Neoteric Algorithm Using Cell Population Data (VCS Parameters) as a Rapid Screening Tool for Haematological Disorders.

Angeli Ambayya,Jameela Sathar,Rosline Hassan

doi:10.3390/diagnostics11091652

Angeli Ambayya, Jameela Sathar + Show 1 more

Open Access

https://doi.org/10.3390/diagnostics11091652

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Abstract

Hitherto, there has been no comprehensive study on the usefulness of cell population data (CPD) parameters as a screening tool in the discrimination of non-neoplastic and neoplastic haematological disorders. Hence, we aimed to develop an algorithm derived from CPD parameters to enable robust screening of neoplastic from non-neoplastic samples and subsequently to aid in differentiating various neoplastic haematological disorders. In this study, the CPD parameters from 245 subtypes of leukaemia and lymphoma were compared against 1103 non-neoplastic cases, and those CPD parameters that were vigorous discriminants were selected for algorithm development. We devised a novel algorithm: [(SD-V-NE*MN-UMALS-LY*SD-AL2-MO)/MN-C-NE] to distinguish neoplastic from non-neoplastic cases. Following that, the single parameter MN-AL2-NE was used as a discriminant to rule out reactive cases from neoplastic cases. We then assessed CPD parameters that were useful in delineating leukaemia subtypes as follows: AML (SD-MALS-NE and SD-UMALS-NE), APL (MN-V-NE and SD-V-MO), ALL (MN-MALS-NE and MN-LMALS-NE) and CLL (SD-C-MO). Prospective studies were carried out to validate the algorithm and single parameter, MN-AL2-NE. We propose these CPD parameter-based discriminant strategies to be adopted as an initial screening and flagging system in the preliminary evaluation of leukocyte morphology.

Highlights

Ampang, which serves as the national referral centre for Haematology in Malaysia. These cases consisted of 62 acute myeloid leukaemia (AML), 30 acute promyelocytic leukaemia (APL), 54 acute lymphoblastic leukaemia (ALL), 47 lymphomas, 28 chronic lymphoblastic leukaemia (CLL), 12 chronic myeloid leukaemia (CML) and 12 myelodysplastic syndrome (MDS)
This study highlighted the utility of leukocyte cell population data (CPD) parameters in distinguishing neoplastic from non-neoplastic cases and the identification of leukaemia subtypes
The neoteric algorithm and single CPD parameters explored in this study have shown excellent correlation with the gold standard of haematological malignancies diagnosis by morphologic and immunophenotypic studies

Summary

Introduction

Haematological malignancies typically arises from two major blood cell lineages: lymphoid and myeloid cells [1], which can progress into different subtypes of leukaemia, lymphoma, and multiple myeloma [2]. Haematological malignancies are the fourth most frequently diagnosed cancer around the world, with an incidence rate of 9% of all cancers [3]. The diagnosis of haematological malignancies has relied on morphologic examinations of the subject’s blood or bone marrow aspirates [4], and by immunophenotype profiling using flow cytometry techniques [5]. The advent of molecular genetics and genomic technologies, such as polymerase chain reaction (PCR) and sequencing-based methods, enabled deeper insight into the diagnosis and prognosis of various haematological malignancies [6]. Due to restricted number of trained personnel, the lack of sophisticated laboratory diagnostic equipment and the need to refer to the tertiary hospitals in rural healthcare settings [7], the diagnostic workups are arduous, leading to delays in initiating patients’ treatment protocols and, in some cases, resulting in untimely death due to these limitations

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