Skeletal muscle density is an independent predictor of diffuse large B‐cell lymphoma outcomes treated with rituximab‐based chemoimmunotherapy
Bibliographic record
Abstract
BACKGROUND: While much cancer research focuses on tumours and their microenvironment, malignancies cause widespread physiologic changes. Cancer and treatment-related sarcopenia, measured with quantitative imaging or as a decrease in overall body mass, are indicative of poor prognosis in elderly diffuse large B-cell lymphoma (DLBCL) patients, skeletal muscle radiodensity (SMD) may be a better prognostic marker. SMD, a measure of muscle radiation attenuation on CT imaging, is more prognostic than sarcopenia or International Prognostic Index (IPI) scores in follicular lymphoma and multiple solid organ malignancies. Low SMD appears to correlate with fat accumulation in muscle and is associated with inflammation. This study set out to examine SMD's prognostic ability in DLBCL. METHODS: All DLBCL patients treated with rituximab-containing therapy between 2004 and 2009 were compared to determine SMD's prognostic ability in this single centre, retrospective study. Pre-treatment CT scans were used to measure SMD and muscle cross-sectional area. Primary endpoints included progression free (PFS) and overall survival (OS) while objective response rates (ORR) were secondary. RESULTS: Of 224 evaluable patients, 116 were identified as having low SMD. Low SMD predicted poorer 5 year PFS, 60 vs. 81% (p = 0.001) and OS, 58 vs. 86% (p < 0.0001). SMD's prognostic ability retained significance in multivariate analysis taking into consideration the Revised International Prognostic Index (R-IPI) and sex. Although high SMD was not predictive of ORR (95.4 vs. 91.4%, p = 0.17), it was strongly associated with radiographic complete response (85 vs. 66%, p = 0.0007). Contrary to previous findings, sarcopenia did not predict for poorer OS but suggested improved OS in elderly DLBCL patients (HR 0.38, p = 0.01). CONCLUSIONS: SMD is a novel prognostic (and potentially treatment predictive) marker independent of R-IPI in DLBCL. It presents an inexpensive yet complementary assessment to R-IPI for prognosticating DLBCL outcomes.
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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 | 0.001 | 0.002 |
| 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".