Feasibility analysis of metabolic parameters based on baseline 18F-FDG PET/CT to predict heterogeneity and recurrence of diffuse large B-cell lymphoma

Objective This study aimed to evaluate the predictive value of intra-tumoral F-18-FDG metabolic heterogeneity in patients with diffuse large B cell lymphoma (DLBCL) in terms of survival. Methods We retrospectively included 245 patients with DLBCL who underwent F-18-FDG PET/CT prior to treatment and analyzed using total metabolic tumor volume (TMTV) and total lesion glycolysis (TLG) as metabolic volume parameters. The linear regression slopes of TMTV and TLG were calculated according to different percentages of SUV thresholds (i.e., 40%, 50%, 60%, 70%, and 80%), respectively, defined as Heterogeneity Factor-1 (HF1) and Heterogeneity Factor-2 (HF2). These indices of heterogeneity were used to predict progression-free survival (PFS). Based on the results of the Cox proportional hazards model, we constructed a multi-parameter prediction model and evaluated the model in the training and validation cohorts by calibration curve, consistency index (C-index) and decision curve analysis (DCA). Results Clinicopathological and PET/CT data from 245 patients were reviewed. 153 patients (62.4%) experienced relapse after treatment. Comparing relapsed and non-relapse patients, all F-18-FDG PET/CT parameters and heterogeneity index showed significant differences. There were significant differences in survival risk stratification according to HF1 and HF2 cut-off classifications (P < 0.0001). In multivariate Cox regression analysis, SUVmax (P < 0.0001), TLG (P < 0.0001), HF1 (P = 0.004), and HF2 (P < 0.0001) showed significant results. Among the clinicopathological parameters, IPI (P = 0.027) and Size (P < 0.0001) were selected as important parameters. Conclusions HF1 and HF2 obtained by the linear regression slope of MTV and TLG may be a novel and useful prognostic marker in DLBCL, which can achieve survival-risk stratification of patients. In addition, multiparametric models have the potential to effectively predict the risk of recurrence in patients.