Deep Learning-Based Body Composition Analysis for Outcome Prediction in Relapsed/Refractory Diffuse Large B-Cell Lymphoma: Insights From the LOTIS-2 Trial.

Abstract

Purpose: The present study aimed to investigate the role of body composition as an independent image-derived biomarker for clinical outcome prediction in a clinical trial cohort of patients with relapsed or refractory (rel/ref) diffuse large B-cell lymphoma (DLBCL) treated with loncastuximab tesirine.

Materials and methods: The imaging cohort consisted of positron emission tomography/computed tomography scans of 140 patients with rel/ref DLBCL treated with loncastuximab tesirine in the LOTIS-2 (ClinicalTrials.gov identifier: NCT03589469) trial. Body composition analysis was conducted using both manual and deep learning-based segmentation of three primary tissue compartments-skeletal muscle (SM), subcutaneous fat (SF), and visceral fat (VF)-at the L3 level from baseline CT scans. From these segmented compartments, body composition ratio indices, including SM*/VF*, SF*/VF*, and SM*/(VF*+SF*), were derived. Pearson's correlation analysis was used to examine the agreement between manual and automated segmentation. Logistic regression analyses were used to assess the association between the derived indices and treatment response. Cox regression analyses were used to determine the effect of body composition indices on time-to-event outcomes. Body composition indices were considered as continuous and binary variables defined by cut points. The Kaplan-Meier method was used to estimate progression-free survival (PFS) and overall survival (OS).

Results: The manual and automated SM*/VF* indices, as dichotomized, were significant predictors in univariable and multivariable logistic models for failure to achieve complete metabolic response. The manual SM*/VF* index as dichotomized was significantly associated with PFS, but not OS, in univariable and multivariable Cox models.

Conclusion: The pretreatment SM*/VF* body composition index shows promise as a biomarker for patients with rel/ref DLBCL undergoing treatment with loncastuximab tesirine. The proposed deep learning-based approach for body composition analysis demonstrated comparable performance to the manual process, presenting a more cost-effective alternative to conventional methods.