Clinical outcome of therapy‐related acute myeloid leukemia patients. Real‐life experience in a University Hospital and a Cancer Center in France
Bibliographic record
Abstract
BACKGROUND: t-AML occurs after a primary malignancy treatment and retains a poor prognosis. AIMS: To determine the impact of primary malignancies, therapeutic strategies, and prognostic factors on clinical outcomes of t-AML. RESULTS: A total of 112 adult patients were included in this study. Fifty-Five patients received intensive chemotherapy (IC), 33 non-IC, and 24 best supportive care. At t-AML diagnosis, 42% and 44% of patients presented an unfavorable karyotype and unfavorable 2010 ELN risk profile, respectively. Among treated patients (n = 88), 43 (49%) achieved complete remission: four out of 33 (12%) and 39 out of 55 (71%) in non-IC and IC groups, respectively. With a median follow-up of 5.5 months, the median overall survival (OS) and disease-free survival (DFS) for the whole population were 9 months and 6.3 months, respectively, and for the 88 treated patients 13.5 months and 8.2 months, respectively. Univariate analysis on OS and DFS showed a significant impact of high white blood cells (WBC) and blast counts at diagnosis, unfavorable karyotype and ELN classification. Multivariate analysis showed a negative impact of WBC count at diagnosis and a positive impact of chemotherapy on OS and DFS in the whole population. It also showed a negative impact of previous auto-HCT and high WBC count on OS and DFS and of IC on OS in treated patients which disappeared when we considered only confounding variables (age, previous cancers, marrow blasts, and 2010 ELN classification). In a pair-matched analysis comparing IC treated t-AML with de novo AML, there was no difference of OS and DFS between the two populations. CONCLUSION: We showed, in this study that t-AML patients with unfavorable features represented almost half of the population. Best outcomes obtained in patients receiving IC must be balanced by known confounding variables and should be improved by using new innovative agents and therapeutic strategies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".