JCO clinical cancer informatics · 2025
Machine Learning Designed for Any Hematologic Flow Cytometry Data Set.
СтатьяНазвание и аннотация: Перевод в очереди
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Mammen Johannes, Manta Calin-Petru, Richter Sarah, Liebers Nora, Roider Tobias, Czernilofsky Felix, Kriegsmann Katharina, Müller-Tidow Carsten, Hundemer Michael, Dietrich Sascha
Аннотация
PURPOSE: Flow cytometry is a key diagnostic technique in hematology that provides protein information at a single-cell level. Traditionally interpreted manually in a sequence of two-dimensional plots, automated analysis techniques have grown in significance in both research and clinics improving interrater reliability and speeding up analysis. Published tools usually require a specific diagnostic setup, which hinders widespread implementation.
METHODS: In this paper, we present the development of a software package and web app (diagnFlow) for the automated analysis of any in-house clinical flow cytometry data set. We exemplify the application of this classifier and its clinical benefit in lymphoma diagnosis and other settings.
RESULTS: Routine performance for the focused diagnostic task was evaluated in a blinded one-examiner setup. Multiple customary workflows solving the task in an automated manner were designed using diagnFlow. Each workflow could improve on the performance of the manual interpretation. The most easily interpretable and computationally efficient workflow out-performed more complicated approaches and was made available as an easy-to-use web app. Same-sample wet laboratory data further elucidated the biological signal the classifier is based on. The approach made available as a web app was validated in additional data sets outperforming a competition-winning clustering-based approach.
CONCLUSION: diagnFlow provides a valuable data set-agnostic approach to flow cytometry data sets previously not leveraged for automatic analysis while maintaining interpretability and resource efficiency.
METHODS: In this paper, we present the development of a software package and web app (diagnFlow) for the automated analysis of any in-house clinical flow cytometry data set. We exemplify the application of this classifier and its clinical benefit in lymphoma diagnosis and other settings.
RESULTS: Routine performance for the focused diagnostic task was evaluated in a blinded one-examiner setup. Multiple customary workflows solving the task in an automated manner were designed using diagnFlow. Each workflow could improve on the performance of the manual interpretation. The most easily interpretable and computationally efficient workflow out-performed more complicated approaches and was made available as an easy-to-use web app. Same-sample wet laboratory data further elucidated the biological signal the classifier is based on. The approach made available as a web app was validated in additional data sets outperforming a competition-winning clustering-based approach.
CONCLUSION: diagnFlow provides a valuable data set-agnostic approach to flow cytometry data sets previously not leveraged for automatic analysis while maintaining interpretability and resource efficiency.