Introduction: Diffuse large B‐cell lymphoma (DLBCL) is the most common subtype of non‐Hodgkin lymphoma, and despite advances in frontline therapies such as rituximab, cyclophosphamide, doxorubicin hydrochloride (hydroxydaunorubicin), vincristine sulfate (Oncovin), and prednisone, approximately 30%–40% of patients develop relapsed or refractory (rel/ref) disease. This subgroup has historically faced poor prognoses with limited treatment options, prompting the development of novel immunotherapeutic strategies. Chimeric antigen receptor T‐cell (CAR T) therapy and bispecific antibodies (BsAbs) have emerged as transformative approaches in this setting. Methods: This narrative review compares these therapies across multiple domains, including mechanisms of action, clinical efficacy, safety profiles, logistics, cost, and accessibility. Results: CAR T therapies have demonstrated durable complete response rates (40%–60%) and extended progression‐free survival (median 11–12.5 months), but they are limited by complex manufacturing, high cost, and potentially severe toxicities. In contrast, BsAbs offer immediate, off‐the‐shelf availability, with promising efficacy and a more favorable safety profile that enables outpatient administration, although long‐term durability remains under investigation. Conclusion: This review provides clinicians with a comprehensive comparison to support evidence‐based treatment selection in rel/ref DLBCL.

The tumor microenvironment [TME] is a dynamic ecosystem where spatial organization and metabolic reprogramming play a crucial role in immune response, tumor progression, and therapeutic response. Recent breakthroughs in spatial transcriptomics, metabolomics, and multiplexed imaging studies have shown that complex immunometabolic niches are involved in therapeutic resistance, including conventional and immunotherapeutic approaches. Artificial intelligence [AI] technology has been recognized as a revolutionary concept that allows the integration of complex data, thereby facilitating the scalable extraction of spatial, molecular, and cellular features from routine histopathology and multi-omics platforms. This review of the current evidence on AI-based spatial immunometabolic studies of the tumor microenvironment aims to provide a comprehensive overview of the current evidence, including AI-based spatial immunometabolic studies of the tumor mi-croenvironment, with special reference to digital pathology, spatial transcriptomics, and multimodal data fusion. The current challenges, including data heterogeneity, model interpretability, generalizability, and biological validation, will be discussed. The emerging trends in AI-based spatial immunometabolism, including multimodal foundation models, federated learning, and spatially resolved target discovery, will be discussed. AI-based spatial immunometabolism will be a cornerstone in precision oncology, with the potential to improve patient stratification, therapeutic approaches, and clinical translation.