aceA machine learning-derived sarcopenia index is associated with survival and nonrelapse mortality in DLBCL
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…
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.
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.
Evidence
- Peer-reviewedBlood Advances2026-09-01
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Truvace Impact Record TRV-2026-0948, v1: “A machine learning-derived sarcopenia index is associated with survival and nonrelapse mortality in DLBCL.” Truvace, 2026-09-01. /record/TRV-2026-0948 (accessed at citation time). sha256 fe62a1111fccc048…
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