Body composition and mortality in men receiving prostate radiotherapy: A pooled analysis of NRG/RTOG 9406 and NRG/RTOG 0126
Bibliographic record
Abstract
PURPOSE: To validate the association between body composition and mortality in men treated with radiation for localized prostate cancer (PCa). Secondarily, to integrate body composition as a factor to classify patients by risk of all-cause mortality. MATERIALS AND METHODS: Participants of NRG/Radiation Therapy Oncology Group (RTOG) 9406 and NRG/RTOG 0126 with archived computed tomography were included. Muscle mass and muscle density were estimated by measuring the area and attenuation of the psoas muscles on a single slice at L4-L5. Bone density was estimated by measuring the attenuation of the vertebral body at mid-L5. Survival analyses, including Cox proportional hazards models, assessed the relationship between body composition and mortality. Recursive partitioning analysis (RPA) was used to create a classification tree to classify participants by risk of death. RESULTS: Data from 2066 men were included in this study. In the final multivariable model, psoas area, comorbidity score, baseline prostate serum antigen, and age were significantly associated with survival. The RPA yielded a classification tree with four prognostic groups determined by age, comorbidity, and psoas area. Notably, the classification among older (≥70 years) men into prognostic groups was determined by psoas area. CONCLUSIONS: This study strongly supports that body composition is related to mortality in men with localized PCa. The inclusion of psoas area in the RPA classification tree suggests that body composition provides additive information to age and comorbidity status for mortality prediction, particularly among older men. More research is needed to determine the clinical impact of body composition on prognostic models in men with PCa.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".