Impact of Body Composition on Progression-Free Survival in Patients with Metastatic Breast Cancer Treated with Ribociclib
Bibliographic record
Abstract
Purpose: This study aims to determine whether body composition parameters affect progression-free survival (PFS) in patients with hormone receptor positive and HER-2 negative metastatic breast cancer treated with ribociclib as first-line therapy. Materials and methods: It was designed as a single-center, retrospective study; therefore, its generalizability is limited. At the start of treatment, 18F-FDG PET/CT scans were performed on patients, and subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) volume, SAT and VAT SUV (standardized uptake value) mean, SAT and VAT index, skeletal muscle index (SMI), and skeletal muscle radiodensity (SMD) were calculated at the L3 vertebra level. The albumin-myosteatosis gauge (AMG) was defined as SMD × albumin. Results: The study included 73 participants. Increased SAT and VAT volumes were associated with worse PFS (23.4 vs. 35.5 months, p: 0.015; 25.4 vs. 33.3 months, p: 0.114). However, in the multivariable cox regression analysis for progression free survival (PFS), an increase in SAT volume [HR 4.96; p: 0.038)] and SAT SUV mean [HR 2.99; p: 0.016)] were identified as independent risk factors. Conclusions: It should be noted that in patients treated with ribociclib, increases in SAT volume and SAT SUV mean are independent risk factors for PFS.
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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 | 0.000 | 0.000 |
| 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".