ArticleCommunications medicine2024
Computational assessment of measurable residual disease in acute myeloid leukemia using mixture models.
Article in Communications medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Wild Boar and Red Deer as Key Players in Coxiella Burnetii Circulation in the Wildlife-Livestock-Human Interface: Serological Analysis Using a Gaussian Mixture Model.Animals : an open access journal from MDPI · 2026Article
- Review
- Review
- Computational measurable residual disease assessment in acute myeloid leukemia: a retrospective validation in the HOVON-SAKK-132 trial.Leukemia · 2025Article
- Review
- Applications of machine learning for immunophenotypic measurable residual disease assessment in acute myeloid leukemia.HemaSphere · 2025Review
- Review
- Computational assessment of measurable residual disease in acute myeloid leukemia using mixture models.Communications medicine · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe proportion of residual leukemic blasts after chemotherapy assessed by multiparameter flow cytometry, is an important prognostic factor for the risk of relapse and overall survival in acute myeloid leukemia (AML). This measurable residual disease (MRD) is used in clinical trials to stratify patients for more or less intensive consolidation therapy. However, an objective and reproducible analysis method to assess MRD status from flow cytometry data is lacking, yet is highly anticipated for broader implementation of MRD testing.
methodsWe propose a computational pipeline based on Gaussian mixture modeling that allows a fully automated assessment of MRD status while remaining completely interpretable for clinical diagnostic experts. Our pipeline requires limited training data, which makes it easily transferable to other medical centers and cytometry platforms.
resultsWe identify all healthy and leukemic immature myeloid cells in with high concordance (Spearman's Rho = 0.974) and classification performance (median F-score = 0.861) compared to manual analysis. Using control samples (n = 18), we calculate a computational MRD percentage with high concordance to expert gating (Spearman's rho = 0.823) and predict MRD status in a cohort of 35 AML follow-up measurements with high accuracy (97%).
conclusionsWe demonstrate that our pipeline provides a powerful tool for fast (~3 s) and objective automated MRD assessment in AML.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.