<p>Extranodal involvement (ENI) is incorporated into routine risk assessment for diffuse large B-cell lymphoma (DLBCL), but treating ENI as a single binary adverse feature may conceal clinically relevant heterogeneity in extranodal burden, anatomical distribution, treatment feasibility, and immune-molecular context. We evaluated ENI as a burden- and site-aware phenotype rather than as a uniform descriptor. We retrospectively studied 710 adults with newly diagnosed DLBCL treated with first-line immunochemotherapy between June 2011 and December 2024. Outcomes were analyzed according to ENI status, number of extranodal sites, and involved organs. Targeted sequencing was available in 176 tumors, and RNA sequencing was performed in 107 quality-controlled tumor samples. Clinical models were interpreted as adjustment models because ENI burden overlaps with established risk factors; site-specific and molecular analyses were prespecified as exploratory and were interpreted with attention to available-case denominators, treatment heterogeneity, sparse subgroups, and multiplicity. ENI was associated with inferior overall survival (OS) and progression-free survival (PFS) in unadjusted analyses. Multisite ENI was enriched for adverse baseline features, including advanced Ann Arbor stage, elevated lactate dehydrogenase (LDH), higher International Prognostic Index (IPI), and impaired performance status. After multivariable adjustment, the independent prognostic contribution of ENI burden was attenuated, indicating substantial clinical overlap with systemic disease burden and host fitness. Several anatomical sites showed candidate adverse signals, but rare-site estimates were limited by small subgroup sizes, wide confidence intervals, and multiple testing. Targeted sequencing and RNA-seq suggested heterogeneous genomic and immune-transcriptional patterns across ENI categories; these results should be regarded as hypothesis-generating because of tissue availability, tumor-only sequencing in many cases, modest molecular sample sizes, and lack of orthogonal immune validation. ENI in DLBCL is best interpreted as a clinically heterogeneous disease descriptor rather than a single uniform prognostic category. ENI burden and anatomical distribution provide useful descriptive and prognostic context, but they should be interpreted alongside established clinical risk, treatment intensity, censoring patterns, tissue-sampling constraints, and molecular ascertainment limitations. Exploratory genomic and immune-transcriptional correlates identified in this study require prospective multicenter validation with harmonized staging, treatment-intensity annotation, matched-normal sequencing, and spatial or cellular immune profiling before ENI-informed biological or therapeutic stratification can be applied clinically.</p> Graphical abstract <p></p> <p>Study design, ENI boundary definitions, statistical safeguards, and multi-omics integration</p>