New Approaches in Characterization of Lesions Dissemination in DLBCL Patients on Baseline PET/CT

Simple Summary Recently, a new PET parameter expressing lymphoma dissemination has been proposed to identify high-risk DLBCL patients: the distance between the two furthest lesions, standardized by body surface area (SDmax). This study aimed to determine the best way to measure the distance between lesions, by comparing different methods of distance measurements. We obtained similar results in terms of prediction of outcome between the different methods further validating the relevance of the dissemination features. We highlighted the possibility to calculate it directly from lymphoma voxels instead of lesion centroids, and thus applied it to a metabolic tumor volume (MTV) determined by deep learning algorithms. This could allow the use in clinical practice of this parameter, characterizing tumor spread, in combination with the tumor burden, for patient risk stratification. Dissemination, expressed recently by the largest Euclidian distance between lymphoma sites (SDmax), appeared a promising risk factor in DLBCL patients. We investigated alternative distance metrics to characterize the robustness of the dissemination information. In 290 patients from the REMARC trial (NCT01122472), the Euclidean (Euc), Manhattan (Man), and Tchebychev (Tch) distances between the furthest lesions, firstly based on the centroid of each lesion and then directly from the two most distant tumor voxels and the Travelling Salesman Problem distance (TSP) were calculated. For PFS, the areas under the ROC curves were between 0.63 and 0.64, and between 0.62 and 0.65 for OS. Patients with high SDmax whatever the method of calculation or high SD_TSP had a significantly poorer outcome than patients with low SDmax or SD_TSP (p < 0.001 for both PFS and OS), with significance maintained in Ann Arbor advanced-stage patients. In multivariate analysis with total metabolic tumor volume and ECOG, each distance feature had an independent prognostic value for PFS. For OS, only SDmax_Tch, SDmax_Euc _Vox, and SDmax_Man _Vox reached significance. The spread of DLBCL lesions measured by the largest distance between lymphoma sites is a strong independent prognostic factor and could be measured directly from tumor voxels, allowing its development in the area of the deep learning segmentation methods.