Follicular &Paracortical Hyperplasia with Prominent CD30-Positive Immunoblasts

Siba El Hussein, MD
2 min readFeb 26, 2022

Lessons From the Friday Unknowns

Histologic sections show needle-shaped fragments of lymph node tissue. Large and hyperplastic lymphoid follicles with prominent germinal centers are present.

The paracortical areas also appear to be expanded by small lymphocytes, large immunoblasts, histiocytes and a few plasma cells and eosinophils.

The antibodies specific for CD20 and PAX-5 highlight increased B-cells in the follicular regions. The follicles contain germinal centers with cells that are positive for CD30 (large subset) and BCL-6, and are negative for BCL-2 and CD10. The paracortical areas show increased T-cells positive for CD3 and CD5 and increased immunoblasts positive for CD30. These immunoblasts are negative for CD15. The CD45 antibody highlights virtually all cells. The CD23 antibody highlights follicular dendritic cells and some mantle zone B-cells in the follicular areas. Plasma cells are positive for CD138 and MUM1 and express polytypic kappa or lambda. Cyclin D1 and keratin (AE1/AE3) are negative. The antibody specific for Ki-67 highlights the germinal centers and shows a proliferation rate of 30–40% in the paracortical areas.


Flow cytometry immunophenotypic analysis showed no evidence of a monotypic B-cell or aberrant T-cell population.

In summary, the diagnosis of lymphoma cannnot be established in this specimen. The etiology for the florid hyperplasia in this lymph node cannot be determined with certainty based on these findings. The unusual number of CD30-positive cells and the immunophenotype of the germinal centers raises the possibility of Epstein-Barr virus (EBV) infection. In situ hybridization to test for EBV may be informative. Other possible explanations for these findings include nearby infection (possibly involving the mouth or teeth), another type of recent viral infection or possibly recent vaccination.

Digital slides : | Case 1

Siba El Hussein, MD

Hematopathology | Cytopathology | Molecular pathology | Digital pathology | Data science | Machine learning