The emerging role of computerized tomography in assessing cancer cachexia
Bibliographic record
Abstract
PURPOSE OF REVIEW: The present review represents an overview of the potential opportunistic use of computerized tomography (CT) to enhance our understanding of abnormal body composition, specifically lean and adipose tissue changes in cancer cachexia. RECENT FINDINGS: One of the characteristics of cancer cachexia is the depletion of muscle with or without adipose tissue loss. Therefore, a body composition tool that specifically distinguishes between these tissues is essential in assessing this syndrome. Cancer patients are routinely evaluated by high resolution imaging such as CT for the purpose of diagnosis and follow-up. Recent work exploiting CT images for body composition analysis has revealed the natural history of cancer cachexia, including progressive alterations in skeletal muscle, adipose tissue, organs, and tumor mass. CT-based quantification of skeletal muscle has permitted identification of individuals with sarcopenia, and links between sarcopenia and functional status, chemotherapy toxicity, time to tumor progression, and mortality. SUMMARY: CT images routinely acquired from health records of cancer patients can be used to quantify specific lean and adipose tissues, to interpret body composition in population-based studies, and to evaluate individual patients in a clinical and therapeutic decision-making setting.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".