Computational flow cytometry provides accurate assessment of measurable residual disease in chronic lymphocytic leukaemia.

Nguyen, Phillip C; Nguyen, Vuong; Baldwin, Kylie; Kankanige, Yamuna; Blombery, Piers; Came, Neil; Westerman, David A

Nguyen, Phillip C; Nguyen, Vuong; Baldwin, Kylie; Kankanige, Yamuna; Blombery, Piers; Came, Neil; Westerman, David A.

Afiliação

Nguyen PC; Department of Pathology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia.
Nguyen V; Department of Pathology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia.
Baldwin K; Department of Pathology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia.
Kankanige Y; Department of Pathology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia.
Blombery P; Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, Victoria, Australia.
Came N; Department of Pathology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia.
Westerman DA; Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, Victoria, Australia.

Br J Haematol ; 202(4): 760-770, 2023 08.

Article em En | MEDLINE | ID: mdl-37052611

Assuntos

Leucemia Linfocítica Crônica de Células B; Neoplasia Residual; Leucemia Linfocítica Crônica de Células B/diagnóstico; Leucemia Linfocítica Crônica de Células B/genética; Neoplasia Residual/diagnóstico; Neoplasia Residual/genética; Citometria de Fluxo; Imunofenotipagem; Humanos; Aprendizado de Máquina

Palavras-chave

chronic lymphocytic leukaemia; flow cytometry; machine learning; measurable residual disease

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Leucemia Linfocítica Crônica de Células B / Neoplasia Residual Tipo de estudo: Diagnostic_studies Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article

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