Diagnosis of B-Cell Non-Hodgkin Lymphomas with Small-/Intermediate-Sized Cells in Cytopathology
Bibliographic record
Abstract
Fine needle sampling is a fast, safe, and potentially cost-effective method of obtaining tissue for cytomorphologic assessment aimed at both initial triage and, in some cases, complete diagnosis of patients that present clinically with lymphadenopathy. The cytologic diagnosis of B-cell non-Hodgkin lymphomas composed of small-/intermediate-sized cells, however, has been seen as an area of great difficulty even for experienced observers due to the morphologic overlap between lymphoma and reactive lymphadenopathies as well as between the lymphoma entities themselves. Although ancillary testing has improved diagnostic accuracy, the results from these tests must be interpreted within the morphological and clinical context to avoid misinterpretation. Importantly, the recognition of specific cytologic features is crucial in guiding the appropriate selection of ancillary tests which will either confirm or refute a tentative diagnosis. For these reasons, we here review the cytologic characteristics particular to five common B-cell non-Hodgkin lymphomas which typically cause the most diagnostic confusion based on cytological assessment alone: marginal zone lymphoma, follicular lymphoma, mantle cell lymphoma, chronic lymphocytic leukemia/small lymphocytic lymphoma, and lymphoplasmacytic lymphoma. We summarize the most pertinent cytomorphologic features for each entity as well as for reactive lymphoid hyperplasia, contrast them with each other to facilitate their recognition, and highlight common diagnostic pitfalls.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".