Introduction
Positron emission tomography-computed tomography (PET/CT) with the radiolabeled glucose analog 18F-Fluorodeoxyglucose ([18F]FDG) has revolutionized imaging, especially in the field of oncology. [18F]FDG is taken up by both normal cells utilizing glucose and also the tumor cells which usually have a high glucose demand. This uptake is mediated by the cell surface carrier molecules designated as glucose transporters (GLUT) [1] and can be measured with the semiquantitative index called standardized uptake value (SUV) [2]. The differential [18F]FDG uptake between normal tissue and tumor leads to a high tumor to background contrast, leading to better lesion detection and higher sensitivity. Therefore, apart from the tumor uptake of [18F]FDG, the level of tracer uptake in normal tissue is of crucial importance. Also, relative [18F]FDG uptake of the tumor as compared to normal tissue such as liver, mediastinal blood pool, and muscle is often used to differentiate physiological from pathological uptake, especially in post-therapy setting [3, 4].
The [18F]FDG uptake in tumors as measured with SUV is dependent on various factors [5]. These include the injected [18F]FDG dose and acquisition time after injection. Patient-related factors such as age, gender, body weight, body mass index (BMI), diabetic status, and serum glucose level also influence [18F]FDG uptake. High blood glucose impairs [18F]FDG uptake in both tumor and normal organs by competing with [18F]FDG for GLUT, as well as, by stimulating endogenous insulin secretion leading to stimulated skeletal muscle [18F]FDG uptake [6]. Since [18F]FDG is excreted via the kidneys, deranged renal function can alter its physiological distribution in the body, especially in the blood pool. Also, highly [18F]FDG avid tumors with large tumor burdens can possibly alter tumor to normal tissue distribution with preferential exsanguination into the tumor, leaving very little [18F]FDG for normal organs.
Few studies have evaluated the impact of patient-related factors and tumoral [18F]FDG uptake on physiological [18F]FDG uptake by normal organs, but overall limited studies are available in this regard, most focussing on blood glucose and diabetes [7]. Büsing et al. [8] in their study demonstrated the impact of these factors on the biodistribution of [18F]FDG in muscles and the brain. Also, the impact of renal function on [18F]FDG biodistribution is not properly known, with studies limited to renal failure patients [9]. In addition, it is to be noted that in most of the previous studies the CT part of PET/CT was non-contrast without using any intravenous iodinated contrast, which is now the norm for most clinically acquired PET/CT studies. We have tried to address these issues in the present study and evaluated how patient-related factors and tumoral [18F]FDG uptake influence the SUV of normal organs in routine clinical PET/CT.
Material and methods
The present study was conducted in accordance with the ethical standards of the Helsinki Declaration of 1975, as revised in 2000. However, since there was a retrospective analysis of data, permission from the ethical committee was not required. Written informed consent was obtained from all patients at the time of PET/CT. All identifying information was omitted. Consecutive patients who had undergone whole body [18F]FDG PET/CT for oncological indication were retrospectively included in this study. Patients in whom malignant disease had spread to organs selected for assessing [18F]FDG biodistribution in healthy tissues were excluded from the study population. A total of 200 patients was finally included in the study. Data of these patients was retrieved from the departmental registry and analyzed. The following patient data were retrieved: age (years), gender (male/female), weight (kg), height (m), body mass index [weight in kilograms divided by the square of height in meters (kg/m2)], serum creatinine (mg/dL), diabetes (present/absent), anti-diabetic medication (if any), blood glucose prior to [18F]FDG injection (BG) and injected [18F]FDG dose [mCi].
Patient preparation
Patient preparation was in accordance with guidelines [10]. All patients fasted for at least 4 hours prior to [18F]FDG injection. Anti-diabetic medications (insulin or oral hypoglycemic agents) if required were taken prior to starting of the fasting period. Parenteral nutrition and intravenous dextrose-containing fluids if any were discontinued 4–6 hours prior to radioisotope injection. Patients were advised to avoid strenuous exercise for at least 6 h before the radioisotope administration, and preferably for 24 h. Blood glucose was measured in all patients prior to [18F]FDG injection. All patients had blood glucose levels below 200 mg/dL as recommended [1]. The patient was then administered [18F]FDG through an intravenous cannula. The administered dose was measured based on a linear relationship formula for PET bed overlap of ≤ 30%, based on EANM guidelines using the formula [FDG activity in MBq for 3D scans = 13.8 × weight/(min/bed) [11]. The actual injected dose was calculated after subtracting pre and post-injection syringe radioactivity. The patient then waited in an adequately warm comfortable room for an uptake prior to 45–60 minutes. None of the patients received oral contrast. They were instructed to drink 1 liter of plain water (if no restriction) during the waiting period which acted as negative oral contrast as well as for hydration.
PET-CT acquisition
Whole body imaging form vortex of the skull to mid-thigh was acquired in all patients with an additional view as and when required. The studies were acquired on a dedicated PET/CT scanner (Discovery 690, GE Healthcare, Waukesha WI, USA). Intravenous iodinated contrast iohexol (1.25 mL/kg with a maximum of 150 mL; Omnipaque 350; GE healthcare, Chicago, Illinois, USA) was administered for the CT part of PET/CT (if no contraindication, depending on estimated glomerular filtration rate). In the PET/CT system, CT acquisition was performed on spiral 16-slice CT with a Kv of 130, mAs of 60, slice thickness of 2 mm, and a pitch of 1. The image was acquired using a matrix of 512 × 512 pixels and a pixel size of 1 mm. After CT, 3D PET acquisition was done for 3 minutes per bed position. PET data was acquired using a matrix of 128 × 128 pixels with a slice thickness of 1.5 mm. CT-based attenuation correction of the emission images was employed. PET images were reconstructed by the iterative method of ordered subset expectation maximization (OSEM; 2 iterations and 8 subsets). After CT acquisition, PET acquisition of the same axial range was done with the patient in the same position. After completion of PET acquisition, the reconstructed attenuation corrected PET images, CT images, and fused images of matching pairs of PET and CT images were available for review in axial, coronal, and sagittal planes, as well as in maximum intensity projections (MIP) and three-dimensional cine mode.
Image analysis
The PET/CT images were re-evaluated by an experienced nuclear medicine physician (P.S., 13 years of experience in PET/CT). The presence or absence of tumors was noted, and if present the most metabolically active tumor was chosen for semiquantitative analysis. Apart from the tumor ten other healthy organ sites viz. brain, lung, heart, liver, spleen, pancreas, bone, muscle, fat, and blood pool were also semiquantitatively evaluated. The semiquantitative analysis was done using the maximum standardized uptake value (SUVmax). The SUV calculation was done via the default method by body weight [(SUV = mean ROI activity (MBq/g)/injected activity (MBq)/body weight (g)]. For measurement of SUVmax of tumor, multiple circular ROIs were drawn covering the tumor over its entire length. For healthy organs, a circular ROI of 10 mm diameter was drawn [brain: left sensorimotor cortex; lung: right lung lower lobe superior segment; heart: lateral wall of left ventricle; liver: right lobe segment 8; spleen: central part; pancreas: body; bone: body of L2 vertebra; muscle: right erector spinae; fat: upper back and blood pool: descending thoracic aorta].
Statistical analysis
Quantitative data were described using mean, standard deviation (SD), median, and range while categorical data were described using frequency and proportion. Pearson correlation coefficients (r) were calculated among continuous variables. Unpaired t-test or Satterthwaite corrected t-test were used to compare the Gaussian continuous outcomes according to binary categorical variables while non-parametric Wilcoxon rank sum test was used for non-Gaussian outcomes. Since the multiple outcomes were correlated, therefore, we used a multivariate linear regression analysis to assess the effects of different patient factors on outcomes after adjusting other cofactors followed by posthoc multiple linear regression analysis after adjusting the levels of significance. Hotelling-Lawley Trace p-values were reported in multivariate analysis. In the posthoc linear regression analysis, we considered the level of significance at 0.01 while a 5% level of significance was considered in univariate analysis. All the statistical analyses were carried out using Statistical Analysis System 9.3 (SAS Institute Inc., Cary, North Carolina, USA).
Results
Patient details are presented in Table 1. Of the 46 diabetic patients, 38 were on oral hypoglycemic agents while 8 were on insulin. Total of 11/200 patients had elevated serum creatinine (> 1.3 mg/dL), of which two were on dialysis. 126 patients had received either radiotherapy or chemotherapy or a combination therapy prior to PET/CT. The rest of them were treatment-naive patients. Eight patients (5 for renal derangement, 3 for prior severe contrast reaction) underwent non-contrast PET/CT, while the remaining 192 patients received intravenous contrast.
|
Parameter |
Mean ± SD |
Range |
|
Age [years] |
55.1 ± 15.0 |
6–86 |
|
BMI [kg/m2] |
24.0 ± 5.3 |
13–60 |
|
Creatinine [mg/dL] |
0.9 ± 0.4 |
0.3–4.4 |
|
Blood glucose [mg/dL] |
119.1 ± 22.7 |
59–194 |
|
Injected [18F]FDG dose [mCi] |
11.3 ± 1.4 |
6–15 |
|
Parameter |
Number (%) |
|
|
Gender |
||
|
Male |
109 (54.5%) |
|
|
Female |
91 (45.5%) |
|
|
Diabetes |
||
|
Absent |
154 (77%) |
|
|
Present |
46 (23%) |
|
|
Obesity (BMI ≥ 25) |
||
|
Yes |
117 (58.5%) |
|
|
No |
83 (41.5%) |
|
|
Indication |
||
|
Lung cancer |
37 (18.5%) |
|
|
Breast cancer |
32 (16.0%) |
|
|
Lymphoma |
41 (20.5%) |
|
|
Head and neck cancer |
25 (12.5%) |
|
|
Esophageal cancer |
12 (6%) |
|
|
Colorectal cancer |
15 (7.5%) |
|
|
Hepatobiliary cancer |
9 (4.5%) |
|
|
Renal cancer |
7 (3.5%) |
|
|
Urinary bladder cancer |
2 (1%) |
|
|
Gynaecological cancer |
16 (8%) |
|
|
Others |
4 (2%) |
[18F]FDG uptake values of tumors and organs are detailed in Table 2. On the assessment of the correlation of tumor [18F]FDG uptake with other organs (Fig. 1), a significant positive correlation was seen between tumor uptake with the pancreas (r = 0.43, p < 0.001) and just significant positive correlation was seen with the heart (r = 0.19, p = 0.049). The [18F]FDG uptake of other sites, including liver (r = 0.46, p = 0.63) and blood pool (r= 0.117, p = 0.229) was not significant, either positively or negatively, correlated with tumor uptake. The results of the correlation analysis between [18F]FDG uptake and different patient parameters are presented in Table 3 (Fig. 2). A significant negative correlation was found between [18F]FDG uptake of the brain with age, while a positive correlation was seen between age and blood pool [18F]FDG uptake. A significant negative correlation was seen between [18F]FDG uptake of brain and heart with BG, while a significant positive correlation was seen with [18F]FDG uptake of fat and BG. BMI showed a significant positive correlation with [18F]FDG uptake of all organs except the pancreas and heart, apart from the tumor. No significant correlation was seen between [18F]FDG uptake of tumor and organs with serum creatinine level and injected [18F]FDG dose. On comparing [18F]FDG uptake in tumors and different organs among male and female patients, significantly higher SUVmax was seen in the brain, spleen, and muscles of females (Tab. 4). On comparing [18F]FDG uptake in tumors and different organs among obese and non-obese patients, a significant difference was seen for all organs except for the pancreas, heart, and tumor (Tab. 5). A comparison of [18F]FDG uptake between non-diabetic and diabetic patients showed a significant difference only for bone (Tab. 6).
|
SUVmax |
Mean ± SD |
Range |
|
Tumor |
5.1 ± 2.9 |
0.9–19.8 |
|
Brain |
5.9 ± 1.6 |
2.1–13.5 |
|
Lung |
0.6 ± 0.5 |
0.2–5.2 |
|
Heart |
3.1 ± 2.3 |
0.7–10.4 |
|
Liver |
1.6 ± 0.5 |
0.8–3.9 |
|
Spleen |
1.3 ± 0.3 |
0.6–3.3 |
|
Pancreas |
1.0 ± 0.5 |
0.4–8.5 |
|
Bone |
1.2 ± 0.5 |
0.6–5.5 |
|
Muscle |
0.6 ± 0.2 |
0.3–2.9 |
|
Fat |
0.6 ± 0.2 |
0.3–1.7 |
|
Blood pool |
1.0 ± 0.3 |
0.6–4 |
|
SUVmax |
Age r (p-value) |
BMI r (p-value) |
Creatinine r (p-value) |
BG r (p-value) |
Injected dose r (p-value) |
|
Tumor |
0.021 (0.825) |
0.021 (0.825) |
−0.043 (0.656) |
−0.011 (0.906) |
−0.107 (0.271) |
|
Brain |
−0.183 (0.009*) |
0.394 (<.0001*) |
−0.117 (0.098) |
−0.449 (< 0.0001*) |
0.032 (0.643) |
|
Lung |
0.032 (0.643) |
0.112 (0.0012*) |
−0.037 (0.597) |
−0.009 (0.896) |
0.024 (0.727) |
|
Heart |
0.056 (0.426) |
−0.051 (0.465) |
0.023 (0.738) |
−0.15 (0.033*) |
−0.02 (0.774) |
|
Liver |
0.092 (0.193) |
0.267 (0.0001*) |
−0.072 (0.304) |
−0.052 (0.458) |
0.021 (0.765) |
|
Spleen |
0.075 (0.29) |
0.367 (< 0.0001*) |
−0.029 (0.675) |
0.108 (0.126) |
0.099 (0.159) |
|
Pancreas |
−0.085 (0.229) |
−0.041 (0.558) |
−0.044 (0.527) |
−0.03 (0.665) |
−0.043 (0.539) |
|
Bone |
−0.027 (0.703) |
0.23 (0.001*) |
0.113 (0.109) |
0.067 (0.345) |
0.003 (0.96) |
|
Muscle |
0.055 (0.438) |
0.203 (0.003*) |
−0.072 (0.31) |
0.125 (0.075) |
0.095 (0.178) |
|
Fat |
0.137 (0.052) |
0.352 (< 0.0001*) |
−0.002 (0.966) |
0.143 (0.043*) |
−0.025 (0.724) |
|
Blood pool |
0.205 (0.003*) |
0.251 (0.0003*) |
−0.125 (0.077) |
0.072 (0.308) |
−0.028 (0.688) |
|
SUVmax (Mean ± SD) |
|||
|
Site |
Male |
Female |
p-value |
|
Tumor |
5.10 ± 2.67 |
5.12 ± 3.21 |
0.974 |
|
Brain |
5.61 ± 1.63 |
6.26 ± 5.91 |
0.005# |
|
Lung* |
0.69 ± 0.59 |
0.69 ± 0.41 |
0.410 |
|
Heart* |
3.19 ± 2.25 |
3.09 ± 2.45 |
0.546 |
|
Liver |
1.66 ± 0.53 |
1.62 ± 0.44 |
0.619 |
|
Spleen |
1.27 ± 0.364 |
1.4 ± 0.33 |
0.01# |
|
Pancreas |
0.96 ± 0.26 |
1.10 ± 0.81 |
0.123 |
|
Bone |
1.21 ± 0.58 |
1.31 ± 0.58 |
0.251 |
|
Muscle |
0.61 ± 0.21 |
0.72 ± 0.3 |
0.007# |
|
Fat |
0.61 ± 0.22 |
0.66 ± 0.2 |
0.149 |
|
Blood pool |
1.05 ± 0.44 |
1.13 ± 0.26 |
0.09 |
|
SUV max (Mean ± SD) |
|||
|
Site |
Non-obese (BMI < 25) |
Obese (BMI ≥ 25) |
p-value |
|
Tumor |
5.03 ± 3.24 |
5.24 ± 2.23 |
0.705 |
|
Brain |
5.34 ± 1.39 |
6.71 ± 1.73 |
< 0.0001# |
|
Lung* |
0.64 ± 0.57 |
0.76 ± 0.41 |
< 0.0001# |
|
Heart* |
3.19 ± 2.4 |
3.08 ± 2.25 |
0.550 |
|
Liver |
1.56 ± 0.51 |
1.75 ± 0.45 |
0.006# |
|
Spleen |
1.24 ± 0.33 |
1.45 ± 0.345 |
< 0.0001# |
|
Pancreas |
1.04 ± 0.74 |
1.00 ± 0.24 |
0.584 |
|
Bone |
1.16 ± 0.58 |
1.38 ± 0.55 |
0.009# |
|
Muscle |
0.63 ± 0.27 |
0.70 ± 0.23 |
0.053 |
|
Fat |
0.58 ± 0.205 |
0.70 ± 0.21 |
0.0002# |
|
Blood pool |
1.02 ± 0.36 |
1.17 ± 0.374 |
0.005# |
|
SUVmax (Mean ± SD) |
|||
|
Site |
Non-diabetic |
Diabetic |
p-value |
|
Tumor |
5.05 ± 2.94 |
5.35 ± 2.73 |
0.67 |
|
Brain |
5.93 ± 1.56 |
5.83 ± 2.04 |
0.769 |
|
Lung* |
0.7 ± 0.61 |
0.66 ± 0.32 |
0.767 |
|
Heart* |
3.29 ± 2.45 |
2.65 ± 1.83 |
0.218 |
|
Liver |
1.64 ± 0.51 |
1.65 ± 0.44 |
0.837 |
|
Spleen |
1.32 ± 0.36 |
1.35 ± 0.31 |
0.532 |
|
Pancreas |
1.04 ± 0.65 |
0.97 ± 0.26 |
0.269 |
|
Bone |
1.3 ± 0.63 |
1.11 ± 0.33 |
0.009# |
|
Muscle |
0.67 ± 0.27 |
0.65 ± 0.19 |
0.628 |
|
Fat |
0.629 ± 0.19 |
0.65 ± 0.27 |
0.507 |
|
Blood pool |
1.08 ± 0.35 |
1.1 ± 0.43 |
0.86 |
Multivariate linear analysis after adjusting for cofactors showed a significant association of [18F]FDG uptake with BMI and BG but not age, gender, and creatinine (Tab. 7). Post hoc multiple regression analysis (Tab. 8, Fig. 3) after adjusting for the level of significance, showed a significant positive association of [18F]FDG uptake of the brain with BMI and a significant negative association with BG. A significant positive association was seen between [18F]FDG uptake of liver and fat with BMI.
|
Variables |
p-value |
|
BMI |
0.0009* |
|
Creatinine |
0.432 |
|
Blood Glucose (BG) |
0.0002* |
|
Age |
0.347 |
|
Gender |
0.526 |
|
SUVmax |
Age |
BMI |
Creatinine |
BG |
Gender |
|
Tumor |
0.005 (0.804) |
0.01 (0.829) |
−0.25 (0.677) |
−0.0002 (0.986) |
−0.072 (0.908) |
|
Brain |
−0.007 (0.485) |
0.118 (< 0.001*) |
0.002 (0.993) |
−0.03 (< 0.0001*) |
0.15 (0.601) |
|
Lung |
−0.002 (0.661) |
0.005 (0.61) |
−0.109 (0.432) |
−0.0004 (0.863) |
−0.045 (0.748) |
|
Heart |
0.003 (0.845) |
−0.04 (0.321) |
0.387 (0.421) |
−0.014 (0.115) |
−0.292 (0.552) |
|
Liver |
0.00002 (0.99) |
0.02 (0.002*) |
−0.16 (0.138) |
0.0004 (0.85) |
−0.19 (0.085) |
|
Spleen |
−0.003 (0.302) |
0.014 (0.038) |
−0.02 (0.744) |
0.001 (0.286) |
0.078 (0.348) |
|
Pancreas |
−0.01 (0.081) |
−0.01 (0.161) |
−0.07 (0.648) |
−0.0004 (0.881) |
0.196 (0.232) |
|
Bone |
−0.004 (0.376) |
0.019 (0.134) |
0.256 (0.09) |
0.001 (0.523) |
0.035 (0.818) |
|
Muscle |
0.0004 (0.828) |
0.01 (0.045) |
−0.01 (0.808) |
0.0008 (0.464) |
0.089 (0.16) |
|
Fat |
0.002 (0.234) |
0.01 (0.006*) |
0.02 (0.544) |
0.0005 (0.547) |
0.02 (0.589) |
|
Blood pool |
0.006 (0.078) |
0.014 (0.063) |
−0.17 (0.061) |
0.001 (0.488) |
−0.06 (0.464) |
Discussion
[18F]FDG PET/CT is now an integral part of the management of a wide array of cancers, with use including but not limited to diagnosis, staging, response evaluation, restaging, and prognostication. SUVmax of the tumor is an important semi-quantitative parameter used for differentiating benign from malignant lesions, with the latter usually showing high values. More significantly, SUVmax is used for the assessment of response to anti-cancer treatment, comparing pre-treatment with post-treatment values. However, since SUV values are affected by many factors and can vary from center to center because of different acquisition systems, for some settings visual scoring systems have been suggested. This includes visually comparing [18F]FDG uptake in a tumor with that of a normal structure as well as numerical uptake ratios [12]. Deauville score for high-grade lymphoma [3] and Hopkin’s score for head and neck cancers [4] are two such examples, with robust validations. This brings forward the issue of [18F]FDG uptake in normal structures/organs which are the denominators for such scores. It is therefore relevant to know how the tumor metabolism and patient-related factors impact [18F]FDG uptake in normal organs. We have tried to evaluate the same in the present study.
On the assessment of the impact of tumor [18F]FDG uptake on other organs, we found a significant positive correlation only with the pancreas and just about a significant positive correlation with the heart. The [18F]FDG uptake of other sites was not significantly, either positively or negatively, correlated with tumor uptake. This implies that high [18F]FDG uptake of tumor does not significantly affect uptake in other organs, especially the liver and blood pool, which are widely employed for response categorization. So, the scary-looking visualization of tumor only PET/CT scans is more a function of normalization. However, this interpretation is with two caveats. Firstly, we excluded patients with widely disseminated disease, so that organ SUV values can be measured without interference. This meant the tumor burden in our study was small to medium. Secondly, the mean tumor SUVmax values in our study were high but not very high.
On the evaluation of the impact of age, we found that among the organs evaluated only SUVmax of brain and blood pool showed a significant correlation with changing age. The SUV of the brain was negatively correlated with age, suggesting a significant reduction with age. This could be because of aging-related neuronal loss [13]. Other factors which might be responsible are the higher prevalence of diabetes with increasing age as well as impaired glucose metabolic capabilities of the aging brain over and above neuronal loss. We also found that blood pool SUVmax significantly increased with age. Impaired overall utilization of [18F]FDG by various organs, increased [18F]FDG uptake in the aging aortic wall with partial volume averaging [14] and increasing prevalence of diabetes in the elderly population could account for this finding. When evaluating the impact of BG, we found its significant negative correlation with [18F]FDG uptake of the brain and heart. In the case of the brain, this could be a result of diminished expression of GLUT-4 transporter, impaired glucose metabolism, and reduced physiological flexibility in chronic hyperglycemia [15]. The same factors can be true for the heart. But a more important reason could be the “metabolic switch” from glucose to fatty acid seen in normal myocardium in fasting states with low BG levels. This finding is similar to that reported by Kaneta et al. [16]. Israel et al. [17] on the other hand reported the opposite finding, but their study population had hyperglycemia and diabetes that could have brought the outcome. We also found a significant positive correlation between [18F]FDG uptake of fat and BG, probably because of the fact that glucose metabolism in white adipose tissue is regulated by insulin-mediated GLUT-4 [18], therefore higher BG with resultant insulin response will lead to an elevated SUV in adipose tissue. Also, adipose tissue inflammation associated with metabolic syndrome could be a reason for this positive correlation [19].
BMI showed a significant positive correlation with [18F]FDG uptake of all organs except the pancreas and heart, as well as tumors. When comparing [18F]FDG uptake among obese and non-obese patients, we found significantly higher SUVmax for all organs in obese group except for the pancreas, heart, and tumor. This could be because of the impact of body weight on SUV values, which has been shown to result in overestimation in an obese patient, both for normal organs and tumors [20]. In fact, to eliminate the confounding impact of body weight, SUV values normalized to lean body mass (SUL) have been recommended by some authors [21]. No significant correlation was seen between [18F]FDG uptake of tumor and organs with serum creatinine level and injected [18F]FDG dose. We didn’t compare the values between those with normal and raised serum creatinine, as the number in the latter group was very small. A similar comparison by Büsing et al. also didn’t find any significant difference [8].
When evaluating the impact of gender on SUVmax of normal organs, we found significantly higher SUVmax in the brain, spleen, and muscles of females, but not for other sites. Many factors could account for this finding including differing glucose metabolism between sexes [22], women having more body fat and lower lean body mass for the same weight [23], and a more active immune system in women [24].
An important finding of the present study was no significant difference in [18F]FDG uptake of tumors and other organs between non-diabetic and diabetic patients, except for bone, where it was higher for non-diabetics. This implies that in patients with well or moderately controlled diabetes with BG ≤ 200 mg/dL at the time of [18F]FDG injection and sufficient interval after antidiabetic medications, the presence of diabetes alone is not an important factor in determining SUVmax values [1]. In fact, as mentioned earlier, it is the pre-injection BG that impacts SUV values of many organs, not the presence or absence of diabetes per se. Many previous studies including that by Büsing et al. [8] have shown that organs, especially the brain and tumor uptake are impaired in diabetics, while muscle and myocardial uptake is increased. However, that could have been because of the fact that many patients in those study groups had higher BG and received insulin to bring it down. High BG leads to competition with the glucose analog [8F]FDG, relocation of GLUT-4 from cell membranes to the cytosol and stimulates the release of insulin which promotes [18F]FDG migration to muscles, including myocardium [25, 26].
Since some of the variables we evaluated were interrelated such as age and diabetic status, gender and BMI, age and BG, etc., we performed a multivariate analysis to look for independent variables impacting [18F]FDG uptake. On multivariate linear analysis after adjusting for cofactors, a significant association of SUVmax (tumor and organ) was seen with BMI and BG but not age, gender, and creatinine. Also on posthoc multiple regression analysis for tumor and organ SUVmax after adjusting for the level of significance, [18F]FDG uptake of the brain showed a significant positive association with BMI and a significant negative association with BG. This is in keeping with previously published studies. A significant positive association was also seen between [18F]FDG uptake of liver and fat with BMI but not with BG. These findings highlight the fact that a rising BMI can lead to high SUVmax values of brain, liver, and fat independent of age, gender and creatinine level therefore a better standardization technique such as using lean body mass might possibly be better. Also, in patients with BG within accepted limits (≤ 200 mg/dL), there is no significant impact of BG on organ and tumor SUV, except for the brain, where higher BG is associated with poorer [18F]FDG uptake. Therefore, better BG control is more stringently needed for patients undergoing [18F]FDG PET/CT for brain pathologies. Also, since hepatic and blood pool uptake are not significantly associated with these factors, scoring systems using them should continue to be useful, with attention towards liver uptake in patients with high BMI.
Our study is not without limitations. Firstly, this was a retrospective analysis with all its associated shortcomings. Secondly, because of the limited number of patients with renal impairment we were not able to compare the SUV values between those with and without renal impairment. Finally, we didn’t have a biochemical proof for many of our hypotheses such as insulin level, tumoral and organ GLUT-4 expression, and markers for inflammation, thus reducing the validity of our arguments. Larger multicentre studies addressing these shortcomings are warranted in the future.
Conclusions
In clinical PET-CT imaging, the level of tumoral [18F]FDG uptake has no significant negative effect on the uptake in organs and has a weak positive correlation with uptake in the pancreas and heart. Only age, gender, BMI, and BG, not creatinine, and injected [18F]FDG dose show positive or negative correlation with uptake in tumor and few other organs, with only BG and BMI being independent significant factors. BMI has a significant positive correlation with brain, hepatic and fat uptake, while brain [18F]FDG uptake was negatively correlated with BG. No independent correlation was seen for other sites and factors.
