Hodgkin lymphoma variant of Richter transformation: morphology, Epstein-Barr virus status, clonality, and survival analysis-with comparison to Hodgkin-like lesion.

Hodgkin/Reed-Sternberg (HRS) cells in the setting of chronic lymphocytic leukemia (CLL) exist in 2 forms: type I with isolated HRS cells in a CLL background (Hodgkin-like lesion) and type II with typical classic Hodgkin lymphoma, a variant of Richter transformation (CHL-RT). The clinical significance of the 2 morphological patterns is unclear, and their biological features have not been compared. We retrospectively reviewed 77 cases: 26 of type I and 51 of type II CHL-RT; 3 cases progressed from type I to type II. We examined clinical features, Epstein-Barr virus (EBV) status, and clonal relatedness after microdissection. Median age for type I was 62 years versus 73 years for type II (P=.01); 27% (type I) versus 73% (type II) had a history of CLL. HRS cells were positive for EBV in 71% (55/77), similar in types I and II. Clonality analysis was performed in 33 cases (type I and type II combined): HRS cells were clonally related to the underlying CLL in 14 and unrelated in 19. ZAP-70 expression of the CLL cells but not EBV status or morphological pattern was correlated with clonal relatedness: all 14 clonally related cases were ZAP-70 negative, whereas 74% (14/19) of clonally unrelated cases were ZAP-70 positive. Overall median survival (types I and II) after diagnosis was 44 months. Advanced age was an adverse risk factor for survival, but not histologic pattern, type I versus type II. HRS-like cells in a background of CLL carries a similar clinical risk to that of CHL-RT and may progress to classic Hodgkin lymphoma in some cases.

Automatic classification of lymphoma images with transform-based global features.

We propose a report on automatic classification of three common types of malignant lymphoma: chronic lymphocytic leukemia, follicular lymphoma, and mantle cell lymphoma. The goal was to find patterns indicative of lymphoma malignancies and allowing classifying these malignancies by type. We used a computer vision approach for quantitative characterization of image content. A unique two-stage approach was employed in this study. At the outer level, raw pixels were transformed with a set of transforms into spectral planes. Simple (Fourier, Chebyshev, and wavelets) and compound transforms (Chebyshev of Fourier and wavelets of Fourier) were computed. Raw pixels and spectral planes were then routed to the second stage (the inner level). At the inner level, the set of multipurpose global features was computed on each spectral plane by the same feature bank. All computed features were fused into a single feature vector. The specimens were stained with hematoxylin (H) and eosin (E) stains. Several color spaces were used: RGB, gray, CIE-L*a*b*, and also the specific stain-attributed H&E space, and experiments on image classification were carried out for these sets. The best signal (98%-99% on earlier unseen images) was found for the HE, H, and E channels of the H&E data set.