Body composition analysis using computed tomography image in patients with advanced lung cancer
Bibliographic record
Abstract
Background: Despite the importance of muscle wasting in lung cancer, body composition in this disease has been mostly assessed using methods that do not allow differentiation between muscle and other tissues. Aim: To compare body composition of patients with lung cancer (1-year survivors versus 1-year non-survivors) using computed tomography (CT scan) image analysis at diagnosis and at the end of follow-up (at 1 year or at death). Method: 39 patients newly diagnosed with advanced lung cancer who had a thoraco-abdominal CT-scan were recruited and separated in two groups according to whether they were alive at one year or not. Following the collection of clinical data, we quantified muscle, visceral fat and subcutaneous fat areas from a single abdominal cross-sectional image at the level of the third lumbar vertebra. Sarcopenia was assessed using CT-based criteria and defined as having a muscle area ≤ 55 cm²/m² for men and ≤ 39 cm²/m² for women. Results: CT scans from 17 males/22 females with advanced lung cancer and a mean age of 64 ±9 years and a body mass index of 26 ±4 Kg/m² were analysed. The one-year survival rate was 51%. At diagnosis, average muscle, visceral fat and subcutaneous fat areas were 141 ±67 cm², 134 ±96 cm² and 141 ±67 cm² respectively. Overall, 28% of patients were sarcopenic at diagnosis but the prevalence tend to be higher in the non-survivors compared to survivors, 37% vs 20% respectively. During the one-year follow-up, the rate of sarcopenia rised in both groups but stayed higher in the non-survivors, 58% vs 40% respectively. Conclusion: Sarcopenia was prevalent in patients with advanced lung cancer and tend to be higher in those who did not survive at one year.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".