Direct Determination of Lean Body Mass by CT in F-18 FDG PET/CT Studies: Comparison with Estimates Using Predictive Equations

SUVfdg: A standard-uptake-value (SUV) body habitus normalizer specific to fluorodeoxyglucose (FDG) in humans

Purpose

To devise a new body-habitus normalizer to be used in the calculation of an SUV that is specific to the PET tracer 18F-FDG.

Methods

A cohort of 481-patients was selected for analysis of 18F-FDG uptake into tissues unaffected by their disease. Among these, 65-patients had only brain concentrations measured and the remaining 416 were randomly divided into an 86-patient test set and a 330-patient training set. Within the test set, normal liver, spleen and blood measures were made. In the training set, only normal liver concentrations were measured. Using data from the training set, a simple polynomial function of height and weight was selected and optimized in a fitting procedure to predict each patient’s mean liver %ID/ml. This function, when used as a normalizer, defines a new SUV metric (SUVfdg) which we compared to SUV metrics normalized by body weight (SUVbw), lean-body mass (SUVlbm) and body surface-area (SUVbsa) in a five-fold cross-validation. SUVfdg was also evaluated in the independent brain-only and whole-body test sets.

Results

For patients of all sizes including pediatric patients, the normal range of liver 18F-FDG uptake at 60 minutes post injection in units of SUVfdg is 1.0 ± 0.16. Liver, blood, and spleen SUVfdg in all comparisons had lower coefficients of variation compared to SUVbw SUVlbm and SUVbsa. Blood had a mean SUVfdg of 0.8 ± 0.11 and showed no correlation with age, height, or weight. Brain SUVfdg measures were significantly higher (P<0.01) in pediatric patients (4.7 ± 0.9) compared to adults (3.1 ± 0.6).

Conclusion

A new SUV metric, SUVfdg, is proposed. It is hoped that SUVfdg will prove to be better at classifying tumor lesions compared to SUV metrics in current use. Other tracers may benefit from similarly tracer-specific body habitus normalizers.

A method for evaluation of patient-specific lean body mass from limited-coverage CT images and its application in PERCIST: comparison with predictive equation

BACKGROUND

Standardized uptake value (SUV) normalized by lean body mass ([LBM] SUL) is recommended as metric by PERCIST 1.0. The James predictive equation (PE) is a frequently used formula for LBM estimation, but may cause substantial error for an individual. The purpose of this study was to introduce a novel and reliable method for estimating LBM by limited-coverage (LC) CT images from PET/CT examinations and test its validity, then to analyse whether SUV normalised by LC-based LBM could change the PERCIST 1.0 response classifications, based on LBM estimated by the James PE.

METHODS

First, 199 patients who received whole-body PET/CT examinations were retrospectively retrieved. A patient-specific LBM equation was developed based on the relationship between LC fat volumes (FV_LC) and whole-body fat mass (FM_WB). This equation was cross-validated with an independent sample of 97 patients who also received whole-body PET/CT examinations. Its results were compared with the measurement of LBM from whole-body CT (reference standard) and the results of the James PE. Then, 241 patients with solid tumours who underwent PET/CT examinations before and after treatment were retrospectively retrieved. The treatment responses were evaluated according to the PE-based and LC-based PERCIST 1.0. Concordance between them was assessed using Cohen's κ coefficient and Wilcoxon's signed-ranks test. The impact of differing LBM algorithms on PERCIST 1.0 classification was evaluated.

RESULTS

The FV_LC were significantly correlated with the FM_WB (r=0.977). Furthermore, the results of LBM measurement evaluated with LC images were much closer to the reference standard than those obtained by the James PE. The PE-based and LC-based PERCIST 1.0 classifications were discordant in 27 patients (11.2%; κ = 0.823, P=0.837). These discordant patients' percentage changes of peak SUL (SUL_peak) were all in the interval above or below 10% from the threshold (±30%), accounting for 43.5% (27/62) of total patients in this region. The degree of variability is related to changes in LBM before and after treatment.

CONCLUSIONS

LBM algorithm-dependent variability in PERCIST 1.0 classification is a notable issue. SUV normalised by LC-based LBM could change PERCIST 1.0 response classifications based on LBM estimated by the James PE, especially for patients with a percentage variation of SUL_peak close to the threshold.

Prognostic value of pretreatment PET/CT lean body mass-corrected parameters in patients with hepatocellular carcinoma

OBJECTIVE

This study was designed to investigate whether pretreatment fluorine-18-fluorodeoxyglucose (F-FDG) PET/computed tomography (CT) lean body mass-corrected parameters could predict the overall survival (OS) better than the established predictors in patients with hepatocellular carcinoma (HCC).

PATIENTS AND METHODS

We retrospectively analyzed 61 patients with HCC with pretreatment F-FDG-PET/CT. Besides obtaining clinical factors, we measured both lean body mass-corrected and body weight-corrected PET/CT parameters, including metabolic tumor volume, maximal standardized uptake value of the tumor, total lesion glycolysis, tumor-to-normal liver uptake ratio, and so on. The prognostic value of those factors for OS was assessed by statistical software.

RESULTS

In the univariate analysis, PET/CT parameters, ascites, serum α-fetoprotein, alkaline phosphatase, aspartate transaminase (AST), tumor number, tumor size of the maximal one, vascular invasion, TNM stage, Child-Pugh class, Barcelona Clinic Liver Cancer (BCLC) staging, and Okuda staging were significant predictors of OS. In multivariate and Kaplan-Meier analyses, lean body mass-corrected maximum standardized uptake value (lbmSUVmax) more than 3.35 g/ml, AST more than 42.00 U/l, and BCLC staging B-C were significant independent predictors of poor OS. When BCLC staging variable was stratified by four categories instead of two in the multivariate analysis, it was not the statistically significant independent predictor anymore, but lbmSUVmax and AST still were.

CONCLUSION

Pretreatment F-FDG-PET/CT lean body mass-corrected parameters can predict the OS in patients with HCC. Moreover, lbmSUVmax and AST, as the independent predictors of OS, could supplement the prognostic value of the BCLC staging system.

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.

The appropriate whole-body index on which to base standardized uptake value in 2-deoxy-2-[(18)F]fludeoxyglucose PET

OBJECTIVE

Tissue uptake of 2-deoxy-2-fluorine-18 fludeoxyglucose ((18)F-FDG) is routinely quantified as standardized uptake value (SUV), which in general is the fraction (F) of administered activity per millilitre of tissue multiplied by an index of body size, usually weight (W), i.e. F/ml × W = SUV or F/ml = SUV × (1/W). Other indices have been suggested as preferable to W, especially lean body mass (LBM) and body surface area (BSA). The second equation mentioned above shows that the reciprocal of the ideal index should correlate closely with F/ml and give a regression line through the origin. The purpose of this study was to determine which of these three indices best meets these criteria.

METHODS

Data were evaluated from 49 males and 51 females undergoing routine (18)F-FDG positron emission tomography/CT. A 3 cm diameter region of interest was drawn over the liver and F/ml recorded. LBM and BSA were estimated from height and weight.

RESULTS

Based on all patients, the reciprocals of the three indices gave similar correlation coefficients with F/ml, but only 1/LBM gave regressions close to the origin. Intercepts were significantly higher for females for 1/W and 1/BSA, consistent with females having more body fat, but there was no significant difference with 1/LBM.

CONCLUSION

LBM is the best index on which to base SUV because adipose tissue accumulates less (18)F-FDG than other soft tissues.

ADVANCES IN KNOWLEDGE

The value of this study lies in its use of a novel, more rational approach than previously to confirm that SUV should be based on LBM.