A Novel Use for Pelvic MRI Scans to Estimate Lean Body Mass and Screen for Sarcopenia in Patients With Rectal Cancer
Bibliographic record
Abstract
BACKGROUND: Sarcopenia, the combination of low lean body mass and decreased muscle strength, is associated with significant morbidity and mortality among patients with colorectal cancer. Standard methods for assessing lean body mass and muscle strength, such as bioelectric impedance analysis and handgrip dynamometry, are rarely obtained clinically. Per National Cancer Center Network recommendations, pelvic MRI is routinely collected for staging and surveillance among patients with rectal cancer. However, there are no data assessing the relationship of pelvic MRI lean body mass measurements at the fifth lumbar vertebrae with bioelectric impedance analysis, handgrip strength, or abdominal CT in patients with rectal cancer. Therefore, we aimed to assess whether pelvic MRI lean body mass correlates with a standard for lean body mass measurement (bioelectric impedance analysis), muscle function (handgrip strength), and an imaging modality frequently used in the literature to identify sarcopenia (abdominal CT at the third lumbar vertebrae). IMPACT OF INNOVATION: Lean body mass measurements from routinely collected pelvic MRI at the fifth lumbar vertebrae accurately and reproducibly estimate lean body mass and modestly correlate with handgrip strength. Rectal cancer pelvic MRI may be repurposed for identifying sarcopenia without increasing inconvenience, ionizing radiation exposure, or expenditure to patients with rectal cancer. TECHNOLOGY, MATERIALS, AND METHODS: Patients with locally advanced rectal cancer with pretreatment bioelectric impedance analysis and handgrip strength measurements within 3 months of their staging pelvic MRI were eligible. Axial skeletal muscle areas were segmented using T1-weighted series pelvic MRI at the fifth lumbar vertebrae and abdominal CT at the third lumbar vertebrae using Slice-O-Matic (Tomovision, Montreal, Canada). Lean body mass (kilograms) was derived from skeletal muscle area with standard equations. Handgrip strength (kilograms) was the maximum of 3 dominant hand attempts in the standing anatomical position. The primary outcome was the agreement between lean body mass measured by pelvic MRI (at the fifth lumbar vertebrae) and bioelectric impedance analysis. Secondary outcomes included the concordance of pelvic MRI lean body mass (at the fifth lumbar vertebrae) with abdominal CT (at the third lumbar vertebrae) and handgrip strength. Additionally, the intra- and interobserver validity, internal consistency, and the mean difference (bias) between lean body mass measurements by pelvic MRI and bioelectric impedance analysis were evaluated. PRELIMINARY RESULTS: Sixteen patients were eligible. The average lean body mass was similar and consistent across 2 observers between bioelectric impedance analysis and pelvic MRI. There was a strong correlation between lean body mass measured on pelvic MRI, bioelectric impedance analysis, and abdominal CT. The reliability of 2 pelvic MRI lean body mass measurements (2 weeks apart by blinded observers) and the correlation of lean body mass between pelvic MRI and bioelectric impedance analysis was strong. Inter- and intraobserver correlation, reliability, and internal consistency were strong for the entire cohort. There was a moderate correlation between pelvic MRI lean body mass and handgrip strength. CONCLUSIONS: Lean body mass measured at the fifth lumbar vertebrae on pelvic MRI is reproducible and correlates strongly with measurements from bioelectric impedance analysis (standard) and abdominal CT at the third lumbar vertebrae and modestly with handgrip strength. These data suggest that MRI lean body mass measurements may be a method to screen patients with rectal cancer for sarcopenia. FUTURE DIRECTIONS: Future studies may evaluate changes in lean body mass on serial pelvic MRI studies among patients with rectal cancer.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".