Can body composition (BC) be predictive for outcomes and severe toxicities (ST) in metastatic solid tumors patients (pts) treated with checkpoint inhibitor (CPI)? An analysis of 145 patients.
Bibliographic record
Abstract
3069 Background: BC parameters have previously been associated with treatments toxicities, and worst outcomes in metastatic solid tumors pts. We studied association between BC parameters and their changes, with ST and outcomes in pts receiving CPI. Methods: Pts consecutively treated with CPI between December 2013 and December 2016 in our institute (Institut Bergonié, Bordeaux, France) for metastatic solid tumor and with a baseline computed tomography (CT0) scan <28 days before CPI beginning were included. BC parameters were assessed with Slice-O-Matic software V4.3 (Tomovision, Montreal, Canada), using third lumbar vertebra as standard landmark, normalized for height (cm2/m2). Results: 145 pts were included (73 female, median age: 62). 124 had a CT scan after 2 months of CPI (CT2). Tumor type was non small cell lung cancer in 80 (55%) pts. 68 pts had received >1 line of chemotherapy before. CPI treatment was: anti PD-1, anti PD-L1, anti PD-L1/CTLA-4 combination in 113, 13, and 19 pts, respectively. ST included: Grade III-V toxicity according to NCI-CTC v4.0, unscheduled hospitalization, definitive CPI treatment discontinuation. 15 pts (10.3%) had ST. None of the baseline clinical, nutritional, and BC parameters was associated with ST. In multivariate analysis, subcutaneous adipose tissue index (SATI) decrease >-10% between CT0 and CT2 was significantly associated with occurrence of ST (OR=5.3, p=0.027). Median Overall survival (OS) was 402 days. Median progression free survival (PFS) was 86 days. In multivariate analysis, 3 BC-related parameters were significantly associated with worse OS: body mass index < 25 (HR=2.375, p=0.030), Skeletal muscle index (SMI) decrease >-10% (HR=4.603, p=0.007), and visceral adipose tissue index (VATI) decrease >-10% (HR=8.470, p=0.030). Only SMI decrease >-10% was predictive of PFS (HR=3.643, p=0.001). Conclusions: Our results demonstrates that body composition is associated with clinical outcomes of cancer patients treated with CPI. Early decrease of SATI is predictive of ST whereas early decrease of skeletal muscle index and of VATI are associated with worse OS.
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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".