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Open AccessArticle

A Clinically Applicable Approach to the Classification of B-Cell Non-Hodgkin Lymphomas with Flow Cytometry and Machine Learning

1
Department of Clinical and Biological Sciences, University of Turin, 10043 Orbassano, Italy
2
Division of Hematology, A.O. SS Antonio e Biagio e Cesare Arrigo, 15121 Alessandria, Italy
3
Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, UT 84112, USA
4
Laboratory of Immunopathology, Division of Pathology, A.O. Ordine Mauriziano, 10128 Turin, Italy
5
University Division of Hematology and Cell Therapy, A.O. Ordine Mauriziano, 10128 Turin, Italy
6
Division of Pathology, San Lazzaro Hospital, ASL CN2, 12051 Alba, Italy
*
Authors to whom correspondence should be addressed.
V.T. and V.G. contributed equally to this manuscript.
Cancers 2020, 12(6), 1684; https://doi.org/10.3390/cancers12061684
Received: 5 May 2020 / Revised: 15 June 2020 / Accepted: 19 June 2020 / Published: 24 June 2020
(This article belongs to the Section Cancer Informatics and Big Data)
The immunophenotype is a key element to classify B-cell Non-Hodgkin Lymphomas (B-NHL); while it is routinely obtained through immunohistochemistry, the use of flow cytometry (FC) could bear several advantages. However, few FC laboratories can rely on a long-standing practical experience, and the literature in support is still limited; as a result, the use of FC is generally restricted to the analysis of lymphomas with bone marrow or peripheral blood involvement. In this work, we applied machine learning to our database of 1465 B-NHL samples from different sources, building four artificial predictive systems which could classify B-NHL in up to nine of the most common clinico-pathological entities. Our best model shows an overall accuracy of 92.68%, a mean sensitivity of 88.54% and a mean specificity of 98.77%. Beyond the clinical applicability, our models demonstrate (i) the strong discriminatory power of MIB1 and Bcl2, whose integration in the predictive model significantly increased the performance of the algorithm; (ii) the potential usefulness of some non-canonical markers in categorizing B-NHL; and (iii) that FC markers should not be described as strictly positive or negative according to fixed thresholds, but they rather correlate with different B-NHL depending on their level of expression. View Full-Text
Keywords: lymphoma; non-hodgkin; classification; artificial intelligence; machine learning; flow cytometry lymphoma; non-hodgkin; classification; artificial intelligence; machine learning; flow cytometry
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Gaidano, V.; Tenace, V.; Santoro, N.; Varvello, S.; Cignetti, A.; Prato, G.; Saglio, G.; De Rosa, G.; Geuna, M. A Clinically Applicable Approach to the Classification of B-Cell Non-Hodgkin Lymphomas with Flow Cytometry and Machine Learning. Cancers 2020, 12, 1684.

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