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Identification of critical hemodilution by artificial intelligence in bone marrow assessed for minimal residual disease analysis in acute myeloid leukemia: The Cinderella method.
Hoffmann, Joerg; Thrun, Michael C; Röhnert, Maximilian A; von Bonin, Malte; Oelschlägel, Uta; Neubauer, Andreas; Ultsch, Alfred; Brendel, Cornelia.
Afiliación
  • Hoffmann J; Department of Hematology, Oncology and Immunology, Philipps University Marburg, University Hospital Giessen and Marburg, Marburg, Germany.
  • Thrun MC; Department of Hematology, Oncology and Immunology, Philipps University Marburg, University Hospital Giessen and Marburg, Marburg, Germany.
  • Röhnert MA; Databionics, Mathematics and Computer Science, Philipps University Marburg, Marburg, Germany.
  • von Bonin M; Department of Medicine I, University Hospital Carl Gustav Carus Dresden, Dresden, Germany.
  • Oelschlägel U; Department of Medicine I, University Hospital Carl Gustav Carus Dresden, Dresden, Germany.
  • Neubauer A; Department of Medicine I, University Hospital Carl Gustav Carus Dresden, Dresden, Germany.
  • Ultsch A; Department of Hematology, Oncology and Immunology, Philipps University Marburg, University Hospital Giessen and Marburg, Marburg, Germany.
  • Brendel C; Databionics, Mathematics and Computer Science, Philipps University Marburg, Marburg, Germany.
Cytometry A ; 103(4): 304-312, 2023 04.
Article en En | MEDLINE | ID: mdl-36030398
Minimal residual disease (MRD) detection is a strong predictor for survival and relapse in acute myeloid leukemia (AML). MRD can be either determined by molecular assessment strategies or via multiparameter flow cytometry. The degree of bone marrow (BM) dilution with peripheral blood (PB) increases with aspiration volume causing consecutive underestimation of the residual AML blast amount. In order to prevent false-negative MRD results, we developed Cinderella, a simple automated method for one-tube simultaneous measurement of hemodilution in BM samples and MRD level. The explainable artificial intelligence (XAI) Cinderella was trained and validated with the digital raw data of a flow cytometric "8-color" AML-MRD antibody panel in 126 BM and 23 PB samples from 35 patients. Cinderella predicted PB dilution with high accordance compared to the results of the Holdrinet formula (Pearson's correlation coefficient r = 0.94, R2  = 0.89, p < 0.001). Unlike conventional neuronal networks Cinderella calculated the distributions of 12 different cell populations that were assigned to true hematopoietic counterparts as a human in the loop (HIL) approach. Besides characteristic BM cells such as myelocytes and myeloid progenitor cells the XAI identified discriminating populations, which were not specific for BM or PB (e.g., T cell/NK cell subpopulations and CD45 negative cells) and considered their frequency differences. Thus, Cinderella represents a HIL-XAI algorithm capable to calculate the degree of hemodilution in BM samples with an AML MRD immunophenotype panel. It is explicable, transparent, and paves a simple way to prevent false negative MRD reports.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Médula Ósea / Leucemia Mieloide Aguda Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Humans Idioma: En Revista: Cytometry A Año: 2023 Tipo del documento: Article País de afiliación: Alemania Pais de publicación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Médula Ósea / Leucemia Mieloide Aguda Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Humans Idioma: En Revista: Cytometry A Año: 2023 Tipo del documento: Article País de afiliación: Alemania Pais de publicación: Estados Unidos