Prognostic factors and nomogram development for adult diffuse large B-cell lymphoma: a single-center retrospective cohort study

BACKGROUND: Diffuse large B-cell lymphoma (DLBCL) exhibits substantial prognostic heterogeneity despite standardized treatment approaches. Integrating clinical, metabolic, and molecular biomarkers into a unified prediction model may enhance risk stratification beyond conventional indices. OBJECTIVE: To identify independent prognostic factors and develop an interanlly validated nomogram for predicting mortality in adult DLBCL patients. METHODS: This retrospective cohort study enrolled 150 consecutive adult DLBCL patients diagnosed between January 2016 and December 2022 at a Cancer Hospital Affiliated to Shanxi Medical University. Primary endpoint was all-cause mortality assessed through August 2025. Clinical characteristics, serum biomarkers (lactate dehydrogenase [LDH], β2-microglobulin [β2-MG]), ¹⁸F-FDG PET/CT metabolic parameters (SUVmax, metabolic tumor volume [MTV], total lesion glycolysis [TLG]), and immunohistochemical markers (Bcl-2, Bcl-6, C-MYC) were analyzed. Notably, all six prognostic variables represent standard-of-care assessments routinely performed in contemporary hematology practice. Multivariable logistic regression with supplementary Cox proportional hazards regression identified independent predictors, which were integrated into a nomogram. Model performance was evaluated using discrimination, calibration metrics, and decision curve analysis. RESULTS: After median follow-up of 54 months, 61 patients (40.7%) died. Among deaths, 54 (88.5%) were attributable to disease progression, 4 (6.6%) to treatment-related complications, and 3 (4.9%) to unrelated causes. Multivariable analysis identified six independent prognostic factors: bone marrow invasion (OR = 3.54; 95%CI:1.34–9.36), elevated LDH (OR = 3.13; 95%CI:1.20–8.15), elevated β2-MG (OR = 3.86; 95%CI:1.15–12.92), high total MTV (OR = 1.22 per 10 mL increase; 95%CI:1.02–1.46), Bcl-2 positivity (OR = 11.45; 95%CI:3.98–32.93), and C-MYC positivity (OR = 8.94; 95%CI:3.47–23.03). The integrated nomogram demonstrated excellent discrimination (AUC = 0.902; 95%CI:0.855–0.949) with 72.1% sensitivity and 92.1% specificity. Cox regression confirmed concordant findings (C-index = 0.847; 95%CI:0.795–0.899). The Brier score was 0.128, with calibration slope of 0.94 and intercept of 0.02. Calibration analysis confirmed strong agreement between predicted and observed outcomes (Hosmer-Lemeshow χ²=10.14, P = 0.255). Decision curve analysis demonstrated superior net benefit compared with IPI and NCCN-IPI across clinically relevant threshold probabilities. CONCLUSIONS: Integration of clinical, metabolic, and molecular biomarkers yields superior prognostic stratification in DLBCL. This internally validated nomogram, composed entirely of routinely available clinical assessments and developed in a real-world patient population, provides a hypothesis-generating tool for identifying high-risk patients that warrants external validation before clinical implementation.