Improved identification of clinically relevant <scp>Acute Leukemia</scp> subtypes using standardized <scp>EuroFlow</scp> panels versus non‐standardized approach
Bibliographic record
Abstract
Rare acute leukemia (AL) components or subtypes such as blastic plasmacytoid dendritic cell neoplasm (BPDCN) or early T-cell precursor acute Lymphoblastic Leukemia (ETP-ALL) can be difficult to detect by routine flow cytometry due to their immunophenotypes overlapping with other poorly differentiated AL. We hypothesized that using standardized EuroFlow™ Consortium approach could better diagnose such entities among cases that previously classified as acute myeloid leukemia (AML)-M0, AML with minimal differentiation, AML with myelodysplasia-related changes without further lineage differentiation, and AL of ambiguous lineage. In order to confirm this hypothesis and assess whether these AL subtypes such as BPDCN and ETP-ALL had previously gone undetected, we reanalyzed 49 banked cryopreserved sample cases using standardized EuroFlow™ Consortium panels. We also performed target sequencing to capture the mutational commonalities between these AL subtypes. Reanalysis led to revised or refined diagnoses for 23 cases (47%). Of these, five diagnoses were modified, uncovering 3 ETP-ALL and 2 typical BPDCN cases. In 12 AML cases, a variable proportion of immature plasmacytoid dendritic cell and/or monocytic component was newly identified. In one AML case, we have identified a megakaryoblastic differentiation. Finally, in five acute lymphoblastic leukemia (ALL) cases, we were able to more precisely determine the maturation stage. The application of standardized EuroFlow flow cytometry immunophenotyping improves the diagnostic accuracy of ALs and could impact treatment decisions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".