Computerized Method for Automatic Evaluation of Lean Body Mass from PET/CT: Comparison with Predictive Equations

Abstract 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 (FVLC) and whole-body fat mass (FMWB). 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 FVLC were significantly correlated with the FMWB (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 (SULpeak) 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 SULpeak close to the threshold.

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Establishment of Prediction Equations of Lean Body Mass Suitable for Chinese Adults

BioMed Research International ◽

10.1155/2019/1757954 ◽

2019 ◽

Vol 2019 ◽

pp. 1-9

Author(s):

Jinhua Li ◽

Jingjie Shang ◽

Bin Guo ◽

Jian Gong ◽

Hao Xu

Keyword(s):

Lean Body Mass ◽

Body Mass ◽

Predictive Ability ◽

Female Group ◽

Chinese Adults ◽

Multilinear Regression ◽

Predictive Equations ◽

Large Sample ◽

Multilinear Regression Analysis ◽

Southern Chinese

Aim. To develop predictive equations of lean body mass (LBM) suitable for healthy southern Chinese adults with a large sample. LBM measured by dual-energy X-ray absorptiometry (DXA) are considered as the standard ones. Methods. Retrospective analysis was conducted on the consecutive people who did total body measurement with DXA from July 2005 to October 2015. People with diseases that might affect LBM were excluded and overall 12,194 subjects were included in this study. Information about the 10,683 subjects (2,987 males and 7,696 females) from July 2005 to November 2014 was used to establish equations. These subjects were grouped by sex and then subdivided according to their body mass index (BMI). The female group was divided into another two subgroups: the premenopausal and postmenopausal subgroups. Equations were developed through stepwise multilinear regression analysis of height, weight, age, and BMI. Information about the 1,511 subjects (395 males and 1116 females) from December 2014 to October 2015 was used to verify the established equations. Results. BMI, height, weight, and age were introduced into the equations as independent variables in the male group, while age was proved to have no influence on LBM in the female group. Regrouping according to BMI or menopause did not increase the predictive ability of equations. Good agreement between LBM evaluated by equation (LBM_PE) and LBM measured by DXA (LBM_DXA) was observed in both the male and female groups. Conclusion. Predictive equations of LBM suitable for healthy southern Chinese adults are established with a large sample. BMI was related to LBM content; however, there is no need for further group based on BMI or menopause while developing LBM questions.

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Evaluation of SUV normalized by lean body mass (SUL) in 68Ga-PSMA11 PET/CT: a bi-centric analysis

EJNMMI Research ◽

10.1186/s13550-019-0572-z ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 3

Author(s):

Andrei Gafita ◽

Jeremie Calais ◽

Charlott Franz ◽

Isabel Rauscher ◽

Hui Wang ◽

...

Keyword(s):

Body Weight ◽

Lean Body Mass ◽

Body Mass ◽

Standardized Uptake Value ◽

Ct Scans ◽

Volume Of Interest ◽

Pet Ct ◽

Positive Correlations ◽

The Right

Abstract Introduction The aim of this analysis was to investigate whether the standardized uptake value (SUV) normalized by lean body mass (SUL) is a more appropriate quantitative parameter compared to the commonly used SUV normalized by patient’s weight in 68Ga-PSMA11 PET/CT. Material and methods 68Ga-PSMA11 PET/CT scans of 121 patients with prostate cancer from two institutions were evaluated. Liver SUV was measured within a 3-cm volume-of-interest (VOI) in the right hepatic lobe and corrected for lean body mass using the Janmahasatian formula. SUV and SUL repeatability between baseline and follow-up scans of the same patients were assessed. Results SUV was significantly positively correlated with body weight (r = 0.35, p = 0.02). In contrast, SUL was not correlated with body weight (r = 0.23, p = 0.07). No significant differences were found between baseline and follow-up scan (p = 0.52). Conclusion The Janmahasatian formula annuls the positive correlations between SUV and body weight, suggesting that SUL is preferable to SUV for quantitative analyses of 68Ga-PSMA11 PET/CT scans.

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A Method to Improve the Semiquantification of 18F-FDG Uptake: Reliability of the Estimated Lean Body Mass Using the Conventional, Low-Dose CT from PET/CT

Journal of Nuclear Medicine ◽

10.2967/jnumed.115.164913 ◽

2015 ◽

Vol 57 (5) ◽

pp. 753-758 ◽

Cited By ~ 7

Author(s):

P. Decazes ◽

D. Metivier ◽

A. Rouquette ◽

J.-N. Talbot ◽

K. Kerrou

Keyword(s):

Lean Body Mass ◽

Body Mass ◽

Low Dose ◽

Low Dose Ct ◽

Fdg Uptake ◽

Pet Ct ◽

18F Fdg

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RA07.06: BASELINE FDG-PET/CT PARAMETERS AS PREDICTOR FOR RESIDUAL TUMOUR AFTER NEOADJUVANT CHEMORADIOTHERAPY IN OESOPHAGEAL CANCER PATIENTS

Diseases of the Esophagus ◽

10.1093/dote/doy089.ra07.06 ◽

2018 ◽

Vol 31 (Supplement_1) ◽

pp. 35-35

Author(s):

Maria Valkema ◽

B Noordman ◽

Bas P L Wijnhoven ◽

M C W Spaander ◽

Sjoerd M Lagarde ◽

...

Keyword(s):

Cancer Patients ◽

Lean Body Mass ◽

Body Mass ◽

Oesophageal Cancer ◽

Fdg Pet ◽

Neoadjuvant Chemoradiotherapy ◽

Tumour Mass ◽

Residual Tumour ◽

Pet Ct ◽

Fdg Pet Ct

Abstract Background An optimal model for predicting pathologic response after neoadjuvant chemoradiotherapy (nCRT) in oesophageal cancer has not been defined yet. FDG-PET/CT is frequently used in response assessments. The aim of this side study of the preSANO trial (NL41732.078.13) was to investigate if the FDG-PET parameters SUVmax, total lesion glycolysis (TLG) and metabolic tumour volume (MTV) were predictive for residual tumour in the resected specimen of oesophageal cancer patients treated with nCRT. Methods Patients underwent FDG-PET/CT at baseline according to the European Association of Nuclear Medicine guidelines 1.0 (2.3MBq/kg F-18-FDG; scanning 60 ± 5min.). All parameters were corrected for lean body mass. MTV was defined as the volume within a 41% of SULmax ( = SUV/lean body mass) isocontour threshold at tumour and lymph nodes. TLG was calculated as SULmean x MTV. Logarithmic transformation was performed because of non-normal distribution of TLG and MTV. Baseline PET parameters were compared to tumour regression grade in the resection specimen (TRG3–4 = > 10% residual tumour vs. TRG1 = complete response). Peroperatively irresectable tumours were recoded as TRG4. Analyses were performed using an independent-samples T-test. Results From a total of 207 patients who underwent FDG-PET/CT before nCRT, 197 were included for analysis (5 were non-FDG avid, 5 had incomplete data). Histological type of tumour: adenocarcinoma (AC) n = 154, squamous cell carcinoma (SCC) n = 42, and one adenosquamous carcinoma. Thirty-seven patients (19%) had TRG1 and 41 patients (21%) had TRG3–4. In complete responders (TRG1), SULmax, TLG and MTV (mean ± SD) were 9.6 ± 5.8, 85.3 ± 85.5 and 13.0 ± 9.9, respectively. In patients with TRG3–4, SULmax, TLG and MTV were 9.4 ± 5.4145.8 ± 164.6 and 21.9 ± 16.2, respectively. SULmax was not significantly different between both groups (P = 0.8), but log(TLG) and log(MTV) (P = 0.008 and P = 0.001) were. In adenocarcinomas, log(TLG) did not differ between groups (P = 0.1). Conclusion Initial FDG tumour mass, expressed as MTV, (rather than SULmax) is the most contributing factor in predicting residual disease after nCRT in both SCC and AC. The effect is stronger in SCC. Therefore, baseline FDG tumour mass should be included in a prediction model, besides other clinical and tumour parameters. Disclosure All authors have declared no conflicts of interest.

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