Automated prediction of acute promyelocytic leukemia from flow cytometry data using a graph neural network pipeline.

Cox, Andrew M; Kim, Daehwan; García, Rolando; Fuda, Franklin S; Weinberg, Olga K; Chen, Weina

Cox, Andrew M; Kim, Daehwan; García, Rolando; Fuda, Franklin S; Weinberg, Olga K; Chen, Weina.

Afiliação

Cox AM; Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX, US.
Kim D; Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX, US.
García R; Department of Pathology, UT Southwestern Medical Center, Dallas, TX, US.
Fuda FS; Department of Pathology, UT Southwestern Medical Center, Dallas, TX, US.
Weinberg OK; Department of Pathology, UT Southwestern Medical Center, Dallas, TX, US.
Chen W; Department of Pathology, UT Southwestern Medical Center, Dallas, TX, US.

Am J Clin Pathol ; 161(3): 264-272, 2024 Mar 01.

Article em En | MEDLINE | ID: mdl-37878540

Assuntos

Sistemas de Apoio a Decisões Clínicas; Leucemia Promielocítica Aguda; Humanos; Leucemia Promielocítica Aguda/diagnóstico; Citometria de Fluxo; Imunofenotipagem; Redes Neurais de Computação

Palavras-chave

acute myeloid leukemia; acute promyelocytic leukemia; flow cytometry; graph neural network; immunophenotype; machine learning

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Leucemia Promielocítica Aguda / Sistemas de Apoio a Decisões Clínicas Limite: Humans Idioma: En Revista: Am J Clin Pathol Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Estados Unidos

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