Lymphoplasmacytic Lymphoma and Marginal Zone Lymphoma in the Bone Marrow
Bibliographic record
Abstract
OBJECTIVES: The differential diagnosis between bone marrow involvement by lymphoplasmacytic lymphoma (LPL) and marginal zone lymphoma (MZL) is challenging because histology and immunophenotype of both diseases overlap. We revisited the diagnostic pathology features of both diseases in the bone marrow. METHODS: We studied a series of bone marrow trephine biopsy specimens from 59 patients with Waldenström macroglobulinemia without extramedullary involvement and bone marrow biopsy specimens from 23 patients with well-characterized MZL who also had bone marrow involvement. H&E- and immunoperoxidase-stained sections of bone marrow trephine biopsy specimens as well as flow cytometry and classic cytogenetics performed on aspirations were reviewed. The study was complemented with MYD88 L265P mutation analysis of all samples. RESULTS: The most distinguishing features of LPL with respect to MZL were focal paratrabecular involvement (P < .001), the presence of lymphoplasmacytoid cells (P < .001) and Dutcher bodies (P < .001), increased numbers of mast cells (P < .001), and the MYD88 L265P mutation (P < .001). CONCLUSIONS: LPL can be reliably distinguished from MZL in the bone marrow by using a combination of pathology characteristics. Our findings stress the diagnostic importance of using the combination of the following parameters for a correct LPL diagnosis: paratrabecular infiltration, the presence of lymphoplasmacytoid cells and cells with Dutcher bodies, and an increased number of mast cells in addition to the presence of MYD88 mutation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".