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.

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

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.

SUVmax of FDG PET-CT Can Be Used As An Identifier of Lesion for AI To Interpret Radiology Reports

2020 ◽  
Author(s):  
Kenji Hirata ◽  
Osamu Manabe ◽  
Keiichi Magota ◽  
Sho Furuya ◽  
Tohru Shiga ◽  
...  
Keyword(s):  
Fdg Pet ◽  
Standardized Uptake Value ◽  
Local Maximum ◽  
Ct Images ◽  
Whole Body ◽  
Training Dataset ◽  
Imaging Biomarkers ◽  
Radiology Reports ◽  
Pet Ct ◽  
Fdg Pet Ct

Abstract Background Radiology reports contribute not only to the particular patient, but also to constructing massive training dataset in the era of artificial intelligence (AI). The maximum standardized uptake value (SUVmax) is often described in daily radiology reports of FDG PET-CT. If SUVmax can be used as an identifier of lesion, that would greatly help AI interpret radiology reports. We aimed to clarify whether the lesion can be localized using SUVmax written in radiology reports.Methods The institutional review board approved this retrospective study. We investigated a total of 112 lesions from 30 FDG PET-CT images acquired with 3 different scanners. SUVmax was calculated from DICOM files based on the latest Quantitative Imaging Biomarkers Alliance (QIBA) publication. The voxels showing the given SUVmax were exhaustively searched in the whole-body images and counted. SUVmax was provided with 5 different degrees of precision: integer (e.g., 3), 1st decimal places (DP) (3.1), 2nd DP (3.14), 3rd DP (3.142), and 4th DP (3.1416). For instance, when SUVmax=3.14 was given, the voxels with 3.135≤SUVmax<3.145 were extracted. We also evaluated whether local maximum restriction could improve the identifying performance, where only the voxels showing the highest intensity within some neighborhood were considered. We defined that “identical detection” was achieved when only single voxel satisfied the criterion.Results A total of 112 lesions from 30 FDG PET-CT images were investigated. SUVmax ranged from 1.3 to 49.1 (median = 5.6, IQR = 5.2). Generally, when larger and more precise SUVmax values were given, fewer voxels satisfied the criterion. The local maximum restriction was very effective. When SUVmax was determined to 4 decimal places (e.g., 3.1416) and the local maximum restriction was applied, identical detection was achieved in 33.3% (lesions with SUVmax<2), 79.5% (2≤SUVmax<5), and 97.8% (5≤SUVmax) of lesions.Conclusions SUVmax of FDG PET-CT can be used as an identifier to localize the lesion if precise SUVmax is provided and local maximum restriction was applied, although the lesions showing SUVmax<2 were difficult to identify. The proposed method may have potential to make use of radiology reports retrospectively for constructing training datasets for AI.

scholarly journals The impact of Hayward green kiwifruit on dietary protein digestion and protein metabolism

2020 ◽  
Author(s):  
Sanghee Park ◽  
David D. Church ◽  
Carlene Starck ◽  
Scott E. Schutzler ◽  
Gohar Azhar ◽  
...  
Keyword(s):  
Amino Acids ◽  
Dietary Protein ◽  
Protein Metabolism ◽  
Plasma Concentrations ◽  
Whole Body ◽  
Protein Digestion ◽  
Body Protein ◽  
Before And After ◽  
The Impact ◽  
Green Kiwifruit

Abstract Purpose The purpose of the study was to determine if an actinidin protease aids gastric digestion and the protein anabolic response to dietary protein. Methods Hayward green kiwifruit (containing an actinidin protease) and Hort 16A gold kiwifruit (devoid of actinidin protease) were given in conjunction with a beef meal to healthy older subjects. Twelve healthy older males (N = 6) and females (N = 6) were studied with a randomized, double-blinded, crossover design to assess muscle and whole-body protein metabolism before and after ingestion of kiwifruit and 100 g of ground beef. Subjects consumed 2 of each variety of kiwifruit daily for 14 d prior to each metabolic study, and again during each study with beef intake. Results Hayward green kiwifruit consumption with beef resulted in a more rapid increase in peripheral plasma essential amino acid concentrations. There were significant time by kiwifruit intake interactions for plasma concentrations of EAAs, branched chain amino acids (BCAAs), and leucine (P < 0.01). However, there was no difference in the total amount of EAAs absorbed. As a result, there were no differences between kiwifruit in any of the measured parameters of protein kinetics. Conclusion Consumption of Hayward green kiwifruit, with a beef meal facilitates protein digestion and absorption of the constituent amino acids as compared to Hort 16A gold kiwifruit. Clinical trial NCT04356573, April 21, 2020 “retrospectively registered”.

Etiology of Solitary Extrapulmonary Positron Emission Tomography and Computed Tomography Findings in Patients With Lung Cancer

2005 ◽  
Vol 23 (28) ◽  
pp. 6846-6853 ◽  
Author(s):  
Didier Lardinois ◽  
Walter Weder ◽  
Marina Roudas ◽  
Gustav K. von Schulthess ◽  
Michaela Tutic ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Positron Emission Tomography ◽  
Ct Imaging ◽  
Emission Tomography ◽  
Whole Body ◽  
Benign Tumors ◽  
Positron Emission ◽  
Pet Ct ◽  
The Impact

Purpose The aim of this prospective study was to assess the incidence and the nature of solitary extrapulmonary [18F] fluorodeoxyglucose (FDG) accumulations in patients with non–small-cell lung cancer (NSCLC) staged with integrated positron emission tomography and computed tomography (PET/CT) and to evaluate the impact on management. Patients and Methods A total of 350 patients with NSCLC underwent whole-body PET/CT imaging. All solitary extrapulmonary FDG accumulations were evaluated by histopathology, further imaging, or clinical follow-up. Results PET/CT imaging revealed extrapulmonary lesions in 110 patients. In 72 patients (21%), solitary lesions were present. A diagnosis was obtained in 69 of these patients, including 37 (54%) with solitary metastases and 32 (46%) with lesions unrelated to the lung primary. Histopathologic examinations of these 32 lesions revealed a second clinically unsuspected malignancy or a recurrence of a previous diagnosed carcinoma in six patients (19%) and a benign tumor or inflammatory lesion in 26 patients (81%). The six malignancies consisted of carcinoma of the breast in two patients, and carcinoma of the orbit, esophagus, prostate, and non-Hodgkin's lymphoma in one patient each. Benign tumors and inflammatory lesions included eight colon adenomas, four Warthin's tumors, one granuloma of the lower jaw, one adenoma of the thyroid gland, one compensatory muscle activity due to vocal chord palsy, two occurrences of arthritis, three occurrences of reflux esophagitis, two occurrences of pancreatitis, two occurrences of diverticulitis, one hemorrhoidal inflammation, and one rib fracture. Conclusion Solitary extrapulmonary FDG accumulations in patients with newly diagnosed lung cancer should be analyzed critically for correct staging and optimal therapy, given that up to half of the lesions may represent unrelated malignancies or benign disease.

scholarly journals Body region localization in whole-body low-dose CT images of PET/CT scans using virtual landmarks

2019 ◽  
Vol 46 (3) ◽  
pp. 1286-1299 ◽  
Author(s):  
Peirui Bai ◽  
Jayaram K. Udupa ◽  
Yubing Tong ◽  
ShiPeng Xie ◽  
Drew A. Torigian
Keyword(s):  
Low Dose ◽  
Ct Images ◽  
Whole Body ◽  
Ct Scans ◽  
Body Region ◽  
Low Dose Ct ◽  
Pet Ct

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 A Preliminary Study to Use SUVmax of FDG PET-CT as an Identifier of Lesion for Artificial Intelligence

2021 ◽  
Vol 8 ◽  
Author(s):  
Kenji Hirata ◽  
Osamu Manabe ◽  
Keiichi Magota ◽  
Sho Furuya ◽  
Tohru Shiga ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Fdg Pet ◽  
Standardized Uptake Value ◽  
Local Maximum ◽  
Ct Images ◽  
Whole Body ◽  
Imaging Biomarkers ◽  
Pet Ct ◽  
Preliminary Study ◽  
Fdg Pet Ct

Background: Diagnostic reports contribute not only to the particular patient, but also to constructing massive training dataset in the era of artificial intelligence (AI). The maximum standardized uptake value (SUVmax) is often described in daily diagnostic reports of [18F] fluorodeoxyglucose (FDG) positron emission tomography (PET) – computed tomography (CT). If SUVmax can be used as an identifier of lesion, that would greatly help AI interpret diagnostic reports. We aimed to clarify whether the lesion can be localized using SUVmax strings.Methods: The institutional review board approved this retrospective study. We investigated a total of 112 lesions from 30 FDG PET-CT images acquired with 3 different scanners. SUVmax was calculated from DICOM files based on the latest Quantitative Imaging Biomarkers Alliance (QIBA) publication. The voxels showing the given SUVmax were exhaustively searched in the whole-body images and counted. SUVmax was provided with 5 different degrees of precision: integer (e.g., 3), 1st decimal places (DP) (3.1), 2nd DP (3.14), 3rd DP (3.142), and 4th DP (3.1416). For instance, when SUVmax = 3.14 was given, the voxels with 3.135 ≤ SUVmax &lt; 3.145 were extracted. We also evaluated whether local maximum restriction could improve the identifying performance, where only the voxels showing the highest intensity within some neighborhood were considered. We defined that “identical detection” was achieved when only single voxel satisfied the criterion.Results: A total of 112 lesions from 30 FDG PET-CT images were investigated. SUVmax ranged from 1.3 to 49.1 (median = 5.6). Generally, when larger and more precise SUVmax values were given, fewer voxels satisfied the criterion. The local maximum restriction was very effective. When SUVmax was determined to 4 decimal places (e.g., 3.1416) and the local maximum restriction was applied, identical detection was achieved in 33.3% (lesions with SUVmax &lt; 2), 79.5% (2 ≤ SUVmax &lt; 5), and 97.8% (5 ≤ SUVmax) of lesions.Conclusion: In this preliminary study, SUVmax of FDG PET-CT could be used as an identifier to localize the lesion if precise SUVmax is provided and local maximum restriction was applied, although the lesions showing SUVmax &lt; 2 were difficult to identify. The proposed method may have potential to make use of diagnostic reports retrospectively for constructing training datasets for AI.

Sign in / Sign up

Export Citation Format

Share Document