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

2021 ◽  
Author(s):  
Jingjie Shang ◽  
Zhiqiang Tan ◽  
Yong Cheng ◽  
Yongjin Tang ◽  
Bin Guo ◽  
...  
Keyword(s):  
Lean Body Mass ◽  
Body Mass ◽  
Whole Body ◽  
Patient Specific ◽  
Predictive Equation ◽  
Reference Standard ◽  
Pet Ct ◽  
Before And After ◽  
Percist 1.0 ◽  
Limited Coverage

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.

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

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Jingjie Shang ◽  
Zhiqiang Tan ◽  
Yong Cheng ◽  
Yongjin Tang ◽  
Bin Guo ◽  
...  
Keyword(s):  
Lean Body Mass ◽  
Body Mass ◽  
Whole Body ◽  
Patient Specific ◽  
Predictive Equation ◽  
Reference Standard ◽  
Pet Ct ◽  
Before And After ◽  
Percist 1.0 ◽  
Limited Coverage

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.

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

2020 ◽  
Author(s):  
Jingjie Shang ◽  
Zhiqiang Tan ◽  
Yong Cheng ◽  
Yongjin Tang ◽  
Bin Guo ◽  
...  
Keyword(s):  
Ct Images ◽  
Whole Body ◽  
Patient Specific ◽  
Predictive Equation ◽  
Reference Standard ◽  
Pet Ct ◽  
Before And After ◽  
Percist 1.0 ◽  
The Impact ◽  
Limited Coverage

Abstract Background : 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 predictive equation (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 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.

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

2018 ◽  
Vol 39 (6) ◽  
pp. 521-526
Author(s):  
Joke Devriese ◽  
Laurence Beels ◽  
Alex Maes ◽  
Christophe van de Wiele ◽  
Hans Pottel
Keyword(s):  
Computed Tomography ◽  
Lean Body Mass ◽  
Body Mass ◽  
Patient Specific ◽  
Computed Tomography Images ◽  
Limited Coverage

Whole-body MRI for initial staging of paediatric lymphoma: prospective comparison to an FDG-PET/CT-based reference standard

2014 ◽  
Vol 24 (5) ◽  
pp. 1153-1165 ◽  
Author(s):  
Annemieke S. Littooij ◽  
Thomas C. Kwee ◽  
Ignasi Barber ◽  
Claudio Granata ◽  
Malou A. Vermoolen ◽  
...  
Keyword(s):  
Fdg Pet ◽  
Whole Body ◽  
Reference Standard ◽  
Whole Body Mri ◽  
Prospective Comparison ◽  
Pet Ct ◽  
Fdg Pet Ct ◽  
Initial Staging

Detection of metastases in breast cancer: Is whole body PET/MR better than PET/CT?

2014 ◽  
Vol 32 (26_suppl) ◽  
pp. 15-15
Author(s):  
Eleonora Teplinsky ◽  
Akshat Pujara ◽  
Francisco J. Esteva ◽  
Linda Moy ◽  
Amy Melsaether ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Treatment Response ◽  
Metastatic Lesion ◽  
Lesion Detection ◽  
Whole Body ◽  
Reference Standard ◽  
Therapeutic Implications ◽  
Metastatic Setting ◽  
Pet Ct ◽  
Pathology Reports

15 Background: Whole body PET/CT is commonly utilized in breast cancer (BC) patients (pts). Limitations include assessment of treatment response in bone metastases (mets), high physiologic uptake in brain and liver, and cumulative radiation exposure. The site of mets can have prognostic and therapeutic implications. PET/MR, an exciting new hybrid technology, delivers less radiation than PET/CT. Our aim was to compare the differences in metastatic lesion detection using PET/CT & PET/MR in all BC subtypes. Methods: After a single 18-FDG injection, pts had whole body PET/CT for staging and assessment of treatment response. They were transported to another NYU facility & then underwent whole body PET/MR. PET/MR & PET/CT images were each read by a radiologist blinded to prior exams or reports. Number of mets (up to 6) per organ was recorded. 2 experienced radiologists unblinded to imaging and pathology reports served as the “reference standard”. Results: Forty-eight BC pts underwent PET/CT & PET/MR (28 in metastatic setting, 5 for staging & 15 to rule out recurrence). Median age: 55; range 32-79 with 31 ER+/HER2-, 8 ER+/HER2+, 2 ER-/HER2+, 6 ER-/HER2+, 1 unknown. 20 pts had no distant mets on scan. In the remaining 28 pts, the reference standard detected 9 liver, 18 bone, 7 lung/pleura, 5 brain & 10 lymph node (LN) metastases; some patients had ≥1 metastatic site. PET/CT had more false positives (FP) and false negatives (FN) in the detection of mets (Table). PET/MR had 1 FP in the liver. PET/MR accurately detected 2 bone (ER+/HER2-), 3 liver (ER+/HER2-), 2 LN (1 ER+/HER2+; 1 ER+/HER2-) and 5 brain lesions (1 ER+/HER2-; 3 ER-/HER2+; 1 ER+/HER2+) in 10 unique pts that were not identified on PET/CT. 1 liver (ER+/HER2-) and 2 brain mets (ER-/HER2+) identified on PET/MR were previously unknown. Conclusions: Our preliminary data suggest that PET/MR outperformed PET/CT in detecting mets in the liver, brain, LN & possibly bone. Prospective studies of PET/MR are warranted to determine whether early detection of mets, including occult brain mets in HER2+ pts, impacts survival.[Table: see text]

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

2019 ◽  
Vol 9 (1) ◽  
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.

scholarly journals The Role of Activity, Scan Duration and Patient's Body Mass Index in the Optimization of 18FDG Imaging Protocols on a TOF-PET/CT Scanner

2020 ◽  
Author(s):  
Roberta Matheoud ◽  
Naema Al-Maymani ◽  
Alessia Oldani ◽  
Gian Mauro Sacchetti ◽  
Marco Brambilla ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Image Quality ◽  
Body Mass ◽  
Activity Concentration ◽  
Background Activity ◽  
Whole Body ◽  
Internal Diameter ◽  
Ct Scanner ◽  
Pet Ct ◽  
Tof Pet

Abstract BackgroundTime-of-flight (TOF) PET technology determines a reduction in the noise and improves the reconstructed image quality in low counts acquisitions, such as in overweight patients, allowing a reduction of administered activity and/or imaging time. However, international guidelines and recommendations on 18F-fluoro-2-deoxyglucose (FDG) activity administration scheme are old or only partially account for TOF technology and advanced reconstruction modalities. The aim of this study was to optimize FDG whole-body studies on a TOF PET/CT scanner by using a multivariate approach to quantify how physical figures of merit related to image quality change with acquisition/reconstruction/patient-dependent parameters in a phantom experiment. MethodsThe NEMA-IQ phantom was used to evaluate contrast recovery coefficient (CRC), background variability (BV) and contrast-to-noise ratio (CNR) as a function of changing emission scan duration (ESD), activity concentration (AC), target internal diameter (ID), target-background activity ratio (TBR), and body mass index (BMI). The phantom was filled with an average concentration of 5.3 kBq/mL of FDG solution and the spheres with TBR of 21.2, 8.8, and 5.0 in 3 different sessions. Images were acquired at varying background activity concentration from 5.1 to 1.3 kBq/mL and images were reconstructed for ESD of 30-151 seconds per bed position with and without Point Spread Function (PSF) correction. The parameters were all considered in a single analysis using multiple linear regression methods. ResultsAs expected, CRC depended only on sphere ID and on PSF application, while BV depended on sphere ID, ESD, AC and BMI of the phantom, in order of decreasing relevance. Noteworthy, ESD and AC resulted as the most significant predictors of CNR variability with a similar relevance, followed by the weight of the patient and TBR of the lesion. ConclusionsAC and ESD proved to be effective tools in modulating CNR. ESD could be increased rather than AC to improve image quality in overweight/obese patients to fulfil ALARA principles.

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