Introduction: Identification of new predictive biomarkers in relapsed/refractory (rel/ref) diffuse large B-cell lymphoma (DLBCL) is essential for individualized treatment selection and optimization of outcomes. Metabolic tumor volume (MTV) is a strong prognostic factor in DLBCL, though, its value in patients treated with loncastuximab tesirine has not yet been explored. We investigated the impact of quantitative PET/CT data on treatment response and survival in patients treated with this compound. Methods: In this post-hoc analysis, we reviewed screening PET/CT images of patients enrolled in the LOTIS-2 trial (NCT03589469). MTV was obtained by adding the metabolic volumes of all individual lesions using the 41% of SUVmax threshold. Total lesion glycolysis (TLG) was calculated as the product of MTV and SUVmean for all individual lesions. SUVmax, MTV and TLG were calculated on screening PET/CT images by a nuclear medicine (NM) radiologist using Hermes Affinity Viewer software. Values calculated by the first reader were independently confirmed by a second NM reader and a deep learning-aided automated method (Kuker R, et al. SNMMI 2022). We tested if these quantitative metrics presented as continuous variables, in the original scale and as log2-transformed data, could predict treatment response, event-free survival (EFS) and overall survival (OS). We also used an outcome-oriented method to determine cutpoints (c) as markers for risk-stratification. Internal validation based on bootstrap was performed to evaluate predictive performance of identified cutpoints. Optimism was calculated using mean of differences between AUC of bootstrap sample and AUC of original dataset. The optimism-corrected AUCs (ocAUC) was considered to take account of model overfitting. The goals of this study were to assess the impact of quantitative PET/CT metrics to predict complete metabolic response (CMR) (primary endpoint) and survival (secondary endpoints) after loncastuximab tesirine treatment. We grouped together patients with no metabolic response (NMR), disease progression (PD), and not evaluable (NE) due to similar survival. Results: At the time of analysis, PET/CT images were available for review in 118 of the 145 patients enrolled. We first investigated the predictive value of PET/CT metrics to determine CMR. Patients achieving CMR exhibited significantly lower values in SUVmax, MTV and TLG (Table 1). We identified high interobserver agreement in MTV values. As continuous variables, log2(SUVmax), log2(MTV) and log2(TLG) were predictive of failure to achieve CMR (1-unit increase odds ratio [OR]= 1.06, 95%CI 1.01-1.10, P=0.008, AUC=0.666; OR=1.52, 95%CI 1.22-1.89, P=0.002, AUC=0.744; and OR=1.49, 95%CI 1.23-1.80, P<.0001, AUC=0.758; respectively). We identified the cutpoints of SUVmax ≥17 (OR=3.94, 95%CI 1.62-9.58, P=0.002, ocAUC= 0.653), MTV ≥43ml (OR=6.82, 95%CI 2.51-18.51, P=0.0002, ocAUC=0.716) and TLG ≥434 (OR=6.84, 95%CI=2.61-17.89, P<.001, ocAUC=0.720) as predictors of failure to achieve CMR. Next, we assessed the association of PET/CT metrics with EFS and OS. Median EFS & OS (months) by treatment response were: CMR= not reached (NR) (95%CI 14.2-NR) & NR (95%CI 16.2-NR); PMR= 3.4 (95%CI 2.8-7.4) & 11.2 (95%CI 7.1-16.4); NMR/PD= 1.4 (95%CI 1.3-2) & 5.8 (95%CI 2.6-6.9); (P<.0001) respectively. Again, log2(MTV) (hazard ratio (HR)=1.28, 95%CI 1.15-1.42, P<.0001 and HR=1.38, 95%CI 1.22-1.55, P<.0001) and log2(TLG) (HR=1.21, 95%CI 1.11-1.32, P<.0001 and HR=1.29, 95%CI 1.17-1.42, P<.0001) predicted shorter EFS and OS, respectively. log2(SUVmax) predicted only shorter OS (HR=1.02, 95%CI 1.00-1.03, P=0.033). We observed SUVmax ≥18 (EFS: HR=1.65, 95%CI 1.06-2.55, P=0.027 & OS: HR=1.7, 95%CI 1.06-2.75, P=0.029), MTV ≥68ml (EFS: HR=3.02, 95%CI 1.94-4.7, P<.0001 & OS: HR=3.26, 95%CI 2.05-5.19, P<.0001) and TLG ≥479 (EFS: HR=2.34, 95%CI 1.52-3.6, P=.0001 & OS: HR=2.46, 95%CI 1.54-3.91, P=.0001) as cutpoints associated with shorter survival. Conclusion: In the present analysis we demonstrated the predictive impact of PET/CT data in rel/ref DLBCL retaining its risk-stratification power as continuous and categorical variables. MTV is an imaging biomarker extracted from routine PET/CT scans and enables individualized treatment selection identifying those patients that will benefit the most from loncastuximab tesirine. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal