A Comparison of FDG PET/CT Segmentation Threshold Methods on Quantification of Brown Adipose Tissue Volume

doi:10.21203/rs.3.rs-1562675/v1

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A Comparison of FDG PET/CT Segmentation Threshold Methods on Quantification of Brown Adipose Tissue Volume

https://doi.org/10.21203/rs.3.rs-1562675/v1

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Background: Human brown adipose tissue (BAT) is a thermogenically active fat that has been identified as a potential target for obesity treatment. BAT presence, activity, and volume are commonly assessed using 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) combined with computed tomography (CT) imaging following a cold activation protocol. Accurate segmentation of BAT is necessary in order to effectively characterize metabolic activity and tissue mass. This study aimed to compare various BAT segmentation methods.

Methods: Healthy volunteers 18-35 years of age, with a body mass index (BMI) of 25 kg/m² or less, were eligible to participate in this prospective, IRB-approved study. Eligible subjects underwent FDG PET/CT following a cold intervention intended to activate BAT. Active BAT was then segmented using the BARCIST 1.0 recommendations and several other published BAT segmentation methods, as well as methods based on the PET response criteria in solid tumors (PERCIST). Areas of FDG uptake where BAT is typically located were segmented manually and then reduced using various thresholds to produce the final BAT volume. In addition, a repeatability assessment was performed on a subset of participants who underwent repeated BAT imaging studies.

Results: A total of 32 participants were enrolled in this study and active BAT was detected in 27 subjects. The median active BAT volumes indicated that the individual BAT volume varied based on the segmentation method used. The highest and lowest median(IQR) segmented BAT volumes were 259.6(195.8) and 17.9(59.6) mL, respectively. The BARCIST and a PERCIST methods resulted in visual scores closest to 0 (0.43 and -0.37, respectively), indicating a strong correlation with the qualitative assessment of active BAT seen on FDG PET/CT images. The inclusion of voxels with standardized uptake values (SUV) greater than 1.0 and Hounsfield unit ranges up to -10 resulted in highly repeatable total BAT volumes (CCC>0.95).

Conclusions: BAT volume and activity, as determined using FDG PET/CT, highly depend on the quantification criteria used. In this study, a simple method employing a PERCIST 1.0 threshold provided similar absolute volumes qualitatively, but the BARCIST 1.0 method showed superior repeatability performance.

In recent decades, obesity and its associated metabolic disorders have become a worldwide problem¹. The accumulation of adipose tissue, which can result in obesity, is connected with several metabolic diseases, including type 2 diabetes and cardiovascular disease². Adipose tissue is a major metabolic organ, which can be classified as white adipose tissue (WAT) or brown adipose tissue (BAT)³. WAT stores energy in adipocytes, whereas BAT dissipates energy as heat through uncoupling protein-1 (UCP-1)-mediated non-shivering thermogenesis⁴.

Heat generation induced by BAT is known to reduce obesity and insulin resistance in rodents⁵. The potential role of BAT as a regulator of energy balance was recognized when transgenic mouse lines deficient in BAT were noted to develop obesity, even in the absence of hyperphagia⁵. Similarly, BAT activation in animal models has been shown to protect against the development of obesity. While BAT has an established role in thermoregulation in newborn humans and small mammals, recent advances in functional imaging technology have also confirmed the presence of BAT in adult human subjects^6–10. Since then, strategies for combating obesity have shifted from preventing fat accumulation to promoting energy expenditure by activation of BAT¹¹. In order to study BAT energy expenditure in individuals, the presence, activity, and volume of BAT is commonly assessed using 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) combined with computed tomography (CT) imaging following a cold activation protocol^12,13. PET/CT imaging of BAT is characterized by moderate to intense uptake of FDG, which is typically distributed in the lower neck, supraclavicular regions, and along the thoracic costovertebral junctions^14,15. Accurate segmentation of BAT tissue is necessary in order to effectively characterize metabolic activity and tissue mass. Therefore, it is essential to select an appropriate combination of PET standardized uptake values (SUV) and CT Hounsfield units (HU) for BAT quantification¹⁶.

Much effort is being invested in understanding how human BAT function is physiologically controlled and how to manage its function. Recently, an expert panel released the Brown Adipose Reporting Criteria in Imaging Studies, version 1.0 (BARCIST 1.0), a set of recommendations for conducting, analyzing and reporting FDG PET/CT studies of human BAT^17,18. BARCIST 1.0 recommends using a HU range between − 190 and − 10, and a SUV threshold of 1.2 (adjusted for lean body mass). The BARCIST criteria marks the first attempt at standardizing BAT segmentation, but numerous BAT segmentation methods have been reported. Understanding how these methods vary in the determination of BAT volume and activity is necessary in order to compare studies and to help define an optimal segmentation algorithm. Martinez-Tellez et al compared three published SUV/HU cutoff combinations with the BARCIST 1.0 recommendations and found significant variability between methods¹⁸. However, that work did not include a visual assessment of the BAT volumes or a repeatability analysis of each method, which would be useful for selecting optimal BAT segmentation thresholds.

Another commonly used threshold method comes from the PET Response Criteria in Solid Tumors, version 1.0 (PERCIST 1.0), which provides a guideline for tumor assessment via FDG PET imaging¹⁹. PERCIST 1.0 has been shown to correlate well with patient outcome, possibly by providing clinicians with more accurate information on therapeutic responses at earlier stages of treatment²⁰. This method has not been used for BAT segmentation, but it may prove useful as it provides a more individualized SUV cutoff by normalizing the threshold to liver FDG uptake.

The present study aims to compare activated BAT volumes segmented using the BARCIST 1.0 criteria and several other published techniques, as well as the PERCIST 1.0 method.

Subjects. This prospective study was approved by the Washington University Institutional Review Board and performed in accordance to the Declaration of Helsinki. All subjects provided written informed consent prior to participation. Healthy adult volunteers ages 18–35 with a BMI under 25 kg/m² were eligible for this study. Individuals with a history of consuming drugs that target the sympathetic nervous system (e.g., nicotine, beta-blockers, and amphetamines) were excluded. A complete list of inclusion and exclusion criteria can be found in Table 1. Subjects were recruited using flyers posted at various locations on the Washington University in St. Louis medical campus. A subset of patients were enrolled in a test-retest study evaluating BAT activation repeatability²¹. Images from patients with repeat PET/CT scans were pooled with images from patients with one PET/CT scan.

Table 1

Subject eligibility criteria.
Inclusion
Age ≥ 18 and ≤ 35 years
Body mass index (BMI) ≤ 25 kg/m2
Ability to tolerate up to 30 min imaging
Ability and willingness to provide informed consent
Exclusion
Uncontrolled intercurrent illness, including (but not limited to) active infections
Current or prior habitual tobacco use
History of cold-related injury
Insulin-dependent diabetes mellitus
Use of medications that may interfere with brown fat activation (e.g., beta blockers)
Candidates who are actively pregnant or nursing

BAT Activation. A study timeline is provided in Fig. 1. Upon arrival at our lab, volunteers were kept warm for approximately 60 minutes. After warming, the cooling period was initiated with the goal of cooling the patient to just above the point of shivering. Volunteers were cooled using a full-body cooling suit (CureWrap; MRTE Advanced Technologies; Yavne, Israel). The initial water temperature was set at 10^oC for all subjects. When shivering was observed or reported, the water temperature was raised 0.5^oC per minute until shivering ceased. The cooling procedure was performed for a total of 120 minutes prior to PET/CT imaging. After 60 minutes of cooling, FDG was administered and cooling and was continued during the 60 minute FDG uptake period. Immediately prior to the start of imaging, subjects were removed from the cooling suit.

Image Acquisition and Segmentation. PET images were acquired from the base of the skull to the umbilicus using a Siemens Biograph True point 64 PET/CT scanner (Siemens Medical Systems; Erlangen, Germany). The PET data were reconstructed using ordered subset expectation maximization (OSEM) with incorporated point-spread function (PSF). A complete list of PET/CT reconstruction parameters can be found in Supplemental Table 1. The emission sequence was initiated approximately 60 min after intravenous injection of FDG (185 MBq). Image analyses (Fig. 2) were performed using MIM 6.9 (MIM Software, Inc; Cleveland, OH). A manual segmentation of BAT was created within areas of the body likely to contain BAT. The order in which patient image datasets were analyzed was randomized so images from patients with repeat scans would likely not have been segmented sequentially. Various thresholding methods were then applied to the manual segmentations in order to create the final BAT volume of interest.

Thresholding Methods. BARCIST 1.0 was tested against previously reported Hounsfield unit (HU) and standardized uptake value (SUV) thresholds, along with PERCIST 1.0 threshold schemes (Table 2). As part of the BARCIST 1.0 and PERCIST 1.0 methods, SUV values were adjusted for lean body mass (SUV_LEAN or SUL), while the remaining methods were normalized to body weight (SUV_BW). The BARCIST 1.0 criteria utilizes a SUV_LEAN value of ≥ 1.2 g/mL and a HU range of -190 to -10. The PERCIST 1.0 method uses cutoffs normalized to liver FDG uptake assessed by placing a 3 cm diameter sphere in the right lobe of the liver and measuring the mean SUV_LEAN and the standard deviation of the SUV_LEAN. The baseline PERCIST 1.0 cutoff is calculated using the following formula: 1.5 × mean liver SUV_LEAN + 2SD. The follow-up PERCIST 1.0 cutoff is calculated using the following formula: 1.0 × mean liver SUV_LEAN + 2SD. These PERCIST 1.0 thresholds were tested alone and also in combination with the BARCIST 1.0 HU range (-190 to -10).

Table 2

BAT segmentation threshold descriptions.
Method	SUV	HU
SUL_1.2HU_−190:−10*	≥ 1.2 SUV_LEAN	-190 to -10
SUV_1.0HU_−180:−10	≥ 1.0 SUV_BW	-180 to -10
SUV_1.0HU_−100:−10	≥ 1.0 SUV_BW	-100 to -10
SUV_1.5HU_−180:−10	≥ 1.5 SUV_BW	-180 to -10
SUV_1.5HU_−200:−50	≥ 1.5 SUV_BW	-200 to -50
SUV_2.0HU_−250:−50	≥ 2.0 SUV_BW	-250 to -50
SUV_2.0HU_−250:−10	≥ 2.0 SUV_BW	-250 to -10
SUL_PERBaHU_−190:−10	PERCIST 1.0 Baseline	-190 to -10
SUL_PERFuHU_−190:−10	PERCIST 1.0 Follow-up	-190 to -10
SUL_PERBaHU_NA	PERCIST 1.0 Baseline	NA
SUL_PERFuHU_NA	PERCIST 1.0 Follow-up	NA
*BARCIST 1.0 method
SUV = standardized uptake value, HU = Hounsfield unit, SUL = SUV adjusted for lean body mass, PERCIST 1.0 = Positron Emission Tomography Response Criteria in Solid Tumors, version 1.0

Visual Analysis. Each segmentation method was also scored visually by an experienced human reader using a Likert scale from − 3 to 3. The order of the images viewed was randomized and the reader was blinded to the segmentation method used. A score of -3 corresponds to a significant underestimation of BAT volume and a score of 3 to a significant overestimation of BAT volume. A score of 0 indicates an ideal estimation of active BAT volume. The visual score was assessed using both PET and CT images.

Data Analysis. The nine semi-automated segmentation distribution results were expressed as BAT volume means, medians, standard deviations, and interquartile ranges (IQRs). Analysis of variance (ANOVA) was used to determine if there was a significant difference between methods. A repeatability analysis was performed by calculating the Lin’s concordance correlation coefficient (CCC) for each method using a subset of repeat images from patients enrolled in a test-retest study of BAT activity. Visual assessments were compared by calculating the mean value of the Likert scale score for each segmentation method. Statistical analysis was performed using IBM SPSS Statistics 19 (SPSS, Chicago, IL) and Microsoft Excel 2016 (Microsoft Corporation).

Subject Characteristics. Between March, 2016, and December, 2019, 32 healthy volunteers (mean age 23.5 ± 3.5 years, 24 females) were enrolled and underwent FDG PET/CT imaging following a cooling procedure intended to activate BAT (subject characteristics provided in Table 3). Since some participants were part of a BAT activation repeatability study, a total of 56 scans were available for analysis. Participants had a mean BMI of 23.0 ± 2.3 kg/m². Active BAT was detected in 27 subjects and on 56 FDG PET/CT scans. Mean administered FDG activity was 194.2 ± 28.3 MBq. Median FDG uptake time was 61.5 min with a range of 59.0–72.6 min. Median blood glucose, measured immediately prior to FDG administration, was 78 mg/dl and ranged from 58–103 mg/dl.

Table 3

Participant characteristics and study conditions.
Characteristics	Case group (n = 32)
Age (years)	23.5\(\pm\)3.47
Height (m)	1.66\(\pm\)0.09
Weight (kg)	62.85\(\pm\)9.14
BMI (kg/m²)	22.69\(\pm\)2.09
LBM (kg)	40.57\(\pm\)5.10
Sex	M = 8, F = 24
Study Period	March 2016 to October 2016
Race	White = 21, Black = 1, Asian = 10
FDG (MBq)	194.25\(\pm\)28.35
PET/CT Scanner	Siemens Biograph True point 64 PET/CT scanner
Cooling Methodology	Full-body cooling suit (CureWrap; MRTE Advanced Technologies; Yavne, Israel)
Time of cold exposure prior PET/CT scan	Cooling period began (t = 60min)
BMI = body mass index, LBM = lean body mass, FDG = 18F-fluorodeoxyglucose

BAT Volume. Representative images of BAT volume segmentations resulting from the different HU and SUV threshold combinations are shown in Fig. 3. Among these methods, SUL_PERBaHU_−190:−10 resulted in the lowest activated BAT volumes and range of volumes with a median(IQR) of 18.0(60.0) ml (Fig. 4). SUV_1.0HU_−180:−10 resulted in the highest activated BAT volumes with a median(IQR) of 259.6(195.8) ml. Compared with BARCIST 1.0, SUV_1.5HU_−180:−10, SUV_1.5HU_−200:−50, SUV_2.0HU_−250:−50, SUV_2.0HU_−250:−10, SUL_PERBaHU_−190:−10, SUL_PERFuHU_−190:−10, and SUL_PERBaHU_NA all consistently showed lower activated BAT volumes while SUV_1.0HU_−180:−10, SUV_1.0HU_−100:−10, and SUL_PERFuHU_NA consistently showed higher activated BAT volumes. Mean activated BAT volume differed the least between BARCIST 1.0 and SUL_PERFuHU_NA (0.5%). Mean activated BAT volume differed the most between BARCIST 1.0 and SUL_PERBaHU_−190:−10 (522.0%).

Segmentation Method Repeatability. A subset of participants (n = 24) were enrolled in a BAT activity test-retest study, so there were repeat FDG PET/CT images available for analysis (Table 4). Methods showing strong repeatability (CCC ≥ 0.95) were SUL_1.2HU_−190:−10 (BARCIST 1.0), SUV_1.0HU_−180:−10, and SUV_1.0HU_−100:−10 (Fig. 5). Moderate repeatability (CCC ≥ 0.90) was shown by SUV_1.5HU_−180:−10, and SUV_1.5HU_−200:−50. The remaining methods showed poor repeatability with CCC values less than 0.90.

Table 4

Median BAT volume, CCC regression values, and visual assessment scores for each BAT volume quantification method.
Method	Median Volume (mL)	IQR	CCC	Visual Score
SUL_1.2HU_−190:−10*	196.62	161.39	0.9504	0.43
SUV_1.0HU_−180:−10	259.61	195.82	0.9534	1.14
SUV_1.0HU_−100:−10	229.93	160.22	0.9567	1.35
SUV_1.5HU_−180:−10	114.71	134.2	0.9191	0.61
SUV_1.5HU_−200:−50	62.88	79.53	0.9032	0.65
SUV_2.0HU_−250:−50	40.65	66.8	0.8417	-1.18
SUV_2.0HU_−250:−10	66.67	110.86	0.8639	-1.33
SUL_PERBaHU_−190:−10	17.94	59.64	0.6713	-1.05
SUL_PERFuHU_−190:−10	42.88	100.07	0.8238	-0.37
SUL_PERBaHU_NA	48.84	124.04	0.7844	-1.93
SUL_PERFuHU_NA	122.51	263.08	0.8132	0.82
*BARCIST 1.0 method
SUV = standardized uptake value, HU = Hounsfield unit, SUL = SUV adjusted for lean body mass, IQR = interquartile range, CCC = Lin’s concordance correlation coefficient

Visual Analysis Results. Figure 4 presents the mean visual determination scores for each segmentation method. By visual assessment, SUL_1.2HU_−190:−10 (BARCIST 1.0), SUV_1.0HU_−180:−10, SUV_1.0HU_−100:−10, SUV_1.5HU_−180:−10, SUV_1.5HU_−200:−50, and SUL_PERFuHU_NA, overestimated BAT volumes and SUV_2.0HU_−250:−50, SUV_2.0HU_−250:−10, SUL_PERBaHU_−190:−10, SUL_PERFuHU_−190:−10, and SUL_PERBaHU_NA underestimated BAT volume. SUL_1.2HU_−190:−10 (BARCIST 1.0) and SUL_PERFuHU_−190:−10 had visual assessment scores closest to 0, indicating a strong estimation of BAT volume. SUV_1.0HU_−100:−10 and SUL_PERBaHU_NA had visual assessment scores furthest from 0, indicating a poor estimation of BAT volume.

The study of human brown adipose tissue has increased substantially over the last decade. There is great interest in assessing interventions intended to increase the magnitude or duration of BAT activity, which requires a standardized process for measuring BAT activity in humans. Several factors are highly important in measuring BAT activity in humans, including the choice of activation method, the consistency of ambient outdoor temperature, and the imaging modality used for BAT assessment^22,23. A major step in the assessment of BAT activity is image segmentation, which includes selecting appropriate region thresholds, especially on FDG PET/CT, which is currently the gold standard in human BAT research¹⁷. However, since optimal BAT image analysis thresholds are not known, current available data on human BAT volume and activity are somewhat speculative and the comparability across studies is impeded by the use of different HU and SUV thresholds. In the present study we compared the BARCIST 1.0 BAT threshold recommendations with several other published thresholds used for BAT volume segmentation, as well as with the PERCIST 1.0 methods. We assessed activated BAT volumes using these methods on 56 FDG PET/CT images of young, healthy participants. We also performed a repeatability analysis of each method and additionally evaluated the images using a visual score.

We found a high degree of variability among thresholding methods, with the highest and lowest mean BAT volumes varying by nearly 700% using SUV_1.0HU_−180:−10 and SUL_PERBaHU_−190:−10. PERCIST is not typically employed for BAT segmentation, but the variability is approximately the same between the 2 published BAT thresholds showing the highest and lowest BAT volumes, SUV_1.0HU_−180:−10 and SUV_2.0HU_−250:−50. Volumes of BAT reported in the literature vary greatly, even among studies enrolling similar populations and utilizing similar BAT activation protocols. For example, Hoeke et al and van der Lans et al both studied a population with a mean age of approximately 23 years and used an individualized cooling approach following a cold acclimation protocol and yet the reported mean BAT volumes differed by a factor of 3^24,25. There are several methodological issues that can impact volume differences, but segmentation/thresholding technique is likely a contributing factor¹⁷.

SUV Threshold. A general trend was seen of a lower SUV cutoff resulting in higher BAT volumes. This effect was compounded by a lack of adjustment for lean body mass with SUV_1.0HU_−180:−10 and SUV_1.0HU_−100:−10. These methods likely incorporated normal tissue, which is evident from the high visual assessment scores indicating an overestimation of BAT volume. The BARCIST 1.0 threshold combination was the only strongly repeatable method that also showed a visual assessment score near 0. The PERCIST follow-up threshold, combined with the BARCIST HU range, also displayed a strong visual assessment score, but was found to be poorly repeatable and showed a high amount of variance. A major strength of the BARCIST 1.0 method is an adjustment for lean body mass, which helps to individualize the SUV cutoff. This was also shown by Martinez-Tellez et al, who compared the effect of four threshold combinations on BAT activity quantification¹⁸. They found that the relative changes in BAT volumes were primarily influenced by the use of an SUV cutoff corrected for lean body mass.

Hounsfield Unit Threshold. This work shows that the inclusion of CT data, and the choice of HU range in BAT segmentation has a major impact on BAT volume. The PERCIST 1.0 cutoffs were tested, using the same images, with and without a HU threshold range applied. Without applying CT thresholds, the calculated BAT volumes were approximately 3-fold higher than BAT volumes calculated with applied CT thresholds. The only difference between SUV_2.0HU_−250:−10 and SUV_2.0HU_−250:−50 was the inclusion of voxels within the − 50 to -10 range in the latter and yet BAT volumes segmented using SUV_2.0HU_−250:−10 were about twice as high. The same pattern was seen with SUV_1.5HU_−180:−10 and SUV_1.5HU_−200:−50, possibly implying that voxels included in the higher end of the HU range are more important than those include from the lower end. It has been shown that BAT radiodensity can change following cold exposure, especially in the higher end of the HU range, which may help explain these results²⁶.

Limitations. This study assessed BARCIST 1.0 thresholds along with several other published threshold combinations used for BAT quantification and PERCIST 1.0 cutoffs. Besides the selection of HU and SUV threshold combinations, quantification of human BAT volume and activity also depends on numerous other methodological issues such as the cooling protocol, FDG PET/CT methodology, segmentation software, intrinsic factors of the participants such as age, sex, or body composition, or extrinsic factors as outdoor temperature or daily light^27,28. Thus, while some threshold combinations in this study, including BARCIST 1.0, showed strong repeatability, this alone may not indicate a useful threshold combination since the true repeatability is not known. Several thresholding schemes in this study produced similar BAT volumes but that may not indicate that the selected ROIs are reproducible. Future studies may benefit from using a voxel-by-voxel analysis to assess the degree of overlap among ROIs created using various thresholding methods. Future studies should also conduct sensitivity analyses with different thresholds in order to understand whether results are driven by the selected HU and/or SUV thresholds.

BAT volume and activity as determined using ¹⁸F-FDG PET/CT highly depend on the quantification criteria used. The use of a SUV correction for lean body mass and inclusion of Hounsfield units in the − 50 to -10 range appear to improve BAT volume assessment. In this study, a simple method employing a PERCIST 1.0 threshold provided similar absolute volumes, but the BARCIST 1.0 method showed superior repeatability performance. The design of the present study precludes providing any conclusive threshold, but before more definitive thresholds for HU and SUV (or other parameters) are available, the BARCIST 1.0 image analysis recommendations provide a useful guide for the evaluation of human BAT activity.

ACKNOWLEDGMENTS

The studies presented in this work were conducted using the scanning and special services in the MIR Center for Clinical Imaging Research located at the Washington University Medical Center. We also thank Lauren Ash, Jessica Cartier, and Lisa Raymond-Schmidt for assistance with data collection.

AUTHORS CONTRIBUTIONS

ML and JC contributed to the study conception and design, data collection, data analysis and interpretation, and manuscript preparation. RW approved the study design and provided substantial edits to manuscript. All authors read and approved the final manuscript.

COMPETING INTERESTS

The authors declare no competing interests.

DATA AVAILABILITY

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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No competing interests reported.

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A Comparison of FDG PET/CT Segmentation Threshold Methods on Quantification of Brown Adipose Tissue Volume

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