Standardization of Measurable Residual Disease in Acute Myeloid Leukemia by Flow Cytometry: A Multicenter Study
Bibliographic record
Abstract
ABSTRACT Introduction Measurable residual disease (MRD) is a strong predictor of the risk of relapse of acute myeloid leukemia (AML). Therefore, for use in clinical decision‐making, methods for MRD assessment must achieve adequate accuracy, sensitivity, specificity, and reproducibility. Multiparametric flow cytometry (MFC) is the most widely used method for assessing AML‐MRD, but its sensitivity varies considerably due to the differing approaches used across centers, in addition to the different experiences of flow cytometrists, especially during clonal evolution. This study aimed to standardize AML‐MRD by MFC in a multicenter project involving 16 Brazilian laboratories. Methods In the first phase, specialists were trained in pre‐analytical standard operating procedures (SOPs) and analysis strategies of pre‐validated 8‐ and 10‐color protocols, followed by a data‐only, that is, a Dry Phase of flow cytometry standard (FCS) file exchange by the coordinating laboratory in a comparability assessment. In the second or Wet Phase, laboratories prepared and analyzed their samples, and the FCS files were submitted for central analysis. Results The agreement of MRD results was 81% and 80% between laboratories and central analysts in the Dry and Wet Phases, respectively. However, non‐suitable application of pre‐analytical SOPs hampered MRD interpretation for 30% of the laboratories in the Wet Phase. Conclusions This study demonstrated that standardized flow cytometry protocols are reproducible as long as rigorous SOPs are implemented. The project's results underscore that continuous education and external quality control are essential to build expertise and ensure reliable AML‐MRD results in clinical practice. Trial Registration :The authors have confirmed clinical trial registration is not needed for this submission.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".