Utility of cell population data (VCS parameters) as a rapid screening tool for Acute Myeloid Leukemia (AML) in resource-constrained laboratories

Recent advances in laboratory hematology reflected by a decade of CCLM publications

On the occasion of the 60th anniversary of Clinical Chemistry and Laboratory Medicine (CCLM) we present a review of recent developments in the discipline of laboratory hematology as these are reflected by papers published in CCLM in the period 2012–2022. Since data on CCLM publications from 1963 to 2012 are also available, we were able to make a comparison between the two periods. This interestingly revealed that the share of laboratory hematology papers has steadily increased and reached now 16% of all papers published in CCLM. It also became evident that blood coagulation and fibrinolysis, erythrocytes, platelets and instrument and method evaluation constituted the ‘hottest’ topics with regard to number of publications. Some traditional, characteristic CCLM categories like reference intervals, standardization and harmonization, were more stable and probably will remain so in the future. With the advent of important newer topics, like new coagulation assays and drugs and cell population data generated by hematology analyzers, laboratory hematology is anticipated to remain a significant discipline in CCLM publications.

Beyond the In-Practice CBC: The Research CBC Parameters-Driven Machine Learning Predictive Modeling for Early Differentiation among Leukemias

A targeted and timely treatment can be a beneficial tool for patients with hematological emergencies (particularly acute leukemias). The key challenges in the early diagnosis of leukemias and related hematological disorders are their symptom-sharing nature and prolonged turnaround time as well as the expertise needed in reporting confirmatory tests. The present study made use of the potential morphological and immature fraction-related parameters (research items or cell population data) generated during complete blood cell count (CBC), through artificial intelligence (AI)/machine learning (ML) predictive modeling for early (at the pre-microscopic level) differentiation of various types of leukemias: acute from chronic as well as myeloid from lymphoid. The routine CBC parameters along with research CBC items from a hematology analyzer in the diagnosis of 1577 study subjects with hematological neoplasms were collected. The statistical and data visualization tools, including heat-map and principal component analysis (PCA,) helped in the evaluation of the predictive capacity of research CBC items. Next, research CBC parameter-driven artificial neural network (ANN) predictive modeling was developed to use the hidden trend (disease’s signature) by increasing the auguring accuracy of these potential morphometric parameters in differentiation of leukemias. The classical statistics for routine and research CBC parameters showed that as a whole, all study items are significantly deviated among various types of leukemias (study groups). The CPD parameter-driven heat-map gave clustering (separation) of myeloid from lymphoid leukemias, followed by the segregation (nodding) of the acute from the chronic class of that particular lineage. Furthermore, acute promyelocytic leukemia (APML) was also well individuated from other types of acute myeloid leukemia (AML). The PCA plot guided by research CBC items at notable variance vindicated the aforementioned findings of the CPD-driven heat-map. Through training of ANN predictive modeling, the CPD parameters successfully differentiate the chronic myeloid leukemia (CML), AML, APML, acute lymphoid leukemia (ALL), chronic lymphoid leukemia (CLL), and other related hematological neoplasms with AUC values of 0.937, 0.905, 0.805, 0.829, 0.870, and 0.789, respectively, at an agreeably significant (10.6%) false prediction rate. Overall practical results of using our ANN model were found quite satisfactory with values of 83.1% and 89.4.7% for training and testing datasets, respectively. We proposed that research CBC parameters could potentially be used for early differentiation of leukemias in the hematology–oncology unit. The CPD-driven ANN modeling is a novel practice that substantially strengthens the predictive potential of CPD items, allowing the clinicians to be confident about the typical trend of the “disease fingerprint” shown by these automated potential morphometric items.

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

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.

Increased circulating basic fibroblast growth factor levels in acute myeloid leukemia: a meta-analysis

Background: Basic fibroblast growth factor (bFGF) plays an important role in the pathogenesis of acute myeloid leukemia (AML). Whether the levels of circulating bFGF are increased or not in untreated AML patients is still not clear. In order to acquire a more definite evaluation, a meta-analysis was performed.Material and methods: We searched PubMed, Embase, the Cochrane Library, China National Knowledge Infrastructure (CNKI), Wanfang, and VIP databases for possible eligible articles. Forest plot was used to present the combined effect values and 95% confidence intervals (CI) through the random-effect model. Subgroup analysis was performed based on sample size, sample type, and region. All statistical analysis was performed in STATA12.0 software.Results: After excluding the articles that did not meet the inclusion criteria, 11 studies that met the inclusion conditions were included in this meta-analysis. Overall, AML patients probably had higher circulating levels of bFGF (SMD = 1.15, 95% CI: 0.35-1.94). The results of sensitivity analysis indicated that the results were stable. Moreover, the trim and fill analysis showed that publication bias had little effect and the results were relatively robust. In addition, AML patients with N < 30 group, serum group, and Asia group (all P < 0.05) had higher circulating bFGF levels, whereas other subgroups showed no significant change.Conclusion: The results of current meta-analysis revealed that AML patients had higher circulating bFGF levels, and it was associated with sample type, sample size, and region. Considering the possible pathogenic role of bFGF in AML, drug development targeting bFGF is very promising for AML patients.

Cell Population Data-Driven Acute Promyelocytic Leukemia Flagging Through Artificial Neural Network Predictive Modeling

A targeted and timely offered treatment can be a benefitting tool for patients with acute promyelocytic leukemia (APML). Current round of study made use of potential morphological and immature fraction–related parameters (cell population data) generated during complete blood cell count (CBC), through artificial neural network (ANN) predictive modeling for early flagging of APML cases. We collected classical CBC items along with cell population data (CPD) from hematology analyzer at diagnosis of 1067 study subjects with hematological neoplasms. For morphological assessment, peripheral blood films were examined. Statistical and machine learning tools including principal component analysis (PCA) helped in the evaluation of predictive capacity of routine and CPD items. Then selected CBC item–driven ANN predictive modeling was developed to smartly use the hidden trend by increasing the auguring accuracy of these parameters in differentiation of APML cases. We found a characteristic triad based on lower (53.73) platelet count (PLT) with decreased/normal (4.72) immature fraction of platelet (IPF) with addition of significantly higher value (65.5) of DNA/RNA content–related neutrophil (NE-SFL) parameter in patients with APML against other hematological neoplasm's groups. On PCA, APML showed exceptionally significant variance for PLT, IPF, and NE-SFL. Through training of ANN predictive modeling, our selected CBC items successfully classify the APML group from non-APML groups at highly significant (0.894) AUC value with lower (2.3 percent) false prediction rate. Practical results of using our ANN model were found acceptable with value of 95.7% and 97.7% for training and testing data sets, respectively. We proposed that PLT, IPF, and NE-SFL could potentially be used for early flagging of APML cases in the hematology-oncology unit. CBC item–driven ANN modeling is a novel approach that substantially strengthen the predictive potential of CBC items, allowing the clinicians to be confident by the typical trend raised by these studied parameters.

Utility of cell population data (VCS parameters) as a rapid screening tool for Acute Myeloid Leukemia (AML) in resource-constrained laboratories

INTRODUCTION

Despite advances in diagnostic techniques, many cases of acute myeloid leukemia (AML) remain underdiagnosed in remote centers unequipped with these technologies. We hypothesized that the automated cellular indices with scatter plots and flags may aid in rapid and cost-effective screening of AML.

METHODS

Cell population data (CPD) parameters from 100 de novo AML samples were analyzed by Coulter LH 780 automated analyzer and were compared with 100 age-matched controls. Similar parameters were also compared with 100 and 50 reactive cases of neutrophilia and monocytosis, respectively. System-generated flags and scatter plot patterns were also analyzed.

RESULTS

Results were compared between AML cases and normal controls; AML FAB M2, M3, M4 vs reactive neutrophilia; AML FAB M4, M5 vs reactive monocytosis. Significant parameters were selected from all comparison groups. Using appropriate statistical tools, we calculated the cutoff values of these parameters and were able to screen out AML cases with 94% sensitivity and 95% specificity. Three statistical equations were generated using two of the most significant parameters which improved the sensitivity to 98% and specificity to 99%. Five hypothetical scatter plot patterns were devised and were classified according to FAB categories of AML. Most common pattern was selected in AML which was seen in 56% of the cases. Output was analyzed combining these patterns and flags with CPD parameters.

CONCLUSION

CPD either alone or in the form of statistical equations along with scatter plots and flags can provide rapid and economic tool in preliminary diagnosis of AML in cost-constrained settings.