A geno-clinical decision model for the diagnosis of myelodysplastic syndromes.
Journal
Blood advances
ISSN: 2473-9537
Titre abrégé: Blood Adv
Pays: United States
ID NLM: 101698425
Informations de publication
Date de publication:
09 11 2021
09 11 2021
Historique:
received:
15
03
2021
accepted:
28
07
2021
pubmed:
1
10
2021
medline:
30
11
2021
entrez:
30
9
2021
Statut:
ppublish
Résumé
The differential diagnosis of myeloid malignancies is challenging and subject to interobserver variability. We used clinical and next-generation sequencing (NGS) data to develop a machine learning model for the diagnosis of myeloid malignancies independent of bone marrow biopsy data based on a 3-institution, international cohort of patients. The model achieves high performance, with model interpretations indicating that it relies on factors similar to those used by clinicians. In addition, we describe associations between NGS findings and clinically important phenotypes and introduce the use of machine learning algorithms to elucidate clinicogenomic relationships.
Identifiants
pubmed: 34592765
pii: 477106
doi: 10.1182/bloodadvances.2021004755
pmc: PMC8579270
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
4361-4369Subventions
Organisme : NCI NIH HHS
ID : K12 CA076917
Pays : United States
Informations de copyright
© 2021 by The American Society of Hematology. Licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0), permitting only noncommercial, nonderivative use with attribution. All other rights reserved.
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