Flow Cytometric Detection of the Double-Positive (CD4+CD8+)/PD-1bright T-Cell Subset Is Useful in Diagnosing Nodular Lymphocyte-Predominant Hodgkin Lymphoma
Bibliographic record
Abstract
CONTEXT.—: Nodular lymphocyte-predominant Hodgkin lymphoma (NLPHL) is characterized by neoplastic lymphocyte-predominant cells frequently rimmed by CD3+/CD57+/programmed death receptor-1 (PD-1)+ T cells. Because of the rarity of lymphocyte-predominant cells in most cases, flow cytometric studies on NLPHL often fail to show evidence of malignancy. OBJECTIVE.—: To evaluate the diagnostic utility of PD-1 in detecting NLPHL by flow cytometry, in conjunction with the CD4:CD8 ratio and the percentage of T cells doubly positive for CD4 and CD8. DESIGN.—: Flow cytometric data obtained from cases of NLPHL (n = 10), classic Hodgkin lymphoma (n = 20), B-cell non-Hodgkin lymphoma (n = 22), T-cell non-Hodgkin lymphoma (n = 5), benign lymphoid lesions (n = 20), angioimmunoblastic T-cell lymphomas (n = 6) and T-cell/histiocyte-rich large B-cell lymphomas (n = 2) were analyzed and compared. RESULTS.—: Compared with the other groups, NLPHL showed significantly higher values in the following parameters: CD4:CD8 ratio, percentage of T cells doubly positive for CD4 and CD8, percentage of PD-1-positive T cells, and median fluorescence intensity of PD-1 expression in the doubly positive for CD4 and CD8 subset. Using a scoring system (0-4) based on arbitrary cutoffs for these 4 parameters, all 10 NLPHL cases scored 3 or higher, as compared with only 3 cases from the other groups, producing an overall sensitivity of 100% and a specificity of 96% (72 of 75). Two of the 3 outliers were non-Hodgkin lymphoma, and both showed definitive immunophenotypic abnormalities leading to the correct diagnosis. The remaining outlier was a case of T-cell/histiocyte-rich large B-cell lymphoma. CONCLUSIONS.—: The inclusion of anti-PD-1 in flow cytometry is useful for detecting NLPHL in fresh tissue samples, most of which would have otherwise been labeled as nondiagnostic or reactive lymphoid processes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".