Work

Automated Assessment of Disease Progression in Acute Myeloid Leukemia by Probabilistic Analysis of Flow Cytometry Data

Year: 2020

Type: article

Abstract: Following the endorsement of the Bethesda International Consensus Group, as well as the guidance for classification of hematolymphoid neoplasms introduced by the WHO's Classification of Tumours of Haematopoietic and Lymphoid Tissues, flow cytometry (FC) immunophenotyping became a standard tool for Acute Myeloid Leukemia (AML) diagnosis and disease monitoring. The goal of the presented work is building a functional prototype of an automated non-parametric Bayesian clinical decision–support system that can not only recognize the difference between normal and abnormal samples, but most importantly also recognize the direction of change in disease progression on the basis of the FC bone-marrow data alone, without the need to supplement the data with morphology and/or genetic analysis.

Source: Purdue e-Pubs (Purdue University System)

DOI: 10.5281/zenodo.3897459

Authors: Rajwa, Bartek, Wallace, Paul, Griffiths, Elizabeth, Dundar, Murat

Institutions: Purdue University West Lafayette, Roswell Park Comprehensive Cancer Center, Indiana University Indianapolis, Indiana University – Purdue University Indianapolis

Language: English

Topic: Acute Myeloid Leukemia Research

Subfield: Hematology

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Science Foundation, National Institutes of Health, National Cancer Institute, National Institute of Biomedical Imaging and Bioengineering