Value of flow cytometry for MRD-based relapse prediction in B-cell precursor ALL in a multicenter setting
Bibliographic record
Abstract
Abstract PCR of TCR/Ig gene rearrangements is considered the method of choice for minimal residual disease (MRD) quantification in BCP-ALL, but flow cytometry analysis of leukemia-associated immunophenotypes (FCM-MRD) is faster and biologically more informative. FCM-MRD performed in 18 laboratories across seven countries was used for risk stratification of 1487 patients with BCP-ALL enrolled in the NOPHO ALL2008 protocol. When no informative FCM-marker was available, risk stratification was based on real-time quantitative PCR. An informative FCM-marker was found in 96.2% and only two patients (0.14%) had non-informative FCM and non-informative PCR-markers. The overall 5-year event-free survival was 86.1% with a cumulative incidence of relapse (CIR5y) of 9.5%. FCM-MRD levels on days 15 (HzR 4.0, p < 0.0001), 29 (HzR 2.7, p < 0.0001), and 79 (HzR 3.5, p < 0.0001) associated with hazard of relapse adjusted for age, cytogenetics, and WBC. The early (day 15) response associated with CIR5y adjusted for day 29 FCM-MRD, with higher levels in adults (median 2.4 × 10−2 versus 5.2 × 10−3, p < 0.0001). Undetectable FCM- and/or PCR-MRD on day 29 identified patients with a very good outcome (CIR5y = 3.2%). For patients who did not undergo transplantation, day 79 FCM-MRD > 10−4 associated with a CIR5y = 22.1%. In conclusion, FCM-MRD performed in a multicenter setting is a clinically useful method for MRD-based treatment stratification in BCP-ALL.
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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 | 0.008 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".