International Journal of Laboratory Hematology最新文献

Introduction

The Scopio Labs X100HT digital morphology system has been designed to automate the classification of white blood cells (WBCs) and identify pathological elements such as blasts, immature myeloid cells, and nucleated red blood cells (NRBCs). This study evaluates its diagnostic performance against manual microscopy, the current gold standard for hematologic morphology.

Methods

A total of 400 peripheral blood smear samples from the routine workload of the Clinical Pathology of “G. Mazzini Hospital” in Teramo were analyzed: 40 from healthy individuals and 360 from patients selected according to internal criteria aligned with the International Consensus Group for Hematology guidelines. Each sample was evaluated using both manual microscopy and the Scopio Labs X100HT Full Field PBS system, in pre-classification and after expert review, by two experienced pathologists. The digital system pre-classified results were compared with manual counts and expert post-classification.

Results

The Scopio Labs X100HT system showed a strong correlation with manual microscopy for neutrophils and lymphocytes (r = 0.93), and a moderate correlation for eosinophils (r = 0.80). Agreement between digital pre-classification and expert post-classification was high across all WBC categories (r ranging from 0.84 to 0.98). Receiver operating characteristic (ROC) analysis demonstrated moderate diagnostic accuracy for detecting blasts (AUC = 0.72), immature myeloid cells (AUC = 0.79), and NRBCs (AUC = 0.71), which improved with expert review.

Conclusion

The Scopio Labs X100HT Full Field PBS system demonstrated reliable performance in WBC differential analysis and identification of abnormal cells. Expert post-classification enhances diagnostic accuracy, supporting its use in clinical workflows alongside traditional hematology analyzers.

Introduction

The current reference method for the white blood cell (WBC) differential is manual smear review as outlined in CLSI H20-A2. As with many manual methods, it suffers from a number of challenges including dependence upon the expertise of the interpreter, the quality of the smear and stain, when dysplastic features make cell identification difficult, imprecision with leucopenia, and enumeration bias due to non-uniform cell distribution.

Methods

This study describes an alternative method for establishing the leucocyte differential using a single-tube, 8-color flow cytometric reference method.

Results

Data presented is from an international comparison of normal (based on analyzer counts, N = 120) and abnormal (N = 496) clinical samples performed at four institutions using four different models of flow cytometers. Here we demonstrate equivalent performance between the flow cytometric method and the current manual reference method, but show improved performance of the proposed reference method for low/infrequent cell populations, for example, monocytes and basophils.

Conclusion

The flow cytometric method also performs well in comparison with hematology analyzers in current clinical use, including good correlation for total white blood cell enumeration. The findings indicate that the flow cytometric method, deemed the “ICSH WBC reference,” could be used in lieu of CLSI H20-A2 as a reference for white blood cell enumeration and differential counting and specifically for the evaluation of automated differential counters.

Background and Objectives

We developed an automated morphological image recognition deep learning system (image recognition DLS) of peripheral blood cells, then constructed the diagnostic assist DLS combining image recognition DLS data with complete blood count (CBC) data. This study aimed to evaluate the clinical performance of the image recognition DLS and the diagnostic assist DLS in routine examinations.

Methods

The image recognition DLS was trained using datasets containing 1 476 727 images of white blood cells (WBCs), nucleated red blood cells (NRBCs), and large platelets to differentiate 14 blood cell types and to recognize 24 morphological characteristics. CBC data were obtained through the automated hematology analyzer (Sysmex XN-9000) and combined with the image recognition DLS data to construct the diagnostic assist DLS. The clinical performance of the image recognition DLS was evaluated using 128 716 blood cell images from 589 smears obtained from healthy subjects, ALL, AML, ML, MPN, and MDS cases in routine examinations.

Results

The image recognition DLS classified 14 blood cell types with an accuracy of 97.3%–99.9%. The accuracy of 11 morphological characteristics exceeded 90%. Blast cells were detected accurately on all slides, where they were identified by manual microscopy. Malignant lymphocytes were classified as blasts and/or lymphocytes with the morphological characteristics of each subtype of lymphoma. The diagnostic assist DLS successfully differentiated MDS, achieving an AUC (area under the curve) of 0.99.

Conclusion

This study demonstrated the potential of the diagnostic assist DLS, utilizing morphological image recognition DLS data combined with CBC parameters, as a promising diagnostic tool.

Background

T follicular helper (TFH) cell lymphoma is complex, and we hope to provide a new perspective for its diagnosis.

Methods

We analysed the immunophenotypes of 89 mature T-cell lymphomas, including 52 nodal lymphomas of TFH origin, as well as 32 benign lymph node samples and 30 healthy bone marrow samples, by flow cytometry (FCM).

Results

Among pan-T cell markers, CD4⁺CD5⁺CD3⁻ is the typical pattern that distinguishes TFH lymphoma from other T-cell lymphomas. Specific markers with high sensitivity for the diagnosis of TFH lymphoma include programmed cell death protein 1 (PD-1) and inducible synergistic co-stimulation molecules (ICOS), which are expressed at lower levels in neoplastic TFH cells than in benign TFH cells. In contrast, the specificity of C-X-C chemokine receptor type 5 (CXCR5) and CD10 is high, and the proportion of CD10-positive cells in neoplastic TFH samples is greater than that in benign TFH samples. Of the 52 TFH lymphoma samples in our study, 7 presented with abnormalities in B cells or plasmablast cells; we considered these to indicate B-cell proliferation rather than composite lymphoma.

Conclusion

Immunophenotypic characterization of neoplastic TFH cells by FCM is unique and has diagnostic value. Each specimen of suspected TFH lymphoma should be analyzed for the presence of specific markers, such as PD-1, ICOS, CXCR5, and CD10, and the clonality of B cells and plasmablast cells should be assessed.

Context

Early detection of acute leukemia (AL) is crucial for timely intervention and improved outcomes. Machine learning (ML) models provide a promising approach for early screening and rapid diagnosis of AL, minimizing delays in referral.

Objectives

Methods

We analyzed 1211 cases, including 810 confirmed AL cases (by morphology, immunophenotype, or molecular methods) and 401 benign cases. Leukocyte parameters and CPD from a Sysmex XN1000 analyzer (WDF Channel) were used for classification. ML models—LightGBM, CatBoost, TabNet, and XGBoost—were trained, and the optimal model was selected based on accuracy from 5-fold cross-validation. Feature contributions were evaluated using SHAP.

Results

Heat maps and UMAP projections effectively differentiated AL from benign cases and AML from ALL. XGBoost achieved the best performance with 88% sensitivity and 94% specificity. ROC-AUC scores were 0.88 for AML, 0.87 for ALL, and 0.99 for benign. Key features identified included NE-WY, MO-WZ, LYMPH, NE-WZ, NEUT, and MONO#.

Conclusion

ML models based on leukocyte and CPD parameters enhance the predictability of AL detection and lineage differentiation at a pre-microscopic level. Integrating these models into hematology analyzers provides a cost-effective, novel tool for detection and differentiation. Interpretable predictions assist experts, reducing subjectivity and expediting final diagnosis through immunophenotyping and molecular studies.

Background

Coagulation screening, consisting of prothrombin time (PT) and activated partial prothrombin time (aPTT), is routinely performed in paediatrics to identify bleeding disorders or guide peri-procedural management. We evaluated the trends in utilisation and diagnostic yield of PT and aPTT testing as part of coagulation screening in a tertiary paediatric centre.

Methods

All PT and aPTT samples received from June to September 2024 were analysed. Total requests, sample rejection rates, abnormal result patterns (isolated PT, isolated APTT, combined), and clinical correlations were recorded. Laboratory cutoffs were PT > 12.5 s and APTT > 30.0 s. Youden's Index determined cutoffs associated with inherited bleeding disorders.

Results

A total of 2808 coagulation profiles from 1207 patients were received, with 15.7% (442/2808) rejected in 268 patients. Of these, 31.7% (85/268) of patients were not re-tested. Among valid requests, 17.0% (402/2366) were abnormal (128 isolated APTT, 173 isolated PT, 101 combined). In a subgroup of 337 randomly selected patients, 28.8% (97/337) had deranged results, leading to 12 new haematological and 34 acute diagnoses. Youden's index determined isolated APTT > 31.4 s associated with inherited disorders (AUC > 0.8). The same was not identified with isolated PT (PT > 13.0 s, AUC < 0.6).

Conclusion

A substantial proportion of samples received are rejected, and some abnormal results remain unaddressed. Most abnormal findings are clinically significant, particularly when APTT > 33.1 s. There is scope to refine utilisation in paediatric practice.

Background

Preanalytical conditions, particularly centrifugation protocols, are critical for producing high-quality platelet-poor plasma in hemostasis testing. Centrifuge braking is debated due to its potential impact on platelet remixing.

Objectives

To evaluate the effect of centrifuge braking on residual platelet counts and a broad panel of hemostasis assays using both fresh and double-centrifuged plasma.

Methods

Fifty-six adult patients provided surplus citrate plasma samples. Three centrifugation protocols were assessed: 2000 g for 15 min with braking (B+/2000/15), 2500 g for 10 min with braking (B+/2500/10), and 2500 g for 10 min without braking (B−/2500/10). Routine assays were performed on fresh plasma. Specialized assays (factors VIII, IX, XI, XII, VWF, protein C, protein S, antithrombin, APC resistance, DRVVT, antiphospholipid antibodies) were performed on frozen plasma after double-centrifugation. Platelet counts and assay concordance were evaluated.

Results

Residual platelet counts were significantly higher in the B+/2500/10 protocol (9 [6–13] × 10⁹/L) compared to B−/2500/10 (2 [2–4] × 10⁹/L, p < 0.001) and B+/2000/15 (3 [2–4] ×10⁹/L, p < 0.01). All frozen samples had platelet counts < 10 × 10⁹/L. Routine coagulation assays were unaffected by protocol choice, except for a slight but statistically significant increase in factor V with braking. Specialized assays showed no meaningful differences across protocols, with the exception of a minor DRVVT confirmation time reduction in the braking group.

Conclusion

Braking during centrifugation reduces processing time but modestly increases residual platelet counts. Nonetheless, it does not compromise the performance of hemostasis assays when protocols are appropriately validated. These findings support the use of braking in clinical laboratories.

Introduction

B-cell acute lymphoblastic leukemia (B-ALL) is genetically heterogeneous. We assessed the utility of FusionPlex ALL targeted RNA sequencing panel in detecting gene fusions and other genomic lesions in B-ALL.

Methods

The high-risk B-ALL, negative for common recurrent gene fusions (RGF), that is, BCR::ABL1, ETV6::RUNX1, TCF3::PBX1 and KMT2A::AFF1, were analysed with RNA-based targeted sequencing 81-gene-panel FusionPlex ALL (IDT, USA). Multiplex ligation-dependent probe amplification (MLPA) was used for IKZF1 deletions and flow-cytometry for CRLF2 expression and ploidy analysis.

Results

Out of 32 samples, 27 were high-risk B-ALL cases (median age 16 (1–41) years) and 5 B-ALL controls with known fusions for validation. The fusions were detected in 6/27 (22%) RGF-negative B-ALL cases; 2 with EPOR::IGH and 1 each P2RY8::IGH, PAX5::ETV6, SNX2::ABL1, IKZF1::CIITA. In addition, IKZF1 and/or PAX5 gene deletions resulting in the formation of oncogenic/novel isoforms were detected in 75% (15/20) samples positive on MLPA. Flow-cytometry CRLF2 overexpression was noted in 60% (9/15) tested samples which correlated well with targeted RNAseq CRLF2 gene expression.

Conclusion

The targeted sequencing approach can help in detecting known and novel fusions in B-ALL, novel breakpoints in the known fusions, gene deletions as oncogenic/novel isoforms and CRLF2 expression.