Rapid Standardized CT-Based Method to Determine Lean Body Mass SUV for PET—A Significant Improvement Over Prediction Equations
Bibliographic record
Abstract
In 18 F-fluorodeoxyglucose ( 18 F-FDG) positron emission tomography (PET) studies, maximum standardized uptake value (SUV max ) is the parameter commonly used to provide a measurement of the metabolic activity of a tumor. SUV normalized by body mass is affected by the proportions of body fat and lean tissue, which present high variability in patients with cancer. SUV corrected by lean body mass (LBM), denoted as SUL, is recommended to provide more accurate, consistent, and reproducible SUV results; however, LBM is frequently estimated rather than measured. Given the increasing importance of a quantitative PET parameter, especially when comparing PET studies over time to evaluate disease response clinically, and its use in oncological clinical trials, we set out to evaluate the commonly used equations originally derived by James (1976) and Janmahasatian et al. (2005) against computerized tomography (CT)-derived measures of LBM. Methods Whole-body 18 F-FDG PET images of 195 adult patients with cancer were analyzed retrospectively. Representative liver SUV mean was normalized by total body mass. SUL was calculated using a quantitative determination of LBM based on the CT component of the PET/CT study (LBM CT ) and compared against the equation-estimated SUL. Bland and Altman plots were generated for SUV-SUL differences. Results This consecutive sample of patients undergoing usual care (men, n = 96; women, n = 99) varied in body mass (38–127 kg) and in Body Mass Index (BMI) (14.7–47.2 kg/m2). LBM CT weakly correlated with body mass (men, r 2 = 0.32; women, r 2 = 0.22), and thus SUV and SUL CT were also weakly correlated (men, r 2 = 0.24; women, r 2 = 0.11). Equations proved inadequate for the assessment of LBM. LBM estimated by James’ equation showed a mean bias (overestimation of LBM compared with LBM CT ) in men (+6.13 kg; 95% CI 4.61–7.65) and in women (+6.32 kg; 95% CI 5.26–7.39). Janmahasatian’s equation provided similarly poor performance. Conclusions CT-based LBM determinations incorporate the patient’s current body composition at the time of a PET/CT study, and the information garnered can provide care teams with information with which to more accurately determine FDG uptake values, allowing comparability over multiple scans and treatment courses and will provide a robust basis for the use of PET Response Criteria in Solid Tumors (PERCIST) in clinical trials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".