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Health·The Trace·Dual reading·Published 2026-09-01

machine learning-derived CT body composition assessment of sarcopenia and survival outcomes in DLBCL patients receiving immunochemotherapy

Source article: A machine learning-derived sarcopenia index is associated with survival and nonrelapse mortality in DLBCL

Abstract: Abstract Body composition analysis (BCA) provides an objective assessment of metabolic states, but its prognostic value in diffuse large B-cell lymphoma (DLBCL) remains unclear. We applied machine learning-supported BCA to computed tomography imaging from patients with newly diagnosed DLBCL enrolled in the prospective phase 3 PETAL trial to quantify radiologic sarcopenia. We assessed BCA results in relation to survival after first-line immunochemotherapy, treatment-related hematologic toxicities, and molecular d…

TRV-2026-0948Peer-reviewedPermanent record — cite & verify
Trace impact reading

Contested: both sides are scored from claims and sources, not community votes.

P 72The P score combines the specificity and measured human impact of the grounded problem claim with the strength of this Trace’s cited sources.G 74The G score combines the specificity and measured human impact of the grounded gain claim with the strength of this Trace’s cited sources.
A machine learning-derived sarcopenia index is associated with survival and nonrelapse mortality in DLBCL

Short-term decreased physical activity with increased sedentary behaviour causes metabolic derangements and altered body composition - effects in individuals with and without a first-degree relative with type 2 diabetes by Kelly A. Bowden Davies, Victoria S. Sprung, Juliette A. Norman, Andrew Thompson, Katie L. Mitchell, Jason C. G. Halford, Jo A. Harrold, John P. H. Wilding, Graham J. Kemp & Daniel J. Cuthbertson. CC BY 4.0 · https://creativecommons.org/licenses/by/4.0

The quick read

In patients with newly diagnosed diffuse large B-cell lymphoma from the PETAL trial, investigators used machine learning-supported body composition analysis of CT imaging to measure skeletal muscle mass. Those in the lowest tertile had inferior survival after adjustment for established risk factors, with cause-specific analyses pointing to nonrelapse mortality rather than lymphoma-specific death.

The findings matter because they position an automated imaging biomarker as a host-vulnerability indicator that predicts both nonrelapse mortality and hematologic toxicity, and identifies early muscle loss during therapy as a separate risk factor. It remains uncertain how much variability is driven by factors beyond age and disease burden, and whether BCA-guided interventions can improve outcomes.

Main points
  • Study used machine learning-supported BCA on CT imaging from newly diagnosed DLBCL patients enrolled in prospective phase 3 PETAL trial.
  • Patients in lowest tertile of normalized skeletal muscle mass had inferior survival after adjustment for established risk factors.
  • Cause-specific analysis showed sarcopenia predicted nonrelapse mortality, not lymphoma-specific death, and was only independent risk factor for higher-grade hematotoxicity.
  • Longitudinal BCA showed early muscle loss during therapy also linked to inferior survival, distinct from baseline sarcopenia.
Gain

Machine learning-supported body composition analysis applied to CT imaging quantifies radiologic sarcopenia and enables risk stratification for survival after first-line immunochemotherapy in newly diagnosed DLBCL.

Problem

In DLBCL patients treated with first-line immunochemotherapy, CT-measured sarcopenia in the lowest tertile of muscle mass is associated with inferior overall survival driven by nonrelapse mortality and higher risk of hematologic toxicity.

The rundown

Researchers applied machine learning-supported body composition analysis to CT scans from the prospective PETAL phase 3 trial of newly diagnosed DLBCL to quantify normalized skeletal muscle mass and define radiologic sarcopenia.

Baseline sarcopenia and early treatment-emergent muscle loss were found to be uncorrelated and independent of molecular clusters, with only a small fraction of muscle mass variability explained by age and lymphoma burden, and no recurrently mutated gene linked to lower muscle mass.

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