MétaCan
Menu
← Retour à la cohorte
Enregistrement W4417009100 · doi:10.1182/blood-2025-5183

Establishing contemporary benchmarks for Acute Myeloid Leukemia outcomes and measurable residual disease: Real-world data utilization from the ELN-david MRD international working group

2025· article· en· W4417009100 sur OpenAlexaff
Gail J. Roboz, Tom Reuvekamp, Jacqueline Cloos, Malte von Bonin, Francesco Buccisano, Lukas H. Haaksma, Maura Rosane Valério Ikoma, Joana Brioso Infante, Dennis Kim, Chrysavgi Lalayanni, David de Leeuw, Josephine Anne Lucero, Francesco Mannelli, Luca Maurillo, Federico Moretti, Josep F Nomdedeu Guinot, Apostolia Papalexandri, Guillermo Ramil López, Maximilian Alexander Röhnert, Christoph Röllig, Anderson João Simione, Daniela Späth, Felicitas Thol, Anne Tierens, Adriano Venditti, François Vergez, Michael Heuser, Konstanze Döhner

Notice bibliographique

RevueBlood · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueAcute Myeloid Leukemia Research
Établissements canadiensPrincess Margaret Cancer Centre
Organismes subventionnairesnon disponible
Mots-clésMyeloid leukemiaClinical trialReal world dataMinimal residual diseaseLeukemiaDiseaseMyeloid

Résumé

récupéré en direct d'OpenAlex

Abstract Background The treatment landscape of acute myeloid leukemia (AML) is rapidly evolving due to advances in novel targeted agents, lower-intensity treatments, and allogeneic stem cell transplantation. Simultaneously, technologic advances are allowing increasingly sensitive detection of measurable residual disease (MRD) to guide treatment decision-making. However, our knowledge of AML patient characteristics, treatments, clinical outcomes and utility of multiparameter flow cytometry and molecular MRD techniques is primarily based on data from clinical trials. There is a need for real-world data to establish AML outcomes and MRD testing in routine practice. Thus, ELN-DAVID, an international European LeukemiaNet (ELN) working group focused on the assessment and validation of MRD in AML, launched the BENCHMARK initiative to establish a regularly updated platform for real-world clinical data and MRD assessment practices in ELN participating centers. Methods Centers provided de-identified, aggregated data from 100 or more unselected, consecutively seen patients with AML starting backwards from December 2022. Centers could include patients in clinical trials, if allowed by the clinical trial protocol. Descriptive data on patient characteristics, treatment strategies, response (morphologic and MRD) and survival were combined for collective analysis. As pooled survival analysis was not possible, the range of the median survival between centers is reported. European LeukemiaNet risk classification is defined as either the 2017 or 2022 version, depending on what the center used and entered. Results To date, data were provided for 1457 patients from 14 international centers treated between 2016 and 2022. Of these patients, 801 (55%) were male and a total of 974 (68%) patients received intensive chemotherapy, 323 (23%) non-intensive treatment, and 136 (9%) supportive care only. Across all treatment groups, most patients were not treated on clinical trials (86%), targeted (gemtuzumab-ozogamicin, FLT3 or IDH inhibitors) treatment was given to 36%, and the majority underwent allogeneic stem cell transplantation (64%). In patients treated with intensive chemotherapy the ELN risk group was 33% favorable, 29% intermediate, and 38% adverse. Of the non-intensively treated patients, most received venetoclax-based therapy (62%). Response evaluations showed complete remission (CR), or CR with incomplete count recovery (CRi) or CR with incomplete platelet recovery (CRp) in 80% of patients that were treated with intensive chemotherapy and in 54% of patients that received non-intensive treatment. Median follow-up time ranges from 4 months to 51 months and the median overall survival in each of the intensively treated ELN risk categories ranges from 16 months to not reached (NR), 8 months to NR, and 5.5 months to NR for favorable, intermediate and adverse risk patients, respectively. MRD data were available for 598 (61%) intensively treated patients after 2 cycles of treatment, 360 (37%) at the end of treatment and 295 (30%) during follow-up. Flow cytometry was the most frequently used MRD technology across all time points (cycle 2: 76%; end of treatment: 64%; follow-up: 62%), followed by quantitative PCR (cycle 2: 40%; end of treatment: 48%; follow-up: 43%). Next-generation sequencing was performed in 8% of patients after two cycles, 12% at end of treatment and 16% during follow-up. Among non-intensively treated patients, 107 (33%) had MRD data available, the majority (73%) was analyzed by flow cytometry. Conclusions The survival data from BENCHMARK-2025, reflecting international practices, are comparable to those expected from recent AML clinical trials. Most centers performed MRD assessment using flow cytometry for patients treated with intensive chemotherapy. MRD was infrequently performed in non-intensively treated patients. A standardized database has been developed to allow future analyses of individual patient data. It is anticipated that data will be updated regularly, giving the opportunity to construct synthetic control cohorts, allowing longitudinal correlation between evolving MRD practices and AML outcomes, and serving as benchmark for contemporary experience.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,066
score de la tête « metaresearch » (Gemma)0,072
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,066
Score d'incertitude au seuil0,348

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0660,072
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0060,009
Études des sciences et des technologies0,0010,001
Communication savante0,0050,002
Science ouverte0,0030,005
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,093
Tête enseignante GPT0,362
Écart entre enseignants0,269 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueBlood→Même sujetAcute Myeloid Leukemia Research→Travaux en français237 207→