A critical evaluation of body composition modalities used to assess adipose and skeletal muscle tissue in cancer
Bibliographic record
Abstract
The majority of cancer patients experience some form of body composition change during the disease trajectory. For example, breast cancer patients undergoing chemotherapy and prostate cancer patients undergoing androgen deprivation therapy gain fat and lose skeletal muscle, which are associated with increased risk of cancer recurrence and clinical comorbidities. In contrast, advanced cancer patients, such as lung and colorectal cancer patients, experience symptoms of cancer cachexia (accelerated loss of skeletal muscle with or without adipose tissue loss), which are associated with decreased treatment response and poorer survival rates in advanced cancers. The heterogeneity of body composition features and their diverse implications across different cancer populations supports the need for accurate quantification of muscle and adipose tissue. Use of appropriate body composition modalities will facilitate an understanding of the complex relationship between body composition characteristics and clinical outcomes. This will ultimately support the development and evaluation of future therapeutic interventions that aim to counter muscle loss and fat gain in cancer populations. Despite the various metabolic complications that may confound the accurate body composition measurement in cancer patients (i.e., dehydration may confound lean tissue measurement), there are no guidelines for selecting the most appropriate modalities to make these measurements. In this review we outline specific considerations for choosing the most optimal approaches of lean and adipose tissue measurements among different cancer populations. Anthropometric measures, bioelectrical impedance analysis, air displacement plethysmography, dual-energy X-ray absorptiometry, computed tomography, and magnetic resonance imaging will be discussed.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".