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Martino F, Ilardi G, Varricchio S, Russo D, Di Crescenzo RM, Staibano S, Merolla F. A deep learning model to predict Ki-67 positivity in oral squamous cell carcinoma. J Pathol Inform 2024; 15:100354. [PMID: 38148967 PMCID: PMC10750186 DOI: 10.1016/j.jpi.2023.100354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 08/14/2023] [Accepted: 11/16/2023] [Indexed: 12/28/2023] Open
Abstract
Anatomical pathology is undergoing its third revolution, transitioning from analogical to digital pathology and incorporating new artificial intelligence technologies into clinical practice. Aside from classification, detection, and segmentation models, predictive models are gaining traction since they can impact diagnostic processes and laboratory activity, lowering consumable usage and turnaround time. Our research aimed to create a deep-learning model to generate synthetic Ki-67 immunohistochemistry from Haematoxylin and Eosin (H&E) stained images. We used 175 oral squamous cell carcinoma (OSCC) from the University Federico II's Pathology Unit's archives to train our model to generate 4 Tissue Micro Arrays (TMAs). We sectioned one slide from each TMA, first stained with H&E and then re-stained with anti-Ki-67 immunohistochemistry (IHC). In digitised slides, cores were disarrayed, and the matching cores of the 2 stained were aligned to construct a dataset to train a Pix2Pix algorithm to convert H&E images to IHC. Pathologists could recognise the synthetic images in only half of the cases in a specially designed likelihood test. Hence, our model produced realistic synthetic images. We next used QuPath to quantify IHC positivity, achieving remarkable levels of agreement between genuine and synthetic IHC. Furthermore, a categorical analysis employing 3 Ki-67 positivity cut-offs (5%, 10%, and 15%) revealed high positive-predictive values. Our model is a promising tool for collecting Ki-67 positivity information directly on H&E slides, reducing laboratory demand and improving patient management. It is also a valuable option for smaller laboratories to easily and quickly screen bioptic samples and prioritise them in a digital pathology workflow.
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Affiliation(s)
- Francesco Martino
- Dedalus HealthCare, Division of Diagnostic Imaging IT, Gertrude-Frohlich-Sandner-Straße 1, Wien 1100, Austria
- Department of Advanced Biomedical Sciences, University of Naples, Via Pansini, 5, Naples 80131, Italy
| | - Gennaro Ilardi
- Department of Advanced Biomedical Sciences, University of Naples, Via Pansini, 5, Naples 80131, Italy
| | - Silvia Varricchio
- Department of Advanced Biomedical Sciences, University of Naples, Via Pansini, 5, Naples 80131, Italy
| | - Daniela Russo
- Department of Advanced Biomedical Sciences, University of Naples, Via Pansini, 5, Naples 80131, Italy
| | - Rosa Maria Di Crescenzo
- Department of Advanced Biomedical Sciences, University of Naples, Via Pansini, 5, Naples 80131, Italy
| | - Stefania Staibano
- Department of Advanced Biomedical Sciences, University of Naples, Via Pansini, 5, Naples 80131, Italy
| | - Francesco Merolla
- Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, Via De Sanctis, Campobasso 86100, Italy
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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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Sulaieva O, Dudin O, Koshyk O, Panko M, Kobyliak N. Digital pathology implementation in cancer diagnostics: towards informed decision-making. Front Digit Health 2024; 6:1358305. [PMID: 38873358 PMCID: PMC11169727 DOI: 10.3389/fdgth.2024.1358305] [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: 12/19/2023] [Accepted: 05/16/2024] [Indexed: 06/15/2024] Open
Abstract
Digital pathology (DP) has become a part of the cancer healthcare system, creating additional value for cancer patients. DP implementation in clinical practice provides plenty of benefits but also harbors hidden ethical challenges affecting physician-patient relationships. This paper addresses the ethical obligation to transform the physician-patient relationship for informed and responsible decision-making when using artificial intelligence (AI)-based tools for cancer diagnostics. DP application allows to improve the performance of the Human-AI Team shifting focus from AI challenges towards the Augmented Human Intelligence (AHI) benefits. AHI enhances analytical sensitivity and empowers pathologists to deliver accurate diagnoses and assess predictive biomarkers for further personalized treatment of cancer patients. At the same time, patients' right to know about using AI tools, their accuracy, strengths and limitations, measures for privacy protection, acceptance of privacy concerns and legal protection defines the duty of physicians to provide the relevant information about AHI-based solutions to patients and the community for building transparency, understanding and trust, respecting patients' autonomy and empowering informed decision-making in oncology.
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Affiliation(s)
- Oksana Sulaieva
- Medical LaboratoryCSD, Kyiv, Ukraine
- Endocrinology Department, Bogomolets National Medical University, Kyiv, Ukraine
| | | | | | | | - Nazarii Kobyliak
- Medical LaboratoryCSD, Kyiv, Ukraine
- Endocrinology Department, Bogomolets National Medical University, Kyiv, Ukraine
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McCaffrey C, Jahangir C, Murphy C, Burke C, Gallagher WM, Rahman A. Artificial intelligence in digital histopathology for predicting patient prognosis and treatment efficacy in breast cancer. Expert Rev Mol Diagn 2024; 24:363-377. [PMID: 38655907 DOI: 10.1080/14737159.2024.2346545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 04/19/2024] [Indexed: 04/26/2024]
Abstract
INTRODUCTION Histological images contain phenotypic information predictive of patient outcomes. Due to the heavy workload of pathologists, the time-consuming nature of quantitatively assessing histological features, and human eye limitations to recognize spatial patterns, manually extracting prognostic information in routine pathological workflows remains challenging. Digital pathology has facilitated the mining and quantification of these features utilizing whole-slide image (WSI) scanners and artificial intelligence (AI) algorithms. AI algorithms to identify image-based biomarkers from the tumor microenvironment (TME) have the potential to revolutionize the field of oncology, reducing delays between diagnosis and prognosis determination, allowing for rapid stratification of patients and prescription of optimal treatment regimes, thereby improving patient outcomes. AREAS COVERED In this review, the authors discuss how AI algorithms and digital pathology can predict breast cancer patient prognosis and treatment outcomes using image-based biomarkers, along with the challenges of adopting this technology in clinical settings. EXPERT OPINION The integration of AI and digital pathology presents significant potential for analyzing the TME and its diagnostic, prognostic, and predictive value in breast cancer patients. Widespread clinical adoption of AI faces ethical, regulatory, and technical challenges, although prospective trials may offer reassurance and promote uptake, ultimately improving patient outcomes by reducing diagnosis-to-prognosis delivery delays.
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Affiliation(s)
- Christine McCaffrey
- UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - Chowdhury Jahangir
- UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - Clodagh Murphy
- UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - Caoimbhe Burke
- UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - William M Gallagher
- UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - Arman Rahman
- UCD School of Medicine, UCD Conway Institute, University College Dublin, Dublin, Ireland
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Magalhães G, Calisto R, Freire C, Silva R, Montezuma D, Canberk S, Schmitt F. Invisible for a few but essential for many: the role of Histotechnologists in the establishment of digital pathology. J Histotechnol 2024; 47:39-52. [PMID: 37869882 DOI: 10.1080/01478885.2023.2268297] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 10/03/2023] [Indexed: 10/24/2023]
Abstract
Digital pathology (DP) is indisputably the future for histopathology laboratories. The process of digital implementation requires deep workflow reorganisation which involves an interdisciplinary team. This transformation may have the greatest impact on the Histotechnologist (HTL) profession. Our review of the literature has clearly revealed that the role of HTLs in the establishment of DP is being unnoticed and guidance is limited. This article aims to bring HTLs from behind-the-scenes into the spotlight. Our objective is to provide them guidance and practical recommendations to successfully contribute to the implementation of a new digital workflow. Furthermore, it also intends to contribute for improvement of study programs, ensuring the role of HTL in DP is addressed as part of graduate and post-graduate education. In our review, we report on the differences encountered between workflow schemes and the limitations observed in this process. The authors propose a digital workflow to achieve its limitless potential, focusing on the HTL's role. This article explores the novel responsibilities of HTLs during specimen gross dissection, embedding, microtomy, staining, digital scanning, and whole slide image quality control. Furthermore, we highlight the benefits and challenges that DP implementation might bring the HTLs career. HTLs have an important role in the digital workflow: the responsibility of achieving the perfect glass slide.
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Affiliation(s)
- Gisela Magalhães
- Histopathology Department, Portsmouth Hospital University NHS Trust, Portsmouth, UK
- Department of Pathological Anatomy, School of Health Polytechnic of Porto (ESS|P.PORTO), Porto, Portugal
| | - Rita Calisto
- Department of Pathological Anatomy, School of Health Polytechnic of Porto (ESS|P.PORTO), Porto, Portugal
- Department of Pathological Anatomy, Hospital do Divino Espírito Santo, Ponta Delgada, Portugal
| | - Catarina Freire
- Department of Pathological Anatomy, School of Health Polytechnic of Porto (ESS|P.PORTO), Porto, Portugal
- Department of Pathological Anatomy, Hospital do Divino Espírito Santo, Ponta Delgada, Portugal
| | - Regina Silva
- Department of Pathological Anatomy, School of Health Polytechnic of Porto (ESS|P.PORTO), Porto, Portugal
- Centro de Investigação em Saúde e Ambiente, ESS,P.PORTO, Porto, Portugal
| | - Diana Montezuma
- Research & Development Unit, IMP Diagnostics, Porto, Portugal
- School of Medicine and Biomedical Sciences, University of Porto (ICBAS-UP), Porto, Portugal
| | - Sule Canberk
- Institute for Research and Innovation in Health (i3S), University of Porto, Porto, Portugal
- Cancer Signalling & Metabolism, Institute of Molecular Pathology and Immunology of the University of Porto (Ipatimup), Porto, Portugal
- Faculty of Medicine of the University of Porto (FMUP), Porto, Portugal
| | - Fernando Schmitt
- Department of Pathology, Faculty of Medicine of University of Porto, Porto, Portugal
- CINTESIS@RISE, Health Research Network, Alameda Prof. Hernâni Monteiro, Portugal
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Iwuajoku V, Haas A, Ekici K, Khan MZ, Stögbauer F, Steiger K, Mogler C, Schüffler PJ. [Digital transformation of a routine histopathology lab : Dos and don'ts!]. PATHOLOGIE (HEIDELBERG, GERMANY) 2024; 45:98-105. [PMID: 38189845 PMCID: PMC10902067 DOI: 10.1007/s00292-023-01291-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 11/15/2023] [Indexed: 01/09/2024]
Abstract
The implementation of digital histopathology in the laboratory marks a crucial milestone in the overall digital transformation of pathology. This shift offers a range of new possibilities, including access to extensive datasets for AI-assisted analyses, the flexibility of remote work and home office arrangements for specialists, and the expedited and simplified sharing of images and data for research, conferences, and tumor boards. However, the transition to a fully digital workflow involves significant technological and personnel-related efforts. It necessitates careful and adaptable change management to minimize disruptions, particularly in the personnel domain, and to prevent the loss of valuable potential from employees who may be resistant to change. This article consolidates our institute's experiences, highlighting technical and personnel-related challenges encountered during the transition to digital pathology. It also presents a comprehensive overview of potential difficulties at various interfaces when converting routine operations to a digital workflow.
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Affiliation(s)
- Viola Iwuajoku
- Institut für Pathologie, TUM School of Medicine and Health, Technische Universität München, Trogerstraße 18, 81675, München, Deutschland
| | - Anette Haas
- Institut für Pathologie, TUM School of Medicine and Health, Technische Universität München, Trogerstraße 18, 81675, München, Deutschland
| | - Kübra Ekici
- Institut für Pathologie, TUM School of Medicine and Health, Technische Universität München, Trogerstraße 18, 81675, München, Deutschland
| | - Mohammad Zaid Khan
- Institut für Pathologie, TUM School of Medicine and Health, Technische Universität München, Trogerstraße 18, 81675, München, Deutschland
| | - Fabian Stögbauer
- Institut für Pathologie, TUM School of Medicine and Health, Technische Universität München, Trogerstraße 18, 81675, München, Deutschland
| | - Katja Steiger
- Institut für Pathologie, TUM School of Medicine and Health, Technische Universität München, Trogerstraße 18, 81675, München, Deutschland
| | - Carolin Mogler
- Institut für Pathologie, TUM School of Medicine and Health, Technische Universität München, Trogerstraße 18, 81675, München, Deutschland
| | - Peter J Schüffler
- Institut für Pathologie, TUM School of Medicine and Health, Technische Universität München, Trogerstraße 18, 81675, München, Deutschland.
- TUM School of Computational Information and Technology, Technische Universität München, München, Deutschland.
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Cima L, Bussola N, Hassell LA, Kiehl TR, Schukow C, Zerbe N, Munari E, Torresani E, Barbareschi M, Cecchini MJ, Cirielli V, Pagliuca F, Ahsan M, Mohanty SK, Arbitrio E, Hughes G, Mirza KM. Evolving educational landscape in pathology: a comprehensive bibliometric and visual analysis including digital teaching and learning resources. J Clin Pathol 2024; 77:87-95. [PMID: 38123966 DOI: 10.1136/jcp-2023-209203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 12/01/2023] [Indexed: 12/23/2023]
Abstract
AIMS Pathology education is a core component of medical training, and its literature is critical for refining educational modalities. We performed a cross-sectional bibliometric analysis to explore publications on pathology education, focusing on new medical education technologies. METHODS The analysis identified 64 pathology journals and 53 keywords. Relevant articles were collected using a web application, PaperScraper, developed to accelerate literature search. Citation data were collected from multiple sources. Descriptive statistics, with time period analysis, were performed using Microsoft Excel and visualised with Flourish Studio. Two article groups were further investigated with a bibliometric software, VOSViewer, to establish co-authorship and keyword relationships. RESULTS 8946 citations were retrieved from 905 selected articles. Most articles were published in the last decade (447, 49.4%). The top journals were Archives of Pathology & Laboratory Medicine (184), Human Pathology (122) and the American Journal of Clinical Pathology (117). The highest number of citations was found for Human Pathology (2120), followed by Archives of Pathology & Laboratory Medicine (2098) and American Journal of Clinical Pathology (1142). Authors with different backgrounds had the greatest number of articles and citations. 12 co-authorship, 3 keyword and 8 co-citation clusters were found for the social media/online resources group, 8 co-authorship, 4 keyword and 7 co-citation clusters for the digital pathology/virtual microscopy/mobile technologies group. CONCLUSIONS The analysis revealed a significant increase in publications over time. The emergence of digital teaching and learning resources played a major role in this growth. Overall, these findings underscore the transformative potential of technology in pathology education.
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Affiliation(s)
- Luca Cima
- Department of Laboratory Medicine, Pathology Unit, Santa Chiara University Hospital, APSS, Trento, Italy
| | - Nicole Bussola
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Lewis A Hassell
- Department of Pathology, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA
| | - Tim-Rasmus Kiehl
- Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Pathology, Berlin, Germany
| | - Casey Schukow
- Department of Pathology, Corewell Health's Beaumont Hospital, Royal Oak, Michigan, USA
| | - Norman Zerbe
- Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Pathology, Berlin, Germany
| | - Enrico Munari
- Department of Molecular and Translational Medicine, Pathology Unit, University of Brescia, Brescia, Italy
| | - Evelin Torresani
- Department of Laboratory Medicine, Pathology Unit, Santa Chiara University Hospital, APSS, Trento, Italy
| | - Mattia Barbareschi
- Department of Laboratory Medicine, Pathology Unit, Santa Chiara University Hospital, APSS, Trento, Italy
- CISMED, Centro Interdipartimentale di Scienze Mediche, University of Trento, Trento, Italy
| | - Matthew J Cecchini
- Department of Pathology and Laboratory Medicine, University of Western Ontario, London, Ontario, Canada
| | - Vito Cirielli
- Department of Prevention, Legal Medicine Unit, Azienda ULSS 8 Berica, Vicenza, Italy
| | - Francesca Pagliuca
- Department of Mental and Physical Health and Preventive Medicine, Pathology Unit, University of Campania 'Luigi Vanvitelli', Naples, Italy
| | - Muhammad Ahsan
- Histopathology Department, Chughtai Institute of Pathology, Lahore, Pakistan
| | - Sambit K Mohanty
- Department of Pathology and Laboratory Medicine, CORE Diagnostics, Gurgaon, Delhi, India
| | | | - Griffin Hughes
- Office of Medical Student Research, Oklahoma State University Center for Health Sciences, Tulsa, Oklahoma, USA
| | - Kamran M Mirza
- Department of Pathology and Laboratory Medicine, Loyola University Chicago Stritch School of Medicine, Maywood, Illinois, USA
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Jensen CL, Thomsen LK, Zeuthen M, Johnsen S, El Jashi R, Nielsen MFB, Hemstra LE, Smith J. Biomedical laboratory scientists and technicians in digital pathology - Is there a need for professional development? Digit Health 2024; 10:20552076241237392. [PMID: 38495864 PMCID: PMC10943708 DOI: 10.1177/20552076241237392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/19/2024] [Indexed: 03/19/2024] Open
Abstract
Objective Digital pathology (DP) is moving into Danish pathology departments at high pace. Conventionally, biomedical laboratory scientists (BLS) and technicians have prepared tissue sections for light microscopy, but workflow alterations are required for the new digital era with whole slide imaging (WSI); digitally assisted image analysis (DAIA) and artificial intelligence (AI). We aim to explore the role of BLS in DP and assess a potential need for professional development. Methods We investigated the roles of BLS in the new digital era through qualitative interviews at Danish Pathology Departments in 2019/2020 before DP implementation (supported by a questionnaire); and in 2022 after DP implementation. Additionally, senior lecturers from three Danish University Colleges reported on how DP was integrated into the 2023 bachelor's degree educational curricula for BLS students. Results At some Danish pathology departments, BLS were involved in the implementation process of DP and their greatest concerns were lack of physical laboratory requirements (69%) and implementation strategies (63%). BLS were generally positive towards working with DP, however, some expressed concern about extended working hours for scanning. Work-task transfers from pathologists were generally greeted positively from both management and pathologists; however, at follow-up interviews after DP implementation, job transfers had not been effectuated. At Danish university colleges, DP had been integrated systematically in the curricula for BLS students, especially WSI. Conclusion Involving BLS in DP implementation and development may benefit the process, as BLS have a hands-on workflow perspective with a focus on quality assurance. Several new work opportunities for BLS may occur with DP including WSI, DAIA and AI, and therefore new qualifications are warranted, which must be considered in future undergraduate programmes for BLS students or postgraduate programmes for BLS.
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Affiliation(s)
- Charlotte Lerbech Jensen
- Center for Engineering and Science, Biomedical Laboratory Science, University College Absalon, Næstved, Denmark
| | - Lisbeth Koch Thomsen
- Center for Engineering and Science, Biomedical Laboratory Science, University College Absalon, Næstved, Denmark
| | - Mette Zeuthen
- Department of Technology, Faculty of Health, University College Copenhagen, Copenhagen, Denmark
| | - Sys Johnsen
- Department of Technology, Faculty of Health, University College Copenhagen, Copenhagen, Denmark
| | - Rima El Jashi
- Department of Biomedical Laboratory Science, Physiotherapy and Radiography, Biomedical Laboratory Science, UCL University College, Odense, Denmark
| | - Michael Friberg Bruun Nielsen
- Department of Biomedical Laboratory Science, Physiotherapy and Radiography, Biomedical Laboratory Science, UCL University College, Odense, Denmark
| | - Line E Hemstra
- Center for Engineering and Science, Biomedical Laboratory Science, University College Absalon, Næstved, Denmark
| | - Julie Smith
- Department of Technology, Faculty of Health, University College Copenhagen, Copenhagen, Denmark
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Schuty B, Martínez S, Guerra A, Lecumberry F, Magliano J, Malacrida L. Quantitative melanoma diagnosis using spectral phasor analysis of hyperspectral imaging from label-free slices. Front Oncol 2023; 13:1296826. [PMID: 38162497 PMCID: PMC10756080 DOI: 10.3389/fonc.2023.1296826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 10/31/2023] [Indexed: 01/03/2024] Open
Abstract
Introduction Melanoma diagnosis traditionally relies on microscopic examination of hematoxylin and eosin (H&E) slides by dermatopathologists to search for specific architectural and cytological features. Unfortunately, no single molecular marker exists to reliably differentiate melanoma from benign lesions such as nevi. This study explored the potential of autofluorescent molecules within tissues to provide molecular fingerprints indicative of degenerated melanocytes in melanoma. Methods Using hyperspectral imaging (HSI) and spectral phasor analysis, we investigated autofluorescence patterns in melanoma compared to intradermal nevi. Using UV excitation and a commercial spectral confocal microscope, we acquired label-free HSI data from the whole-slice samples. Results Our findings revealed distinct spectral phasor distributions between melanoma and intradermal nevi, with melanoma displaying a broader phasor phase distribution, signifying a more heterogeneous autofluorescence pattern. Notably, longer wavelengths associated with larger phases correlated with regions identified as melanoma by expert dermatopathologists using H&E staining. Quantitative analysis of phase and modulation histograms within the phasor clusters of five melanomas (with Breslow thicknesses ranging from 0.5 mm to 6 mm) and five intradermal nevi consistently highlighted differences between the two groups. We further demonstrated the potential for the discrimination of several melanocytic lesions using center-of-mass comparisons of phase and modulation variables. Remarkably, modulation versus phase center of mass comparisons revealed strong statistical significance among the groups. Additionally, we identified the molecular endogenous markers responsible for tissue autofluorescence, including collagen, elastin, NADH, FAD, and melanin. In melanoma, autofluorescence is characterized by a higher phase contribution, indicating an increase in FAD and melanin in melanocyte nests. In contrast, NADH, elastin, and collagen dominate the autofluorescence of the nevus. Discussion This work underscores the potential of autofluorescence and HSI-phasor analysis as valuable tools for quantifying tissue molecular fingerprints, thereby supporting more effective and quantitative melanoma diagnosis.
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Affiliation(s)
- Bruno Schuty
- Unidad de Bioimagenología Avanzada, Institut Pasteur de Montevideo, Hospital de Clínicas Universidad de la República, Montevideo, Uruguay
| | - Sofía Martínez
- Unidad de Bioimagenología Avanzada, Institut Pasteur de Montevideo, Hospital de Clínicas Universidad de la República, Montevideo, Uruguay
- Unidad Academica de Dermatología, Hospital de Clínicas, Facultad de Medicina, Universidad de la República, Montevideo, Uruguay
| | - Analía Guerra
- Unidad Academica de Dermatología, Hospital de Clínicas, Facultad de Medicina, Universidad de la República, Montevideo, Uruguay
| | - Federico Lecumberry
- Instituto de Ingeniería Eléctrica, Facultad de Ingeniería, Universidad de la República, Montevideo, Uruguay
| | - Julio Magliano
- Unidad Academica de Dermatología, Hospital de Clínicas, Facultad de Medicina, Universidad de la República, Montevideo, Uruguay
| | - Leonel Malacrida
- Unidad de Bioimagenología Avanzada, Institut Pasteur de Montevideo, Hospital de Clínicas Universidad de la República, Montevideo, Uruguay
- Unidad Academica de Fisiopatología, Hospital de Clínicas, Facultad de Medicina, Universidad de la República, Montevideo, Uruguay
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10
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Zhang J, Wu J, Zhou XS, Shi F, Shen D. Recent advancements in artificial intelligence for breast cancer: Image augmentation, segmentation, diagnosis, and prognosis approaches. Semin Cancer Biol 2023; 96:11-25. [PMID: 37704183 DOI: 10.1016/j.semcancer.2023.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 08/03/2023] [Accepted: 09/05/2023] [Indexed: 09/15/2023]
Abstract
Breast cancer is a significant global health burden, with increasing morbidity and mortality worldwide. Early screening and accurate diagnosis are crucial for improving prognosis. Radiographic imaging modalities such as digital mammography (DM), digital breast tomosynthesis (DBT), magnetic resonance imaging (MRI), ultrasound (US), and nuclear medicine techniques, are commonly used for breast cancer assessment. And histopathology (HP) serves as the gold standard for confirming malignancy. Artificial intelligence (AI) technologies show great potential for quantitative representation of medical images to effectively assist in segmentation, diagnosis, and prognosis of breast cancer. In this review, we overview the recent advancements of AI technologies for breast cancer, including 1) improving image quality by data augmentation, 2) fast detection and segmentation of breast lesions and diagnosis of malignancy, 3) biological characterization of the cancer such as staging and subtyping by AI-based classification technologies, 4) prediction of clinical outcomes such as metastasis, treatment response, and survival by integrating multi-omics data. Then, we then summarize large-scale databases available to help train robust, generalizable, and reproducible deep learning models. Furthermore, we conclude the challenges faced by AI in real-world applications, including data curating, model interpretability, and practice regulations. Besides, we expect that clinical implementation of AI will provide important guidance for the patient-tailored management.
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Affiliation(s)
- Jiadong Zhang
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Jiaojiao Wu
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Xiang Sean Zhou
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China.
| | - Dinggang Shen
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China; Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China; Shanghai Clinical Research and Trial Center, Shanghai, China.
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11
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Kriegsmann M, Kriegsmann K, Steinbuss G, Zgorzelski C, Albrecht T, Heinrich S, Farkas S, Roth W, Dang H, Hausen A, Gaida MM. Implementation of deep learning in liver pathology optimizes diagnosis of benign lesions and adenocarcinoma metastasis. Clin Transl Med 2023; 13:e1299. [PMID: 37415390 PMCID: PMC10326372 DOI: 10.1002/ctm2.1299] [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: 05/22/2023] [Accepted: 05/28/2023] [Indexed: 07/08/2023] Open
Abstract
INTRODUCTION Differentiation of histologically similar structures in the liver, including anatomical structures, benign bile duct lesions, or common types of liver metastases, can be challenging with conventional histological tissue sections alone. Accurate histopathological classification is paramount for the diagnosis and adequate treatment of the disease. Deep learning algorithms have been proposed for objective and consistent assessment of digital histopathological images. MATERIALS AND METHODS In the present study, we trained and evaluated deep learning algorithms based on the EfficientNetV2 and ResNetRS architectures to discriminate between different histopathological classes. For the required dataset, specialized surgical pathologists annotated seven different histological classes, including different non-neoplastic anatomical structures, benign bile duct lesions, and liver metastases from colorectal and pancreatic adenocarcinoma in a large patient cohort. Annotation resulted in a total of 204.159 image patches, followed by discrimination analysis using our deep learning models. Model performance was evaluated on validation and test data using confusion matrices. RESULTS Evaluation of the test set based on tiles and cases revealed overall highly satisfactory prediction capability of our algorithm for the different histological classes, resulting in a tile accuracy of 89% (38 413/43 059) and case accuracy of 94% (198/211). Importantly, the separation of metastasis versus benign lesions was certainly confident on case level, confirming the classification model performed with high diagnostic accuracy. Moreover, the whole curated raw data set is made publically available. CONCLUSIONS Deep learning is a promising approach in surgical liver pathology supporting decision making in personalized medicine.
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Affiliation(s)
- Mark Kriegsmann
- Institute of PathologyHeidelberg UniversityHeidelbergGermany
- Pathology WiesbadenWiesbadenGermany
| | - Katharina Kriegsmann
- Department of HematologyOncology and RheumatologyHeidelberg UniversityHeidelbergGermany
- Laborarztpraxis Rhein‐Main MVZ GbRFrankfurt am MainFrankfurtGermany
| | - Georg Steinbuss
- Department of HematologyOncology and RheumatologyHeidelberg UniversityHeidelbergGermany
| | | | - Thomas Albrecht
- Institute of PathologyHeidelberg UniversityHeidelbergGermany
| | - Stefan Heinrich
- Department of SurgeryJGU‐MainzUniversity Medical Center MainzMainzGermany
| | - Stefan Farkas
- Department of SurgerySt. Josefs‐ HospitalWiesbadenGermany
| | - Wilfried Roth
- Institute of PathologyJGU‐MainzUniversity Medical Center MainzMainzGermany
| | - Hien Dang
- Department of SurgeryDepartment of Surgical ResearchThomas Jefferson UniversityPhiladelphiaPennsylvaniaUSA
| | - Anne Hausen
- Institute of PathologyJGU‐MainzUniversity Medical Center MainzMainzGermany
| | - Matthias M. Gaida
- Institute of PathologyJGU‐MainzUniversity Medical Center MainzMainzGermany
- TRONJGU‐MainzTranslational Oncology at the University Medical CenterMainzGermany
- Research Center for ImmunotherapyJGU‐MainzUniversity Medical Center MainzMainzGermany
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12
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Piya S, Lennerz JK. Sustainable development goals applied to digital pathology and artificial intelligence applications in low- to middle-income countries. Front Med (Lausanne) 2023; 10:1146075. [PMID: 37256085 PMCID: PMC10225661 DOI: 10.3389/fmed.2023.1146075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 04/27/2023] [Indexed: 06/01/2023] Open
Abstract
Digital Pathology (DP) and Artificial Intelligence (AI) can be useful in low- and middle-income countries; however, many challenges exist. The United Nations developed sustainable development goals that aim to overcome some of these challenges. The sustainable development goals have not been applied to DP/AI applications in low- to middle income countries. We established a framework to align the 17 sustainable development goals with a 27-indicator list for low- and middle-income countries (World Bank/WHO) and a list of 21 essential elements for DP/AI. After categorization into three domains (human factors, IT/electronics, and materials + reagents), we permutated these layers into 153 concatenated statements for prioritization on a four-tiered scale. The two authors tested the subjective ranking framework and endpoints included ranked sum scores and visualization across the three layers. The authors assigned 364 points with 1.1-1.3 points per statement. We noted the prioritization of human factors (43%) at the indicator layer whereas IT/electronic (36%) and human factors (35%) scored highest at the essential elements layer. The authors considered goal 9 (industry, innovation, and infrastructure; average points 2.33; sum 42), goal 4 (quality education; 2.17; 39), and goal 8 (decent work and economic growth; 2.11; 38) most relevant; intra-/inter-rater variability assessment after a 3-month-washout period confirmed these findings. The established framework allows individual stakeholders to capture the relative importance of sustainable development goals for overcoming limitations to a specific problem. The framework can be used to raise awareness and help identify synergies between large-scale global objectives and solutions in resource-limited settings.
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Affiliation(s)
- Sumi Piya
- Nepal Medical College and Teaching Hospital (NMCTH), Kathmandu, Nepal
- Nepal Cancer Hospital and Research Center, Lalitpur, Nepal
- Department of Pathology, Center for Integrated Diagnostics, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Jochen K. Lennerz
- Department of Pathology, Center for Integrated Diagnostics, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
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13
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Rojansky R, Jhun I, Dussaq AM, Chirieleison SM, Nirschl JJ, Born D, Fralick J, Hetherington W, Kerr AM, Lavezo J, Lawrence DB, Lummus S, Macasaet R, Montine TJ, Ryan E, Shen J, Shoemaker J, Tan B, Vogel H, Waraich PS, Yang E, Young A, Folkins A. Rapid Deployment of Whole Slide Imaging for Primary Diagnosis in Surgical Pathology at Stanford Medicine: Responding to Challenges of the COVID-19 Pandemic. Arch Pathol Lab Med 2023; 147:359-367. [PMID: 35802938 PMCID: PMC9904534 DOI: 10.5858/arpa.2021-0438-oa] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/22/2022] [Indexed: 11/06/2022]
Abstract
CONTEXT.— Stanford Pathology began stepwise subspecialty implementation of whole slide imaging (WSI) in 2018 soon after the first US Food and Drug Administration approval. In 2020, during the COVID-19 pandemic, the Centers for Medicare & Medicaid Services waived the requirement for pathologists to perform diagnostic tests in Clinical Laboratory Improvement Amendments (CLIA)-licensed facilities. This encouraged rapid implementation of WSI across all surgical pathology subspecialties. OBJECTIVE.— To present our experience with validation and implementation of WSI at a large academic medical center encompassing a caseload of more than 50 000 cases per year. DESIGN.— Validation was performed independently for 3 subspecialty services with a diagnostic concordance threshold above 95%. Analysis of user experience, staffing, infrastructure, and information technology was performed after department-wide expansion. RESULTS.— Diagnostic concordance was achieved in 96% of neuropathology cases, 100% of gynecologic pathology cases, and 98% of immunohistochemistry cases. After full implementation, 8 high-capacity scanners were operational, with whole slide images generated on greater than 2000 slides per weekday, accounting for approximately 80% of histologic slides at Stanford Medicine. Multiple modifications in workflow and information technology were needed to improve performance. Within months of full implementation, most attending pathologists and trainees had adopted WSI for primary diagnosis. CONCLUSIONS.— WSI across all surgical subspecialities is achievable at scale at an academic medical center; however, adoption required flexibility to adjust workflows and develop tailored solutions. WSI at scale supported the health and safety of medical staff while facilitating high-quality patient care and education during COVID-19 restrictions.
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Affiliation(s)
- Rebecca Rojansky
- From the Department of Pathology, School of Medicine, Stanford University, Stanford, California (Rojansky, Jhun, Dussaq, Chirieleison, Nirschl, Born, Montine, Ryan, Shen, Tan, Vogel, Yang, Folkins)
| | - Iny Jhun
- From the Department of Pathology, School of Medicine, Stanford University, Stanford, California (Rojansky, Jhun, Dussaq, Chirieleison, Nirschl, Born, Montine, Ryan, Shen, Tan, Vogel, Yang, Folkins)
| | - Alex M Dussaq
- From the Department of Pathology, School of Medicine, Stanford University, Stanford, California (Rojansky, Jhun, Dussaq, Chirieleison, Nirschl, Born, Montine, Ryan, Shen, Tan, Vogel, Yang, Folkins)
| | - Steven M Chirieleison
- From the Department of Pathology, School of Medicine, Stanford University, Stanford, California (Rojansky, Jhun, Dussaq, Chirieleison, Nirschl, Born, Montine, Ryan, Shen, Tan, Vogel, Yang, Folkins)
| | - Jeffrey J Nirschl
- From the Department of Pathology, School of Medicine, Stanford University, Stanford, California (Rojansky, Jhun, Dussaq, Chirieleison, Nirschl, Born, Montine, Ryan, Shen, Tan, Vogel, Yang, Folkins)
| | - Don Born
- From the Department of Pathology, School of Medicine, Stanford University, Stanford, California (Rojansky, Jhun, Dussaq, Chirieleison, Nirschl, Born, Montine, Ryan, Shen, Tan, Vogel, Yang, Folkins)
| | - Jennifer Fralick
- Anatomic Pathology and Clinical Laboratories (Fralick, Hetherington, Macasaet, Young), Stanford Health Care, Stanford, California
| | - William Hetherington
- Anatomic Pathology and Clinical Laboratories (Fralick, Hetherington, Macasaet, Young), Stanford Health Care, Stanford, California
| | - Alison M Kerr
- Clinical Operations (Kerr), Stanford Health Care, Stanford, California
| | - Jonathan Lavezo
- The Department of Pathology, Health Sciences Center, Texas Tech University, El Paso (Lavezo)
| | - Daniel B Lawrence
- Information Technology (Lawrence, Shoemaker, Waraich), Stanford Health Care, Stanford, California
| | - Seth Lummus
- The Department of Human Physiology and Nutrition, University of Colorado, Colorado Springs (Lummus)
| | - Ronald Macasaet
- Anatomic Pathology and Clinical Laboratories (Fralick, Hetherington, Macasaet, Young), Stanford Health Care, Stanford, California
| | - Thomas J Montine
- From the Department of Pathology, School of Medicine, Stanford University, Stanford, California (Rojansky, Jhun, Dussaq, Chirieleison, Nirschl, Born, Montine, Ryan, Shen, Tan, Vogel, Yang, Folkins)
| | - Emily Ryan
- From the Department of Pathology, School of Medicine, Stanford University, Stanford, California (Rojansky, Jhun, Dussaq, Chirieleison, Nirschl, Born, Montine, Ryan, Shen, Tan, Vogel, Yang, Folkins)
| | - Jeanne Shen
- From the Department of Pathology, School of Medicine, Stanford University, Stanford, California (Rojansky, Jhun, Dussaq, Chirieleison, Nirschl, Born, Montine, Ryan, Shen, Tan, Vogel, Yang, Folkins)
| | - Jonathan Shoemaker
- Information Technology (Lawrence, Shoemaker, Waraich), Stanford Health Care, Stanford, California
| | - Brent Tan
- From the Department of Pathology, School of Medicine, Stanford University, Stanford, California (Rojansky, Jhun, Dussaq, Chirieleison, Nirschl, Born, Montine, Ryan, Shen, Tan, Vogel, Yang, Folkins)
| | - Hannes Vogel
- From the Department of Pathology, School of Medicine, Stanford University, Stanford, California (Rojansky, Jhun, Dussaq, Chirieleison, Nirschl, Born, Montine, Ryan, Shen, Tan, Vogel, Yang, Folkins)
| | - Puneet Singh Waraich
- Information Technology (Lawrence, Shoemaker, Waraich), Stanford Health Care, Stanford, California
| | - Eric Yang
- From the Department of Pathology, School of Medicine, Stanford University, Stanford, California (Rojansky, Jhun, Dussaq, Chirieleison, Nirschl, Born, Montine, Ryan, Shen, Tan, Vogel, Yang, Folkins)
| | - April Young
- Anatomic Pathology and Clinical Laboratories (Fralick, Hetherington, Macasaet, Young), Stanford Health Care, Stanford, California
| | - Ann Folkins
- From the Department of Pathology, School of Medicine, Stanford University, Stanford, California (Rojansky, Jhun, Dussaq, Chirieleison, Nirschl, Born, Montine, Ryan, Shen, Tan, Vogel, Yang, Folkins)
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Samaddar P, Mishra AK, Gaddam S, Singh M, Modi VK, Gopalakrishnan K, Bayer RL, Igreja Sa IC, Khanal S, Hirsova P, Kostallari E, Dey S, Mitra D, Roy S, Arunachalam SP. Machine Learning-Based Classification of Abnormal Liver Tissues Using Relative Permittivity. SENSORS (BASEL, SWITZERLAND) 2022; 22:9919. [PMID: 36560303 PMCID: PMC9781624 DOI: 10.3390/s22249919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 12/04/2022] [Accepted: 12/08/2022] [Indexed: 06/17/2023]
Abstract
The search for non-invasive, fast, and low-cost diagnostic tools has gained significant traction among many researchers worldwide. Dielectric properties calculated from microwave signals offer unique insights into biological tissue. Material properties, such as relative permittivity (εr) and conductivity (σ), can vary significantly between healthy and unhealthy tissue types at a given frequency. Understanding this difference in properties is key for identifying the disease state. The frequency-dependent nature of the dielectric measurements results in large datasets, which can be postprocessed using artificial intelligence (AI) methods. In this work, the dielectric properties of liver tissues in three mouse models of liver disease are characterized using dielectric spectroscopy. The measurements are grouped into four categories based on the diets or disease state of the mice, i.e., healthy mice, mice with non-alcoholic steatohepatitis (NASH) induced by choline-deficient high-fat diet, mice with NASH induced by western diet, and mice with liver fibrosis. Multi-class classification machine learning (ML) models are then explored to differentiate the liver tissue groups based on dielectric measurements. The results show that the support vector machine (SVM) model was able to differentiate the tissue groups with an accuracy up to 90%. This technology pipeline, thus, shows great potential for developing the next generation non-invasive diagnostic tools.
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Affiliation(s)
- Poulami Samaddar
- Microwave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology & Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Anup Kumar Mishra
- GIH Artificial Intelligence Laboratory (GAIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Sunil Gaddam
- Microwave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology & Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Vaishnavi K. Modi
- Microwave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology & Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Keerthy Gopalakrishnan
- Microwave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology & Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Rachel L. Bayer
- Gastroenterology Research, Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN 55905, USA
| | - Ivone Cristina Igreja Sa
- Gastroenterology Research, Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN 55905, USA
- Department of Biological and Medical Sciences, Faculty of Pharmacy in Hradec Králové, Charles University, Heyrovskeho 1203, 500 05 Hradec Kralove, Czech Republic
| | - Shalil Khanal
- Gastroenterology Research, Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN 55905, USA
| | - Petra Hirsova
- Gastroenterology Research, Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN 55905, USA
| | - Enis Kostallari
- Gastroenterology Research, Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN 55905, USA
| | - Shuvashis Dey
- Microwave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology & Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Department of Electrical and Computer Engineering, North Dakota State University, Fargo, ND 58105, USA
| | - Dipankar Mitra
- Microwave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology & Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Department of Computer Science, University of Wisconsin, La Crosse, WI 54601, USA
| | - Sayan Roy
- Microwave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology & Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Department of Electrical Engineering and Computer Science, South Dakota Mines, Rapid City, SD 57701, USA
| | - Shivaram P. Arunachalam
- Microwave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology & Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
- GIH Artificial Intelligence Laboratory (GAIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
- Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
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15
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Dileep G, Gianchandani Gyani SG. Artificial Intelligence in Breast Cancer Screening and Diagnosis. Cureus 2022; 14:e30318. [PMID: 36381716 PMCID: PMC9650950 DOI: 10.7759/cureus.30318] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 10/15/2022] [Indexed: 11/05/2022] Open
Abstract
Cancer is a disease that continues to plague our modern society. Among all types of cancer, breast cancer is now the most common type of cancer occurring in women worldwide. Various factors, including genetics, lifestyle, and the environment, have contributed to the rise in the prevalence of breast cancer among women of all socioeconomic strata. Therefore, proper screening for early diagnosis and treatment becomes a major factor when fighting the disease. Artificial intelligence (AI) continues to revolutionize various spheres of our lives with its numerous applications. Using AI in the existing screening process makes obtaining results even easier and more convenient. Faster, more accurate results are some of the benefits of AI methods in breast cancer screening. Nonetheless, there are many challenges in the process of the integration of AI that needs to be addressed systematically. The following is a review of the application of AI in breast cancer screening.
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Digital Pathology Implementation in Private Practice: Specific Challenges and Opportunities. Diagnostics (Basel) 2022; 12:diagnostics12020529. [PMID: 35204617 PMCID: PMC8871027 DOI: 10.3390/diagnostics12020529] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 02/15/2022] [Accepted: 02/16/2022] [Indexed: 01/27/2023] Open
Abstract
Digital pathology (DP) is being deployed in many pathology laboratories, but most reported experiences refer to public health facilities. In this paper, we report our experience in DP transition at a high-volume private laboratory, addressing the main challenges in DP implementation in a private practice setting and how to overcome these issues. We started our implementation in 2020 and we are currently scanning 100% of our histology cases. Pre-existing sample tracking infrastructure facilitated this process. We are currently using two high-capacity scanners (Aperio GT450DX) to digitize all histology slides at 40×. Aperio eSlide Manager WebViewer viewing software is bidirectionally linked with the laboratory information system. Scanning error rate, during the test phase, was 2.1% (errors detected by the scanners) and 3.5% (manual quality control). Pre-scanning phase optimizations and vendor feedback and collaboration were crucial to improve WSI quality and are ongoing processes. Regarding pathologists' validation, we followed the Royal College of Pathologists recommendations for DP implementation (adapted to our practice). Although private sector implementation of DP is not without its challenges, it will ultimately benefit from DP safety and quality-associated features. Furthermore, DP deployment lays the foundation for artificial intelligence tools integration, which will ultimately contribute to improving patient care.
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17
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Guo R, Hou M, Han Y, Feng XL. Access, charge and quality of tele-dermatology e-consults in China: A standardized patients study. Digit Health 2022; 8:20552076221140763. [PMID: 36465986 PMCID: PMC9716584 DOI: 10.1177/20552076221140763] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 11/02/2022] [Indexed: 12/03/2023] Open
Abstract
OBJECTIVE Direct-to-consumer telemedicine is rapidly changing the way that patients seek medical care. This study provided the first report on access, cost and quality of text- and image-based tele-dermatology e-consults, in China. METHODS We adopted the Standardized Patients approach to evaluate the services of tele-dermatology e-consults in two metropolitan cities, that is, Beijing and Hangzhou, in China. We measured quality from four dimensions: service process, diagnosis accuracy, prescription and treatment comprehensiveness, based on China's national clinical guidelines. We performed logistic regressions to investigate factors that were associated with high-quality care. RESULTS For 114 physicians eligible for inclusion, we succeeded in 87 (76%) validated visits. The median waiting time was 100 minutes (IQR 19-243 minutes) and the median length of consultation was 636 minutes (about 10 hours, IQR 188-1528 minutes). Per visit costs varied from $0 to $38, with a median of $8 (IQR 4-9). Among all, 15% of visits showed high quality in service process, 84% arrived in the correct diagnosis, 24% provided high-quality prescriptions and 71% provided comprehensive treatment. Providing images was associated with high quality in service process (OR 7.22, 95% CI 1.49-34.88). Visits in metropolitan Beijing and on non-work days had better prescription quality than that in metropolitan Hangzhou (OR 6.05, 95% CI 1.75-20.95) and that on workdays (OR 3.75, 95%CI 1.27-11.04), respectively. CONCLUSIONS Tele-dermatology e-consults seem to be easy to access and less expensive in China. However, great efforts are warranted to ensure that service processes and prescriptions adhere to clinical guidelines.
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Affiliation(s)
- Rui Guo
- School of Public Health, Capital Medical University, Beijing, China
| | - Mengchi Hou
- China Aerospace Science & Industry Corporation 731 Hospital, Beijing, China
| | - Yangyang Han
- Beijing Chinese Medical Hospital Affiliated to Capital Medical University, Beijing, China
| | - Xing Lin Feng
- Department of Health Policy and Management, School of Public Health, Peking University, Beijing, China
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Patel A, Balis UGJ, Cheng J, Li Z, Lujan G, McClintock DS, Pantanowitz L, Parwani A. Contemporary Whole Slide Imaging Devices and Their Applications within the Modern Pathology Department: A Selected Hardware Review. J Pathol Inform 2021; 12:50. [PMID: 35070479 PMCID: PMC8721869 DOI: 10.4103/jpi.jpi_66_21] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 09/22/2021] [Indexed: 12/21/2022] Open
Abstract
Digital pathology (DP) has disrupted the practice of traditional pathology, including applications in education, research, and clinical practice. Contemporary whole slide imaging (WSI) devices include technological advances that help address some of the challenges facing modern pathology, such as increasing workloads with fewer subspecialized pathologists, expanding integrated delivery networks with global reach, and greater customization when working up cases for precision medicine. This review focuses on integral hardware components of 43 market available and soon-to-be released digital WSI devices utilized throughout the world. Components such as objective lens type and magnification, scanning camera, illumination, and slide capacity were evaluated with respect to scan time, throughput, accuracy of scanning, and image quality. This analysis of assorted modern WSI devices offers essential, valuable information for successfully selecting and implementing a digital WSI solution for any given pathology practice.
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Affiliation(s)
- Ankush Patel
- Department of Pathology, The Ohio State University, Columbus, Ohio, USA
| | | | - Jerome Cheng
- Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA
| | - Zaibo Li
- Department of Pathology, The Ohio State University, Columbus, Ohio, USA
| | - Giovanni Lujan
- Department of Pathology, The Ohio State University, Columbus, Ohio, USA
| | | | - Liron Pantanowitz
- Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA
| | - Anil Parwani
- Department of Pathology, The Ohio State University, Columbus, Ohio, USA
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