High-Sensitivity Flow Cytometric Detection of a Small Circulating Population of Nodal T-Follicular Helper Cell Lymphoma Angioimmunoblastic Type Cells
Bibliographic record
Abstract
Nodal T-follicular helper cell lymphoma angioimmunoblastic type (nTFHL-AI) is a rare and aggressive neoplasm of mature T-follicular helper cells. nTFHL-AI is characterized by polyclonal hypergammaglobulinemia, hemolytic anemia, circulating immune complexes, and cold agglutinins. nTFHL-AI is also often associated with B-cell or plasma cell expansion, mimicking B-cell lymphomas or plasma cell neoplasms. Therefore, the diagnosis of nTFHL-AI can sometimes be challenging and requires a specific immunophenotypic panel. However, the peripheral blood involvement in nTFHL-AI seems rare and has not been frequently addressed in the literature. We report the case of a 54-year-old man with multiple lymphadenopathies, hepatosplenomegaly, and skin rash, complaining of asthenia. Peripheral blood smear showed plasmacytoid cells and red cell rouleaux. A first flow cytometry screening panel of peripheral blood disclosed marked polyclonal plasmacytosis (12%). No mature B lymphocytes were detectable. In the suspicion of an nTFHL-AI, another flow cytometric panel was performed, including CD3, CD4, CD5, CD7, CD8, and CD10. The high-sensitivity flow cytometry analysis disclosed a small circulating population of atypical T cells (0.07%) expressing CD4+, CD3+, CD5+, CD10+, partially CD7+, and negative for CD8. Moreover, anti-TCRβ-chain constant region 1 (TRBC1) antibody (JOVI-1) was used to confirm the T-cell clonal restriction of this abnormal population. Immunohistochemistry on excised lymph node sections was carried out and confirmed the diagnosis of nTFHL-AI. In this case, the unexpected detection of a small circulating population of nTFHL-AI cells by high-sensitivity flow cytometry has prompted an extensive diagnostic workup leading rapidly to the correct diagnosis.
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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 | 0.000 |
| 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) | 0.001 | 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".