Splenic Diffuse Red Pulp Small B-Cell Lymphoma with Overlapping Clinical and Immunophenotypic Features with Hairy Cell Leukaemia: A Case Report and a Review of the Literature
Bibliographic record
Abstract
Background: Splenic B-cell lymphomas and leukaemias include hairy cell leukaemia, splenic marginal zone lymphoma, splenic diffuse red pulp small B-cell lymphoma, and splenic B-cell lymphoma/leukaemia with prominent nucleoli. The main diagnostic challenge is to differentiate between splenic B-cell lymphomas and leukaemias due to highly overlapping clinical, morphologic, and phenotypic features in the absence of splenectomies for diagnostic purposes. Case presentation: We describe a case of a 78-year-old woman who presented with weight loss and was subsequently found to have pancytopenia, lymphocytosis, and splenomegaly. Peripheral blood smear showed a homogenous population of small- to medium-sized lymphocytes having oval nuclei, condensed chromatin, and villous cytoplasmic projections. Bone marrow showed B-cell infiltrate in a predominantly intrasinusoidal pattern. Except for cyclin D1 and BRAF, the immunophenotype was similar to that of hairy cell leukaemia. This was further supported by the lack of BRAF p.V600E mutation by polymerase chain reaction. A diagnosis of splenic diffuse red pulp small B-cell lymphoma was thus favored based on the lack of cyclin D1 expression and pattern of infiltration in the bone marrow biopsy. Conclusions: Awareness of this infrequent clinical presentation and immunophenotype of splenic diffuse red pulp small B-cell lymphoma is crucial for diagnosis and devising appropriate therapeutic strategies for the patient.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".