Influence of Contrast Administration on Computed Tomography–Based Analysis of Visceral Adipose and Skeletal Muscle Tissue in Clear Cell Renal Cell Carcinoma
Bibliographic record
Abstract
Abstract Background Computed tomography (CT) scans are being utilized to examine the influence of skeletal muscle and visceral adipose quantity and quality on health‐related outcomes in clinical populations. However, little is known about the influence of contrast administration on these parameters. Methods Precontrast, arterial, and 3‐minute postcontrast CT images of 45 patients with clear cell renal cell carcinoma were downloaded from The Cancer Imaging Archive and retrospectively analyzed for visceral adipose cross‐sectional area (CSA) and density, and muscle CSA and density at the third lumbar vertebrae. Low muscle CSA index was defined as ≤38.9 cm 2 /m 2 for women and ≤55.4 cm 2 /m 2 for men. Low muscle density was defined as <41 Hounsfield units (HU) for body mass index (BMI) <24.9 kg/m 2 and <33 HU for BMI ≥25.0 kg/m 2 . Results In both the arterial and 3‐minute phases, contrast administration decreased visceral adipose CSA (−20.9 and −20.9 cm 2 ; P < .001) and increased visceral adipose density (4.8 and 5.8 HU; P < .001), relative to precontrast images. Muscle CSA index marginally increased in the arterial (0.6 cm 2 /m 2 ; P = .007) and 3‐minute phases (0.8 cm 2 /m 2 ; P < .001). This likely represents clinically insignificant changes because it does not alter the identification of low muscle CSA (44.4% vs 42.2%; P = 1.00). Skeletal muscle density increased in the arterial (6.4 HU; P < .001) and 3‐minute phases (8.7 HU; P < .001), which altered the identification of low muscle density (6.7% vs 31.1%; P < .001). Conclusions Future analyses should consider the phase of contrast during CT imaging because it may alter the interpretations of several parameters.
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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 | 0.000 | 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) | 0.000 | 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".