MRI quantitation of abdominal skeletal muscle correlates with CT-based analysis: implications for sarcopenia measurement
Bibliographic record
Abstract
Sarcopenia is associated with poor outcomes in a variety of conditions, including malignancy. Abdominal skeletal muscle area (SMA) segmentation using computed tomography (CT) has been shown to be an accurate surrogate for identifying sarcopenia. While magnetic resonance imaging (MRI) segmentation of SMA has been validated in cadaver limbs, few studies have validated abdominal SMA segmentation using MRI at lumbar level mid-L3. Our objective was to assess the reproducibility and concordance of CT and MRI segmentation analyses of SMA at mid-L3. This retrospective analysis included a random sample of 10 patients with renal cell carcinoma (RCC) and CT abdomen/pelvis, used to assess intra-observer variability of SMA measurements using CT. An additional sample of 9 patients with RCC and both CT and T2-weighted (T2w) MRI abdomen/pelvis was used to assess intra-observer variability of SMA using MRI and concordance of SMA between MRI and CT. SMA was segmented using Slice-O-Matic. SMA reproducibility was assessed using intraclass correlation coefficient (ICC). SMA concordance was analyzed using Bland–Altman plot and Pearson correlation coefficient. The intra-observer variability of CT and MRI SMA at mid-L3 was low, with ICC of 0.998 and 0.985, respectively. Bland–Altman analysis revealed bias of 0.74% for T2w MRI over CT. The Pearson correlation coefficient was 0.997 (p < 0.0001), demonstrating strong correlation between CT and T2w MRI. Abdominal SMA at mid-L3 is reproducibly segmented for both CT and T2w MRI, with strong correlation between the 2 modalities. T2w MRI can be used interchangeably with CT for assessment of SMA and sarcopenia. This finding has important clinical implications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 source (direct Gemma or distilled Codex), 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".