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Zhang M, Zhang Y, Zhang J, Zhang J, Gao S, Li Z, Tao K, Liang X, Pan J, Zhu M. An automatic analysis and quality assurance method for lymphocyte subset identification. Clin Chem Lab Med 2024; 62:1411-1420. [PMID: 38217085 DOI: 10.1515/cclm-2023-1141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 12/20/2023] [Indexed: 01/15/2024]
Abstract
OBJECTIVES Lymphocyte subsets are the predictors of disease diagnosis, treatment, and prognosis. Determination of lymphocyte subsets is usually carried out by flow cytometry. Despite recent advances in flow cytometry analysis, most flow cytometry data can be challenging with manual gating, which is labor-intensive, time-consuming, and error-prone. This study aimed to develop an automated method to identify lymphocyte subsets. METHODS We propose a knowledge-driven combined with data-driven method which can gate automatically to achieve subset identification. To improve accuracy and stability, we have implemented a Loop Adjustment Gating to optimize the gating result of the lymphocyte population. Furthermore, we have incorporated an anomaly detection mechanism to issue warnings for samples that might not have been successfully analyzed, ensuring the quality of the results. RESULTS The evaluation showed a 99.2 % correlation between our method results and manual analysis with a dataset of 2,000 individual cases from lymphocyte subset assays. Our proposed method attained 97.7 % accuracy for all cases and 100 % for the high-confidence cases. With our automated method, 99.1 % of manual labor can be saved when reviewing only the low-confidence cases, while the average turnaround time required is only 29 s, reducing by 83.7 %. CONCLUSIONS Our proposed method can achieve high accuracy in flow cytometry data from lymphocyte subset assays. Additionally, it can save manual labor and reduce the turnaround time, making it have the potential for application in the laboratory.
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Affiliation(s)
- MinYang Zhang
- Department of Digital Management Center, Guangzhou KingMed Diagnostics Group Co., Ltd., Guangzhou Kingmed Center for Clinical Laboratory Co., Ltd., Guangzhou, Guandong, P.R. China
| | - YaLi Zhang
- Department of Digital Management Center, Guangzhou KingMed Diagnostics Group Co., Ltd., Guangzhou Kingmed Center for Clinical Laboratory Co., Ltd., Guangzhou, Guandong, P.R. China
| | - JingWen Zhang
- Department of Clinical Hematology and Flow Cytometry Lab, Guangzhou Kingmed Center for Clinical Laboratory Co., Ltd., Guangzhou, Guandong, P.R. China
| | - JiaLi Zhang
- Department of Clinical Hematology and Flow Cytometry Lab, Guangzhou Kingmed Center for Clinical Laboratory Co., Ltd., Guangzhou, Guandong, P.R. China
| | - SiYuan Gao
- Department of Digital Management Center, Guangzhou KingMed Diagnostics Group Co., Ltd., Guangzhou Kingmed Center for Clinical Laboratory Co., Ltd., Guangzhou, Guandong, P.R. China
| | - ZeChao Li
- Department of Digital Management Center, Guangzhou KingMed Diagnostics Group Co., Ltd., Guangzhou Kingmed Center for Clinical Laboratory Co., Ltd., Guangzhou, Guandong, P.R. China
| | - KangPei Tao
- Department of Digital Management Center, Guangzhou KingMed Diagnostics Group Co., Ltd., Guangzhou Kingmed Center for Clinical Laboratory Co., Ltd., Guangzhou, Guandong, P.R. China
| | - XiaoDan Liang
- Department of Digital Management Center, Guangzhou KingMed Diagnostics Group Co., Ltd., Guangzhou Kingmed Center for Clinical Laboratory Co., Ltd., Guangzhou, Guandong, P.R. China
| | - JianHua Pan
- Department of Clinical Hematology and Flow Cytometry Lab, Guangzhou Kingmed Center for Clinical Laboratory Co., Ltd., Guangzhou, Guandong, P.R. China
| | - Min Zhu
- Department of Digital Management Center, Guangzhou KingMed Diagnostics Group Co., Ltd., Guangzhou Kingmed Center for Clinical Laboratory Co., Ltd., Guangzhou, Guandong, P.R. China
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Robinson JP, Ostafe R, Iyengar SN, Rajwa B, Fischer R. Flow Cytometry: The Next Revolution. Cells 2023; 12:1875. [PMID: 37508539 PMCID: PMC10378642 DOI: 10.3390/cells12141875] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 07/06/2023] [Accepted: 07/13/2023] [Indexed: 07/30/2023] Open
Abstract
Unmasking the subtleties of the immune system requires both a comprehensive knowledge base and the ability to interrogate that system with intimate sensitivity. That task, to a considerable extent, has been handled by an iterative expansion in flow cytometry methods, both in technological capability and also in accompanying advances in informatics. As the field of fluorescence-based cytomics matured, it reached a technological barrier at around 30 parameter analyses, which stalled the field until spectral flow cytometry created a fundamental transformation that will likely lead to the potential of 100 simultaneous parameter analyses within a few years. The simultaneous advance in informatics has now become a watershed moment for the field as it competes with mature systematic approaches such as genomics and proteomics, allowing cytomics to take a seat at the multi-omics table. In addition, recent technological advances try to combine the speed of flow systems with other detection methods, in addition to fluorescence alone, which will make flow-based instruments even more indispensable in any biological laboratory. This paper outlines current approaches in cell analysis and detection methods, discusses traditional and microfluidic sorting approaches as well as next-generation instruments, and provides an early look at future opportunities that are likely to arise.
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Affiliation(s)
- J Paul Robinson
- Department of Basic Medical Sciences, Purdue University, West Lafayette, IN 47907, USA
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 47907, USA
| | - Raluca Ostafe
- Molecular Evolution, Protein Engineering and Production Facility (PI4D), Purdue University, West Lafayette, IN 47907, USA
| | | | - Bartek Rajwa
- Bindley Bioscience Center, Purdue University, West Lafayette, IN 47907, USA
| | - Rainer Fischer
- Department of Comparative Pathobiology, College of Veterinary Medicine, Purdue University, West Lafayette, IN 47907, USA
- Purdue Institute of Inflammation, Immunology and Infectious Diseases, Purdue University, West Lafayette, IN 47907, USA
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3
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Fuda F, Chen M, Chen W, Cox A. Artificial intelligence in clinical multiparameter flow cytometry and mass cytometry-key tools and progress. Semin Diagn Pathol 2023; 40:120-128. [PMID: 36894355 DOI: 10.1053/j.semdp.2023.02.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 02/22/2023] [Accepted: 02/23/2023] [Indexed: 03/07/2023]
Abstract
There are many research studies and emerging tools using artificial intelligence (AI) and machine learning to augment flow and mass cytometry workflows. Emerging AI tools can quickly identify common cell populations with continuous improvement of accuracy, uncover patterns in high-dimensional cytometric data that are undetectable by human analysis, facilitate the discovery of cell subpopulations, perform semi-automated immune cell profiling, and demonstrate potential to automate aspects of clinical multiparameter flow cytometric (MFC) diagnostic workflow. Utilizing AI in the analysis of cytometry samples can reduce subjective variability and assist in breakthroughs in understanding diseases. Here we review the diverse types of AI that are being applied to clinical cytometry data and how AI is driving advances in data analysis to improve diagnostic sensitivity and accuracy. We review supervised and unsupervised clustering algorithms for cell population identification, various dimensionality reduction techniques, and their utilities in visualization and machine learning pipelines, and supervised learning approaches for classifying entire cytometry samples.Understanding the AI landscape will enable pathologists to better utilize open source and commercially available tools, plan exploratory research projects to characterize diseases, and work with machine learning and data scientists to implement clinical data analysis pipelines.
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Affiliation(s)
- Franklin Fuda
- Department of Pathology and Laboratory Medicine, University of Texas, Southwestern Medical Center, Dallas, Texas, USA
| | - Mingyi Chen
- Department of Pathology and Laboratory Medicine, University of Texas, Southwestern Medical Center, Dallas, Texas, USA
| | - Weina Chen
- Department of Pathology and Laboratory Medicine, University of Texas, Southwestern Medical Center, Dallas, Texas, USA
| | - Andrew Cox
- Lyda Hill Department of Bioinformatics, University of Texas, Southwestern Medical Center, Dallas, Texas, USA; Department of Cell and Molecular Biology, University of Texas, Southwestern Medical Center, Dallas, Texas, USA.
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4
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Soh KT, Conway A, Liu X, Wallace PK. Development of a 27-color panel for the detection of measurable residual disease in patients diagnosed with acute myeloid leukemia. Cytometry A 2022; 101:970-983. [PMID: 35716345 DOI: 10.1002/cyto.a.24667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Revised: 04/26/2022] [Accepted: 06/15/2022] [Indexed: 01/27/2023]
Abstract
Acute myeloid leukemia (AML) measurable residual disease (MRD) evaluated by multiparametric flow cytometry (MFC) is a surrogate for progression-free and overall survival in clinical trials and patient management. Due to the limited number of detection channels available in conventional flow cytometers, panels used for assessing AML MRD are typically split into multiple tubes. This cripples the simultaneous and correlated assessment of all myeloblast measurements. In response, we prototyped a single-tube 27-color MFC assay for the evaluation of AML MRD, incorporating all recommended markers. Marrow aspirates from 22 patients were processed for analysis using full spectrum flow cytometry (FSFC). The signal resolution of each marker was compared between samples stained with single antibody vs. the fully stained panel. The analytical accuracy for quantifying hematopoietic cells between our established 8-color assay and the new 27-color method were compared. Variations within an operator and between separate operators were assessed to evaluate the assays reproducibility. The limited of blank (LOB), limit of detection (LOD), and lower limit of quantification (LLOQ) of the 27-color method were empirically determined using limiting dilution experiments. The stability of antibody cocktails over a period of 120 h was also studied using cryopreserved marrow cells. The stain indices for all antibodies were lower in the fully stained panel compared to cells stained with one antibody but clear separations between negative and positive signals were achieved for all antibodies. Our results demonstrated a high concordance between the established 8-color method and the new 27-color assay for enumerating myeloblasts and MRD interpretation within and between operators. The data further showed that the single-tube 27-color assay easily achieved the minimum required detection sensitivity of 0.1%. When antibodies were combined, however, expression intensity of some antigens deteriorated significantly when stored. Our single-tube 27-color panel is a suitable, high sensitivity flow cytometric approach that can be used for AML MRD testing, which improves the correlation of aberrant antigens and detection of asynchronous differentiation patterns. Based on the stability study, we recommend the full panel be made prior to staining.
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Affiliation(s)
- Kah Teong Soh
- Department of Flow and Image Cytometry, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA
| | - Alexis Conway
- Department of Flow and Image Cytometry, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA
| | - Xiaojun Liu
- Department of Flow and Image Cytometry, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA
| | - Paul K Wallace
- Department of Flow and Image Cytometry, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA
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Li JL, Lin YC, Wang YF, Monaghan SA, Ko BS, Lee CC. A Chunking-for-Pooling Strategy for Cytometric Representation Learning for Automatic Hematologic Malignancy Classification. IEEE J Biomed Health Inform 2022; 26:4773-4784. [PMID: 35588419 DOI: 10.1109/jbhi.2022.3175514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Differentiating types of hematologic malignancies is vital to determine therapeutic strategies for the newly diagnosed patients. Flow cytometry (FC) can be used as diagnostic indicator by measuring the multi-parameter fluorescent markers on thousands of antibody-bound cells, but the manual interpretation of large scale flow cytometry data has long been a time-consuming and complicated task for hematologists and laboratory professionals. Past studies have led to the development of representation learning algorithms to perform sample-level automatic classification. In this work, we propose a chunking-for-pooling strategy to include large-scale FC data into a supervised deep representation learning procedure for automatic hematologic malignancy classification. The use of discriminatively-trained representation learning strategy and the fixed-size chunking and pooling design are key components of this framework. It improves the discriminative power of the FC sample-level embedding and simultaneously addresses the robustness issue due to an inevitable use of down-sampling in conventional distribution based approaches for deriving FC representation. We evaluated our framework on two datasets. Our framework outperformed other baseline methods and achieved 92.3% unweighted average recall (UAR) for four-class recognition on the UPMC dataset and 85.0% UAR for five-class recognition on the hema.to dataset. We further compared the robustness of our proposed framework with that of the traditional downsampling approach. Analysis of the effects of the chunk size and the error cases revealed further insights about different hematologic malignancy characteristics in the FC data.
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6
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Monaghan SA, Li JL, Liu YC, Ko MY, Boyiadzis M, Chang TY, Wang YF, Lee CC, Swerdlow SH, Ko BS. A Machine Learning Approach to the Classification of Acute Leukemias and Distinction From Nonneoplastic Cytopenias Using Flow Cytometry Data. Am J Clin Pathol 2022; 157:546-553. [PMID: 34643210 DOI: 10.1093/ajcp/aqab148] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 08/01/2021] [Indexed: 11/14/2022] Open
Abstract
OBJECTIVES Flow cytometry (FC) is critical for the diagnosis and monitoring of hematologic malignancies. Machine learning (ML) methods rapidly classify multidimensional data and should dramatically improve the efficiency of FC data analysis. We aimed to build a model to classify acute leukemias, including acute promyelocytic leukemia (APL), and distinguish them from nonneoplastic cytopenias. We also sought to illustrate a method to identify key FC parameters that contribute to the model's performance. METHODS Using data from 531 patients who underwent evaluation for cytopenias and/or acute leukemia, we developed an ML model to rapidly distinguish among APL, acute myeloid leukemia/not APL, acute lymphoblastic leukemia, and nonneoplastic cytopenias. Unsupervised learning using gaussian mixture model and Fisher kernel methods were applied to FC listmode data, followed by supervised support vector machine classification. RESULTS High accuracy (ACC, 94.2%; area under the curve [AUC], 99.5%) was achieved based on the 37-parameter FC panel. Using only 3 parameters, however, yielded similar performance (ACC, 91.7%; AUC, 98.3%) and highlighted the significant contribution of light scatter properties. CONCLUSIONS Our findings underscore the potential for ML to automatically identify and prioritize FC specimens that have critical results, including APL and other acute leukemias.
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Affiliation(s)
- Sara A Monaghan
- Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- UPMC Presbyterian, Pittsburgh, PA, USA
| | - Jeng-Lin Li
- Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan
| | - Yen-Chun Liu
- Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Department of Pathology, St Jude Children’s Research Hospital, Memphis, TN, USA
| | - Ming-Ya Ko
- Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan
| | - Michael Boyiadzis
- Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- UPMC Hillman Cancer Center, Pittsburgh, PA, USA
| | | | | | - Chi-Chun Lee
- Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan
| | - Steven H Swerdlow
- Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- UPMC Presbyterian, Pittsburgh, PA, USA
| | - Bor-Sheng Ko
- Department of Hematological Oncology, National Taiwan University Cancer Center, Taipei, Taiwan
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
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7
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Brestoff JR, Frater JL. Contemporary Challenges in Clinical Flow Cytometry: Small Samples, Big Data, Little Time. J Appl Lab Med 2022; 7:931-944. [DOI: 10.1093/jalm/jfab176] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 11/15/2021] [Indexed: 12/13/2022]
Abstract
Abstract
Background
Immunophenotypic analysis of cell populations by flow cytometry has an established role in primary diagnosis and disease monitoring of many hematologic diseases. A persistent problem in evaluation of specimens is suboptimal cell counts and low cell viability, which results in an undesirable rate of analysis failure. In addition, the increased amount of data generated in flow cytometry challenges existing data analysis and reporting paradigms.
Content
We describe current and emerging technological improvements in cell analysis that allow the clinical laboratory to perform multiparameter analysis of specimens, including those with low cell counts and other quality issues. These technologies include conventional multicolor flow cytometry and new high-dimensional technologies, such as spectral flow cytometry and mass cytometry that enable detection of over 40 antigens simultaneously. The advantages and disadvantages of each approach are discussed. We also describe new innovations in flow cytometry data analysis, including artificial intelligence-aided techniques.
Summary
Improvements in analytical technology, in tandem with innovations in data analysis, data storage, and reporting mechanisms, help to optimize the quality of clinical flow cytometry. These improvements are essential because of the expanding role of flow cytometry in patient care.
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Affiliation(s)
- Jonathan R Brestoff
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO
| | - John L Frater
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO
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8
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Light sheet based volume flow cytometry (VFC) for rapid volume reconstruction and parameter estimation on the go. Sci Rep 2022; 12:78. [PMID: 34997009 PMCID: PMC8741756 DOI: 10.1038/s41598-021-03902-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 12/06/2021] [Indexed: 11/08/2022] Open
Abstract
Optical imaging is paramount for disease diagnosis and to access its progression over time. The proposed optical flow imaging (VFC/iLIFE) is a powerful technique that adds new capabilities (3D volume visualization, organelle-level resolution, and multi-organelle screening) to the existing system. Unlike state-of-the-art point-illumination-based biomedical imaging techniques, the sheet-based VFC technique is capable of single-shot sectional visualization, high throughput interrogation, real-time parameter estimation, and instant volume reconstruction with organelle-level resolution of live specimens. The specimen flow system was realized on a multichannel (Y-type) microfluidic chip that enables visualization of organelle distribution in several cells in-parallel at a relatively high flow-rate (2000 nl/min). The calibration of VFC system requires the study of point emitters (fluorescent beads) at physiologically relevant flow-rates (500-2000 nl/min) for determining flow-induced optical aberration in the system point spread function (PSF). Subsequently, the recorded raw images and volumes were computationally deconvolved with flow-variant PSF to reconstruct the cell volume. High throughput investigation of the mitochondrial network in HeLa cancer cell was carried out at sub-cellular resolution in real-time and critical parameters (mitochondria count and size distribution, morphology, entropy, and cell strain statistics) were determined on-the-go. These parameters determine the physiological state of cells, and the changes over-time, revealing the metastatic progression of diseases. Overall, the developed VFC system enables real-time monitoring of sub-cellular organelle organization at a high-throughput with high-content capacity.
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9
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Computational analysis of flow cytometry data in hematological malignancies: future clinical practice? Curr Opin Oncol 2020; 32:162-169. [PMID: 31876546 DOI: 10.1097/cco.0000000000000607] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
PURPOSE OF REVIEW This review outlines the advancements that have been made in computational analysis for clinical flow cytometry data in hematological malignancies. RECENT FINDINGS In recent years, computational analysis methods have been applied to clinical flow cytometry data of hematological malignancies with promising results. Most studies combined dimension reduction (principle component analysis) or clustering methods (FlowSOM, generalized mixture models) with machine learning classifiers (support vector machines, random forest). For diagnosis and classification of hematological malignancies, many studies have reported results concordant with manual expert analysis, including B-cell chronic lymphoid leukemia detection and acute leukemia classification. Other studies, e.g. concerning diagnosis of myelodysplastic syndromes and classification of lymphoma, have shown to be able to increase diagnostic accuracy. With respect to treatment response monitoring, studies have focused on, for example, computational minimal residual disease detection in multiple myeloma and posttreatment classification of healthy or diseased in acute myeloid leukemia. The results of these studies are encouraging, although accurate relapse prediction remains challenging. To facilitate clinical implementation, collaboration and (prospective) validation in multicenter setting are necessary. SUMMARY Computational analysis methods for clinical flow cytometry data hold the potential to increase ease of use, objectivity and accuracy in the clinical work-up of hematological malignancies.
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Lucchesi S, Furini S, Medaglini D, Ciabattini A. From Bivariate to Multivariate Analysis of Cytometric Data: Overview of Computational Methods and Their Application in Vaccination Studies. Vaccines (Basel) 2020; 8:E138. [PMID: 32244919 PMCID: PMC7157606 DOI: 10.3390/vaccines8010138] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Revised: 03/17/2020] [Accepted: 03/18/2020] [Indexed: 12/15/2022] Open
Abstract
Flow and mass cytometry are used to quantify the expression of multiple extracellular or intracellular molecules on single cells, allowing the phenotypic and functional characterization of complex cell populations. Multiparametric flow cytometry is particularly suitable for deep analysis of immune responses after vaccination, as it allows to measure the frequency, the phenotype, and the functional features of antigen-specific cells. When many parameters are investigated simultaneously, it is not feasible to analyze all the possible bi-dimensional combinations of marker expression with classical manual analysis and the adoption of advanced automated tools to process and analyze high-dimensional data sets becomes necessary. In recent years, the development of many tools for the automated analysis of multiparametric cytometry data has been reported, with an increasing record of publications starting from 2014. However, the use of these tools has been preferentially restricted to bioinformaticians, while few of them are routinely employed by the biomedical community. Filling the gap between algorithms developers and final users is fundamental for exploiting the advantages of computational tools in the analysis of cytometry data. The potentialities of automated analyses range from the improvement of the data quality in the pre-processing steps up to the unbiased, data-driven examination of complex datasets using a variety of algorithms based on different approaches. In this review, an overview of the automated analysis pipeline is provided, spanning from the pre-processing phase to the automated population analysis. Analysis based on computational tools might overcame both the subjectivity of manual gating and the operator-biased exploration of expected populations. Examples of applications of automated tools that have successfully improved the characterization of different cell populations in vaccination studies are also presented.
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Affiliation(s)
- Simone Lucchesi
- Laboratory of Molecular Microbiology and Biotechnology (LA.M.M.B.), Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy; (S.L.); (D.M.)
| | - Simone Furini
- Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy;
| | - Donata Medaglini
- Laboratory of Molecular Microbiology and Biotechnology (LA.M.M.B.), Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy; (S.L.); (D.M.)
| | - Annalisa Ciabattini
- Laboratory of Molecular Microbiology and Biotechnology (LA.M.M.B.), Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy; (S.L.); (D.M.)
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Li JL, Wang YF, Ko BS, Li CC, Tang JL, Lee CC. Learning a Cytometric Deep Phenotype Embedding for Automatic Hematological Malignancies Classification. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:1733-1736. [PMID: 31946232 DOI: 10.1109/embc.2019.8856728] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Identification of minimal residual disease (MRD) is important in assessing the prognosis of acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS). The current best clinical practice relies heavily on Flow Cytometry (FC) examination. However, the current FC diagnostic examination requires trained physicians to perform lengthy manual interpretation on high-dimensional FC data measurements of each specimen. The difficulty in handling idiosyncrasy between interpreters along with the time-consuming diagnostic process has become one of the major bottlenecks in advancing the treatment of hematological diseases. In this work, we develop an automatic MRD classifications (AML, MDS, normal) algorithm based on learning a deep phenotype representation from a large cohort of retrospective clinical data with over 2000 real patients' FC samples. We propose to learn a cytometric deep embedding through cell-level autoencoder combined with specimen-level latent Fisher-scoring vectorization. Our method achieves an average AUC of 0.943 across four different hematological malignancies classification tasks, and our analysis further reveals that with only half of the FC markers would be sufficient in obtaining these high recognition accuracies.
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12
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Badea M, Gaman L, Delia C, Ilea A, Leașu F, Henríquez-Hernández LA, Luzardo OP, Rădoi M, Rogozea L. Trends of Lipophilic, Antioxidant and Hematological Parameters Associated with Conventional and Electronic Smoking Habits in Middle-Age Romanians. J Clin Med 2019; 8:E665. [PMID: 31083602 PMCID: PMC6571835 DOI: 10.3390/jcm8050665] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 05/01/2019] [Accepted: 05/09/2019] [Indexed: 01/15/2023] Open
Abstract
It is known that cigarette smoking is correlated with medical associated inquires. New electronic cigarettes are intensively advertised as an alternative to conventional smoking, but only a few studies demonstrate their harmful potential. A cross-sectional study was designed using 150 subjects from Brasov (Romania), divided into three groups: non-smokers (NS = 58), conventional cigarettes smokers (CS = 58) and electronic cigarettes users (ECS = 34). The aim of this study was to determine levels of some plasma lipophilic and hematological components, and the total antioxidant status that could be associated with the smoking status of the subjects. Serum low density lipoproteins (LDL) cholesterol increased significantly for ECS participants versus NS group (18.9% difference) (p < 0.05). Also, the CS group is characterized by an increase of serum LDL cholesterol (7.9% difference vs. NS), but with no significant statistical difference. The variation of median values of serum very low density lipoproteins (VLDL) was in order NS < ECS < CS, with statistical difference between NS and CS groups (34.6% difference; p = 0.023). When comparing the antioxidant status of the three groups, significant differences (p < 0.05) were obtained between NS vs. CS and NS vs. ECS. Similar behavior was identified for CS and ECS. Statistically significant changes (p < 0.0001) for both vitamin A and vitamin E were identified in the blood of NS vs. CS and NS vs. ECS, and also when comparing vitamin A in the blood of the CS group versus the ECS group (p < 0.05). When all groups were compared, the difference in the white blood cell (WBC) was (p = 0.008). A slight increase in the red blood cell (RBC) count was observed, but with no statistical difference between groups. These results indicated that conventional cigarette and e-cigarette usage promotes the production of excess reactive oxygen species, involving different pathways, different antioxidants and bioactive molecules.
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Affiliation(s)
- Mihaela Badea
- Faculty of Medicine, Transilvania University of Brasov, Brasov 500019, Romania.
| | - Laura Gaman
- "Carol Davila" University of Medicine and Pharmacy, Bucharest 050474, Romania.
| | - Corina Delia
- National Institute for Mother and Child Health "Alessandrescu-Rusescu", Bucharest 20395, Romania.
| | - Anca Ilea
- Faculty of Medicine, Transilvania University of Brasov, Brasov 500019, Romania.
| | - Florin Leașu
- Faculty of Medicine, Transilvania University of Brasov, Brasov 500019, Romania.
| | - Luis Alberto Henríquez-Hernández
- Toxicology Unit, Clinical Sciences Department, Universidad de Las Palmas de Gran Canaria, Paseo Blas Cabrera Felipe, s/n, 35019 Las Palmas de Gran Canaria, Spain.
| | - Octavio P Luzardo
- Toxicology Unit, Clinical Sciences Department, Universidad de Las Palmas de Gran Canaria, Paseo Blas Cabrera Felipe, s/n, 35019 Las Palmas de Gran Canaria, Spain.
| | - Mariana Rădoi
- Faculty of Medicine, Transilvania University of Brasov, Brasov 500019, Romania.
| | - Liliana Rogozea
- Faculty of Medicine, Transilvania University of Brasov, Brasov 500019, Romania.
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13
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Ko BS, Wang YF, Li JL, Li CC, Weng PF, Hsu SC, Hou HA, Huang HH, Yao M, Lin CT, Liu JH, Tsai CH, Huang TC, Wu SJ, Huang SY, Chou WC, Tien HF, Lee CC, Tang JL. Clinically validated machine learning algorithm for detecting residual diseases with multicolor flow cytometry analysis in acute myeloid leukemia and myelodysplastic syndrome. EBioMedicine 2018; 37:91-100. [PMID: 30361063 PMCID: PMC6284584 DOI: 10.1016/j.ebiom.2018.10.042] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2018] [Revised: 10/14/2018] [Accepted: 10/14/2018] [Indexed: 12/27/2022] Open
Abstract
Background Multicolor flow cytometry (MFC) analysis is widely used to identify minimal residual disease (MRD) after treatment for acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS). However, current manual interpretation suffers from drawbacks of time consuming and interpreter idiosyncrasy. Artificial intelligence (AI), with the expertise in assisting repetitive or complex analysis, represents a potential solution for these drawbacks. Methods From 2009 to 2016, 5333 MFC data from 1742 AML or MDS patients were collected. The 287 MFC data at post-induction were selected as the outcome set for clinical outcome validation. The rest were 4:1 randomized into the training set (n = 4039) and the validation set (n = 1007). AI algorithm learned a multi-dimensional MFC phenotype from the training set and input it to support vector machine (SVM) classifier after Gaussian mixture model (GMM) modeling, and the performance was evaluated in The validation set. Findings Promising accuracies (84·6% to 92·4%) and AUCs (0·921–0·950) were achieved by the developed algorithms. Interestingly, the algorithm from even one testing tube achieved similar performance. The clinical significance was validated in the outcome set, and normal MFC interpreted by the AI predicted better progression-free survival (10·9 vs 4·9 months, p < 0·0001) and overall survival (13·6 vs 6·5 months, p < 0·0001) for AML. Interpretation Through large-scaled clinical validation, we showed that AI algorithms can produce efficient and clinically-relevant MFC analysis. This approach also possesses a great advantage of the ability to integrate other clinical tests. Fund This work was supported by the Ministry of Science and Technology (107-2634-F-007-006 and 103–2314-B-002-185-MY2) of Taiwan.
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Affiliation(s)
- Bor-Sheng Ko
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Yu-Fen Wang
- Tai-Cheng Stem Cell Therapy Center, National Taiwan University, Taipei, Taiwan
| | - Jeng-Lin Li
- Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan
| | - Chi-Cheng Li
- Tai-Cheng Stem Cell Therapy Center, National Taiwan University, Taipei, Taiwan; Center of Stem Cell and Precision Medicine, Buddhist Tzu Chi General Hospital, Hualien, Taiwan
| | - Pei-Fang Weng
- Tai-Cheng Stem Cell Therapy Center, National Taiwan University, Taipei, Taiwan
| | - Szu-Chun Hsu
- Department of Laboratory Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Hsin-An Hou
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Huai-Hsuan Huang
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Ming Yao
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Chien-Ting Lin
- Tai-Cheng Stem Cell Therapy Center, National Taiwan University, Taipei, Taiwan
| | - Jia-Hau Liu
- Tai-Cheng Stem Cell Therapy Center, National Taiwan University, Taipei, Taiwan
| | - Cheng-Hong Tsai
- Tai-Cheng Stem Cell Therapy Center, National Taiwan University, Taipei, Taiwan
| | - Tai-Chung Huang
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Shang-Ju Wu
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Shang-Yi Huang
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Wen-Chien Chou
- Department of Laboratory Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Hwei-Fang Tien
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Chi-Chun Lee
- Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan; Joint Research Center for AI Technology and All Vista Healthcare, Ministry of Science and Technology, Taiwan.
| | - Jih-Luh Tang
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan; Tai-Cheng Stem Cell Therapy Center, National Taiwan University, Taipei, Taiwan.
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14
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Alsalem MA, Zaidan AA, Zaidan BB, Hashim M, Albahri OS, Albahri AS, Hadi A, Mohammed KI. Systematic Review of an Automated Multiclass Detection and Classification System for Acute Leukaemia in Terms of Evaluation and Benchmarking, Open Challenges, Issues and Methodological Aspects. J Med Syst 2018; 42:204. [PMID: 30232632 DOI: 10.1007/s10916-018-1064-9] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2018] [Accepted: 09/06/2018] [Indexed: 10/28/2022]
Abstract
This study aims to systematically review prior research on the evaluation and benchmarking of automated acute leukaemia classification tasks. The review depends on three reliable search engines: ScienceDirect, Web of Science and IEEE Xplore. A research taxonomy developed for the review considers a wide perspective for automated detection and classification of acute leukaemia research and reflects the usage trends in the evaluation criteria in this field. The developed taxonomy consists of three main research directions in this domain. The taxonomy involves two phases. The first phase includes all three research directions. The second one demonstrates all the criteria used for evaluating acute leukaemia classification. The final set of studies includes 83 investigations, most of which focused on enhancing the accuracy and performance of detection and classification through proposed methods or systems. Few efforts were made to undertake the evaluation issues. According to the final set of articles, three groups of articles represented the main research directions in this domain: 56 articles highlighted the proposed methods, 22 articles involved proposals for system development and 5 papers centred on evaluation and comparison. The other taxonomy side included 16 main and sub-evaluation and benchmarking criteria. This review highlights three serious issues in the evaluation and benchmarking of multiclass classification of acute leukaemia, namely, conflicting criteria, evaluation criteria and criteria importance. It also determines the weakness of benchmarking tools. To solve these issues, multicriteria decision-making (MCDM) analysis techniques were proposed as effective recommended solutions in the methodological aspect. This methodological aspect involves a proposed decision support system based on MCDM for evaluation and benchmarking to select suitable multiclass classification models for acute leukaemia. The said support system is examined and has three sequential phases. Phase One presents the identification procedure and process for establishing a decision matrix based on a crossover of evaluation criteria and acute leukaemia multiclass classification models. Phase Two describes the decision matrix development for the selection of acute leukaemia classification models based on the integrated Best and worst method (BWM) and VIKOR. Phase Three entails the validation of the proposed system.
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Affiliation(s)
- M A Alsalem
- Department of Computing, Universiti Pendidikan Sultan Idris, Tanjong Malim, Perak, Malaysia
| | - A A Zaidan
- Department of Computing, Universiti Pendidikan Sultan Idris, Tanjong Malim, Perak, Malaysia.
| | - B B Zaidan
- Department of Computing, Universiti Pendidikan Sultan Idris, Tanjong Malim, Perak, Malaysia
| | - M Hashim
- Department of Computing, Universiti Pendidikan Sultan Idris, Tanjong Malim, Perak, Malaysia
| | - O S Albahri
- Department of Computing, Universiti Pendidikan Sultan Idris, Tanjong Malim, Perak, Malaysia
| | - A S Albahri
- Department of Computing, Universiti Pendidikan Sultan Idris, Tanjong Malim, Perak, Malaysia
| | - Ali Hadi
- Department of Computing, Universiti Pendidikan Sultan Idris, Tanjong Malim, Perak, Malaysia
| | - K I Mohammed
- Department of Computing, Universiti Pendidikan Sultan Idris, Tanjong Malim, Perak, Malaysia
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15
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Duetz C, Westers TM, van de Loosdrecht AA. Clinical Implication of Multi-Parameter Flow Cytometry in Myelodysplastic Syndromes. Pathobiology 2018; 86:14-23. [PMID: 30227408 PMCID: PMC6482988 DOI: 10.1159/000490727] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2018] [Accepted: 06/08/2018] [Indexed: 12/16/2022] Open
Abstract
Myelodysplastic syndromes (MDS) are a challenging group of diseases for clinicians and researchers, as both disease course and pathobiology are highly heterogeneous. In (suspected) MDS patients, multi-parameter flow cytometry can aid in establishing diagnosis, risk stratification and choice of therapy. This review addresses the developments and future directions of multi-parameter flow cytometry scores in MDS. Additionally, we propose an integrated diagnostic algorithm for suspected MDS.
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Affiliation(s)
- Carolien Duetz
- Department of Hematology, Cancer Center Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
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16
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Alsalem MA, Zaidan AA, Zaidan BB, Hashim M, Madhloom HT, Azeez ND, Alsyisuf S. A review of the automated detection and classification of acute leukaemia: Coherent taxonomy, datasets, validation and performance measurements, motivation, open challenges and recommendations. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 158:93-112. [PMID: 29544792 DOI: 10.1016/j.cmpb.2018.02.005] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2017] [Revised: 01/19/2018] [Accepted: 02/02/2018] [Indexed: 06/08/2023]
Abstract
CONTEXT Acute leukaemia diagnosis is a field requiring automated solutions, tools and methods and the ability to facilitate early detection and even prediction. Many studies have focused on the automatic detection and classification of acute leukaemia and their subtypes to promote enable highly accurate diagnosis. OBJECTIVE This study aimed to review and analyse literature related to the detection and classification of acute leukaemia. The factors that were considered to improve understanding on the field's various contextual aspects in published studies and characteristics were motivation, open challenges that confronted researchers and recommendations presented to researchers to enhance this vital research area. METHODS We systematically searched all articles about the classification and detection of acute leukaemia, as well as their evaluation and benchmarking, in three main databases: ScienceDirect, Web of Science and IEEE Xplore from 2007 to 2017. These indices were considered to be sufficiently extensive to encompass our field of literature. RESULTS Based on our inclusion and exclusion criteria, 89 articles were selected. Most studies (58/89) focused on the methods or algorithms of acute leukaemia classification, a number of papers (22/89) covered the developed systems for the detection or diagnosis of acute leukaemia and few papers (5/89) presented evaluation and comparative studies. The smallest portion (4/89) of articles comprised reviews and surveys. DISCUSSION Acute leukaemia diagnosis, which is a field requiring automated solutions, tools and methods, entails the ability to facilitate early detection or even prediction. Many studies have been performed on the automatic detection and classification of acute leukaemia and their subtypes to promote accurate diagnosis. CONCLUSIONS Research areas on medical-image classification vary, but they are all equally vital. We expect this systematic review to help emphasise current research opportunities and thus extend and create additional research fields.
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Affiliation(s)
- M A Alsalem
- Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Malaysia
| | - A A Zaidan
- Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Malaysia.
| | - B B Zaidan
- Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Malaysia
| | - M Hashim
- Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Malaysia
| | - H T Madhloom
- Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Malaysia
| | - N D Azeez
- Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Malaysia
| | - S Alsyisuf
- Faculty of on information Science and Engineering, Management and Science university, Shah Alam, Malaysia
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17
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Cosma G, McArdle SE, Reeder S, Foulds GA, Hood S, Khan M, Pockley AG. Identifying the Presence of Prostate Cancer in Individuals with PSA Levels <20 ng ml -1 Using Computational Data Extraction Analysis of High Dimensional Peripheral Blood Flow Cytometric Phenotyping Data. Front Immunol 2017; 8:1771. [PMID: 29326690 PMCID: PMC5741695 DOI: 10.3389/fimmu.2017.01771] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2017] [Accepted: 11/27/2017] [Indexed: 11/23/2022] Open
Abstract
Determining whether an asymptomatic individual with Prostate-Specific Antigen (PSA) levels below 20 ng ml−1 has prostate cancer in the absence of definitive, biopsy-based evidence continues to present a significant challenge to clinicians who must decide whether such individuals with low PSA values have prostate cancer. Herein, we present an advanced computational data extraction approach which can identify the presence of prostate cancer in men with PSA levels <20 ng ml−1 on the basis of peripheral blood immune cell profiles that have been generated using multi-parameter flow cytometry. Statistical analysis of immune phenotyping datasets relating to the presence and prevalence of key leukocyte populations in the peripheral blood, as generated from individuals undergoing routine tests for prostate cancer (including tissue biopsy) using multi-parametric flow cytometric analysis, was unable to identify significant relationships between leukocyte population profiles and the presence of benign disease (no prostate cancer) or prostate cancer. By contrast, a Genetic Algorithm computational approach identified a subset of five flow cytometry features (CD8+CD45RA−CD27−CD28− (CD8+ Effector Memory cells); CD4+CD45RA−CD27−CD28− (CD4+ Terminally Differentiated Effector Memory Cells re-expressing CD45RA); CD3−CD19+ (B cells); CD3+CD56+CD8+CD4+ (NKT cells)) from a set of twenty features, which could potentially discriminate between benign disease and prostate cancer. These features were used to construct a prostate cancer prediction model using the k-Nearest-Neighbor classification algorithm. The proposed model, which takes as input the set of flow cytometry features, outperformed the predictive model which takes PSA values as input. Specifically, the flow cytometry-based model achieved Accuracy = 83.33%, AUC = 83.40%, and optimal ROC points of FPR = 16.13%, TPR = 82.93%, whereas the PSA-based model achieved Accuracy = 77.78%, AUC = 76.95%, and optimal ROC points of FPR = 29.03%, TPR = 82.93%. Combining PSA and flow cytometry predictors achieved Accuracy = 79.17%, AUC = 78.17% and optimal ROC points of FPR = 29.03%, TPR = 85.37%. The results demonstrate the value of computational intelligence-based approaches for interrogating immunophenotyping datasets and that combining peripheral blood phenotypic profiling with PSA levels improves diagnostic accuracy compared to using PSA test alone. These studies also demonstrate that the presence of cancer is reflected in changes in the peripheral blood immune phenotype profile which can be identified using computational analysis and interpretation of complex flow cytometry datasets.
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Affiliation(s)
- Georgina Cosma
- School of Science and Technology, Nottingham Trent University, Nottingham, United Kingdom
| | - Stéphanie E McArdle
- John van Geest Cancer Research Centre, School of Science and Technology, Nottingham Trent University, Nottingham, United Kingdom
| | - Stephen Reeder
- John van Geest Cancer Research Centre, School of Science and Technology, Nottingham Trent University, Nottingham, United Kingdom
| | - Gemma A Foulds
- John van Geest Cancer Research Centre, School of Science and Technology, Nottingham Trent University, Nottingham, United Kingdom
| | - Simon Hood
- John van Geest Cancer Research Centre, School of Science and Technology, Nottingham Trent University, Nottingham, United Kingdom
| | - Masood Khan
- University Hospitals of Leicester NHS Trust, Leicester, United Kingdom
| | - A Graham Pockley
- John van Geest Cancer Research Centre, School of Science and Technology, Nottingham Trent University, Nottingham, United Kingdom
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18
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Banjar H, Adelson D, Brown F, Chaudhri N. Intelligent Techniques Using Molecular Data Analysis in Leukaemia: An Opportunity for Personalized Medicine Support System. BIOMED RESEARCH INTERNATIONAL 2017; 2017:3587309. [PMID: 28812013 PMCID: PMC5547708 DOI: 10.1155/2017/3587309] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/05/2017] [Revised: 06/12/2017] [Accepted: 06/15/2017] [Indexed: 12/05/2022]
Abstract
The use of intelligent techniques in medicine has brought a ray of hope in terms of treating leukaemia patients. Personalized treatment uses patient's genetic profile to select a mode of treatment. This process makes use of molecular technology and machine learning, to determine the most suitable approach to treating a leukaemia patient. Until now, no reviews have been published from a computational perspective concerning the development of personalized medicine intelligent techniques for leukaemia patients using molecular data analysis. This review studies the published empirical research on personalized medicine in leukaemia and synthesizes findings across studies related to intelligence techniques in leukaemia, with specific attention to particular categories of these studies to help identify opportunities for further research into personalized medicine support systems in chronic myeloid leukaemia. A systematic search was carried out to identify studies using intelligence techniques in leukaemia and to categorize these studies based on leukaemia type and also the task, data source, and purpose of the studies. Most studies used molecular data analysis for personalized medicine, but future advancement for leukaemia patients requires molecular models that use advanced machine-learning methods to automate decision-making in treatment management to deliver supportive medical information to the patient in clinical practice.
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Affiliation(s)
- Haneen Banjar
- School of Computer Science, University of Adelaide, Adelaide, SA, Australia
- Department of Computer Science, King Abdulaziz University, Jeddah, Saudi Arabia
| | - David Adelson
- School of Molecular and Biomedical Science, University of Adelaide, Adelaide, SA, Australia
| | - Fred Brown
- School of Computer Science, University of Adelaide, Adelaide, SA, Australia
| | - Naeem Chaudhri
- Oncology Centre, Section of Hematology, HSCT, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
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Measurement and Clinical Significance of Biomarkers of Oxidative Stress in Humans. OXIDATIVE MEDICINE AND CELLULAR LONGEVITY 2017; 2017:6501046. [PMID: 28698768 PMCID: PMC5494111 DOI: 10.1155/2017/6501046] [Citation(s) in RCA: 432] [Impact Index Per Article: 61.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/14/2017] [Revised: 04/26/2017] [Accepted: 05/21/2017] [Indexed: 12/11/2022]
Abstract
Oxidative stress is the result of the imbalance between reactive oxygen species (ROS) formation and enzymatic and nonenzymatic antioxidants. Biomarkers of oxidative stress are relevant in the evaluation of the disease status and of the health-enhancing effects of antioxidants. We aim to discuss the major methodological bias of methods used for the evaluation of oxidative stress in humans. There is a lack of consensus concerning the validation, standardization, and reproducibility of methods for the measurement of the following: (1) ROS in leukocytes and platelets by flow cytometry, (2) markers based on ROS-induced modifications of lipids, DNA, and proteins, (3) enzymatic players of redox status, and (4) total antioxidant capacity of human body fluids. It has been suggested that the bias of each method could be overcome by using indexes of oxidative stress that include more than one marker. However, the choice of the markers considered in the global index should be dictated by the aim of the study and its design, as well as by the clinical relevance in the selected subjects. In conclusion, the clinical significance of biomarkers of oxidative stress in humans must come from a critical analysis of the markers that should give an overall index of redox status in particular conditions.
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