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Novel flowcytometry-based approach of malignant cell detection in body fluids using an automated hematology analyzer.

ABSTRACT: Morphological microscopic examinations of nucleated cells in body fluid (BF) samples are performed to screen malignancy. However, the morphological differentiation is time-consuming and labor-intensive. This study aimed to develop a new flowcytometry-based gating analysis mode "XN-BF gating algorithm" to detect malignant cells using an automated hematology analyzer, Sysmex XN-1000. XN-BF mode was equipped with WDF white blood cell (WBC) differential channel. We added two algorithms to the WDF channel: Rule 1 detects larger and clumped cell signals compared to the leukocytes, targeting the clustered malignant cells; Rule 2 detects middle sized mononuclear cells containing less granules than neutrophils with similar fluorescence signal to monocytes, targeting hematological malignant cells and solid tumor cells. BF samples that meet, at least, one rule were detected as malignant. To evaluate this novel gating algorithm, 92 various BF samples were collected. Manual microscopic differentiation with the May-Grunwald Giemsa stain and WBC count with hemocytometer were also performed. The performance of these three methods were evaluated by comparing with the cytological diagnosis. The XN-BF gating algorithm achieved sensitivity of 63.0% and specificity of 87.8% with 68.0% for positive predictive value and 85.1% for negative predictive value in detecting malignant-cell positive samples. Manual microscopic WBC differentiation and WBC count demonstrated 70.4% and 66.7% of sensitivities, and 96.9% and 92.3% of specificities, respectively. The XN-BF gating algorithm can be a feasible tool in hematology laboratories for prompt screening of malignant cells in various BF samples.

SUBMITTER: Ai T

PROVIDER: S-EPMC5806859 | biostudies-other | 2018

REPOSITORIES: biostudies-other

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Novel flowcytometry-based approach of malignant cell detection in body fluids using an automated hematology analyzer.

Ai Tomohiko T Tabe Yoko Y Takemura Hiroyuki H Kimura Konobu K Takahashi Toshihiro T Yang Haeun H Tsuchiya Koji K Konishi Aya A Uchihashi Kinya K Horii Takashi T Ohsaka Akimichi A

PloS one 20180209 2

Morphological microscopic examinations of nucleated cells in body fluid (BF) samples are performed to screen malignancy. However, the morphological differentiation is time-consuming and labor-intensive. This study aimed to develop a new flowcytometry-based gating analysis mode "XN-BF gating algorithm" to detect malignant cells using an automated hematology analyzer, Sysmex XN-1000. XN-BF mode was equipped with WDF white blood cell (WBC) differential channel. We added two algorithms to the WDF ch ...[more]

PMID: 29425230

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Project description:BackgroundDistinguishing arboviral infections from bacterial causes of febrile illness is of great importance for clinical management. The Infection Manager System (IMS) is a novel diagnostic algorithm equipped on a Sysmex hematology analyzer that evaluates the host response using novel techniques that quantify cellular activation and cell membrane composition. The aim of this study was to train and validate the IMS to differentiate between arboviral and common bacterial infections in Southeast Asia and compare its performance against C-reactive protein (CRP) and procalcitonin (PCT).Methodology/principal findings600 adult Indonesian patients with acute febrile illness were enrolled in a prospective cohort study and analyzed using a structured diagnostic protocol. The IMS was first trained on the first 200 patients and subsequently validated using the complete cohort. A definite infectious etiology could be determined in 190 of 463 evaluable patients (41%), including 89 arboviral infections (81 dengue and 8 chikungunya), 94 bacterial infections (26 murine typhus, 16 salmonellosis, 6 leptospirosis and 46 cosmopolitan bacterial infections), 3 concomitant arboviral-bacterial infections, and 4 malaria infections. The IMS detected inflammation in all but two participants. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the IMS for arboviral infections were 69.7%, 97.9%, 96.9%, and 77.3%, respectively, and for bacterial infections 77.7%, 93.3%, 92.4%, and 79.8%. Inflammation remained unclassified in 19.1% and 22.5% of patients with a proven bacterial or arboviral infection. When cases of unclassified inflammation were grouped in the bacterial etiology group, the NPV for bacterial infection was 95.5%. IMS performed comparable to CRP and outperformed PCT in this cohort.Conclusions/significanceThe IMS is an automated, easy to use, novel diagnostic tool that allows rapid differentiation between common causes of febrile illness in Southeast Asia.

Project description:BackgroundAccurate and timely diagnosis of malaria is essential for disease management and surveillance. Thin and thick blood smear microscopy and malaria rapid diagnostic tests (RDTs) are standard malaria diagnostics, but both methods have limitations. The novel automated hematology analyzer XN-30 provides standard complete blood counts (CBC) as well as quantification of malaria parasitemia at the price of a CBC. This study assessed the accuracy of XN-30 for malaria detection in a controlled human malaria infection (CHMI) study and a phase 3 diagnostic accuracy study in Burkina Faso.MethodsSixteen healthy, malaria-naive CHMI participants were challenged with five Plasmodium falciparum-infected mosquitoes. Blood was sampled daily for XN-30, blood smear microscopy, and malaria qPCR. The accuracy study included patients aged > 3 months presenting with acute febrile illness. XN-30, microscopy, and rapid diagnostic tests (HRP-2/pLDH) were performed on site; qPCR was done in retrospect. The malaria reference standard was microscopy, and results were corrected for sub-microscopic cases.ResultsAll CHMI participants became parasitemic by qPCR and XN-30 with a strong correlation for parasite density (R2 = 0.91; p < .0001). The XN-30 accurately monitored treatment and allowed detection of recrudescence. Out of 908 patients in the accuracy study, 241 had microscopic malaria (density 24-491,802 parasites/μL). The sensitivity and specificity of XN-30 compared to microscopy were 98.7% and 99.4% (PPV = 98.7%, NPV = 99.4%). Results were corrected for qPCR-confirmed sub-microscopic cases. Three microscopy-confirmed cases were not detected by XN-30. However, XN-30 detected 19/134 (14.2%) qPCR-confirmed cases missed by microscopy. Among qPCR-confirmed cases, XN-30 had a higher sensitivity (70.9% versus 66.4%; p = .0009) and similar specificity (99.6% versus 100%; p = .5) as microscopy. The accuracy of XN-30 for microscopic malaria was equal to or higher than HRP-2 and pLDH RDTs, respectively.ConclusionsThe XN-30 is a novel, automated hematology analyzer that combines standard hemocytometry with rapid, objective, and robust malaria detection and quantification, ensuring prompt treatment of malaria and malaria anemia and follow-up of treatment response.Trial registrationBoth trials were registered on clinicaltrials.gov with respective identifiers NCT02836002 (CHMI trial) and NCT02669823 (diagnostic accuracy study).

Dataset Information

Novel flowcytometry-based approach of malignant cell detection in body fluids using an automated hematology analyzer.

Publications

Novel flowcytometry-based approach of malignant cell detection in body fluids using an automated hematology analyzer.

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