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Unlabelled: The most accurate way to estimate the glucose metabolic rate (or its influx constant) from (18)F-FDG PET is to perform a full kinetic analysis (or its simplified Patlak version), requiring dynamic imaging and the knowledge of arterial activity as a function of time. To avoid invasive arterial blood sampling, a simplified kinetic analysis (SKA) has been proposed, based on blood curves measured from a control group. Here, we extend the SKA by allowing for a greater variety of arterial input function (A(t)) curves among patients than in the original SKA and by accounting for unmetabolized (18)F-FDG in the tumor., Methods: Ten A(t)s measured in patients were analyzed using a principal-component analysis to derive 2 principal components describing most of the variability of the A(t). The mean distribution volume of (18)F-FDG in tumors for these patients was used to estimate the corresponding quantity in other patients. In subsequent patient studies, the A(t) was described as a linear combination of the 2 principal components, for which the 2 scaling factors were obtained from an early and a late venous sample drawn for the patient. The original and extended SKA (ESKA) were assessed using fifty-seven (18)F-FDG PET scans with various tumor types and locations and using different injection and acquisition protocols, with the K(i) derived from Patlak analysis as a reference., Results: ESKA improved the accuracy or precision of the input function (area under the blood curve) for all protocols examined. The mean errors (±SD) in K(i) estimates were -12% ± 33% for SKA and -7% ± 22% for ESKA for a 20-s injection protocol with a 55-min postinjection PET scan, 20% ± 42% for SKA and 1% ± 29% for ESKA (P < 0.05) for a 120-s injection protocol with a 55-min postinjection PET scan, and -37% ± 19% for SKA and -4% ± 6% for ESKA (P < 0.05) for a 20-s injection protocol with a 120-min postinjection PET scan. Changes in K(i) between the 2 PET scans in the same patients also tended to be estimated more accurately and more precisely with ESKA than with SKA., Conclusion: ESKA, compared with SKA, significantly improved the accuracy and precision of K(i) estimates in (18)F-FDG PET. ESKA is more robust than SKA with respect to various injection and acquisition protocols.

Unlabelled: Standardized uptake value (SUV) is often used to quantify (18)F-FDG tumor use. Although useful, SUV suffers from known quantitative inaccuracies. Simplified kinetic analysis (SKA) methods have been proposed to overcome the shortcomings of SUV. Most SKA methods rely on a single time point (SKA-S), not on tumor uptake rate. We describe a hybrid between Patlak analysis and existing SKA-S methods, using multiple time points (SKA-M) but reduced imaging time and without measurement of an input function. We compared SKA-M with a published SKA-S method and with Patlak analysis., Methods: Twenty-seven dynamic (18)F-FDG scans were performed on 11 cancer patients. A population-based (18)F-FDG input function was generated from an independent patient population. SKA-M was calculated using this population input function and either a short, late, dynamic acquisition over the tumor (starting 25-35 min after injection and ending approximately 55 min after injection) or dynamic imaging 10 or 25 min to approximately 55 min after injection but using only every second or third time point, to permit a 2- or 3-field-of-view study. SKA-S was also calculated. Both SKA-M and SKA-S were compared with the gold standard, Patlak analysis., Results: Both SKA-M (1 field of view) and SKA-S correlated well with Patlak slope (r > 0.99, P < 0.001, and r = 0.96, P < 0.001, respectively), as did multilevel SKA-M (r > 0.99 and P < 0.001 for both). Mean values of SKA-M (25-min start time) and SKA-S were statistically different from Patlak analysis (P < 0.001 and P < 0.04, respectively). One-level SKA-M differed from the Patlak influx constant by only -1.0% +/- 1.4%, whereas SKA-S differed by 15.1% +/- 3.9%. With 1-level SKA-M, only 2 of 27 studies differed from K(i) by more than 20%, whereas with SKA-S, 10 of 27 studies differed by more than 20% from K(i)., Conclusion: Both SKA-M and SKA-S compared well with Patlak analysis. SKA-M (1 or multiple levels) had lower variability and bias than did SKA-S, compared with Patlak analysis. SKA-M may be preferred over SUV or SKA-S when a large unmetabolized (18)F-FDG fraction is expected and 1-3 fields of view are sufficient.

The standardized uptake value (SUV) and the slope of the Patlak plot ( K) have both been proposed as indices to monitor the progress of disease during cancer therapy. Although a good correlation has been reported between SUV and K, they are not equivalent, and may not be equally affected by metabolic changes occurring during disease progression or therapy. We wished to compare changes in tumor SUV with changes in K during serial positron emission tomography (PET) scans for monitoring therapy. Thirteen patients enrolled in a protocol to treat renal cell carcinoma metastases were studied. Serial dynamic fluorodeoxyglucose (FDG) PET scans and computed tomography (CT) and magnetic resonance (MR) scans were performed once prior to treatment, once at 36+/-2 days after the start of treatment, and (in 7/13 subjects, 16/27 lesions) a third time at 92+/-9 days after the start of treatment. This resulted in a total of 33 scans, and 70 tumor Patlak and SUV values (one value for each lesion at each time point). SUV and K were measured over one to four predefined tumors/patient at each time point. The input function was obtained from regions of interest over the heart, combined, if necessary, with late blood samples. Over all tumors and scans, SUV and K correlated well ( r=0.97, P<0.0001). However, change in SUV with treatment over all tumor scan pairs was much less well correlated with the corresponding change in K ( r=0.73, P<0.0001). The absolute difference in % change was outside the 95% confidence limits expected from previous variability studies in 6 of 43 pairs of tumor scans, and greater than 50% in 2 of 43 tumor scan pairs. In four of the six cases, the two indices predicted opposing therapeutic outcomes. Similar results were obtained for SUV normalized by body weight or body surface area and for SUVs using mean or maximum count. Changes in CT and MR tumor cross-product dimensions correlated poorly with each other ( r=0.47, P=NS), and so could not be used to determine the "correct" PET index. Absolute values of SUV and K correlated well over the patient population. However, when monitoring individual patient therapy serially, large differences in the % changes in the two indices were occasionally found, sometimes sufficient to produce opposing conclusions regarding the progression of disease.