Tao Chan1. 1. Department of Diagnostic Radiology, University of Hong Kong. taochan@hku.hk
Abstract
UNLABELLED: CT has become an established method for calculating body composition, but it requires data from the whole body, which are not typically obtained in routine PET/CT examinations. A computerized scheme that evaluates whole-body lean body mass (LBM) based on CT data from limited-whole-body coverage was developed. The LBM so obtained was compared with results from conventional predictive equations. METHODS: LBM can be obtained automatically from limited-whole-body CT data by 3 means: quantification of body composition from CT images in the limited-whole-body scan, based on thresholding of CT attenuation; determination of the range of coverage based on a characteristic trend of changing composition across different levels and pattern recognition of specific features at strategic positions; and estimation of the LBM of the whole body on the basis of a predetermined relationship between proportion of fat mass and extent of coverage. This scheme was validated using 18 whole-body PET/CT examinations truncated at different lengths to emulate limited-whole-body data. LBM was also calculated using predictive equations that had been reported for use in SUV normalization. RESULTS: LBM derived from limited-whole-body data using the proposed method correlated strongly with LBM derived from whole-body CT data, with correlation coefficients ranging from 0.991 (shorter coverage) to 0.998 (longer coverage) and SEMs of LBM ranging from 0.14 to 0.33 kg. These were more accurate than results from different predictive equations, which ranged in correlation coefficient from 0.635 to 0.970 and in SEM from 0.64 to 2.40 kg. CONCLUSION: LBM of the whole body could be automatically estimated from CT data of limited-whole-body coverage typically acquired in PET/CT examinations. This estimation allows more accurate and consistent quantification of metabolic activity of tumors based on LBM-normalized standardized uptake value.
UNLABELLED: CT has become an established method for calculating body composition, but it requires data from the whole body, which are not typically obtained in routine PET/CT examinations. A computerized scheme that evaluates whole-body lean body mass (LBM) based on CT data from limited-whole-body coverage was developed. The LBM so obtained was compared with results from conventional predictive equations. METHODS: LBM can be obtained automatically from limited-whole-body CT data by 3 means: quantification of body composition from CT images in the limited-whole-body scan, based on thresholding of CT attenuation; determination of the range of coverage based on a characteristic trend of changing composition across different levels and pattern recognition of specific features at strategic positions; and estimation of the LBM of the whole body on the basis of a predetermined relationship between proportion of fat mass and extent of coverage. This scheme was validated using 18 whole-body PET/CT examinations truncated at different lengths to emulate limited-whole-body data. LBM was also calculated using predictive equations that had been reported for use in SUV normalization. RESULTS: LBM derived from limited-whole-body data using the proposed method correlated strongly with LBM derived from whole-body CT data, with correlation coefficients ranging from 0.991 (shorter coverage) to 0.998 (longer coverage) and SEMs of LBM ranging from 0.14 to 0.33 kg. These were more accurate than results from different predictive equations, which ranged in correlation coefficient from 0.635 to 0.970 and in SEM from 0.64 to 2.40 kg. CONCLUSION: LBM of the whole body could be automatically estimated from CT data of limited-whole-body coverage typically acquired in PET/CT examinations. This estimation allows more accurate and consistent quantification of metabolic activity of tumors based on LBM-normalized standardized uptake value.
Authors: Eline E Verwer; Floris H P van Velden; Idris Bahce; Maqsood Yaqub; Robert C Schuit; Albert D Windhorst; Pieter Raijmakers; Adriaan A Lammertsma; Egbert F Smit; Ronald Boellaard Journal: Eur J Nucl Med Mol Imaging Date: 2013-06-06 Impact factor: 9.236
Authors: Terence A Riauka; Vickie E Baracos; Rebecca Reif; Freimut D Juengling; Don M Robinson; Marguerite Wieler; Alexander J B McEwan Journal: Front Oncol Date: 2022-07-07 Impact factor: 5.738