دورية أكاديمية
Automated Deep Learning-Based Diagnosis and Molecular Characterization of Acute Myeloid Leukemia Using Flow Cytometry.
| العنوان: | Automated Deep Learning-Based Diagnosis and Molecular Characterization of Acute Myeloid Leukemia Using Flow Cytometry. |
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| المؤلفون: | Lewis JE; Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts., Cooper LAD; Department of Pathology, Northwestern University, Chicago, Illinois., Jaye DL; Department of Pathology and Laboratory Medicine, Emory University, Atlanta, Georgia., Pozdnyakova O; Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts. Electronic address: opozdnyakova@bwh.harvard.edu. |
| المصدر: | Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc [Mod Pathol] 2024 Jan; Vol. 37 (1), pp. 100373. Date of Electronic Publication: 2023 Nov 03. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Elsevier Inc Country of Publication: United States NLM ID: 8806605 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1530-0285 (Electronic) Linking ISSN: 08933952 NLM ISO Abbreviation: Mod Pathol Subsets: MEDLINE |
| أسماء مطبوعة: | Publication: 2023- : [New York] : Elsevier Inc. Original Publication: Baltimore, MD : Williams & Wilkins, c1988- |
| مواضيع طبية MeSH: | Deep Learning* , Leukemia, Myeloid, Acute*/diagnosis , Leukemia, Myeloid, Acute*/genetics , Leukemia, Myeloid, Acute*/metabolism, Humans ; Flow Cytometry/methods ; Acute Disease ; Cytogenetics |
| مستخلص: | The current flow cytometric analysis of blood and bone marrow samples for diagnosis of acute myeloid leukemia (AML) relies heavily on manual intervention in the processing and analysis steps, introducing significant subjectivity into resulting diagnoses and necessitating highly trained personnel. Furthermore, concurrent molecular characterization via cytogenetics and targeted sequencing can take multiple days, delaying patient diagnosis and treatment. Attention-based multi-instance learning models (ABMILMs) are deep learning models that make accurate predictions and generate interpretable insights regarding the classification of a sample from individual events/cells; nonetheless, these models have yet to be applied to flow cytometry data. In this study, we developed a computational pipeline using ABMILMs for the automated diagnosis of AML cases based exclusively on flow cytometric data. Analysis of 1820 flow cytometry samples shows that this pipeline provides accurate diagnoses of acute leukemia (area under the receiver operating characteristic curve [AUROC] 0.961) and accurately differentiates AML vs B- and T-lymphoblastic leukemia (AUROC 0.965). Models for prediction of 9 cytogenetic aberrancies and 32 pathogenic variants in AML provide accurate predictions, particularly for t(15;17)(PML::RARA) [AUROC 0.929], t(8;21)(RUNX1::RUNX1T1) (AUROC 0.814), and NPM1 variants (AUROC 0.807). Finally, we demonstrate how these models generate interpretable insights into which individual flow cytometric events and markers deliver optimal diagnostic utility, providing hematopathologists with a data visualization tool for improved data interpretation, as well as novel biological associations between flow cytometric marker expression and cytogenetic/molecular variants in AML. Our study is the first to illustrate the feasibility of using deep learning-based analysis of flow cytometric data for automated AML diagnosis and molecular characterization. (Copyright © 2023 United States & Canadian Academy of Pathology. Published by Elsevier Inc. All rights reserved.) |
| التعليقات: | Update of: bioRxiv. 2023 Sep 25;:. (PMID: 37808719) |
| فهرسة مساهمة: | Keywords: acute myeloid leukemia; attention-based multi-instance learning models; automated deep-learning based analysis; automated diagnosis; flow cytometry |
| تواريخ الأحداث: | Date Created: 20231104 Date Completed: 20240122 Latest Revision: 20240122 |
| رمز التحديث: | 20240122 |
| DOI: | 10.1016/j.modpat.2023.100373 |
| PMID: | 37925056 |
| قاعدة البيانات: | MEDLINE |
| تدمد: | 1530-0285 |
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| DOI: | 10.1016/j.modpat.2023.100373 |