Abstract
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.
Highlights
Lymphoid neoplasms are a very heterogeneous group of malignancies, ranging from indolent lymphomas to extremely aggressive entities
Samples derive from different sources (PB, bone marrow (BM) aspirates, lymph node and tissue biopsies or fine needle aspiration (FNA), liquor, bronchoalveolar lavage, pleural and peritoneal effusions) of patients affected by the most common mature B-cell Non-Hodgkin Lymphomas (B-non-Hodgkin lymphomas (NHL)), and in particular: Chronic Lymphocytic Leukemia (CLL), Diffuse Large B-cell
This aspect is important for MIB1 and Bcl2, as these two intracellular markers are mostly utilized on samples other than peripheral blood (PB) or BM aspirates, i.e., lymph nodes, tissue biopsies, and effusions
Summary
Lymphoid neoplasms are a very heterogeneous group of malignancies, ranging from indolent lymphomas to extremely aggressive entities. Different lymphomas require different therapies, show different responses to treatments and are generally associated with very different prognosis. For these reasons, the WHO classification [1,2] of lymphoid neoplasms is of huge importance in routine clinical practice, and it is based on morphology, immunophenotype, genetic abnormalities, and clinical features. FC provides faster and less biased results compared to IHC, since data are expressed in a quantitative way; even though the gating strategy may introduce some subjectivity in FC, when gates are designed to identify a specific population (defined by the immunological markers) the subjectivity is limited
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