−1) is defined as the total tracer activity at time t per tissue volume unit that includes both trapped tracer and free tracer in the blood and interstitial volumes. Ap(t) (in kBq mL−1) is the blood activity at time t per blood volume unit, that is, the 18F-FDG IF.
In each patient, Patlak’s graphical analysis was implemented from Eq. 1, setting kb = 0. Eleven cancer tissue TAC data points and the corresponding 11 data points of the experimental 18F-FDG IF, ranging 7.5–57.5 min post-injection, were used. The lower limit of 7.5 min for this range was chosen in order to limit the analysis to data collected for the period t > t* after injection, as required by Eq. 1 validity [7, 8]. Ki was determined as the slope of the linear fitting of the Patlak’s plot showing AT(t)/Ap(t) versus the ratio of time integral of the right hand side of Eq. 1 to Ap(t), i.e., the so-called stretched time.
In Eq. 3, the 18F physical decay constant is λ, and Ki, kb, and F were obtained in each patient by fitting the 18F-FDG tissue TAC (XLSTAT, Microsoft; Levenberg-Marquardt algorithm), ranging 7.5–57.5 min post-injection, uncorrected for 18F physical decay. Note that previous studies used 18F-FDG tissue data obtained at late dynamic PET imaging, i.e., beyond 2 h after injection, in comparison with the current ones obtained at early imaging (7.5–57.5 min post-injection) [12, 15]. However, the rationale for deriving Eq. 3 remains identical, whatever the time of acquisition (>t* after injection).
A normal distribution of the α2 and α3 values (Eq. 2) in the current study and in Hunter’s study could not be clearly showed for each IF time constants; therefore, comparisons between the two studies were made by means of non-parametric Mann-Whitney’s test (GraphPad Prism 6 software; two-tailed; 95% confidence level). Bland-Altman analysis was used for Ki comparison between 3P-KMA and Patlak’s analysis, as well as for further comparisons between 3P-KMA and 5P-KMA, and between 5P-KMA and Patlak’s analysis (GraphPad Prism 6 software; 95% confidence level) .
Fitting results in each patient for Patlak’s analysis, 3P-KMA and 5P-KMA (A0 in kBq mL−1; α2, α3, k b k2-4 in min−1; Ki and K1 in mL min−1 mL−1; Vb no unit)
Values of Ki, kb, and Vb obtained from 3P-KMA and values of K1, k2-3-4, Vb, Ki, and kb (computed from Eqs. 4 and 5) obtained from 5P-KMA are presented in Table 1. Range of correlation coefficients for 5P-KMA fitting over the patient series was 0.977–0.999 (mean: 0.989). 3P-KMA Ki was found to be strongly correlated with 5P-KMA Ki (R = 0.989; P < 0.001). No significant difference was found between 3P-KMA Ki and 5P-KMA Ki: Ki ratio (i.e., 3P-KMA/5P-KMA) which was 1.017 ± 0.054 on average (95% confidence limits), with 95% limits of agreement of 0.230. No significant difference was found between 5P-KMA Ki and Patlak’s Ki: Ki ratio (Table 1) (i.e., 5P-KMA/Patlak) which was 1.056 ± 0.074 on average (95% confidence limits), with 95% limits of agreement of 0.317. 3P-KMA kb was found to be significantly correlated with 5P-KMA kb (R = 0.60; P < 0.01). No significant difference was found between 3P-KMA kb and 5P-KMA kb: kb difference (i.e., 3P-KMA minus5P-KMA; kb ratio is not allowed since division by zero is not allowed) which was 0.00041 ± 0.00083 min−1 on average (95% confidence limits), with 95% limits of agreement of 0.00359 min−1. No significant correlation was found between 3P-KMA Vb and 5P-KMA Vb (R = 0.12).
In each patient, 11 data points of the 18F-FDG IF were fitted by using a three-exponential decreasing function. These data points ranged 7.5–57.5 min post-injection, that is, after an equilibrium has been reached between compartments in order to satisfy the Patlak’s condition t > t*. This function was derived from Hunter’s results, of which A0, α2, and α3 were obtained by fitting (Eq. 2) . Indeed, the relative part of each exponential function to the IF area-under-curve (i.e., the total number of molecules that are available to the tissues after injection) is 1.14, 9.62, and 89.24% (by using the mean value of α1 by Hunter and of α2 and α3 reported in Table 1), respectively. In other words, the part of the first exponential function in the whole IF, which mainly covers the IF peak, is very limited, suggesting that the mean value of α1 reported by Hunter may be used in each individual . Comparison between the fitted IFs of the current study and those reported by Hunter et al. shows that the former α2 and α3 values were significantly lower than the latter ones (P < 0.0001) . The IF fitting correlation coefficients were high (range, 0.989–0.999; P < 0.001; Fig. 1). The major role of a reliable analytical IF as a sum of exponential functions for implementing 3P-KMA (Eq. 3) is emphasized by the significant correlation between IF fitting correlation coefficients and those of 3P-KMA (R = 0.631; P < 0.01). It is noteworthy that, although the current study with 18F-FDG used a three-exponential decreasing function, 3P-KMA may also be efficient with either a mono- or a bi-exponential IF depending on the tracer. Moreover, the ways the exponentially decaying IF can be obtained may be various: either from arterial or venous blood sampling, or image-derived, or from population-based IF models possibly scaled to later dynamic measurements on blood pool ROIs [3, 19].
3P-KMA Ki was found to be 6.0% greater than Patlak’s Ki, on average, with reasonable 95% limits of agreement of 17.1%, according to Bland-Altman analysis (Fig. 4). Moreover, when patients with kb > 0 were excluded, 3P-KMA Ki was no more significantly greater than Patlak’s Ki, with 95% limits of agreement of 9.8%. These findings were in agreement with Patlak and Blasberg’s comment suggesting that, in case of reversible trapping (kb > 0), linear fitting of a concave curve rather than that of a (true) linear one (kb = 0), results in an underestimation of the slope and hence to Ki underestimation . These findings also suggest that 3P-KMA and Patlak’s analysis may be used interchangeably to assess 18F-FDG uptake in lung cancer lesions. However, the strong correlation between Ki ratio (i.e., 3P-KMA/Patlak) and kb (and hence, between Ki underestimation by Patlak’s analysis and kb; Fig. 5) suggests that 3P-KMA may be more appropriate than Patlak’s analysis to accurately assess Ki in various 18F-FDG-positive cancer lesions, possibly showing greater trapping reversibility than lung cancer lesions [9–11].
No significant difference over the current series was found between 3P-KMA Ki and 5P-KMA Ki and between 3P-KMA kb and 5P-KMA kb. A further comparison strengthens the current findings, between the current 3P-KMA outcomes and published ones by Dimitrakopoulou-Strauss et al., who implemented 5P-KMA in nine patients with lung tumors: Ki = 0.0414 ± 0.0288 min−1 (SD) and kb = 0.0009 ± 0.0016 min−1 (SD) for 3P-KMA (Table 1) versus the mean value of 0.0304 and 0.0009 min−1 for 5P-KMA by Dimitrakopoulou-Strauss et al., respectively . However, it should be emphasized that the measurement uncertainty of the 3P-KMA outcomes may be expected to be lower than that of the 5P-KMA one because Ki and kb from 5P-KMA has to be computed by using three independent kinetic (micro)parameters (Eqs. 4 and 5). As a result, the measurement uncertainty of Ki and kb from 5P-KMA combines that of the three (micro)parameters, whereas the measurement uncertainty of Ki and kb from 3P-KMA may be obtained without any further combination . (Note that MU of 3P-KMA outcomes was not available from XLSTAT that did not allow a possible comparison with MU of 5P-KMA outcomes.) The proposed line of argument may be associated with Galli et al.’s results showing that close values of Ki may be computed from different set of (micro)parameter values . It may also explain why, unlike for the comparison between 3P-KMA Ki and Patlak’s Ki, no significant difference was found between 5P-KMA Ki and Patlak’s Ki and, hence, that the Ki underestimation by Patlak’s analysis was not revealed by the latter comparison. It may also be illustrated by the comparison of limits of agreement of 17.1 versus 31.7% that were found for the comparison between 3P-KMA and Patlak’s analysis versus the comparison between 5P-KMA and Patlak’s analysis, respectively. Furthermore, no significant correlation was found between 3P-KMA Vb and 5P-KMA Vb (R = 0.12), and the Vb mean value over the current series was found to be 0.16 ± 0.09 (SD) and 0.05 ± 0.03 (SD), respectively (Table 1). Consistently with the above-proposed comparison for Ki and kb, comparison of the current 3P-KMA Vb value of 0.16 ± 0.09 (SD) with that of 0.17 ± 0.07 (SD) previously published for 5P-KMA by Dimitrakopoulou-Strauss et al. further strengthens the findings of the current study .
Unlike Patlak’s analysis, the 3P-KMA approach allows expressing the whole tissue TAC as an analytical formula (Eq. 3). Therefore, at each time point, it is possible to compare it to that of its two components, that is, to the trapped tracer TAC and to the free tracer TAC. This comparison is shown in Fig. 6 by using mean values for IF and for 3P-KMA kinetic parameters that were obtained over the current lung cancer series (Table 1). Furthermore, this graph shows that the (mean) peak time for tissue TAC and trapped tracer TAC is 84 and 88 min, which could serve as landmarks to determine the optimal injection-acquisition time delay in clinical practice.
A limitation of the study is that, although the current 3P-KMA results obtained for Ki, kb, and Vb were in agreement with previous literature results by Dimitrakopoulou-Strauss et al., the kb/Ki ratio was low, about 2% on average in the current lung tumor series (Table 1) . One could argue that 3P-KMA has been previously applied at late imaging to healthy liver that showed greater kb values than those of lung tumors; however, 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 [9–11, 15]. Furthermore, we also suggest that future studies should compare the performance of the 3P-KMA non-linear fitting with that of a step-wise approach replotting non-linear graphical data with different values of kb in order to recover a linear fitting and hence to obtain Ki . Finally, the current study did not use respiratory gating and, in the case of lesions in the lower lobes, respiratory artifacts may very likely have affected outcomes of both 3P-KMA, Patlak’s analysis, and 5P-KMA and thus might have had an influence on the reported SDs and limits of agreements.
Comparison between 3P-KMA and standard Patlak’s analysis showed that the latter significantly underestimates, on average, the net influx constant (Ki) value in comparison with the former, because it arbitrarily set the release rate constant (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 3P-KMA efficiency in various tissues, either physiological or pathological, showing greater 18F-FDG trapping reversibility than lung cancer lesions.
The authors declare that they have no source of funding.
Availability of data and materials
Please contact author for data request.
EL conceived the 3P-KMA and participated in the study design and coordination and in the manuscript writing. MLC, GG, AG, AC, and LI carried out the PET scan acquisitions, obtained the IF and TAC data in all patients, implemented Patlak’s analysis and 5P-KMA, and participated in the manuscript writing. RM participated in the study design, in the model interpretation, and in the manuscript writing. All authors read and approved the final manuscript.
Ethics approval and consent to participate
This is a retrospective analysis of a prospective study. All patients who were enrolled in the prospective study provided written informed consent before participating. The further retrospective study received the approval of the ethics committees of our teaching hospitals (Comité Régional Ethique Aquitaine and Comitato Etico, Università Cattolica del S. Cuore, Roma).
Consent for publication
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- Som P, Atkins HL, Bandoypadhyay D, et al. A fluorinated glucose analog, 2-fluoro-2-deoxy-d-glucose (F-18): nontoxic tracer for rapid tumor detection. J Nucl Med. 1980;21:670–5.PubMedGoogle Scholar
- Huang HSC. Anatomy of SUV. Nucl Med Biol. 2000;27:643–6.View ArticlePubMedGoogle Scholar
- Galli G, Indovina L, Calcagni ML, Mansi L, Giordano A. The quantification with FDG as seen by a physician. Nucl Med Biol. 2013;40:720–30.View ArticlePubMedGoogle Scholar
- Laffon E, de Clermont H, Begueret H, Vernejoux J-M, Thumerel M, Marthan R, Ducassou D. Assessment of dual time point 18F-FDG imaging for pulmonary lesions. Nucl Med Commun. 2009;30:455–61.View ArticlePubMedGoogle Scholar
- Sokoloff L, Reivich M, Kennedy C, et al. The |14C]deoxyglucose method for the measurement of local cerebral glucose utilization: theory, procedure, and normal values in the conscious and anesthetized albino rat J Neurochem 1977;28:897–916.Google Scholar
- Phelps ME, Huang SC, Hoffman EJ, Selin C, Sokoloff L, Kuhl DE. Tomographic measurement of local cerebral glucose metabolic rate in humans with (F-18)2-Fluoro-2-deoxy-d-glucose: validation of method. Ann Neuro. 1979;6:371–88.View ArticleGoogle Scholar
- Patlak CS, Blasberg RG, Fenstermacher JD. Graphical evaluation of blood-to brain transfer constants from multiple-time uptake data. J Cereb Blood Flow Metab. 1983;3:1–7.View ArticlePubMedGoogle Scholar
- Patlak CS, Blasberg RG. Graphical evaluation of blood-to-brain transfer constants from multiple-time uptake data: generalizations. J Cereb Blood Flow Metab. 1985;5:584–90.View ArticlePubMedGoogle Scholar
- Okazumi S, Isono K, Enomoto K, et al. Evaluation of liver tumors using fluorine-18-fluorodeoxyglucose PET: characterization of tumor and assessment of effect of treatment. J Nucl Med. 1992;33:333–9.PubMedGoogle Scholar
- Torizuka T, Tamaki N, Inokuma T, et al. In vivo assessment of glucose metabolism in hepatocellular carcinoma with FDG-PET. J Nucl Med. 1995;36:1811–7.PubMedGoogle Scholar
- Dimitrakopoulou-Strauss A, Georgoulias V, Eisenhut M, et al. Quantitative assessment of SSTR2 expression in patients with non-small cell lung cancer using 68Ga-DOTATOC PET and comparison with 18F-FDG PET. Eur J Nucl Med Mol Imaging. 2006;33:823–30.View ArticlePubMedGoogle Scholar
- Laffon E, de Clermont H, Vernejoux JM, Jougon J, Marthan R. Feasibility of assessing [18F]FDG lung metabolism with late dynamic PET imaging. Mol Imaging Biol. 2011;13:378–84.View ArticlePubMedGoogle Scholar
- Calcagni ML, Indovina L, Di Franco D, et al. Are the simplified methods to estimate Ki in 18F-FDG PET studies feasible in clinical routine? Comparison between three simplified methods. Q J Nucl Med Mol Imaging. 2014;5 [Epub ahead of print]Google Scholar
- Lammertsma AA. Forward to the past: the case for quantitative PET imaging. J Nucl Med. 2017;58:1019–24.View ArticlePubMedGoogle Scholar
- Laffon E, Adhoute X, de Clermont H, Marthan R. Is liver SUV stable over time in 18F-FDG PET imaging? J Nucl Med Technol. 2011;39:1–6.View ArticleGoogle Scholar
- Hunter GJ, Hamberg LM, Alpert NM, Choi NC, Fischman AJ. Simplified measurement of deoxyglucose utilization rate. J Nucl Med. 1996;37:950–5.PubMedGoogle Scholar
- Vriens D, de Geus-Oei L-F, Oyen WJG, Visser EP. A curve-fitting approach to estimate the arterial plasma input function for the assessment of glucose metabolic rate and response to treatment. J Nucl Med. 2009;50:1933–9.View ArticlePubMedGoogle Scholar
- Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986;1:307–10.View ArticlePubMedGoogle Scholar
- de Geus-Oei LF, Visser EP, Krabbe PF, et al. Comparison of image-derived and arterial input functions for estimating the rate of glucose metabolism in therapy-monitoring18F-FDG PET studies. J Nucl Med. 2006;47:945–9.PubMedGoogle Scholar
- JCGM 100: 2008 . Evaluation of measurement data—guide to the expression of uncertainty in measurement; 2008. Available at: http://www.bipm.org. [Accessed September 2008].