Purpose: The aim of the study was to evaluate the inter- and intrapatient variability of positron emission tomography (PET) measurements of perfusion in advanced solid cancers.

Experimental Design: Thirty-seven patients with predominantly intra-abdominal tumors underwent PET imaging using inhaled C15O2. Repeat data were obtained by scanning five patients twice, 1 week apart, with no intervening therapy. Regional flow and the volume of distribution (Vd) were measured from dynamic images by use of a one-compartment model. Inter- and intrapatient variability were measured as the coefficient of variability (CV). Data were also obtained for regions of interest in normal liver, spleen, and kidney.

Results: The mean (±SD) regional flow in the tumors was 0.46 ± 0.19 mlblood/min/mltissue, and the mean Vd was 0.74 ± 0.15 mlblood/mltissue. Variability in tumor flow was greater between (n = 37; CV = 41%) than within (n = 5; CV = 11%) patients. Variability in tumor Vd was greater between (CV = 21%) than within (CV = 6%) patients. There was a good correlation between the repeat tumor data for both regional flow (ρ = 0.82; P = 0.023) and Vd (ρ = 0.89; P = 0.007). Normal tissue variability was also greater between than within patients. In all cases, no statistically significant differences were seen between repeat measurements in the same patient.

Conclusions: Dynamic C15O2 PET measurements of regional flow are reproducible in patients with predominantly intra-abdominal malignancies and may be useful for the pharmacodynamic evaluation of novel antivascular and antiangiogenic cancer therapeutic agents.

The measurement of vascular physiology and hemodynamic parameters is a growing area of cancer research that is linked with the development of drugs that target the tumor vasculature. This is because the maximum tolerated dose used for dose and schedule-finding studies of conventional antiproliferative chemotherapeutic agents may be less applicable in antiangiogenic/vascular approaches. The lack of antiproliferative toxicity associated with these new agents has led to interest in the use of surrogate endpoints of efficacy that could be used in their early clinical evaluation. One of the rationales behind the development of functional imaging of vascular physiology in cancer research, therefore, is to provide pharmacodynamic methods that can be applied in Phase I/II studies of antiangiogenic/vascular drugs. Several endpoints are of interest that include the measurement of tumor blood flow.

The principle underlying positron emission tomography (PET) measurements of tumor blood flow is that the transcapillary exchange of a freely diffusible, biologically inert, and nonmetabolized tracer is limited by blood flow and not by diffusion or metabolism (1, 2). With a constant infusion of a short-lived isotope, its concentration in a tissue at equilibrium is related to blood flow. The first quantitative PET techniques used either i.v. H215O (3) or inhaled C15O2(4), which is converted to H215O in the lungs by carbonic anhydrase. To calculate regional flow, only two measurements are required: the steady-state tracer concentration in the tissue, determined by PET, and the corresponding arterial tracer concentration, measured by blood sampling. This steady-state method, however, assumes that the partition coefficient of water is the same in healthy and diseased tissue and that the volume of distribution of water (Vd) is fixed. Vd is the proportion of a region of interest (ROI) in which the radioactive water is distributed, and a value of 0.5 corresponds to the ROI containing half the concentration of plasma. Because the steady-state method can lead to the underestimation of blood flow in heterogeneous tissues and where Vd is variable, a dynamic method was developed in which both flow and Vd are measured simultaneously (5, 6, 7).

Methods for quantifying regional flow by use of 15O-labeled tracers have been well validated. Data obtained with PET were shown to correlate with other methods for measuring blood flow in the brain, myocardium, muscle, and kidney (8). The methods are widely used in the brain and heart, but fewer studies have been conducted in human tumors. The regional flow of brain tumors was shown to be variable with no consistent finding of whether it is higher or lower than in the normal brain (9, 10, 11). Breast tumors were shown to be better perfused than normal breast tissue, with the Vd being considerably higher (7). Other tracers are also being studied, such as 62Cu-pyruvaldehyde bis(N-4-methylthiosemicarbazone) (PTSM; Ref.12), but are generally considered less useful in tumors than the 15oxygen-labeled molecules because they rely on metabolite trapping (8). Recently, studies have demonstrated the efficacy of using H215O PET (13, 14, 15) and 62Cu-PTSM PET (12) measurements of blood flow for the early evaluation of novel antitumor agents.

For a method to be useful in the clinical evaluation of new anticancer agents, it must be quantitative and reproducible. H215O PET blood flow measurements have been shown to be reproducible in the brain (16), spleen (17), and heart (18). The lack of published data for human tumors and for the C15O2 inhalation method was the rationale behind the study reported here: an examination of the reproducibility of C15O2 PET measurements of regional flow in solid, predominantly intra-abdominal, tumors.

Patients.

The regional flow studies were approved by the ethical committee of the Imperial College School of Medicine, Hammersmith Hospital, London, United Kingdom. Permission to administer the radioactive tracers was obtained from the Administration of Radioactive Substances Advisory Committee of the United Kingdom. Patients with advanced, predominantly intra-abdominal malignancies were enrolled, and all gave written informed consent. Patients had a performance status ≤1. To minimize inter- and intrapatient variability in hepatic and portal venous flow and the effect of circadian variations in metabolism, patients in both studies fasted for a minimum of 4 h before scanning and were scanned at a similar time of day (19).

Patient Imaging.

Labeled oxygen was produced by deuteron bombardment of a 14N target in the United Kingdom Medical Research Council Scanditronix MC40 cyclotron, and was exposed to a nitrogen plus 1% CO2 gas mixture to give C15O2(20). The tracer was delivered at 4 MBq/ml and a flow rate of 500 ml/min via a light face mask (MC oxygen mask; Henleys Medical, Welwyn, United Kingdom) for 210 s, 30 s after the start of scanning. Patients were scanned for 10 min, and the data were collected into 30 time frames with the highest temporal sampling at the beginning of the scan (time frame 1 = 30 s; time frames 2–7 = 5 s; time frames 8–13 = 10 s; time frames 14–19 = 20 s; time frames 20–21 = 10 s; time frames 22–23 = 20 s; time frames 24–27 = 30 s; time frames 28–30 = 60 s). Imaging was performed on an ECAT 931-08/12 scanner (CTI PET Systems, Knoxville, TN) at the Hammersmith Hospital, London, United Kingdom, after insertion of an arterial line for blood sampling. Patient position was defined by X-ray simulation of the area of interest after localization of the tumor from a recent computed tomography scan. The axial field of view was 10.8 cm. A 68Ge phantom was used for attenuation correction and calibration. During the flow scan, the radioactivity of arterial blood was monitored continually by passage through an on-line bismuth germinate detector calibrated with a well counter (21). Separate analysis of discrete blood samples enabled calibration of the radioactive counts.

PET Image Analysis.

Sinogram data were reconstructed into tomographic images by use of filtered back-projection after transfer to a Sun SPARC Workstation (Sun Microsystems, Mountain View, CA). Voxel dimensions were 2.1 × 2.1 × 6.4 mm. Inspection of a recent computed tomography scan was used to assist the delineation of tumor and normal tissue regions. ROI were defined by use of Analyze image analysis software (Biomedical Imaging Resource, Mayo Foundation) on summed integral images (five plane minimum) rather than individual time frames. To ensure that only viable tumor was analyzed, tumor rims were defined by use of the computed tomography images and the regional flow scans. Once ROI were defined, mean pixel counts for each time frame were measured and plotted against midframe time to produce a time–activity curve. The same ROI were applied for the repeat studies by repositioning on an identical slice and by visual comparison.

Quantification of Regional Flow and Vd.

Tissue and arterial 15O concentrations were measured by use of the tomograph and bismuth germinate on-line detector, respectively. Data were calibrated with a well counter. Values for regional flow (mlblood/min/mltissue) and the volume of tissue distribution of the tracer (Vd; mlblood/mltissue) were obtained by use of the model originally described by Kety and Schmidt (22). The model requires a measure of the delay in tracer delivery between the point of sampling and its delivery to tissue, in addition to a measure of tracer dispersion in blood. The values for these parameters were adjusted to obtain the best fit to the model. After inhalation of C15O2, 15O is transferred rapidly to the form of H215O by the action of carbonic anhydrase in the lung. Because H215O is a freely diffusible tracer, its behavior in tissue can be described by a single-tissue compartmental model:

\[dC_{t}(t)/dt\ {=}\ FC_{a}t(t)\ {-}\ (F/V_{d}\ {+}\ {\lambda})C_{t}(t)\]

Where Ct(t) is the regional tissue concentration of H215O in Bq/ml as a function of time t; Ca(t) is the arterial whole blood concentration of H215O in Bq/ml as a function of time; F is the regional flow in mlblood/min/mltissue; Vd is the volume of distribution of water in mlblood/mltissue; and λ = 0.338 min−1, the decay constant of 15O. The solution to the differential equation is given by:

\[C_{t}(t)\ {=}\ FC_{a}(t)\ {\otimes}\ exp{[}{-}(F/V_{d}\ {+}\ {\lambda})t{]}\]

Where ⊗ denotes the convolution; and Ct(t) represents the tissue response to an arterial input function Ca(t). When Ca(t) and Ct(t) are measured over time with dynamic PET, best estimates of both F and Vd can be obtained by standard nonlinear regression analysis (a simple least-squares algorithm) and minimizing the sum of squares of differences between the model output and the tissue data. Data were fitted by use of PET interactive data language (idl) software, and in-house Matlab (version 4.2; The Mathworks, Inc.) software was used for data analysis and presentation. It should be noted that Ca(t) and Ct(t) should not be corrected for decay because that has already been incorporated into equation B. In addition, parameters for the liver are only an index of regional flow and Vd because it has a dual blood supply from the hepatic artery and portal vein, and are thus only approximated by the one-compartment flow model (23).

Statistical Analyses.

The variability in regional flow was expressed as a coefficient of variation (CV; SD/mean × 100, expressed as a percentage). Differences between the data obtained in paired scans on the same individual were tested by the Wilcoxon matched-pairs signed-rank test. The relationship between paired data were also tested with Spearman’s correlation coefficient. A probability of P < 0.05 was used throughout to indicate a statistically significant result.

Interpatient Variability in Regional Flow.

Table 1 summarizes the variety of tumors studied. Most of the tumors were intra-abdominal (35 of 37) and of gastrointestinal origin (31 of 37). Tumor flow and Vd data were obtained for all of the patients (Table 2; Fig. 1). For some of the patients, data were also obtained for ROI set in the liver, spleen, or kidney (Table 2; Fig. 2). The data are summarized in Table 3. Regional flow was highest in the kidney and lowest in the tumor and liver, both of which had similar levels of flow. There was more interpatient variability in tumor regional flow than in any of the normal tissues studied. Comparison of patients’ weights, measured immediately before each scanning session, showed no changes in weight between sessions, providing some indication that patients had a similar volume status and level of hydration during their repeat scans. The Vd was similar in all of the tissues, with a similar degree of interpatient variability except in the spleen, where the level of variability was small.

Intrapatient Variability in Tissue Regional Flow.

A subset of 5 of the 37 patients were scanned twice, 1 week apart, with no intervening therapy. Table 4 lists the tumor data obtained, which included measurements made in both non-necrotic and necrotic regions. As expected, the regional flow and Vd of necrotic regions were poor. Intrapatient variability was less than that seen between patients. There was no statistically significant difference in the paired scan data for regional flow or Vd. The relationships between the paired tumor regional flow and Vd data are shown in Fig. 3. Table 5 lists the repeat data for the normal tissues studied. Intrapatient variability was also less than interpatient variability for regional flow and Vd measured in normal liver, spleen, and kidney (Table 3). Again, there were no statistically significant differences between the paired data. ROI were drawn in the left and right kidney in three patients, and the regional flow and Vd data were obtained during a single scan. The intrapatient regional flow CV were similar (9–14%) for all of the tissues studied, but the CV for Vd varied from 3 to 13% and were smallest in the kidney.

For some conventional chemotherapeutic agents, the correlation between plasma drug levels and toxicity can be used to individualize and optimize drug dose levels (24). In these cases plasma levels form a surrogate for tumor drug doses. Tumors are heterogeneous, with variable perfusion (25, 26), which leads to uncertainty in the assessment of the relationship between plasma and tumor drug levels. This, along with the fact that many new anticancer agents lack the toxicity associated with conventional chemotherapeutic agents and also may target tumor vasculature, makes a reliable measurement of tumor perfusion attractive for Phase I studies of new antitumor agents. There is also interest in the relationship between tumor blood flow and patient response to therapy. Recent data showed that the combination of tumor 18F-fluorodeoxyglucose metabolic and H215O blood flow measurements predicted disease-free survival in patients with locally advanced breast cancer undergoing chemotherapy (14). In addition, H215O blood flow measurements have been used to measure pharmacodynamic vascular changes in Phase I studies of the antiangiogenic agent endostatin (15) and the antivascular agent combretastatin A4-phospate (13).

Until recently, there has been a paucity of data on the use of PET to measure tumor vascular physiology in vivo. Table 6 summarizes some of the recent data obtained in patients with predominantly intra-abdominal cancer. Most of the studies listed in Table 6 involved the use of labeled water rather than carbon dioxide. Although injection of water is more widely used than inhalation of carbon dioxide for the administration of 15O, Table 6 illustrates the similarity of the data obtained using the different approaches. In the study reported here, PET measurements of regional flow were variable among patients, in keeping with reports in the literature involving non-PET methods (25, 26). Regional flow was also more variable in the tumors than in the normal tissues studied, and higher regional flows were seen in the spleen and kidney than in either tumor or normal liver. The reduced regional flow measured in necrotic regions of tumors was expected and illustrates the sensitivity of PET for measurement of differences in regional flow.

The advantage of the dynamic over the steady-state method for measuring flow is that it allows for the Vd of the radiolabeled water and avoids the tendency to underestimate mean tumor flow in the presence of flow heterogeneity within a ROI. In normal breast, a Vd of 0.14 ml/ml was reported, with the low value attributed to the high fat content of the tissue (7). In breast tumor, a Vd of 0.56 and in cerebral gray matter a Vd of 0.86 have been reported (27). The Vd value of 0.74 mlblood/mltissue for mainly intra-abdominal tumors falls within the range reported by others in other tissues (Table 6). Again, as for the regional flow data, the low Vd obtained in the necrotic areas of the tumors illustrates the sensitivity of PET for measurement of regional differences in vascular hemodynamic parameters.

No term was used to account for radioactivity from the blood volume in either the imaged tissue or adjacent vascular structures (spillover). The lack of a term might lead to an overestimate of the measurements of regional flow and Vd in highly vascularized tumors or in tumors close to major blood vessels. This contrasts with cardiac studies in which imaging is carried out adjacent to vascular structures and a spillover term is required. A term is not generally applied to tumor PET flow studies when the tumors imaged are not near highly vascularized structures, as was the case in the work reported here. In the study by Hoekstra et al.(28) of lung tumors, however, a single-compartment model was used both with and without an arterial blood volume component. Better fits to the data were obtained with the incorporation of a blood volume component. This might be attributable to the highly vascular nature of the surrounding normal lung tissue, resulting in a significant spillover term. However, it might be of interest, therefore, to explore the use of the term spillover in future PET studies of regional flow in tumors.

This study has shown that the measurement of regional flow by C15O2 PET is a reproducible method in patients with solid, predominantly intra-abdominal, tumors. The intrapatient reproducibility is also in keeping with that reported for the use of H215O PET to measure cerebral (16) and spleenic (17) blood flow. Recently 62Cu-PTSM measurements of tumor flow were also shown to be reproducible when a single patient with colorectal liver metastases was scanned on three separate occasions (29). In the study reported here, data were also available for within-scan intrapatient variability. A similar level of variability was seen for the kidney regional flow data (CV = 12%), where the variability between right and left kidney data obtained during the same scan were compared, as for the between-scan intrapatient variability in the other tissues examined (CV = 11–14%). These data suggest that machine, set-up, and analysis errors are minimal for PET flow studies in intra-abdominal malignancies. The within-scan intrapatient variability in Vd was smaller (kidney CV = 3%) than the between-scan intrapatient variability in the other tissues studied (CV = 6–13%) and suggests that the machine/set-up/analysis errors might contribute approximately half of the measured intrapatient variability.

The conclusion from this study is that PET measurements of regional flow are reproducible in patients with advanced, predominantly intra-abdominal, tumors. This study supports the continuing development of PET measurements of tumor blood flow for the pharmacodynamic evaluation of antiangiogenic/vascular agents.

Grant support: Medical Research Council of the United Kingdom and Cancer Research United Kingdom Grants C153/A1797 and C153/A1802.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

Requests for reprints: Pat Price, Academic Department of Radiation Oncology, Wolfson Molecular Imaging Centre, Christie NHS Trust Hospital, Wilmslow Road, Manchester M20 4BX, United Kingdom. Phone: 44-161-446-8003; Fax: 44-446-161-8111; E-mail: Anne.Mason@man.ac.uk

Fig. 1.

Frequency histogram showing the distribution of tumor regional flow and volume of distribution (Vd) measured by C15O2 PET. Data are for 37 patients with advanced, predominantly intra-abdominal, malignancies.

Fig. 1.

Frequency histogram showing the distribution of tumor regional flow and volume of distribution (Vd) measured by C15O2 PET. Data are for 37 patients with advanced, predominantly intra-abdominal, malignancies.

Close modal
Fig. 2.

Perfusion data in patients with advanced tumors. The X axis represents a consecutive series of patients.

Fig. 2.

Perfusion data in patients with advanced tumors. The X axis represents a consecutive series of patients.

Close modal
Fig. 3.

Relationship between repeat measurements of regional flow (top) and volume of distribution (Vd; bottom) in patients with gastrointestinal tumors. The lines represent a theoretical exact correlation between the two measurements. There was a statistically significant correlation between the paired tumor regional flow (ρ = 0.82; P = 0.023) and Vd (ρ = 0.89; P = 0.007) data.

Fig. 3.

Relationship between repeat measurements of regional flow (top) and volume of distribution (Vd; bottom) in patients with gastrointestinal tumors. The lines represent a theoretical exact correlation between the two measurements. There was a statistically significant correlation between the paired tumor regional flow (ρ = 0.82; P = 0.023) and Vd (ρ = 0.89; P = 0.007) data.

Close modal
Table 1

Tumors studied

PrimaryMetastasisn
Large bowel Liver 13 
 Lung 
 Ovary 
Liver  
Carcinoid Liver 
Pancreas  
 Liver 
Anal  
Stomach  
Renal  
Squamous cell carcinoma of the cheek  
Endometrial sarcoma  
Primitive neuro-ectodermal of shoulder  
Bladder  
Unknown Liver 
 Lung 
PrimaryMetastasisn
Large bowel Liver 13 
 Lung 
 Ovary 
Liver  
Carcinoid Liver 
Pancreas  
 Liver 
Anal  
Stomach  
Renal  
Squamous cell carcinoma of the cheek  
Endometrial sarcoma  
Primitive neuro-ectodermal of shoulder  
Bladder  
Unknown Liver 
 Lung 
Table 2

Raw dataa

PatientTumorLiverSpleenKidney
FlowbV                  d                  cFlowV                  dFlowV                  dFlowV                  d
0.52 0.92       
0.65 1.07   0.19 0.95   
0.78 0.83 0.32 0.83   0.61 0.72 
0.41 0.58       
0.65 0.76       
0.49 0.82   0.89 0.93 0.74 0.74 
0.29 0.81       
0.17 0.63       
0.46 0.89 0.45 1.00 0.53 0.74   
10 0.39 0.76 0.34 0.75 0.98 0.79   
11 0.48 0.48       
12 0.42 0.85 0.46 0.84 0.55 0.86 0.69 0.65 
13 0.32 0.84 0.44 0.92 1.05 0.77   
14 0.33 0.85 0.35 0.77 0.54 0.79   
15 0.37 0.60 0.04 0.21   0.98 0.62 
16 0.63 0.78 0.55 0.73 1.03 0.74   
17 0.66 0.50       
18 0.35 0.72   0.97 0.78 0.58 0.5 
19 0.97 0.82 0.53 0.74 0.49 0.75   
20 0.18 0.77 0.22 0.69 0.62 0.79 0.99 0.64 
21 0.26 0.58 0.25 0.58 0.80 0.71 0.70 0.52 
22 0.48 0.70 0.70 0.77 0.82 0.58   
23 0.38 0.71 0.40 0.7 1.11 0.73   
24 0.71 0.68 0.47 0.75 1.02 0.76   
25 0.40 0.71 0.48 0.72 0.92 0.72   
26 0.51 0.77 0.36 0.67 0.86 0.70   
27 0.85 0.75   1.17 0.70   
28 0.49 0.87 0.45 0.91 1.14 0.90 0.97 0.83 
29 0.52 0.83 0.45 0.91 0.53 0.79   
30 0.64 1.04     1.04 0.76 
31 0.27 0.40       
32 0.15 0.41       
33 0.33 0.54       
34 0.34 0.71       
35 0.34 0.80 0.37 0.91 0.77 0.90 1.69 0.68 
36 0.47 0.70 0.57 0.70 0.88 0.73   
37 0.35 0.90 0.35 0.80 0.89 0.89 1.04 0.76 
PatientTumorLiverSpleenKidney
FlowbV                  d                  cFlowV                  dFlowV                  dFlowV                  d
0.52 0.92       
0.65 1.07   0.19 0.95   
0.78 0.83 0.32 0.83   0.61 0.72 
0.41 0.58       
0.65 0.76       
0.49 0.82   0.89 0.93 0.74 0.74 
0.29 0.81       
0.17 0.63       
0.46 0.89 0.45 1.00 0.53 0.74   
10 0.39 0.76 0.34 0.75 0.98 0.79   
11 0.48 0.48       
12 0.42 0.85 0.46 0.84 0.55 0.86 0.69 0.65 
13 0.32 0.84 0.44 0.92 1.05 0.77   
14 0.33 0.85 0.35 0.77 0.54 0.79   
15 0.37 0.60 0.04 0.21   0.98 0.62 
16 0.63 0.78 0.55 0.73 1.03 0.74   
17 0.66 0.50       
18 0.35 0.72   0.97 0.78 0.58 0.5 
19 0.97 0.82 0.53 0.74 0.49 0.75   
20 0.18 0.77 0.22 0.69 0.62 0.79 0.99 0.64 
21 0.26 0.58 0.25 0.58 0.80 0.71 0.70 0.52 
22 0.48 0.70 0.70 0.77 0.82 0.58   
23 0.38 0.71 0.40 0.7 1.11 0.73   
24 0.71 0.68 0.47 0.75 1.02 0.76   
25 0.40 0.71 0.48 0.72 0.92 0.72   
26 0.51 0.77 0.36 0.67 0.86 0.70   
27 0.85 0.75   1.17 0.70   
28 0.49 0.87 0.45 0.91 1.14 0.90 0.97 0.83 
29 0.52 0.83 0.45 0.91 0.53 0.79   
30 0.64 1.04     1.04 0.76 
31 0.27 0.40       
32 0.15 0.41       
33 0.33 0.54       
34 0.34 0.71       
35 0.34 0.80 0.37 0.91 0.77 0.90 1.69 0.68 
36 0.47 0.70 0.57 0.70 0.88 0.73   
37 0.35 0.90 0.35 0.80 0.89 0.89 1.04 0.76 
a

Normal tissue data were not obtained for some patients because of an inadequate volume of normal tissue in the PET image.

b

Regional flow in mlblood/min/mltissue.

c

Vd, volume of distribution, in mlblood/mltissue.

Table 3

Summary of inter- and intrapatient variability in tumor and normal tissue regional flow

ParameterTissuenMean ± SDRangeCVa (%)
InterpatientIntrapatientb
Flow (mlblood/min/mltissueTumor 37 0.46 ± 0.19 0.15–0.97 40 11 
 Liver 21 0.41 ± 0.14 0.04–0.70 34 14 
 Spleen 23 0.82 ± 0.25 0.19–1.17 31 
 Kidney 11 0.90 ± 0.31 0.58–1.69 34 12 
Vd (mlblood/mltissueTumor 37 0.74 ± 0.15 0.40–1.07 21 
 Liver 21 0.75 ± 0.16 0.21–1.00 21 13 
 Spleen 23 0.78 ± 0.09 0.58–0.95 12 
 Kidney 11 0.68 ± 0.11 0.50–0.88 18 
ParameterTissuenMean ± SDRangeCVa (%)
InterpatientIntrapatientb
Flow (mlblood/min/mltissueTumor 37 0.46 ± 0.19 0.15–0.97 40 11 
 Liver 21 0.41 ± 0.14 0.04–0.70 34 14 
 Spleen 23 0.82 ± 0.25 0.19–1.17 31 
 Kidney 11 0.90 ± 0.31 0.58–1.69 34 12 
Vd (mlblood/mltissueTumor 37 0.74 ± 0.15 0.40–1.07 21 
 Liver 21 0.75 ± 0.16 0.21–1.00 21 13 
 Spleen 23 0.78 ± 0.09 0.58–0.95 12 
 Kidney 11 0.68 ± 0.11 0.50–0.88 18 
a

CV, coefficient of variation; Vd, volume of distribution.

b

Intrapatient CV was studied in a subset of five (tumor) or four (liver, spleen) patients with measurement in the same patients made 1 week apart. For the kidney, both right and left kidneys were imaged in three patients on the same day.

Table 4

Intrapatient variability in tumor regional flow and volume of distribution

PatientImaged siteNecroticRegional flow (mlblood/min/mltissue)Vda (mlblood/mltissue)
Reading 1Reading 2Reading 1Reading 2
Liver No 0.39 0.44 0.76 0.76 
Lung No 0.48 0.38 0.48 0.53 
Liver No 0.33 0.35 0.85 0.78 
Liver No 0.49 0.36 0.87 0.75 
Liver No 0.35 0.34 0.90 0.81 
Liver Yes 0.12 0.19 0.39 0.31 
Liver Yes 0.04 0.06 0.27 0.29 
PatientImaged siteNecroticRegional flow (mlblood/min/mltissue)Vda (mlblood/mltissue)
Reading 1Reading 2Reading 1Reading 2
Liver No 0.39 0.44 0.76 0.76 
Lung No 0.48 0.38 0.48 0.53 
Liver No 0.33 0.35 0.85 0.78 
Liver No 0.49 0.36 0.87 0.75 
Liver No 0.35 0.34 0.90 0.81 
Liver Yes 0.12 0.19 0.39 0.31 
Liver Yes 0.04 0.06 0.27 0.29 
a

Vd, volume of distribution.

Table 5

Intrapatient variability in normal tissue regional flow and volume of distribution

OrganRegional flow (mlblood/min/mltissue)Vda (mlblood/mltissue)
Reading 1Reading 2Reading 1Reading 2
Liver 0.34 0.49 0.75 0.81 
 0.35 0.46 0.77 0.51 
 0.45 0.46 0.91 0.75 
 0.35 0.30 0.80 0.76 
Spleen 0.98 1.13 0.79 0.79 
 0.54 0.63 0.79 0.59 
 1.14 1.05 0.90 0.81 
 0.89 0.80 0.89 0.85 
Kidney 0.61 0.98 0.72 0.67 
 0.98 0.98 0.62 0.62 
 0.97 0.92 0.83 0.81 
OrganRegional flow (mlblood/min/mltissue)Vda (mlblood/mltissue)
Reading 1Reading 2Reading 1Reading 2
Liver 0.34 0.49 0.75 0.81 
 0.35 0.46 0.77 0.51 
 0.45 0.46 0.91 0.75 
 0.35 0.30 0.80 0.76 
Spleen 0.98 1.13 0.79 0.79 
 0.54 0.63 0.79 0.59 
 1.14 1.05 0.90 0.81 
 0.89 0.80 0.89 0.85 
Kidney 0.61 0.98 0.72 0.67 
 0.98 0.98 0.62 0.62 
 0.97 0.92 0.83 0.81 
a

Vd, volume of distribution.

Table 6

Between-study comparison of H215O PET measurements of regional flow and volume of distribution

StudySitePatientsFlow (mlblood/min/mltissue)Vda (mlblood/mltissue)
Wilson et al., 1992 (7) Normal breast 20 0.06 ± 0.01 0.14 ± 0.05 
Mankoff et al., 2002 (14) Normal breast 37 0.06 0.18 
Wilson et al., 1992 (7) Breast tumor 20 0.30 ± 0.17 0.56 ± 0.15 
Mankoff et al., 2002 (14) Breast tumor 37 0.32 0.58 
Yamaguchi et al., 2000 (30)b CRC liver metastases 15 0.36 ± 0.04c  
Herbst et al., 2002 (15) Advanced solid tumors 25 0.36 ± 0.27  
Lehtio et al., 2001 (31) Head and neck tumors 0.38 ± 0.15  
This studyb Intra-abdominal tumors 37 0.46 ± 0.19 0.74 ± 0.15 
Anderson et al., 2003 (13) Intra-abdominal tumors 13 0.54 ± 0.46 0.78 ± 0.21 
Hoekstra et al., 2002 (28) NSCLC 10 0.59 ± 0.37 0.63 ± 0.10 
Anderson et al., 2003 (13) Normal spleen 13 1.05 ± 0.34 0.88 ± 0.13 
Anderson et al., 2003 (13) Normal kidney 13 1.32 ± 0.30 0.77 ± 0.08 
StudySitePatientsFlow (mlblood/min/mltissue)Vda (mlblood/mltissue)
Wilson et al., 1992 (7) Normal breast 20 0.06 ± 0.01 0.14 ± 0.05 
Mankoff et al., 2002 (14) Normal breast 37 0.06 0.18 
Wilson et al., 1992 (7) Breast tumor 20 0.30 ± 0.17 0.56 ± 0.15 
Mankoff et al., 2002 (14) Breast tumor 37 0.32 0.58 
Yamaguchi et al., 2000 (30)b CRC liver metastases 15 0.36 ± 0.04c  
Herbst et al., 2002 (15) Advanced solid tumors 25 0.36 ± 0.27  
Lehtio et al., 2001 (31) Head and neck tumors 0.38 ± 0.15  
This studyb Intra-abdominal tumors 37 0.46 ± 0.19 0.74 ± 0.15 
Anderson et al., 2003 (13) Intra-abdominal tumors 13 0.54 ± 0.46 0.78 ± 0.21 
Hoekstra et al., 2002 (28) NSCLC 10 0.59 ± 0.37 0.63 ± 0.10 
Anderson et al., 2003 (13) Normal spleen 13 1.05 ± 0.34 0.88 ± 0.13 
Anderson et al., 2003 (13) Normal kidney 13 1.32 ± 0.30 0.77 ± 0.08 
a

Vd, volume of distribution; CRC, colorectal cancer; NSCLC, non-small cell lung cancer.

b

C15O2 used rather than H215O.

c

Regional flow was 0.53 ± 0.17 mlblood/min/mltissue in 5 tumors with high vascularization, 0.32 ± 0.04 in 10 tumors with similar vascularization to normal, and 0.32 ± 0.06 in 7 tumors with poor vascularization.

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