Rapid Standardized CT-Based Method to Determine Lean Body Mass SUV for PET—A Significant Improvement Over Prediction Equations

In 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) studies, maximum standardized uptake value (SUVmax) is the parameter commonly used to provide a measurement of the metabolic activity of a tumor. SUV normalized by body mass is affected by the proportions of body fat and lean tissue, which present high variability in patients with cancer. SUV corrected by lean body mass (LBM), denoted as SUL, is recommended to provide more accurate, consistent, and reproducible SUV results; however, LBM is frequently estimated rather than measured. Given the increasing importance of a quantitative PET parameter, especially when comparing PET studies over time to evaluate disease response clinically, and its use in oncological clinical trials, we set out to evaluate the commonly used equations originally derived by James (1976) and Janmahasatian et al. (2005) against computerized tomography (CT)-derived measures of LBM.

Methods

Whole-body 18F-FDG PET images of 195 adult patients with cancer were analyzed retrospectively. Representative liver SUVmean was normalized by total body mass. SUL was calculated using a quantitative determination of LBM based on the CT component of the PET/CT study (LBMCT) and compared against the equation-estimated SUL. Bland and Altman plots were generated for SUV-SUL differences.

Results

This consecutive sample of patients undergoing usual care (men, n = 96; women, n = 99) varied in body mass (38–127 kg) and in Body Mass Index (BMI) (14.7–47.2 kg/m2). LBMCT weakly correlated with body mass (men, r2 = 0.32; women, r2 = 0.22), and thus SUV and SULCT were also weakly correlated (men, r2 = 0.24; women, r2 = 0.11). Equations proved inadequate for the assessment of LBM. LBM estimated by James’ equation showed a mean bias (overestimation of LBM compared with LBMCT) in men (+6.13 kg; 95% CI 4.61–7.65) and in women (+6.32 kg; 95% CI 5.26–7.39). Janmahasatian’s equation provided similarly poor performance.

Conclusions

CT-based LBM determinations incorporate the patient’s current body composition at the time of a PET/CT study, and the information garnered can provide care teams with information with which to more accurately determine FDG uptake values, allowing comparability over multiple scans and treatment courses and will provide a robust basis for the use of PET Response Criteria in Solid Tumors (PERCIST) in clinical trials.

Comparison of the Variability of SUV Normalized by Skeletal Volume with the Variability of SUV Normalized by Body Weight in 18F-Fluoride PET/CT

Our objective was to test the hypothesis that variability in SUV normalized by skeletal volume (SV) in ¹⁸F-fluoride (¹⁸F-NaF) PET/CT studies is lower than variability in SUV normalized by body weight (BW). Methods: The mean SUV (SUV_mean) was obtained for whole skeletal volume of interest (wsVOI) in 163 selected ¹⁸F-NaF PET/CT studies. These studies were performed to investigate bone metastases and were considered to have normal results. SUV_mean was calculated with normalization by BW (BW SUV_mean), with normalization by SV (SV SUV_mean), and without normalization (WN SUV_mean). The total SV for each patient was also estimated on the basis of the wsVOI defined on the CT component of the PET/CT study. SUV_mean variability for each patient was estimated as the absolute value of the difference between the SUV_mean for the patient and the mean of the SUV_mean for the whole group of patients, divided by the mean of the SUV_mean for the whole group of patients. The variabilities of SUV_mean calculated by the 3 methods were compared using a paired 1-tailed Wilcoxon test. Results: The mean variability for the BW, SV, and WN SUV_mean was 0.16, 0.13, and 0.16, respectively. There were statistically significant differences between SV and BW SUV_mean variability (P = 0.03) and between SV and WN SUV_mean variability (P < 0.01). There was no statistically significant difference between BW and WN SUV_mean variability (P = 0.4). Conclusion: In patients with normal ¹⁸F-NaF PET/CT results, SV SUV_mean presents lower variability than BW SUV_mean.

Patient-specific lean body mass can be estimated from limited-coverage computed tomography images

OBJECTIVE

In PET/CT, quantitative evaluation of tumour metabolic activity is possible through standardized uptake values, usually normalized for body weight (BW) or lean body mass (LBM). Patient-specific LBM can be estimated from whole-body (WB) CT images. As most clinical indications only warrant PET/CT examinations covering head to midthigh, the aim of this study was to develop a simple and reliable method to estimate LBM from limited-coverage (LC) CT images and test its validity.

PATIENTS AND METHODS

Head-to-toe PET/CT examinations were retrospectively retrieved and semiautomatically segmented into tissue types based on thresholding of CT Hounsfield units. LC was obtained by omitting image slices. Image segmentation was validated on the WB CT examinations by comparing CT-estimated BW with actual BW, and LBM estimated from LC images were compared with LBM estimated from WB images. A direct method and an indirect method were developed and validated on an independent data set.

RESULTS

Comparing LBM estimated from LC examinations with estimates from WB examinations (LBMWB) showed a significant but limited bias of 1.2 kg (direct method) and nonsignificant bias of 0.05 kg (indirect method).

CONCLUSION

This study demonstrates that LBM can be estimated from LC CT images with no significant difference from LBMWB.

The Effect of New Formulas for Lean Body Mass on Lean-Body-Mass-Normalized SUV in Oncologic 18F-FDG PET/CT

Because of better precision and intercompatibility, the use of lean body mass (LBM) as a mass estimate in the calculation of SUV (SUL) has become more common in research and clinical studies today. Thus, the equations deciding this quantity must be those that best represent the actual body composition. Methods: LBM was calculated for 44 patients examined with ¹⁸F-FDG PET/CT scans by means of the sex-specific predictive equations of James and Janmahasatians, and the results were validated using a CT-based method that makes use of the eyes-to-thighs CT component of the PET/CT aquisition and segments the voxels according to Hounsfield units. Intraclass correlation coefficients and Bland-Altman plots were used to assess agreement between the various methods. Results: A mean difference of 6.3 kg (limits of agreement, -15.1 to 2.5 kg) between [Formula: see text] and [Formula: see text] was found. This difference was higher than the 3.8-kg difference observed between [Formula: see text] and [Formula: see text] (limits of agreement, -12.5 to 4.9 kg). In addition, [Formula: see text] had a higher intraclass correlation coefficient with [Formula: see text] (0.87; 95% confidence interval, 0.60-0.94) than with [Formula: see text] (0.77; 95% confidence interval, 0.11-0.91). Thus, we obtained better agreement between [Formula: see text] and [Formula: see text] Although there were exceptions, the overall effect on SUL was that [Formula: see text] was greater than [Formula: see text] Conclusion: We have verified the reliability of the suggested [Formula: see text] formulas with a CT-derived reference standard. Compared with the more traditional and available set of [Formula: see text] equations, the [Formula: see text] formulas tend to yield better agreement.

Technical Note: Scintillation well counters and particle counting digital autoradiography devices can be used to detect activities associated with genomic profiling adequacy of biopsy specimens obtained after a low activity 18 F-FDG injection

PURPOSE

Genomic profiling of biopsied tissue is the basis for precision cancer therapy. However, biopsied materials may not contain sufficient amounts of tumor deoxyribonucleonic acid needed for the analysis. We propose a method to determine the adequacy of specimens for performing genomic profiling by quantifying their metabolic activity.

METHODS

We estimated the average density of tumor cells in biopsy specimens needed to successfully perform genomic analysis following the Memorial Sloan Kettering Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT) protocol from the minimum amount of deoxyribonucleonic acid needed and the volume of tissue typically used for analysis. The average ¹⁸ F-FDG uptake per cell was assessed by incubating HT-29 adenocarcinoma tumor cells in ¹⁸ F-FDG containing solution and then measuring their activity with a scintillation well counter. Consequently, we evaluated the response of two devices around the minimum expected activities which would indicate genomic profiling adequacy of biopsy specimens obtained under ¹⁸ F-FDG PET/CT guidance. Surrogate samples obtained using 18G core needle biopsies of gels containing either ¹⁸ F-FDG-loaded cells in the expected concentrations or the corresponding activity were measured using autoradiography and a scintillation well counter. Autoradiography was performed using a CCD-based device with real-time image display as well as with digital autoradiography imaging plates following a 30-min off-line protocol for specimen activity determination against previously established calibration.

RESULTS

Cell incubation experiments and estimates obtained from quantitative autoradiography of biopsy specimens (QABS) indicate that specimens acquired under ¹⁸ F-FDG PET/CT guidance that contained the minimum amount of cells needed for genomic profiling would have an average activity concentration in the range of about 3 to about 9 kBq/mL. When exposed to specimens with similar activity concentration, both a CCD-based autoradiography device and a scintillation well counter produced signals with sufficient signal-to-background ratio for specimen genomic adequacy identification in less than 10 min, which is short enough to allow procedure guidance.

CONCLUSION

Scintillation well counter measurements and CCD-based autoradiography have adequate sensitivity to detect the tumor burden needed for genomic profiling during ¹⁸ F-FDG PET/CT-guided 18G core needle biopsies of liver adenocarcinoma metastases.

SUV Normalized by Skeletal Volume on 18F-Fluoride PET/CT Studies

OBJECTIVE

To propose a technique for SUV normalization on F-fluoride PET/CT (F-NaF) studies based on skeletal volume and to compare the SUVs normalized by this technique with the ones normalized by body weight.

METHODS

SUVs were obtained in volumes of interest (VOIs) in proximal diaphyseal regions of the right humerus (HD) and right femur (FD) in 12 selected F-NaF studies. The 12 studies presented both regions considered normal by visual examination on PET and CT and were performed in patients presenting body weight below 50 kg (B50) or above 90 kg (A90) (6 patients in each group). The maximum SUVs were calculated in these 2 bone regions in both groups of patients using body weight (SUV BW) and skeletal volume (SUV SV) methodologies. The total skeletal volume for each patient was estimated based on whole skeletal VOIs automatically defined on the CT component of the PET/CT study. The maximum SUVs calculated using the 2 methodologies were compared.

RESULTS

The maximum SUVs BW were statistically higher in the group A90 in both regions, with a P < 0.001 and P < 0.008 for FD and HD, respectively. The maximum SUVs SV in the 2 regions were not statistically different between the groups B50 and A90, P values of 0.27 and 0.87 for FD and HD, respectively.

CONCLUSIONS

The SUVs normalized by skeletal volume present similar results in groups of patients with extremes of body weight. Therefore, this methodology could be more adequate than the one normalized by body weight to semiquantitatively analyze F-NaF studies.

How to assess intra- and inter-observer agreement with quantitative PET using variance component analysis: a proposal for standardisation

BACKGROUND

Quantitative measurement procedures need to be accurate and precise to justify their clinical use. Precision reflects deviation of groups of measurement from another, often expressed as proportions of agreement, standard errors of measurement, coefficients of variation, or the Bland-Altman plot. We suggest variance component analysis (VCA) to estimate the influence of errors due to single elements of a PET scan (scanner, time point, observer, etc.) to express the composite uncertainty of repeated measurements and obtain relevant repeatability coefficients (RCs) which have a unique relation to Bland-Altman plots. Here, we present this approach for assessment of intra- and inter-observer variation with PET/CT exemplified with data from two clinical studies.

METHODS

In study 1, 30 patients were scanned pre-operatively for the assessment of ovarian cancer, and their scans were assessed twice by the same observer to study intra-observer agreement. In study 2, 14 patients with glioma were scanned up to five times. Resulting 49 scans were assessed by three observers to examine inter-observer agreement. Outcome variables were SUVmax in study 1 and cerebral total hemispheric glycolysis (THG) in study 2.

RESULTS

In study 1, we found a RC of 2.46 equalling half the width of the Bland-Altman limits of agreement. In study 2, the RC for identical conditions (same scanner, patient, time point, and observer) was 2392; allowing for different scanners increased the RC to 2543. Inter-observer differences were negligible compared to differences owing to other factors; between observer 1 and 2: -10 (95 % CI: -352 to 332) and between observer 1 vs 3: 28 (95 % CI: -313 to 370).

CONCLUSIONS

VCA is an appealing approach for weighing different sources of variation against each other, summarised as RCs. The involved linear mixed effects models require carefully considered sample sizes to account for the challenge of sufficiently accurately estimating variance components.

Usefulness of standardized uptake value normalized by individual CT-based lean body mass in application of PET response criteria in solid tumors (PERCIST)

Our aim in this study was to verify the usefulness of the standardized uptake value (SUV) normalized by individual CT-based lean body mass (LBMCT) in application of PET response criteria in solid tumors (PERCIST).We retrospectively investigated 14 patients (4 male and 10 female) with malignant lymphoma who were undergoing chemotherapy. (18)F-FDG PET/CT examinations were performed before and after chemotherapy. The LBMCT was calculated by estimation of fat weight from CT data (from skull base to pelvis). The mean ± standard deviation (SD) and the Bland-Altman plot were used for comparison among body weight, LBMCT, and LBM derived from a predictive equation (LBMPE). Indices for FDG uptake in the liver were: SUV, SUV based on LBMPE (SULPE), and SUV based on LBMCT (SULCT). Overall differences between the uptake values were analyzed by one-way ANOVA. If the ANOVA showed significance, differences between uptake values were investigated further by use of the Tukey-Kramer test. The mean values of body weight, LBMPE, and LBMCT were: 55.4 ± 14.9 (39.0-112.0), 43.0 ± 10.5 (31.3-75.2), and 35.3 ± 9.8 (23.4-75.8) kg, respectively. There was a wide dispersion between LBMPE and LBMCT (differences, 7.6 ± 3.6 kg; 95 % CI, 6.42-8.85). LBMPE was higher than LBMCT in all the cases except in Case 11. The mean uptake values significantly differed among SUV, SULPE, and SULCT (F = 68.3, p < 0.05). Whereas SULPE deviated from PERCIST criteria in seven patients, SULCT satisfied the criteria except in one case. These results suggest that liver SULCT is useful for application of PERCIST.