Mitoxantrone Versus Liposomal Daunorubicin in Induction of Pediatric AML With Risk Stratification Based on Flow Cytometry Measurement of Residual Disease
Bibliographic record
Abstract
PURPOSE Measurable residual disease (MRD) by using flow cytometry after induction therapy is strongly prognostic in pediatric AML, and hematopoietic stem-cell transplant (hSCT) may counteract a poor response. We designed a phase III study with intensified response-guided induction and MRD-based risk stratification and treated poor induction response with hSCT. The efficacy of liposomal daunorubicin (DNX) in induction was compared with mitoxantrone. METHODS The study planned to randomly assign 300 patients, but the production of DNX ceased in 2017. One hundred ninety-four patients were randomly assigned to mitoxantrone or experimental DNX in induction 1. Ninety-three non–randomly assigned patients served as an observation cohort. Primary end point was fraction of patients with MRD <0.1% on day 22 after induction 1. Patients with MRD ≥15% after induction 1 or ≥0.1% after induction 2 or FLT3-ITD with NPM1 wildtype were stratified to high-risk therapy, including hSCT. RESULTS Outcome for all 287 children was good with 5-year event-free survival (EFS 5y ) 66.7% (CI, 61.4 to 72.4) and 5-year overall survival (OS 5y ) 79.6% (CI, 75.0 to 84.4). Overall, 75% were stratified to standard-risk and 19% to high-risk. There was no difference in the proportion of patients with MRD <0.1% on day 22 after induction 1 (34% mitoxantrone, etoposide, araC [MEC], 30% DNX, P = .65), but the proportion increased to 61% for MEC versus 47% for DNX ( P = .061) at the last evaluation before induction 2. EFS 5y was significantly lower, 56.6% (CI, 46.7 to 66.5) versus 71.9% (CI, 63.0 to 80.9), and cumulative incidence of relapse (CIR) was higher, 35.1% (CI, 25.7 to 44.7) versus 18.8% (CI, 11.6 to 27.2) for DNX. The inferior outcome for DNX was only in standard-risk patients with EFS 5y 55.3% (CI, 45.1 to 67.7) versus 79.9% (CI, 71.1 to 89.9), CIR 39.5% (CI, 28.4 to 50.3) versus 18.7% (CI, 10.5 to 28.7), and OS 5y 76.2% (CI, 67.2 to 86.4) versus 88.6% (CI, 81.4 to 96.3). As-treated analyses, including the observation cohort, supported these results. For all high-risk patients, 85% received hSCT, and EFS 5y was 77.7 (CI, 67.3 to 89.7) and OS 5y was 83.0 (CI, 73.5 to 93.8). CONCLUSION The intensification of induction therapy with risk stratification on the basis of response to induction and hSCT for high-risk patients led to improved outcomes. Mitoxantrone had a superior anti-leukemic effect than liposomal daunorubicin.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| 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".