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Bruce C, Prassas I, Mokhtar M, Clarke B, Youssef E, Wang C, Yousef GM. Transforming diagnostics: The implementation of digital pathology in clinical laboratories. Histopathology 2024; 85:207-214. [PMID: 38516992 DOI: 10.1111/his.15178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 01/18/2024] [Accepted: 03/04/2024] [Indexed: 03/23/2024]
Abstract
Digital pathology (DP) has emerged as a cutting-edge technology that promises to revolutionise diagnostics in clinical laboratories. This perspective article explores the implementation planning and considerations of DP in a single multicentre institution in Canada, the University Health Network, discussing benefits, challenges, potential implications and considerations for future adopters. We examine the transition from traditional microscopy to digital slide scanning and its impact on pathology practice, patient care and medical research. Furthermore, we address the regulatory, infrastructure and change management considerations for successful integration into clinical laboratories. By highlighting the advantages and addressing concerns, we aim to shed light on the transformative potential of DP and its role in shaping the future of diagnostics.
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Affiliation(s)
- Christine Bruce
- Laboratory Medicine Program, University Health Network, Toronto, ON, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
| | - Ioannis Prassas
- Laboratory Medicine Program, University Health Network, Toronto, ON, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
| | - Mark Mokhtar
- Laboratory Medicine Program, University Health Network, Toronto, ON, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
| | - Blaise Clarke
- Laboratory Medicine Program, University Health Network, Toronto, ON, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
| | - Elaria Youssef
- Laboratory Medicine Program, University Health Network, Toronto, ON, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
| | - Catherine Wang
- Laboratory Medicine Program, University Health Network, Toronto, ON, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
| | - George M Yousef
- Laboratory Medicine Program, University Health Network, Toronto, ON, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
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Hilgers L, Ghaffari Laleh N, West NP, Westwood A, Hewitt KJ, Quirke P, Grabsch HI, Carrero ZI, Matthaei E, Loeffler CML, Brinker TJ, Yuan T, Brenner H, Brobeil A, Hoffmeister M, Kather JN. Automated curation of large-scale cancer histopathology image datasets using deep learning. Histopathology 2024; 84:1139-1153. [PMID: 38409878 DOI: 10.1111/his.15159] [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: 09/19/2023] [Revised: 12/29/2023] [Accepted: 02/09/2024] [Indexed: 02/28/2024]
Abstract
BACKGROUND Artificial intelligence (AI) has numerous applications in pathology, supporting diagnosis and prognostication in cancer. However, most AI models are trained on highly selected data, typically one tissue slide per patient. In reality, especially for large surgical resection specimens, dozens of slides can be available for each patient. Manually sorting and labelling whole-slide images (WSIs) is a very time-consuming process, hindering the direct application of AI on the collected tissue samples from large cohorts. In this study we addressed this issue by developing a deep-learning (DL)-based method for automatic curation of large pathology datasets with several slides per patient. METHODS We collected multiple large multicentric datasets of colorectal cancer histopathological slides from the United Kingdom (FOXTROT, N = 21,384 slides; CR07, N = 7985 slides) and Germany (DACHS, N = 3606 slides). These datasets contained multiple types of tissue slides, including bowel resection specimens, endoscopic biopsies, lymph node resections, immunohistochemistry-stained slides, and tissue microarrays. We developed, trained, and tested a deep convolutional neural network model to predict the type of slide from the slide overview (thumbnail) image. The primary statistical endpoint was the macro-averaged area under the receiver operating curve (AUROCs) for detection of the type of slide. RESULTS In the primary dataset (FOXTROT), with an AUROC of 0.995 [95% confidence interval [CI]: 0.994-0.996] the algorithm achieved a high classification performance and was able to accurately predict the type of slide from the thumbnail image alone. In the two external test cohorts (CR07, DACHS) AUROCs of 0.982 [95% CI: 0.979-0.985] and 0.875 [95% CI: 0.864-0.887] were observed, which indicates the generalizability of the trained model on unseen datasets. With a confidence threshold of 0.95, the model reached an accuracy of 94.6% (7331 classified cases) in CR07 and 85.1% (2752 classified cases) for the DACHS cohort. CONCLUSION Our findings show that using the low-resolution thumbnail image is sufficient to accurately classify the type of slide in digital pathology. This can support researchers to make the vast resource of existing pathology archives accessible to modern AI models with only minimal manual annotations.
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Affiliation(s)
- Lars Hilgers
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany
| | - Narmin Ghaffari Laleh
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany
| | - Nicholas P West
- Pathology & Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
| | - Alice Westwood
- Pathology & Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
| | - Katherine J Hewitt
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany
| | - Philip Quirke
- Pathology & Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
| | - Heike I Grabsch
- Pathology & Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
- Department of Pathology, GROW - Research Institute for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Zunamys I Carrero
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany
| | - Emylou Matthaei
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany
| | - Chiara M L Loeffler
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany
| | - Titus J Brinker
- Digital Biomarkers for Oncology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Tanwei Yuan
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Alexander Brobeil
- Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany
- Tissue Bank, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany
| | - Michael Hoffmeister
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jakob Nikolas Kather
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany
- Pathology & Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
- Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany
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Alchami FS, Iqbal Z, Björkhammer CN, Saeed MO, Ramakrishnan R, Clelland C, Ahmad F, Charles A. Whole Slide Imaging Integration with Lab Information Systems, a Study of the Requirements, Processes and Procedures Enabling a Reporting-Based Workflow. PATHOLOGY AND LABORATORY MEDICINE INTERNATIONAL 2023. [DOI: 10.2147/plmi.s388981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
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Go H. Digital Pathology and Artificial Intelligence Applications in Pathology. Brain Tumor Res Treat 2022; 10:76-82. [PMID: 35545826 PMCID: PMC9098984 DOI: 10.14791/btrt.2021.0032] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 01/17/2022] [Accepted: 03/13/2022] [Indexed: 11/20/2022] Open
Abstract
Digital pathology is revolutionizing pathology. The introduction of digital pathology made it possible to comprehensively change the pathology diagnosis workflow, apply and develop pathological artificial intelligence (AI) models, generate pathological big data, and perform telepathology. AI algorithms, including machine learning and deep learning, are used for the detection, segmentation, registration, processing, and classification of digitized pathological images. Pathological AI algorithms can be helpfully utilized for diagnostic screening, morphometric analysis of biomarkers, the discovery of new meanings of prognosis and therapeutic response in pathological images, and improvement of diagnostic efficiency. In order to develop a successful pathological AI model, it is necessary to consider the selection of a suitable type of image for a subject, utilization of big data repositories, the setting of an effective annotation strategy, image standardization, and color normalization. This review will elaborate on the advantages and perspectives of digital pathology, AI-based approaches, the applications in pathology, and considerations and challenges in the development of pathological AI models.
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Affiliation(s)
- Heounjeong Go
- Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
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Digital Pathology Transformation in a Supraregional Germ Cell Tumour Network. Diagnostics (Basel) 2021; 11:diagnostics11122191. [PMID: 34943429 PMCID: PMC8700654 DOI: 10.3390/diagnostics11122191] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 11/16/2021] [Accepted: 11/23/2021] [Indexed: 01/21/2023] Open
Abstract
Background: In this article we share our experience of creating a digital pathology (DP) supraregional germ cell tumour service, including full digitisation of the central laboratory. Methods: DP infrastructure (Philips) was deployed across our hospital network to allow full central digitisation with partial digitisation of two peripheral sites in the supraregional testis germ cell tumour network. We used a survey-based approach to capture the quantitative and qualitative experiences of the multidisciplinary teams involved. Results: The deployment enabled case sharing for the purposes of diagnostic reporting, second opinion, and supraregional review. DP was seen as a positive step forward for the departments involved, and for the wider germ cell tumour network, and was completed without significant issues. Whilst there were challenges, the transition to DP was regarded as worthwhile, and examples of benefits to patients are already recognised. Conclusion: Pathology networks, including highly specialised services, such as in this study, are ideally suited to be digitised. We highlight many of the benefits but also the challenges that must be overcome for such clinical transformation. Overall, from the survey, the change was seen as universally positive for our service and highlights the importance of engagement of the whole team to achieve success.
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van den Brand M, Nooijen PTGA, van der Laan KD, de Bruin PC, van Leeuwen AMG, Leeuwis JW, Meijer JW, Otte-Höller I, Hebeda KM. Discrepancies in digital hematopathology diagnoses for consultation and expert panel analysis. Virchows Arch 2021; 478:535-540. [PMID: 32840673 PMCID: PMC7973407 DOI: 10.1007/s00428-020-02907-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Revised: 07/23/2020] [Accepted: 08/10/2020] [Indexed: 10/26/2022]
Abstract
Digital pathology with whole-slide imaging (WSI) has a large potential to make the process of expert consultation and expert panel diagnosis more rapid and more efficient. However, comparison with the current methods is necessary for validation of the technique. In this study, we determined if digital assessment of whole-slide images of hematopathology specimens with a focus on the assessment of lymphoma can be used for consultation and panel diagnostics. Ninety-three histological specimens with a suspicion for lymphoma were assessed both with conventional microscopy and digital microscopy with a wash out period between assessments. A consensus diagnosis was based on full concordance between the pathologists or, in case of discordances, was reached at a joint session at a multi-headed microscope. In 81% of the cases, there was a full concordance between digital and light microscopical assessment for all three pathologists. Discordances between conventional microscopy and digital pathology were present in 3% of assessments. In comparison with the consensus diagnosis, discordant diagnoses were made in 5 cases with digital microscopy and in 3 cases with light microscopy. The reported level of confidence and need for additional investigations were similar between assessment by conventional and by digital microscopy. In conclusion, the performance of assessment by digital pathology is in general comparable with that of conventional light microscopy and pathologists feel confident using digital pathology for this subspecialty.
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Affiliation(s)
- Michiel van den Brand
- Department of Pathology, Radboud University Medical Center, P. O. Box 9101, 6500, HB, Nijmegen, The Netherlands.
- Pathology-DNA, Rijnstate Hospital, Arnhem, The Netherlands.
| | | | - Kimberly D van der Laan
- Department of Pathology, Radboud University Medical Center, P. O. Box 9101, 6500, HB, Nijmegen, The Netherlands
| | - Peter C de Bruin
- Pathology-DNA, St. Antonius Hospital, Nieuwegein, The Netherlands
| | | | | | - Jos W Meijer
- Pathology-DNA, Rijnstate Hospital, Arnhem, The Netherlands
| | - Irene Otte-Höller
- Department of Pathology, Radboud University Medical Center, P. O. Box 9101, 6500, HB, Nijmegen, The Netherlands
| | - Konnie M Hebeda
- Department of Pathology, Radboud University Medical Center, P. O. Box 9101, 6500, HB, Nijmegen, The Netherlands
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Jahn SW, Plass M, Moinfar F. Digital Pathology: Advantages, Limitations and Emerging Perspectives. J Clin Med 2020; 9:E3697. [PMID: 33217963 PMCID: PMC7698715 DOI: 10.3390/jcm9113697] [Citation(s) in RCA: 103] [Impact Index Per Article: 25.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 10/27/2020] [Accepted: 11/13/2020] [Indexed: 12/11/2022] Open
Abstract
Digital pathology is on the verge of becoming a mainstream option for routine diagnostics. Faster whole slide image scanning has paved the way for this development, but implementation on a large scale is challenging on technical, logistical, and financial levels. Comparative studies have published reassuring data on safety and feasibility, but implementation experiences highlight the need for training and the knowledge of pitfalls. Up to half of the pathologists are reluctant to sign out reports on only digital slides and are concerned about reporting without the tool that has represented their profession since its beginning. Guidelines by international pathology organizations aim to safeguard histology in the digital realm, from image acquisition over the setup of work-stations to long-term image archiving, but must be considered a starting point only. Cost-efficiency analyses and occupational health issues need to be addressed comprehensively. Image analysis is blended into the traditional work-flow, and the approval of artificial intelligence for routine diagnostics starts to challenge human evaluation as the gold standard. Here we discuss experiences from past digital pathology implementations, future possibilities through the addition of artificial intelligence, technical and occupational health challenges, and possible changes to the pathologist's profession.
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Affiliation(s)
- Stephan W. Jahn
- Diagnostic and Research Institute of Pathology, Medical University of Graz, Neue Stiftingtalstraße 6, 8010 Graz, Austria; (M.P.); (F.M.)
| | - Markus Plass
- Diagnostic and Research Institute of Pathology, Medical University of Graz, Neue Stiftingtalstraße 6, 8010 Graz, Austria; (M.P.); (F.M.)
| | - Farid Moinfar
- Diagnostic and Research Institute of Pathology, Medical University of Graz, Neue Stiftingtalstraße 6, 8010 Graz, Austria; (M.P.); (F.M.)
- Department of Pathology, Ordensklinikum/Hospital of the Sisters of Charity, Seilerstätte 4, 4010 Linz, Austria
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Clunie DA. DICOM Format and Protocol Standardization-A Core Requirement for Digital Pathology Success. Toxicol Pathol 2020; 49:738-749. [PMID: 33063645 DOI: 10.1177/0192623320965893] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
As the use of digital techniques in toxicologic pathology expands, challenges of scalability and interoperability come to the fore. Proprietary formats and closed single-vendor platforms prevail but depend on the availability and maintenance of multiformat conversion libraries. Expedient for small deployments, this is not sustainable at an industrial scale. Primarily known as a standard for radiology, the Digital Imaging and Communications in Medicine (DICOM) standard has been evolving to support other specialties since its inception, to become the single ubiquitous standard throughout medical imaging. The adoption of DICOM for whole slide imaging (WSI) has been sluggish. Prospects for widespread commercially viable clinical use of digital pathology change the incentives. Connectathons using DICOM have demonstrated its feasibility for WSI and virtual microscopy. Adoption of DICOM for digital and computational pathology will allow the reuse of enterprise-wide infrastructure for storage, security, and business continuity. The DICOM embedded metadata allows detached files to remain useful. Bright-field and multichannel fluorescence, Z-stacks, cytology, and sparse and fully tiled encoding are supported. External terminologies and standard compression schemes are supported. Color consistency is defined using International Color Consortium profiles. The DICOM files can be dual personality Tagged Image File Format (TIFF) for legacy support. Annotations for computational pathology results can be encoded.
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Taghipour MM, Sepehri MM. Designing a novel hybrid healthcare teleconsultation network: a benchtop study of telepathology in Iran and a systematic review. BMC Med Inform Decis Mak 2020; 20:186. [PMID: 32787833 PMCID: PMC7477836 DOI: 10.1186/s12911-020-01170-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2019] [Accepted: 06/26/2020] [Indexed: 12/03/2022] Open
Abstract
BACKGROUND Growing demand for medical services has increased patient waiting time due to the limited number or unbalanced distribution of healthcare centers. Healthcare teleconsultation networks are one of the potentially powerful systems to overcome this problem. Medical pathology can hugely benefit from teleconsultation networks because having second opinions is precious for many cases; however, resource planning (i.e., assignment and distribution of pathology consultation requests) is challenging due to bulky medical images of patients. This results in high setup and operational costs. The aim of this study is to design an optimal teleconsultation network for pathology labs under the supervision of medical sciences universities in Tehran, Iran. METHODS To avoid the setup cost, we first propose a modified hybrid peer-to-peer (P2P) overlay architecture for our telepathology network, using Iran's National Healthcare Information Network (SHAMS) as the underlying infrastructure. Then we apply optimization techniques to solve the request assignment and distribution problems in the network. Finally, we present a novel mathematical model with the objective of minimizing the variable operational costs of the system. RESULTS The efficiency of the proposed method was evaluated by a set of practical-sized network instances simulated based on the characteristics of SHAMS. The results show that the presented model and architecture can obtain optimal solutions for network instances up to 350 nodes, which covers our target network. CONCLUSIONS We believe that the proposed method can be beneficial for designing large-scale medical teleconsultation networks by adjusting the constraints according to the rules and conditions of each country. Our findings showed that teleconsultation networks in countries with strong information technology (IT) infrastructures are under the influence of consultation fees, while in countries with weak IT infrastructure, the transmission costs are more critical. To the best of our knowledge, no research has so far addressed resource planning in medical teleconsultation networks using optimization techniques. Besides, the target network, i.e., pathology labs under the supervision of medical sciences universities in Tehran and the SHAMS network, are discussed for the first time in this work.
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Affiliation(s)
- Mohammad Mahdi Taghipour
- The Laboratory for Healthcare Systems Optimization, Engineering, and Informatics, Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, 1411713116 Iran
| | - Mohammad Mehdi Sepehri
- The Laboratory for Healthcare Systems Optimization, Engineering, and Informatics, Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, 1411713116 Iran
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Stathonikos N, van Varsseveld NC, Vink A, van Dijk MR, Nguyen TQ, Leng WWJD, Lacle MM, Goldschmeding R, Vreuls CPH, van Diest PJ. Digital pathology in the time of corona. J Clin Pathol 2020; 73:706-712. [PMID: 32699117 PMCID: PMC7588598 DOI: 10.1136/jclinpath-2020-206845] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Accepted: 06/09/2020] [Indexed: 12/29/2022]
Abstract
The 2020 COVID-19 crisis has had and will have many implications for healthcare, including pathology. Rising number of infections create staffing shortages and other hospital departments might require pathology employees to fill more urgent positions. Furthermore, lockdown measures and social distancing cause many people to work from home. During this crisis, it became clearer than ever what an asset digital diagnostics is to keep pathologists, residents, molecular biologists and pathology assistants engaged in the diagnostic process, allowing social distancing and a ‘need to be there’ on-the-premises policy, while working effectively from home. This paper provides an overview of our way of working during the 2020 COVID-19 crisis with emphasis on the virtues of digital pathology.
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Affiliation(s)
| | | | - Aryan Vink
- Pathology, Universitair Medisch Centrum Utrecht, Utrecht, The Netherlands
| | - Marijke R van Dijk
- Pathology, Universitair Medisch Centrum Utrecht, Utrecht, The Netherlands
| | - Tri Q Nguyen
- Pathology, Universitair Medisch Centrum Utrecht, Utrecht, The Netherlands
| | - Wendy W J de Leng
- Pathology, Universitair Medisch Centrum Utrecht, Utrecht, The Netherlands
| | - Miangela M Lacle
- Pathology, Universitair Medisch Centrum Utrecht, Utrecht, The Netherlands
| | - Roel Goldschmeding
- Pathology, Universitair Medisch Centrum Utrecht, Utrecht, The Netherlands
| | - Celien P H Vreuls
- Pathology, Universitair Medisch Centrum Utrecht, Utrecht, The Netherlands
| | - Paul J van Diest
- Pathology, Universitair Medisch Centrum Utrecht, Utrecht, The Netherlands
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Ščupáková K, Balluff B, Tressler C, Adelaja T, Heeren RM, Glunde K, Ertaylan G. Cellular resolution in clinical MALDI mass spectrometry imaging: the latest advancements and current challenges. Clin Chem Lab Med 2020; 58:914-929. [PMID: 31665113 PMCID: PMC9867918 DOI: 10.1515/cclm-2019-0858] [Citation(s) in RCA: 70] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2019] [Accepted: 10/07/2019] [Indexed: 02/07/2023]
Abstract
Mass spectrometry (MS) is the workhorse of metabolomics, proteomics and lipidomics. Mass spectrometry imaging (MSI), its extension to spatially resolved analysis of tissues, is a powerful tool for visualizing molecular information within the histological context of tissue. This review summarizes recent developments in MSI and highlights current challenges that remain to achieve molecular imaging at the cellular level of clinical specimens. We focus on matrix-assisted laser desorption/ionization (MALDI)-MSI. We discuss the current status of each of the analysis steps and remaining challenges to reach the desired level of cellular imaging. Currently, analyte delocalization and degradation, matrix crystal size, laser focus restrictions and detector sensitivity are factors that are limiting spatial resolution. New sample preparation devices and laser optic systems are being developed to push the boundaries of these limitations. Furthermore, we review the processing of cellular MSI data and images, and the systematic integration of these data in the light of available algorithms and databases. We discuss roadblocks in the data analysis pipeline and show how technology from other fields can be used to overcome these. Finally, we conclude with curative and community efforts that are needed to enable contextualization of the information obtained.
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Affiliation(s)
- Klára Ščupáková
- Maastricht MultiModal Molecular Imaging Institute (M4I), University of Maastricht, Maastricht, The Netherlands
| | - Benjamin Balluff
- Maastricht MultiModal Molecular Imaging Institute (M4I), University of Maastricht, Maastricht, The Netherlands
| | - Caitlin Tressler
- Russell H. Morgan Department of Radiology and Radiological Science, Division of Cancer Imaging Research, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Tobi Adelaja
- Russell H. Morgan Department of Radiology and Radiological Science, Division of Cancer Imaging Research, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Ron M.A. Heeren
- Corresponding author: Ron M.A. Heeren, Maastricht MultiModal Molecular Imaging Institute (M4I), University of Maastricht, Maastricht, The Netherlands,
| | - Kristine Glunde
- Russell H. Morgan Department of Radiology and Radiological Science, Division of Cancer Imaging Research, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; and The Sidney Kimmel Comprehensive Cancer Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Gökhan Ertaylan
- Unit Health, Flemish Institute for Technological Research (VITO), Mol, Belgium
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Church DL, Naugler C. Essential role of laboratory physicians in transformation of laboratory practice and management to a value-based patient-centric model. Crit Rev Clin Lab Sci 2020; 57:323-344. [PMID: 32180485 DOI: 10.1080/10408363.2020.1720591] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
The laboratory is a vital part of the continuum of patient care. In fact, there are few programs in the healthcare system that do not rely on ready access and availability of complex diagnostic laboratory services. The existing transactional model of laboratory "medical practice" will not be able to meet the needs of the healthcare system as it rapidly shifts toward value-based care and precision medicine, which demands that practice be based on total system indicators, clinical effectiveness, and patient outcomes. Laboratory "value" will no longer be focused primarily on internal testing quality and efficiencies but rather on the relative cost of diagnostic testing compared to direct improvement in clinical and system outcomes. The medical laboratory as a "business" focused on operational efficiency and cost-controls must transform to become an essential clinical service that is a tightly integrated equal partner in direct patient care. We would argue that this paradigm shift would not be necessary if laboratory services had remained a "patient-centric" medical practice throughout the last few decades. This review is focused on the essential role of laboratory physicians in transforming laboratory practice and management to a value-based patient-centric model. Value-based practice is necessary not only to meet the challenges of the new precision medicine world order but also to bring about sustainable healthcare service delivery.
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Affiliation(s)
- Deirdre L Church
- Department of Pathology and Laboratory Medicine, Faculty of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Medicine, Faculty of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Community Health Sciences, Faculty of Medicine, University of Calgary, Calgary, AB, Canada
| | - Christopher Naugler
- Department of Pathology and Laboratory Medicine, Faculty of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Community Health Sciences, Faculty of Medicine, University of Calgary, Calgary, AB, Canada
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Huss R, Coupland SE. Software‐assisted decision support in digital histopathology. J Pathol 2020; 250:685-692. [DOI: 10.1002/path.5388] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Revised: 01/09/2020] [Accepted: 01/17/2020] [Indexed: 12/16/2022]
Affiliation(s)
- Ralf Huss
- Institute of Pathology and Molecular Diagnostics University Hospital Augsburg Augsburg Germany
| | - Sarah E Coupland
- Department of Cellular and Molecular Pathology University of Liverpool Liverpool UK
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Turnquist C, Roberts-Gant S, Hemsworth H, White K, Browning L, Rees G, Roskell D, Verrill C. On the Edge of a Digital Pathology Transformation: Views from a Cellular Pathology Laboratory Focus Group. J Pathol Inform 2019; 10:37. [PMID: 31897354 PMCID: PMC6909548 DOI: 10.4103/jpi.jpi_38_19] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Accepted: 07/08/2019] [Indexed: 01/18/2023] Open
Abstract
Introduction: Digital pathology has the potential to revolutionize the way clinical diagnoses are made while improving safety and quality. With a few notable exceptions in the UK, few National Health Service (NHS) departments have deployed digital pathology platforms. Thus, in the next few years, many departments are anticipated to undergo the transition to digital pathology. In this period of transition, capturing attitudes and experiences can elucidate issues to be addressed and foster collaboration between NHS Trusts. This study aims to qualitatively ascertain the benefits and challenges of transitioning to digital pathology from the perspectives of pathologists and biomedical scientists in a department about to undergo the transition from diagnostic reporting via traditional microscopy to digital pathology. Methods: A focus group discussion was held in the setting of a large NHS teaching hospital's cellular pathology department which was on the brink of transitioning to digital pathology. A set of open questions were developed and posed to a group of pathologists and biomedical scientists in a focus group setting. Notes of the discussion were made along with an audio recording with permission. The discussion was subsequently turned into a series of topic headings and analyzed using content analysis. Results: Identified benefits of digital pathology included enhanced collaboration, teaching, cost savings, research, growth of specialty, multidisciplinary teams, and patient-centered care. Barriers to transitioning to digital pathology included standardization, validation, national implementation, storage and backups, training, logistical implementation, cost-effectiveness, privacy, and legality. Conclusion: Many benefits of digital pathology were identified, but key barriers need to be addressed in order to fully implement digital pathology on a trust and national level.
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Affiliation(s)
- Casmir Turnquist
- University of Oxford Medical School, John Radcliffe Hospital, Oxford, UK
| | - Sharon Roberts-Gant
- Department of Cellular Pathology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Helen Hemsworth
- Department of Cellular Pathology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Kieron White
- Department of Cellular Pathology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Lisa Browning
- Department of Cellular Pathology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Gabrielle Rees
- Department of Cellular Pathology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Derek Roskell
- Department of Cellular Pathology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Clare Verrill
- Department of Cellular Pathology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.,Nuffield Department of Surgical Sciences, Oxford NIHR Biomedical Research Centre, University of Oxford, Oxford, UK
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