Quantification of Muscle Wasting in CKD by Texture Analysis on 1H-Magnetic Resonance Images
Bibliographic record
Abstract
Background: Functionally significant muscle wasting is prevalent in chronic kidney disease (CKD). Currently, muscle quality assessment requires biopsy and microscopy. 1H-Magnetic Resonance Imaging (MRI) is non-invasive and can be used to assess changes in skeletal muscle composition. This study aims to utilize 1H-MRI to establish a quantitative metric for muscle heterogeneity, a potential biomarker of muscle quality and composition in patients with CKD, both requiring hemodialysis (HD) and earlier stages. Methods: 1H T1-weighted axial images (3 Tesla) of the calf were acquired on 43 CKD, 34 HD, and 8 with cardiorenal syndrome (CR). Gastrocnemius and soleus muscles were delineated and the heterogeneity quantification algorithm was applied. The magnitude of pixel intensity gradation between pixel-pair combinations was computed, resulting in a value, zeta, to represent the mean heterogeneity. A one-way ANOVA was performed for significance in heterogeneity between the three cohorts. Combining all participants (n=85), Pearson correlations was completed for blood markers of kidney function in relation to muscle heterogeneity. Results: Muscle heterogeneity of HD and CR were comparable but significantly more heterogeneous relative to CKD (Figure 1). Negative correlations were seen with albumin and 1,25 Vitamin D with relation to muscle heterogeneity (Figure 2A, B). A positive association was seen with PTH with respect to muscle heterogeneity (Figure 2C). Conclusion: Muscle heterogeneity of HD and CR may be indicative of fibrosis and wasting that is more pronounced than progressive CKD not on dialysis. Relationship between blood markers of kidney function and muscle heterogeneity suggest texture analysis to be a useful tool for non-invasive evaluation of kidney disease on skeletal muscle structure and function. Funding: Government Support – Non-U.S.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".