ESCCA Industrial Partner Presentations (IPP) are scheduled as plenary session in the programme.

WEDNESDAY 17 September

15:00-15:30 - Sysmex

More infornation will follow

16:00-16:30 - BD

More infornation will follow

ThURSDAY 18 september

11:15-11:45 - Beckman Coulter

More information will follow soon.

Friday 19 September

12:15-12:45 - Cytek Biosciences

Title: Pairing Of Spectral Flow Cytometry And Machine Learning Based Decision Support System For Accurate Diagnosis Of Leukemia and Lymphoma

Speaker:
Joseph C. Lownik, M.D./Ph.D.
Hematopathologist, Pathology & Laboratory Medicine
Cedars-Sinai

Abstract:
Flow cytometry is an essential methodology in the diagnosis and prognostication of leukemias and lymphomas (L&L). While flow cytometry data quality has improved with the increasing performance of instrumentation and the availability of novel fluorophores, the analysis of this data is complex, requiring significant training and time. Profiling >40 markers in a single tube is possible using spectral flow cytometry, but visualizing and analyzing these assays using standard bivariate plots is complex and inefficient in a clinical laboratory workflow. Here, we present an integrated solution for clinical workflows that covers the validation and implementation of laboratory developed tests (LDTs). The LDTs were developed as L&L diagnosis panels, consisting of reagents from multiple manufacturers, on the Cytek Northern Lights™ instrument, together with a novel machine learning algorithm and hematopathologist-developed clinical decision support software. This machine learning based method incorporates automatic fluidic abnormality detection, doublet detection, as well as red blood cell removal. Additionally, the machine learning based approach allows for automated adjustments of unmixing, decreasing technician time and effort. Overall, we demonstrate how the use of a machine learning based approach improves the clinical workflow by reducing the burden of data analysis on laboratory technicians and improving overall laboratory efficiency. Our work demonstrates that clinical decision support software can be implemented alongside spectral flow cytometry in routine diagnostics of L&L.