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Gómez‐Rojas S, Segura GP, Ollé J, Carreño Gómez‐Tarragona G, Medina JG, Aguado JM, Guerrero EV, Santaella MP, Martínez‐López J. A machine learning tool for the diagnosis of SARS-CoV-2 infection from hemogram parameters. J Cell Mol Med 2023; 27:3423-3430. [PMID: 37882471 PMCID: PMC10660618 DOI: 10.1111/jcmm.17864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 06/20/2023] [Accepted: 07/05/2023] [Indexed: 10/27/2023] Open
Abstract
Monocytes and neutrophils play key roles in the cytokine storm triggered by SARS-CoV-2 infection, which changes their conformation and function. These changes are detectable at the cellular and molecular level and may be different to what is observed in other respiratory infections. Here, we applied machine learning (ML) to develop and validate an algorithm to diagnose COVID-19 using blood parameters. In this retrospective single-center study, 49 hemogram parameters from 12,321 patients with clinical suspicion of COVID-19 and tested by RT-PCR (4239 positive and 8082 negative) were analysed. The dataset was randomly divided into training and validation sets. Blood cell parameters and patient age were used to construct the predictive model with the support vector machine (SVM) tool. The model constructed from the training set (5936 patients) achieved an accuracy for diagnosis of SARS-CoV-2 infection of 0.952 (95% CI: 0.875-0.892). Test sensitivity and specificity was 0.868 and 0.899, respectively, with a positive (PPV) and negative (NPV) predictive value of 0.896 and 0.872, respectively (prevalence 0.50). The validation set model (4964 patients) achieved an accuracy of 0.894 (95% CI: 0.883-0.903). Test sensitivity and specificity was 0.8922 and 0.8951, respectively, with a positive (PPV) and negative (NPV) predictive value of 0.817 and 0.94, respectively (prevalence 0.34). The area under the receiver operating characteristic curve was 0.952 for the algorithm performance. This algorithm may allow to rule out COVID-19 diagnosis with 94% of probability. This represents a great advance for early diagnostic orientation and guiding clinical decisions.
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Affiliation(s)
- S. Gómez‐Rojas
- Department of HematologyHospital Universitario 12 octubreMadridSpain
| | - G. Pérez Segura
- Department of HematologyHospital Universitario 12 octubreMadridSpain
| | - J. Ollé
- Conceptos Claros CoBarcelonaSpain
| | | | - J. González Medina
- Department of HematologyHospital Universitario Fundación Jiménez DíazMadridSpain
| | - J. M. Aguado
- Unit of Infectious DiseasesHospital Universitario "12 de Octubre", Instituto de Investigación Sanitaria Hospital "12 de Octubre" (i+12), CIBERINFEC, ISCIIIMadridSpain
- Department of Medicine, School of MedicineUniversidad ComplutenseMadridSpain
| | - E. Vera Guerrero
- Department of HematologyHospital Universitario 12 octubreMadridSpain
| | - M. Poza Santaella
- Department of HematologyHospital Universitario 12 octubreMadridSpain
| | - J. Martínez‐López
- Department of HematologyHospital Universitario 12 octubreMadridSpain
- Department of Medicine, School of MedicineUniversidad ComplutenseMadridSpain
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2
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Tsai HF, Podder S, Chen PY. Microsystem Advances through Integration with Artificial Intelligence. MICROMACHINES 2023; 14:826. [PMID: 37421059 DOI: 10.3390/mi14040826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 04/04/2023] [Accepted: 04/06/2023] [Indexed: 07/09/2023]
Abstract
Microfluidics is a rapidly growing discipline that involves studying and manipulating fluids at reduced length scale and volume, typically on the scale of micro- or nanoliters. Under the reduced length scale and larger surface-to-volume ratio, advantages of low reagent consumption, faster reaction kinetics, and more compact systems are evident in microfluidics. However, miniaturization of microfluidic chips and systems introduces challenges of stricter tolerances in designing and controlling them for interdisciplinary applications. Recent advances in artificial intelligence (AI) have brought innovation to microfluidics from design, simulation, automation, and optimization to bioanalysis and data analytics. In microfluidics, the Navier-Stokes equations, which are partial differential equations describing viscous fluid motion that in complete form are known to not have a general analytical solution, can be simplified and have fair performance through numerical approximation due to low inertia and laminar flow. Approximation using neural networks trained by rules of physical knowledge introduces a new possibility to predict the physicochemical nature. The combination of microfluidics and automation can produce large amounts of data, where features and patterns that are difficult to discern by a human can be extracted by machine learning. Therefore, integration with AI introduces the potential to revolutionize the microfluidic workflow by enabling the precision control and automation of data analysis. Deployment of smart microfluidics may be tremendously beneficial in various applications in the future, including high-throughput drug discovery, rapid point-of-care-testing (POCT), and personalized medicine. In this review, we summarize key microfluidic advances integrated with AI and discuss the outlook and possibilities of combining AI and microfluidics.
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Affiliation(s)
- Hsieh-Fu Tsai
- Department of Biomedical Engineering, Chang Gung University, Taoyuan City 333, Taiwan
- Department of Neurosurgery, Chang Gung Memorial Hospital, Keelung, Keelung City 204, Taiwan
- Center for Biomedical Engineering, Chang Gung University, Taoyuan City 333, Taiwan
| | - Soumyajit Podder
- Department of Biomedical Engineering, Chang Gung University, Taoyuan City 333, Taiwan
| | - Pin-Yuan Chen
- Department of Biomedical Engineering, Chang Gung University, Taoyuan City 333, Taiwan
- Department of Neurosurgery, Chang Gung Memorial Hospital, Keelung, Keelung City 204, Taiwan
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3
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Allam S, Nasr K, Khalid F, Shah Z, Khan Suheb MZ, Mulla S, Vikash S, Bou Zerdan M, Anwer F, Chaulagain CP. Liquid biopsies and minimal residual disease in myeloid malignancies. Front Oncol 2023; 13:1164017. [PMID: 37213280 PMCID: PMC10196237 DOI: 10.3389/fonc.2023.1164017] [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: 02/11/2023] [Accepted: 04/25/2023] [Indexed: 05/23/2023] Open
Abstract
Minimal residual disease (MRD) assessment through blood component sampling by liquid biopsies (LBs) is increasingly being investigated in myeloid malignancies. Blood components then undergo molecular analysis by flow cytometry or sequencing techniques and can be used as a powerful tool for prognostic and predictive purposes in myeloid malignancies. There is evidence and more is evolving about the quantification and identification of cell-based and gene-based biomarkers in myeloid malignancies to monitor treatment response. MRD based acute myeloid leukemia protocol and clinical trials are currently incorporating LB testing and preliminary results are encouraging for potential widespread use in clinic in the near future. MRD monitoring using LBs are not standard in myelodysplastic syndrome (MDS) but this is an area of active investigation. In the future, LBs can replace more invasive techniques such as bone marrow biopsies. However, the routine clinical application of these markers continues to be an issue due to lack of standardization and limited number of studies investigating their specificities. Integrating artificial intelligence (AI) could help simplify the complex interpretation of molecular testing and reduce errors related to operator dependency. Though the field is rapidly evolving, the applicability of MRD testing using LB is mostly limited to research setting at this time due to the need for validation, regulatory approval, payer coverage, and cost issues. This review focuses on the types of biomarkers, most recent research exploring MRD and LB in myeloid malignancies, ongoing clinical trials, and the future of LB in the setting of AI.
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Affiliation(s)
- Sabine Allam
- Department of Medicine and Medical Sciences, University of Balamand, Dekwaneh, Lebanon
| | - Kristina Nasr
- Department of Medicine and Medical Sciences, University of Balamand, Dekwaneh, Lebanon
| | - Farhan Khalid
- Department of Internal Medicine, Monmouth Medical Center, Long Branch, NJ, United States
| | - Zunairah Shah
- Department of Internal Medicine, Weiss Memorial Hospital, Chicago, IL, United States
| | | | - Sana Mulla
- Department of Internal Medicine, St Mary’s Medical Center, Apple Valley, CA, United States
| | - Sindhu Vikash
- Department of Medicine, Jacobi Medical center/AECOM Bronx, Bronx, NY, United States
| | - Maroun Bou Zerdan
- Department of Internal Medicine, SUNY Upstate Medical University, New York, NY, United States
| | - Faiz Anwer
- Department of Hematology and Oncology, Taussig Cancer Center, Cleveland Clinic, Cleveland, OH, United States
| | - Chakra P. Chaulagain
- Department of Hematology and Oncology, Maroone Cancer Center, Cleveland Clinic Florida, Weston, FL, United States
- *Correspondence: Chakra P. Chaulagain,
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Raji H, Tayyab M, Sui J, Mahmoodi SR, Javanmard M. Biosensors and machine learning for enhanced detection, stratification, and classification of cells: a review. Biomed Microdevices 2022; 24:26. [PMID: 35953679 DOI: 10.1007/s10544-022-00627-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/22/2022] [Indexed: 12/16/2022]
Abstract
Biological cells, by definition, are the basic units which contain the fundamental molecules of life of which all living things are composed. Understanding how they function and differentiating cells from one another, therefore, is of paramount importance for disease diagnostics as well as therapeutics. Sensors focusing on the detection and stratification of cells have gained popularity as technological advancements have allowed for the miniaturization of various components inching us closer to Point-of-Care (POC) solutions with each passing day. Furthermore, Machine Learning has allowed for enhancement in the analytical capabilities of these various biosensing modalities, especially the challenging task of classification of cells into various categories using a data-driven approach rather than physics-driven. In this review, we provide an account of how Machine Learning has been applied explicitly to sensors that detect and classify cells. We also provide a comparison of how different sensing modalities and algorithms affect the classifier accuracy and the dataset size required.
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Affiliation(s)
- Hassan Raji
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, 08854, USA
| | - Muhammad Tayyab
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, 08854, USA
| | - Jianye Sui
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, 08854, USA
| | - Seyed Reza Mahmoodi
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, 08854, USA
| | - Mehdi Javanmard
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, 08854, USA.
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Machine Learning and Deep Learning Applications in Multiple Myeloma Diagnosis, Prognosis, and Treatment Selection. Cancers (Basel) 2022; 14:cancers14030606. [PMID: 35158874 PMCID: PMC8833500 DOI: 10.3390/cancers14030606] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 01/20/2022] [Accepted: 01/24/2022] [Indexed: 02/01/2023] Open
Abstract
Simple Summary Multiple myeloma is a malignant neoplasm of plasma cells with complex pathogenesis. With major progresses in multiple myeloma research, it is essential that we reconsider our methods for diagnosing and monitoring multiple myeloma disease. This fact needs the integration of serology, histology, radiology, and genetic data; therefore, multiple myeloma study has generated massive quantities of granular high-dimensional data exceeding human understanding. With improved computational techniques, artificial intelligence tools for data processing and analysis are becoming more and more relevant. Artificial intelligence represents a wide set of algorithms for which machine learning and deep learning are presently among the most impactful. This review focuses on artificial intelligence applications in multiple myeloma research, first illustrating machine learning and deep learning procedures and workflow, followed by how these algorithms are used for multiple myeloma diagnosis, prognosis, bone lesions identification, and evaluation of response to the treatment. Abstract Artificial intelligence has recently modified the panorama of oncology investigation thanks to the use of machine learning algorithms and deep learning strategies. Machine learning is a branch of artificial intelligence that involves algorithms that analyse information, learn from that information, and then employ their discoveries to make abreast choice, while deep learning is a field of machine learning basically represented by algorithms inspired by the organization and function of the brain, named artificial neural networks. In this review, we examine the possibility of the artificial intelligence applications in multiple myeloma evaluation, and we report the most significant experimentations with respect to the machine and deep learning procedures in the relevant field. Multiple myeloma is one of the most common haematological malignancies in the world, and among them, it is one of the most difficult ones to cure due to the high occurrence of relapse and chemoresistance. Machine learning- and deep learning-based studies are expected to be among the future strategies to challenge this negative-prognosis tumour via the detection of new markers for their prompt discovery and therapy selection and by a better evaluation of its relapse and survival.
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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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7
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Talwar V, Chufal KS, Joga S. Artificial Intelligence: A New Tool in Oncologist's Armamentarium. Indian J Med Paediatr Oncol 2021. [DOI: 10.1055/s-0041-1735577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022] Open
Abstract
AbstractArtificial intelligence (AI) has become an essential tool in human life because of its pivotal role in communications, transportation, media, and social networking. Inspired by the complex neuronal network and its functions in human beings, AI, using computer-based algorithms and training, had been explored since the 1950s. To tackle the enormous amount of patients' clinical data, imaging, histopathological data, and the increasing pace of research on new treatments and clinical trials, and ever-changing guidelines for treatment with the advent of novel drugs and evidence, AI is the need of the hour. There are numerous publications and active work on AI's role in the field of oncology. In this review, we discuss the fundamental terminology of AI, its applications in oncology on the whole, and its limitations. There is an inter-relationship between AI, machine learning and, deep learning. The virtual branch of AI deals with machine learning. While the physical branch of AI deals with the delivery of different forms of treatment—surgery, targeted drug delivery, and elderly care. The applications of AI in oncology include cancer screening, diagnosis (clinical, imaging, and histopathological), radiation therapy (image acquisition, tumor and organs at risk segmentation, image registration, planning, and delivery), prediction of treatment outcomes and toxicities, prediction of cancer cell sensitivity to therapeutics and clinical decision-making. A specific area of interest is in the development of effective drug combinations tailored to every patient and tumor with the help of AI. Radiomics, the new kid on the block, deals with the planning and administration of radiotherapy. As with any new invention, AI has its fallacies. The limitations include lack of external validation and proof of generalizability, difficulty in data access for rare diseases, ethical and legal issues, no precise logic behind the prediction, and last but not the least, lack of education and expertise among medical professionals. A collaboration between departments of clinical oncology, bioinformatics, and data sciences can help overcome these problems in the near future.
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Affiliation(s)
- Vineet Talwar
- Department of Medical Oncology, Rajiv Gandhi Cancer Institute & Research Centre, New Delhi, India
| | - Kundan Singh Chufal
- Department of Radiation Oncology, Rajiv Gandhi Cancer Institute & Research Centre, New Delhi, India
| | - Srujana Joga
- Department of Medical Oncology, Rajiv Gandhi Cancer Institute & Research Centre, New Delhi, India
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8
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Maurer-Granofszky M, Schumich A, Buldini B, Gaipa G, Kappelmayer J, Mejstrikova E, Karawajew L, Rossi J, Suzan AÇ, Agriello E, Anastasiou-Grenzelia T, Barcala V, Barna G, Batinić D, Bourquin JP, Brüggemann M, Bukowska-Strakova K, Burnusuzov H, Carelli D, Deniz G, Dubravčić K, Feuerstein T, Gaillard MI, Galeano A, Giordano H, Gonzalez A, Groeneveld-Krentz S, Hevessy Z, Hrusak O, Iarossi MB, Jáksó P, Kloboves Prevodnik V, Kohlscheen S, Kreminska E, Maglia O, Malusardi C, Marinov N, Martin BM, Möller C, Nikulshin S, Palazzi J, Paterakis G, Popov A, Ratei R, Rodríguez C, Sajaroff EO, Sala S, Samardzija G, Sartor M, Scarparo P, Sędek Ł, Slavkovic B, Solari L, Svec P, Szczepanski T, Taparkou A, Torrebadell M, Tzanoudaki M, Varotto E, Vernitsky H, Attarbaschi A, Schrappe M, Conter V, Biondi A, Felice M, Campbell M, Kiss C, Basso G, Dworzak MN. An Extensive Quality Control and Quality Assurance (QC/QA) Program Significantly Improves Inter-Laboratory Concordance Rates of Flow-Cytometric Minimal Residual Disease Assessment in Acute Lymphoblastic Leukemia: An I-BFM-FLOW-Network Report. Cancers (Basel) 2021; 13:cancers13236148. [PMID: 34885257 PMCID: PMC8656726 DOI: 10.3390/cancers13236148] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 11/25/2021] [Accepted: 11/30/2021] [Indexed: 11/30/2022] Open
Abstract
Simple Summary Standardization of flow-cytometric assessment of minimal residual disease in acute lymphoid leukemia (ALL) is necessary to allow concordant multicentric application of the methodology. This is a prerequisite for internationally collaborative trials, such as the AIEOP-BFM-ALL and the ALL IC-BFM trial. We developed and applied a comprehensive training and quality control program involving a large number of international laboratories within the I-BFM consortium to complement standardization of the methodology with an educational component as well as with persistent quality control measures to allow large ALL treatment trials which use multi-laboratory FCM-MRD assessments for risk stratification of pediatric patients with ALL. Abstract Monitoring of minimal residual disease (MRD) by flow cytometry (FCM) is a powerful prognostic tool for predicting outcomes in acute lymphoblastic leukemia (ALL). To apply FCM-MRD in large, collaborative trials, dedicated laboratory staff must be educated to concordantly high levels of expertise and their performance quality should be continuously monitored. We sought to install a unique and comprehensive training and quality control (QC) program involving a large number of reference laboratories within the international Berlin-Frankfurt-Münster (I-BFM) consortium, in order to complement the standardization of the methodology with an educational component and persistent quality control measures. Our QC and quality assurance (QA) program is based on four major cornerstones: (i) a twinning maturation program, (ii) obligatory participation in external QA programs (spiked sample send around, United Kingdom National External Quality Assessment Service (UK NEQAS)), (iii) regular participation in list-mode-data (LMD) file ring trials (FCM data file send arounds), and (iv) surveys of independent data derived from trial results. We demonstrate that the training of laboratories using experienced twinning partners, along with continuous educational feedback significantly improves the performance of laboratories in detecting and quantifying MRD in pediatric ALL patients. Overall, our extensive education and quality control program improved inter-laboratory concordance rates of FCM-MRD assessments and ultimately led to a very high conformity of risk estimates in independent patient cohorts.
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Affiliation(s)
| | - Angela Schumich
- Children’s Cancer Research Institute, Medical University of Vienna, 1090 Vienna, Austria; (M.M.-G.); (A.S.)
| | - Barbara Buldini
- Pediatric Hematology, Oncology and Stem Cell Transplant Division, Maternal and Child Health Department, University of Padova, 35122 Padova, Italy; (B.B.); (P.S.); (E.V.); (G.B.)
| | - Giuseppe Gaipa
- M. Tettamanti Foundation Research Center, Department of Pediatrics, University of Milano-Bicocca, 20900 Monza, Italy; (G.G.); (O.M.); (S.S.)
| | - Janos Kappelmayer
- Department of Laboratory Medicine, University of Debrecen, 4032 Debrecen, Hungary; (J.K.); (Z.H.)
| | - Ester Mejstrikova
- Department of Paediatric Haematology and Oncology, University Hospital Motol, 150 06 Prague, Czech Republic; (E.M.); (O.H.)
| | - Leonid Karawajew
- Department of Pediatric Oncology and Hematology, Charité Berlin, 10117 Berlin, Germany; (L.K.); (S.G.-K.)
| | - Jorge Rossi
- Cellular Immunology Laboratory, Hospital de Pediatria “Dr. Juan P. Garrahan”, Buenos Aires C1245, Argentina; (J.R.); (E.O.S.)
| | - Adın Çınar Suzan
- Department of Immunology, Aziz Sancar Institute of Experimental Medicine, Istanbul University, 34452 Istanbul, Turkey; (A.Ç.S.); (G.D.)
| | - Evangelina Agriello
- LEB Laboratorio, Servicio de Hematologia Hospital Penna, Bahia Blanca B8000, Argentina;
| | | | - Virna Barcala
- Laboratory—Flow Cytometry, Citomlab, Buenos Aires C1406AWK, Argentina;
| | - Gábor Barna
- 1st Department of Pathology and Experimental Cancer Research, Semmelweis University, 1085 Budapest, Hungary;
| | - Drago Batinić
- Division of Laboratory Immunology, Department of Laboratory Diagnostics, University Hospital Centre Zagreb & School of Medicine, 10000 Zagreb, Croatia; (D.B.); (K.D.)
| | - Jean-Pierre Bourquin
- Department of Oncology and Children’s Cancer Research Center, University Children’s Hospital, 8032 Zurich, Switzerland; (J.-P.B.); (C.M.)
| | - Monika Brüggemann
- Department of Hematology, University Hospital Schleswig-Holstein, 24105 Kiel, Germany; (M.B.); (S.K.)
| | - Karolina Bukowska-Strakova
- Department of Clinical Immunology, Institute of Pediatrics, Jagiellonian University Medical College, 31-008 Krakow, Poland;
| | - Hasan Burnusuzov
- Center of Competence “PERIMED”, Department of Pediatrics, Department of Microbiology and Clinical Immunology, Medical University Plovdiv, 4002 Plovdiv, Bulgaria;
| | | | - Günnur Deniz
- Department of Immunology, Aziz Sancar Institute of Experimental Medicine, Istanbul University, 34452 Istanbul, Turkey; (A.Ç.S.); (G.D.)
| | - Klara Dubravčić
- Division of Laboratory Immunology, Department of Laboratory Diagnostics, University Hospital Centre Zagreb & School of Medicine, 10000 Zagreb, Croatia; (D.B.); (K.D.)
| | - Tamar Feuerstein
- The Rina Zaizov Division of Pediatric Hematology-Oncology, Schneider’s Children’s Medical Center, Petah Tikva 4920235, Israel;
| | - Marie Isabel Gaillard
- Bioquimica, Inmunologia, Hospital de Ninos Rocardo Gutierrez, Buenos Aires C1425EFD, Argentina;
| | - Adriana Galeano
- Flow Cytometry Laboratory, FUNDALEU, Buenos Aires C1114, Argentina;
| | - Hugo Giordano
- Fundación Pérez Scremini, Pediatric Hematology-Oncology Service, Pereira Rossell Hospital, Montevideo 11600, Uruguay;
| | | | - Stefanie Groeneveld-Krentz
- Department of Pediatric Oncology and Hematology, Charité Berlin, 10117 Berlin, Germany; (L.K.); (S.G.-K.)
| | - Zsuzsanna Hevessy
- Department of Laboratory Medicine, University of Debrecen, 4032 Debrecen, Hungary; (J.K.); (Z.H.)
| | - Ondrej Hrusak
- Department of Paediatric Haematology and Oncology, University Hospital Motol, 150 06 Prague, Czech Republic; (E.M.); (O.H.)
| | - Maria Belen Iarossi
- Flow Cytometry Laboratory, Provincial Histocompatibility Reference Centre, CUCAIBA, Buenos Aires C1114, Argentina;
| | - Pál Jáksó
- Flow Cytometry Laboratory, Department of Pathology, Clinical Centre, University of Pécs, 7622 Pécs, Hungary;
| | - Veronika Kloboves Prevodnik
- Department of Cytopathology, Institute of Oncology, 1000 Ljubljana, Slovenia;
- Faculty of Medicine, University of Ljubljana, 1000 Ljubljana, Slovenia
| | - Saskia Kohlscheen
- Department of Hematology, University Hospital Schleswig-Holstein, 24105 Kiel, Germany; (M.B.); (S.K.)
| | - Elena Kreminska
- Clinical Laboratory Diagnostics and Metrology of NCSH “OHMATDYT”, Ministry of Heath of Ukraine, 01601 Kiev, Ukraine;
| | - Oscar Maglia
- M. Tettamanti Foundation Research Center, Department of Pediatrics, University of Milano-Bicocca, 20900 Monza, Italy; (G.G.); (O.M.); (S.S.)
| | - Cecilia Malusardi
- Hospital de Clinica Jose de San Martin, Buenos Aires C1120, Argentina;
| | - Neda Marinov
- PINDA, Chilean National Pediatric Oncology Group, Hospital Roberto del Rio, Universidad de Chile, Santiago 8380418, Chile; (N.M.); (M.C.)
| | | | - Claudia Möller
- Department of Oncology and Children’s Cancer Research Center, University Children’s Hospital, 8032 Zurich, Switzerland; (J.-P.B.); (C.M.)
| | - Sergey Nikulshin
- Hematopathology and Flow Cytometry Division, Children’s Clinical University Hospital, LV-1004 Riga, Latvia;
| | | | | | - Alexander Popov
- Laboratory of Leukemia Immunophenotyping, Dmitry Rogachev National Medical Research Center of Pediatric Hematology, Oncology and Immunology, 117997 Moscow, Russia;
| | - Richard Ratei
- Clinic for Hematology and Tumor Immunology, HELIOS Klinikum Berlin-Buch, 13125 Berlin, Germany;
| | - Cecilia Rodríguez
- Hospital Nacional de Clínicas, Universidad Nacional de Córdoba, Cordoba X5000HUA, Argentina;
| | - Elisa Olga Sajaroff
- Cellular Immunology Laboratory, Hospital de Pediatria “Dr. Juan P. Garrahan”, Buenos Aires C1245, Argentina; (J.R.); (E.O.S.)
| | - Simona Sala
- M. Tettamanti Foundation Research Center, Department of Pediatrics, University of Milano-Bicocca, 20900 Monza, Italy; (G.G.); (O.M.); (S.S.)
| | - Gordana Samardzija
- Laboratory for Flow Cytometry and Immunology, Institute for Health and Protection of Mother and Child of Serbia, 11070 Belgrade, Serbia; (G.S.); (B.S.)
| | - Mary Sartor
- The Children’s Hospital at Westmead, Sydney, NSW 2145, Australia;
| | - Pamela Scarparo
- Pediatric Hematology, Oncology and Stem Cell Transplant Division, Maternal and Child Health Department, University of Padova, 35122 Padova, Italy; (B.B.); (P.S.); (E.V.); (G.B.)
| | - Łukasz Sędek
- Department of Microbiology and Immunology, Medical University of Silesia, 40-055 Katowice, Poland;
| | - Bojana Slavkovic
- Laboratory for Flow Cytometry and Immunology, Institute for Health and Protection of Mother and Child of Serbia, 11070 Belgrade, Serbia; (G.S.); (B.S.)
| | - Liliana Solari
- Servicio de Bioquimica, Hospital Posadas, Buenos Aires B1684, Argentina;
| | - Peter Svec
- National Institute of Children’s Diseases, 831 01 Bratislava, Slovakia;
| | - Tomasz Szczepanski
- Department of Pediatric Hematology and Oncology, Zabrze, Medical University of Silesia, 40-055 Katowice, Poland;
| | - Anna Taparkou
- Department of Pediatric Oncology Hippokration General Hospital, 546 42 Thessaloniki, Greece;
| | | | - Marianna Tzanoudaki
- Department of Immunology & Histocompatibility, “Agia Sophia” Children’s Hospital, 115 27 Athens, Greece;
| | - Elena Varotto
- Pediatric Hematology, Oncology and Stem Cell Transplant Division, Maternal and Child Health Department, University of Padova, 35122 Padova, Italy; (B.B.); (P.S.); (E.V.); (G.B.)
| | - Helly Vernitsky
- Hematology Lab, Sheba Medical Center, Ramat Gan 52621, Israel;
| | - Andishe Attarbaschi
- St. Anna Children’s Hospital, Department of Pediatrics, Medical University of Vienna, 1090 Vienna, Austria;
| | - Martin Schrappe
- Department of Pediatrics, University Medical Center SchleswigHolstein, Christian-Albrechts-University of Kiel, 24118 Kiel, Germany;
| | - Valentino Conter
- Clinica Pediatrica University degli Studi di Milano Biococca, Fondazione MBBM, 20900 Monza, Italy; (V.C.); (A.B.)
| | - Andrea Biondi
- Clinica Pediatrica University degli Studi di Milano Biococca, Fondazione MBBM, 20900 Monza, Italy; (V.C.); (A.B.)
| | - Marisa Felice
- Department of Hematology and Oncology, Hospital de Pediatria “Dr. Juan P. Garrahan”, Buenos Aires C1245, Argentina;
| | - Myriam Campbell
- PINDA, Chilean National Pediatric Oncology Group, Hospital Roberto del Rio, Universidad de Chile, Santiago 8380418, Chile; (N.M.); (M.C.)
| | - Csongor Kiss
- Division of Pediatric Hematology-Oncology, Department of Pediatrics, Faculty of Medicine, University of Debrecen, 4032 Debrecen, Hungary;
| | - Giuseppe Basso
- Pediatric Hematology, Oncology and Stem Cell Transplant Division, Maternal and Child Health Department, University of Padova, 35122 Padova, Italy; (B.B.); (P.S.); (E.V.); (G.B.)
| | - Michael N. Dworzak
- Children’s Cancer Research Institute, Medical University of Vienna, 1090 Vienna, Austria; (M.M.-G.); (A.S.)
- St. Anna Children’s Hospital, Department of Pediatrics, Medical University of Vienna, 1090 Vienna, Austria;
- Correspondence: ; Tel.: +43-1-40470-4064
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9
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Yang S, Kay NE, Shi M, Ossenkoppele G, Walter RB, Gale RP. Measurable residual disease testing in chronic lymphocytic leukaemia: hype, hope neither or both? Leukemia 2021; 35:3364-3370. [PMID: 34580401 DOI: 10.1038/s41375-021-01419-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 09/03/2021] [Accepted: 09/07/2021] [Indexed: 02/08/2023]
Affiliation(s)
- Shenmiao Yang
- Peking University Peoples Hospital, Peking University Institute of Hematology, Beijing, China
| | - Neil E Kay
- Mayo Clinic, Department of Medicine, Division of Hematology, Rochester, MN, USA
| | - Min Shi
- Mayo Clinic, Department of Laboratory Medicine and Pathology, Rochester, MN, USA
| | - Gert Ossenkoppele
- Department of Hematology, Amsterdam UMC, Vrije Universiteit Amsterdam, Cancer Center Amsterdam, Amsterdam, The Netherlands
| | - Roland B Walter
- Fred Hutchinson Cancer Research Center, Clinical Research Division, Seattle, WA, USA
| | - Robert Peter Gale
- Haematology Research Centre, Department of Immunology and Inflammation, Imperial College London, London, UK.
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10
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Clichet V, Harrivel V, Delette C, Guiheneuf E, Gautier M, Morel P, Assouan D, Merlusca L, Beaumont M, Lebon D, Caulier A, Marolleau JP, Matthes T, Vergez F, Garçon L, Boyer T. Accurate classification of plasma cell dyscrasias is achieved by combining artificial intelligence and flow cytometry. Br J Haematol 2021; 196:1175-1183. [PMID: 34730236 DOI: 10.1111/bjh.17933] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 10/13/2021] [Accepted: 10/18/2021] [Indexed: 12/19/2022]
Abstract
Monoclonal gammopathy of unknown significance (MGUS), smouldering multiple myeloma (SMM), and multiple myeloma (MM) are very common neoplasms. However, it is often difficult to distinguish between these entities. In the present study, we aimed to classify the most powerful markers that could improve diagnosis by multiparametric flow cytometry (MFC). The present study included 348 patients based on two independent cohorts. We first assessed how representative the data were in the discovery cohort (123 MM, 97 MGUS) and then analysed their respective plasma cell (PC) phenotype in order to obtain a set of correlations with a hypersphere visualisation. Cluster of differentiation (CD)27 and CD38 were differentially expressed in MGUS and MM (P < 0·001). We found by a gradient boosting machine method that the percentage of abnormal PCs and the ratio PC/CD117 positive precursors were the most influential parameters at diagnosis to distinguish MGUS and MM. Finally, we designed a decisional algorithm allowing a predictive classification ≥95% when PC dyscrasias were suspected, without any misclassification between MGUS and SMM. We validated this algorithm in an independent cohort of PC dyscrasias (n = 87 MM, n = 41 MGUS). This artificial intelligence model is freely available online as a diagnostic tool application website for all MFC centers worldwide (https://aihematology.shinyapps.io/PCdyscrasiasToolDg/).
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Affiliation(s)
- Valentin Clichet
- Service d'Hématologie Biologique, CHU Amiens-Picardie, Amiens, France
| | | | - Caroline Delette
- Service d'Hématologie Clinique et de Thérapie Cellulaire, CHU Amiens-Picardie, Amiens, France
| | - Eric Guiheneuf
- Service d'Hématologie Biologique, CHU Amiens-Picardie, Amiens, France
| | - Murielle Gautier
- Service d'Hématologie Biologique, CHU Amiens-Picardie, Amiens, France
| | - Pierre Morel
- Service d'Hématologie Clinique et de Thérapie Cellulaire, CHU Amiens-Picardie, Amiens, France
| | - Déborah Assouan
- Service d'Hématologie Clinique et de Thérapie Cellulaire, CHU Amiens-Picardie, Amiens, France
| | - Lavinia Merlusca
- Service d'Hématologie Clinique et de Thérapie Cellulaire, CHU Amiens-Picardie, Amiens, France
| | - Marie Beaumont
- Service d'Hématologie Clinique et de Thérapie Cellulaire, CHU Amiens-Picardie, Amiens, France
| | - Delphine Lebon
- Service d'Hématologie Clinique et de Thérapie Cellulaire, CHU Amiens-Picardie, Amiens, France.,Université Picardie Jules Verne, HEMATIM, UR 4666, F80025, Amiens, France
| | - Alexis Caulier
- Service d'Hématologie Clinique et de Thérapie Cellulaire, CHU Amiens-Picardie, Amiens, France.,Université Picardie Jules Verne, HEMATIM, UR 4666, F80025, Amiens, France
| | - Jean-Pierre Marolleau
- Service d'Hématologie Clinique et de Thérapie Cellulaire, CHU Amiens-Picardie, Amiens, France.,Université Picardie Jules Verne, HEMATIM, UR 4666, F80025, Amiens, France
| | - Thomas Matthes
- Service d'Hématologie, Hôpital Universitaire de Genève, Genève, Suisse
| | - François Vergez
- Laboratoire d'Hématologie, Institut Universitaire du Cancer de Toulouse, Toulouse, France
| | - Loïc Garçon
- Service d'Hématologie Biologique, CHU Amiens-Picardie, Amiens, France.,Université Picardie Jules Verne, HEMATIM, UR 4666, F80025, Amiens, France
| | - Thomas Boyer
- Service d'Hématologie Biologique, CHU Amiens-Picardie, Amiens, France.,Université Picardie Jules Verne, HEMATIM, UR 4666, F80025, Amiens, France
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11
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Radakovich N, Nagy M, Nazha A. Artificial Intelligence in Hematology: Current Challenges and Opportunities. Curr Hematol Malig Rep 2020; 15:203-210. [PMID: 32239350 DOI: 10.1007/s11899-020-00575-4] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
PURPOSE OF REVIEW Artificial intelligence (AI), and in particular its subcategory machine learning, is finding an increasing number of applications in medicine, driven in large part by an abundance of data and powerful, accessible tools that have made AI accessible to a larger circle of investigators. RECENT FINDINGS AI has been employed in the analysis of hematopathological, radiographic, laboratory, genomic, pharmacological, and chemical data to better inform diagnosis, prognosis, treatment planning, and foundational knowledge related to benign and malignant hematology. As more widespread implementation of clinical AI nears, attention has also turned to the effects this will have on other areas in medicine. AI offers many promising tools to clinicians broadly, and specifically in the practice of hematology. Ongoing research into its various applications will likely result in an increasing utilization of AI by a broader swath of clinicians.
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Affiliation(s)
- Nathan Radakovich
- Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH, USA
| | - Matthew Nagy
- Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH, USA
| | - Aziz Nazha
- Center for Clinical Artificial Intelligence, Cleveland Clinic, Cleveland, OH, USA.
- Department of Hematology and Medical Oncology, Taussig Cancer Institute, Cleveland Clinic, Desk R35 9500 Euclid Ave., Cleveland, OH, 44195, USA.
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12
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Eckardt JN, Bornhäuser M, Wendt K, Middeke JM. Application of machine learning in the management of acute myeloid leukemia: current practice and future prospects. Blood Adv 2020; 4:6077-6085. [PMID: 33290546 PMCID: PMC7724910 DOI: 10.1182/bloodadvances.2020002997] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 10/26/2020] [Indexed: 12/19/2022] Open
Abstract
Machine learning (ML) is rapidly emerging in several fields of cancer research. ML algorithms can deal with vast amounts of medical data and provide a better understanding of malignant disease. Its ability to process information from different diagnostic modalities and functions to predict prognosis and suggest therapeutic strategies indicates that ML is a promising tool for the future management of hematologic malignancies; acute myeloid leukemia (AML) is a model disease of various recent studies. An integration of these ML techniques into various applications in AML management can assure fast and accurate diagnosis as well as precise risk stratification and optimal therapy. Nevertheless, these techniques come with various pitfalls and need a strict regulatory framework to ensure safe use of ML. This comprehensive review highlights and discusses recent advances in ML techniques in the management of AML as a model disease of hematologic neoplasms, enabling researchers and clinicians alike to critically evaluate this upcoming, potentially practice-changing technology.
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Affiliation(s)
- Jan-Niklas Eckardt
- Department of Internal Medicine I, University Hospital Carl Gustav Carus, Dresden, Germany
| | - Martin Bornhäuser
- Department of Internal Medicine I, University Hospital Carl Gustav Carus, Dresden, Germany
- National Center for Tumor Diseases, Dresden (NCT/UCC), Dresden, Germany
- German Consortium for Translational Cancer Research, DKFZ, Heidelberg, Germany; and
| | - Karsten Wendt
- Institute of Circuits and Systems, Technical University Dresden, Dresden, Germany
| | - Jan Moritz Middeke
- Department of Internal Medicine I, University Hospital Carl Gustav Carus, Dresden, Germany
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13
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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: 25] [Impact Index Per Article: 6.3] [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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14
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Machine learning in haematological malignancies. LANCET HAEMATOLOGY 2020; 7:e541-e550. [PMID: 32589980 DOI: 10.1016/s2352-3026(20)30121-6] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Revised: 04/02/2020] [Accepted: 04/14/2020] [Indexed: 02/06/2023]
Abstract
Machine learning is a branch of computer science and statistics that generates predictive or descriptive models by learning from training data rather than by being rigidly programmed. It has attracted substantial attention for its many applications in medicine, both as a catalyst for research and as a means of improving clinical care across the cycle of diagnosis, prognosis, and treatment of disease. These applications include the management of haematological malignancy, in which machine learning has created inroads in pathology, radiology, genomics, and the analysis of electronic health record data. As computational power becomes cheaper and the tools for implementing machine learning become increasingly democratised, it is likely to become increasingly integrated into the research and practice landscape of haematology. As such, machine learning merits understanding and attention from researchers and clinicians alike. This narrative Review describes important concepts in machine learning for unfamiliar readers, details machine learning's current applications in haematological malignancy, and summarises important concepts for clinicians to be aware of when appraising research that uses machine learning.
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15
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Buldini B, Maurer-Granofszky M, Varotto E, Dworzak MN. Flow-Cytometric Monitoring of Minimal Residual Disease in Pediatric Patients With Acute Myeloid Leukemia: Recent Advances and Future Strategies. Front Pediatr 2019; 7:412. [PMID: 31681710 PMCID: PMC6798174 DOI: 10.3389/fped.2019.00412] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/11/2019] [Accepted: 09/25/2019] [Indexed: 01/10/2023] Open
Abstract
Minimal residual disease (MRD) by multiparametric flow cytometry (MFC) has been recently shown as a strong and independent prognostic marker of relapse in pediatric AML (pedAML) when measured at specific time points during Induction and/or Consolidation therapy. Hence, MFC-MRD has the potential to refine the current strategies of pedAML risk stratification, traditionally based on the cytogenetic and molecular genetic aberrations at diagnosis. Consequently, it may guide the modulation of therapy intensity and clinical decision making. However, the use of non-standardized protocols, including different staining panels, analysis, and gating strategies, may hamper a broad implementation of MFC-MRD monitoring in clinical routine. Besides, the thresholds of MRD positivity still need to be validated in large, prospective and multi-center clinical studies, as well as optimal time points of MRD assessment during therapy, to better discriminate patients with different prognosis. In the present review, we summarize the most relevant findings on MFC-MRD testing in pedAML. We examine the clinical significance of MFC-MRD and the recent advances in its standardization, including innovative approaches with an automated analysis of MFC-MRD data. We also touch upon other technologies for MRD assessment in AML, such as quantitative genomic breakpoint PCR, current challenges and future strategies to enable full incorporation of MFC-MRD into clinical practice.
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Affiliation(s)
- Barbara Buldini
- Laboratory of Hematology-Oncology, Department of Woman's and Child's Health, University of Padova, Padova, Italy
| | | | - Elena Varotto
- Laboratory of Hematology-Oncology, Department of Woman's and Child's Health, University of Padova, Padova, Italy
| | - Michael N Dworzak
- Children's Cancer Research Institute (CCRI), St. Anna Kinderkrebsforschung, Vienna, Austria
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16
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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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17
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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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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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Hourigan CS, Gale RP, Gormley NJ, Ossenkoppele GJ, Walter RB. Measurable residual disease testing in acute myeloid leukaemia. Leukemia 2017; 31:1482-1490. [PMID: 28386105 DOI: 10.1038/leu.2017.113] [Citation(s) in RCA: 171] [Impact Index Per Article: 24.4] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2017] [Revised: 03/15/2017] [Accepted: 03/21/2017] [Indexed: 12/24/2022]
Abstract
There is considerable interest in developing techniques to detect and/or quantify remaining leukaemia cells termed measurable or, less precisely, minimal residual disease (MRD) in persons with acute myeloid leukaemia (AML) in complete remission defined by cytomorphological criteria. An important reason for AML MRD-testing is the possibility of estimating the likelihood (and timing) of leukaemia relapse. A perfect MRD-test would precisely quantify leukaemia cells biologically able and likely to cause leukaemia relapse within a defined interval. AML is genetically diverse and there is currently no uniform approach to detecting such cells. Several technologies focused on immune phenotype or cytogenetic and/or molecular abnormalities have been developed, each with advantages and disadvantages. Many studies report a positive MRD-test at diverse time points during AML therapy identifies persons with a higher risk of leukaemia relapse compared with those with a negative MRD-test even after adjusting for other prognostic and predictive variables. No MRD-test in AML has perfect sensitivity and specificity for relapse prediction at the cohort- or subject levels and there are substantial rates of false-positive and -negative tests. Despite these limitations, correlations between MRD-test results and relapse risk have generated interest in MRD-test result-directed therapy interventions. However, convincing proof that a specific intervention will reduce relapse risk in persons with a positive MRD-test is lacking and needs testing in randomized trials. Routine clinical use of MRD-testing requires further refinements and standardization/harmonization of assay platforms and results reporting. Such data are needed to determine whether results of MRD-testing can be used as a surrogate end point in AML therapy trials. This could make drug-testing more efficient and accelerate regulatory approvals. Although MRD-testing in AML has advanced substantially, much remains to be done.
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Affiliation(s)
- C S Hourigan
- Myeloid Malignancies Section, Hematology Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - R P Gale
- Haematology Research Centre, Division of Experimental Medicine, Department of Medicine, Imperial College London, London, UK
| | - N J Gormley
- Division of Hematology Products, Office of Hematology and Oncology Products, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD, USA
| | - G J Ossenkoppele
- Division of Hematology, VU University Medical Center, Amsterdam, The Netherlands
| | - R B Walter
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.,Department of Medicine, Division of Hematology, University of Washington, Seattle, WA, USA.,Department of Epidemiology, University of Washington, Seattle, WA, USA
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