Automated Assessment of Disease Progression in Acute Myeloid Leukemia by Probabilistic Analysis of Flow Cytometry Data
Authors/Creators
- 1. Purdue University
- 2. Roswell Park Cancer Institute
- 3. IUPUI
Description
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.
Files
ICSS poster 2015.pdf
Additional details
Funding
- National Institutes of Health
- Automated spectral data transformations and analysis pipeline for high-throughput 5R21EB015707-02
- National Institutes of Health
- ROSWELL PARK MEMORIAL INSTITUTE CORE CENTER GRANT 5P30CA016056-11
- U.S. National Science Foundation
- CAREER: Self-adjusting Models as a New Direction in Machine Learning 1252648
References
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- Rajwa B, Wallace PK, Griffiths EA, Dundar M. Automated assessment of disease progression in acute myeloid leukemia by probabilistic analysis of flow cytometry data. IEEE Trans Biomed Eng. 2017;64(5):1089–98. Available at: https://doi.org/10.1109/TBME.2016.2590950
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