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The prognostic impact of the presence of Epstein–Barr virus (EBV) in classical Hodgkin lymphoma (cHL) is controversial. Previous studies reported heterogeneous results, rendering difficult the clinical validation of EBV as a prognostic biomarker in this lymphoma. The objective of this study was to evaluate the survival impact of the expression of EBV Latent-Membrane Protein 1 (EBV-LMP1) in tumoral Hodgkin–Reed–Sternberg (HRS) cells of primary diagnostic samples of cHL. Formalin-Fixed Paraffin-Embedded (FFPE) lymph node samples from 88 patients with cHL were analyzed. Patients were treated with the standard first-line chemotherapy (CT) with Adriamycin, Bleomycin, Vinblastine and Dacarbazine (ABVD) followed by radiotherapy. The Kaplan–Meier method and the Cox proportional hazards model were used for carrying out the survival analysis. In order to investigate whether the influence of EBV was age-dependent, analyses were performed both for patients of all ages and for age-stratified subgroups. In bivariate analysis, the expression of EBV was associated with older age (p = 0.011), mixed cellularity subtype cHL (p < 0.001) and high risk International Prognostic Score (IPS) (p = 0.023). Overall survival (OS) and progression-free survival (PFS) were associated with the presence of bulky disease (p = 0.009) and advanced disease at diagnosis (p = 0.016). EBV-positive cases did not present a significantly lower OS and PFS in comparison with EBV-negative cases, for all ages and when stratifying for age. When adjusted for covariates, absence of bulky disease at diagnosis (HR: 0.102, 95% CI: 0.02–0.48, p = 0.004) and limited disease stages (I–II) (HR: 0.074, 95% CI: 0.01–0.47, p = 0.006) were associated with a significant better OS. For PFS, limited-disease stages also retained prognostic impact in the multivariate Cox regression (HR: 0.145, 95% CI: 0.04–0.57, p = 0.006). These results are of importance as the early identification of prognostic biomarkers in cHL is critical for guiding and personalizing therapeutic decisions. The prognostic role of EBV in cHL could be modulated by the type of CT protocol employed and interact with the rest of presenting features.

Genomic analysis and digitalization of medical records have led to a big data scenario within hematopathology. Artificial intelligence and machine learning tools are increasingly used to integrate clinical, histopathological, and genomic data in lymphoid neoplasms. In this study, we identified global trends, cognitive, and social framework of this field from 1990 to 2020. Metadata were obtained from the Clarivate Analytics Web of Science database in January 2021. A total of 525 documents were assessed by document type, research areas, source titles, organizations, and countries. SciMAT and VOSviewer package were used to perform scientific mapping analysis. Geographical distribution showed the USA and People’s Republic of China as the most productive countries, reporting up to 190 (36.19%) of all documents. A third-degree polynomic equation predicts that future global production in this area will be three-fold the current number, near 2031. Thematically, current research is focused on the integration of digital image analysis and genomic sequencing in Non-Hodgkin lymphomas, prediction of chemotherapy response and validation of new prognostic models. These findings can serve pathology departments to depict future clinical and research avenues, but also, public institutions and administrations to promote synergies and optimize funding allocation.