Computational assessment of measurable residual disease in acute myeloid leukemia using mixture models
We demonstrate that our pipeline provides a powerful tool for fast (~3 s) and objective automated MRD assessment in AML.
Authors
- Angèle Kelder (ORCID: https://orcid.org/0000-0002-9643-4932)
- Arjan A. van de Loosdrecht (ORCID: https://orcid.org/0000-0001-8311-983X)
- Lok Lam Ngai (ORCID: https://orcid.org/0000-0003-0969-3766)
- Patrycja L. Gradowska (ORCID: https://orcid.org/0000-0002-4620-9163)
- Costa Bachas (ORCID: https://orcid.org/0000-0001-5983-6193)
- Jacqueline J Cloos (ORCID: https://orcid.org/0000-0001-9150-8026)
- Tim R. Mocking (ORCID: https://orcid.org/0000-0001-9170-4131)
- Tom Reuvekamp (ORCID: https://orcid.org/0009-0001-1346-6606)
- Philip Rutten
Institutions
- Dutch Cancer Society (NL)
- Amsterdam Neuroscience (NL)
- Hovon (NL)
- Erasmus MC Cancer Institute (NL)
- Amsterdam University Medical Centers (NL)
- Cancer Center Amsterdam (NL)
- Vrije Universiteit Amsterdam (NL)
- University of Amsterdam (NL)
Publication Details
- Journal
- Communications Medicine
- Published
- 2024-12-19
- DOI
- https://doi.org/10.1038/s43856-024-00700-x
- Citations
- 8
- Primary Topic
- Acute Myeloid Leukemia Research
- Type
- article
- Field-Weighted Citation Impact
- 3.64
Funders
- Vanderbilt University Medical Center
- Cancer Center Amsterdam