Exploring blast composition in myelodysplastic syndromes and myelodysplastic/myeloproliferative neoplasms: <scp>CD45RA</scp> and <scp>CD371</scp> improve diagnostic value of flow cytometry through assessment of myeloblast heterogeneity and stem cell aberrancy
Bibliographic record
Abstract
BACKGROUND: Flow cytometry immunophenotyping (FCIP) can improve diagnosis of myelodysplastic syndromes (MDS) and myelodysplastic/myeloproliferative neoplasms (MDS/MPN), although its application is challenging due to difficulties in standardization, complexity of antibody panels and subjective interpretation of data. Since blasts are invariably affected in these disorders, we developed a FCIP approach for detailed and objective analysis of the blast population. METHODS: FCIP using a one-tube 10-color (13-marker) antibody panel was performed on bone marrow samples from 23 MDS and 8 MDS/MPN patients, 21 cytopenic patients non-diagnostic for MDS (Non-MDS), and 16 Control samples. RESULTS: MDS and MDS/MPN cases demonstrated one to several immunophenotypic abnormalities including: increased myeloblasts, decreased stage-1 hematogones, aberrant stem cells, abnormal myeloblast heterogeneity/divergence from normal, increased or decreased CD45 intensity, increased CD117 or CD123 intensity, decreased CD38 intensity, and aberrant expression of lineage markers (CD5, CD19, CD56). A Blast score was developed that showed sensitivity of 80.6% and specificity of 90.5% for immunophenotypic diagnosis of MDS and MDS/MPN. Expression levels of CD45RA and CD371 were used to evaluate abnormal myeloblast heterogeneity and stem cell aberrancy. Both these features were, for the first time, incorporated into a scoring system and resulted in 19% increase in the sensitivity of the assay for lower-risk MDS. CONCLUSION: Deep immunophenotypic analysis of the blast population is valuable for diagnosis of MDS and MDS/MPN and can potentially provide sensitivity and specificity figures comparable to those previously described using more comprehensive panels that assess maturing myelomonocytic and erythroid elements in addition to progenitor cells.
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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) | 0.001 | 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".