1
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Ardon O, Asa SL, Lloyd MC, Lujan G, Parwani A, Santa-Rosario JC, Van Meter B, Samboy J, Pirain D, Blakely S, Hanna MG. Understanding the financial aspects of digital pathology: A dynamic customizable return on investment calculator for informed decision-making. J Pathol Inform 2024; 15:100376. [PMID: 38736870 PMCID: PMC11087961 DOI: 10.1016/j.jpi.2024.100376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2024] [Accepted: 04/05/2024] [Indexed: 05/14/2024] Open
Abstract
Background The adoption of digital pathology has transformed the field of pathology, however, the economic impact and cost analysis of implementing digital pathology solutions remain a critical consideration for institutions to justify. Digital pathology implementation requires a thorough evaluation of associated costs and should identify and optimize resource allocation to facilitate informed decision-making. A dynamic cost calculator to estimate the financial implications of deploying digital pathology systems was needed to estimate the financial effects on transitioning to a digital workflow. Methods A systematic approach was used to comprehensively assess the various components involved in implementing and maintaining a digital pathology system. This consisted of: (1) identification of key cost categories associated with digital pathology implementation; (2) data collection and analysis of cost estimation; (3) cost categorization and quantification of direct and indirect costs associated with different use cases, allowing customization of each factor based on specific intended uses and market rates, industry standards, and regional variations; (4) opportunities for savings realized by digitization of glass slides and (5) integration of the cost calculator into a unified framework for a holistic view of the financial implications associated with digital pathology implementation. The online tool enables the user to test various scenarios specific to their institution and provides adjustable parameters to assure organization specific relatability. Results The Digital Pathology Association has developed a web-based calculator as a companion tool to provide an exhaustive list of the necessary concepts needed when assessing the financial implications of transitioning to a digital pathology system. The dynamic return on investment (ROI) calculator successfully integrated relevant cost and cost-saving components associated with digital pathology implementation and maintenance. Considerations include factors such as digital pathology infrastructure, clinical operations, staffing, hardware and software, information technology, archive and retrieval, medical-legal, and potential reimbursements. The ROI calculator developed for digital pathology workflows offers a comprehensive, customizable tool for institutions to assess their anticipated upfront and ongoing annual costs as they start or expand their digital pathology journey. It also offers cost-savings analysis based on specific user case volume, institutional geographic considerations, and actual costs. In addition, the calculator also serves as a tool to estimate number of required whole slide scanners, scanner throughput, and data storage (TB). This tool is intended to estimate the potential costs and cost savings resulting from the transition to digital pathology for business plan justifications and return on investment calculations. Conclusions The digital pathology online cost calculator provides a comprehensive and reliable means of estimating the financial implications associated with implementing and maintaining a digital pathology system. By considering various cost factors and allowing customization based on institution-specific variables, the calculator empowers pathology laboratories, healthcare institutions, and administrators to make informed decisions and optimize resource allocation when adopting or expanding digital pathology technologies. The ROI calculator will enable healthcare institutions to assess the financial feasibility and potential return on investment on adopting digital pathology, facilitating informed decision-making and resource allocation.
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Affiliation(s)
- Orly Ardon
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Sylvia L. Asa
- University Hospitals Cleveland Medical Center, Case Western Reserve University, Cleveland OH 44106, USA
| | | | - Giovanni Lujan
- Department of Pathology, Wexner Medical Center, The Ohio State University, Columbus, OH 43210, USA
| | - Anil Parwani
- Department of Pathology, Wexner Medical Center, The Ohio State University, Columbus, OH 43210, USA
| | | | | | | | | | | | - Matthew G. Hanna
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
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2
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Hosseini MS, Bejnordi BE, Trinh VQH, Chan L, Hasan D, Li X, Yang S, Kim T, Zhang H, Wu T, Chinniah K, Maghsoudlou S, Zhang R, Zhu J, Khaki S, Buin A, Chaji F, Salehi A, Nguyen BN, Samaras D, Plataniotis KN. Computational pathology: A survey review and the way forward. J Pathol Inform 2024; 15:100357. [PMID: 38420608 PMCID: PMC10900832 DOI: 10.1016/j.jpi.2023.100357] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 12/21/2023] [Accepted: 12/23/2023] [Indexed: 03/02/2024] Open
Abstract
Computational Pathology (CPath) is an interdisciplinary science that augments developments of computational approaches to analyze and model medical histopathology images. The main objective for CPath is to develop infrastructure and workflows of digital diagnostics as an assistive CAD system for clinical pathology, facilitating transformational changes in the diagnosis and treatment of cancer that are mainly address by CPath tools. With evergrowing developments in deep learning and computer vision algorithms, and the ease of the data flow from digital pathology, currently CPath is witnessing a paradigm shift. Despite the sheer volume of engineering and scientific works being introduced for cancer image analysis, there is still a considerable gap of adopting and integrating these algorithms in clinical practice. This raises a significant question regarding the direction and trends that are undertaken in CPath. In this article we provide a comprehensive review of more than 800 papers to address the challenges faced in problem design all-the-way to the application and implementation viewpoints. We have catalogued each paper into a model-card by examining the key works and challenges faced to layout the current landscape in CPath. We hope this helps the community to locate relevant works and facilitate understanding of the field's future directions. In a nutshell, we oversee the CPath developments in cycle of stages which are required to be cohesively linked together to address the challenges associated with such multidisciplinary science. We overview this cycle from different perspectives of data-centric, model-centric, and application-centric problems. We finally sketch remaining challenges and provide directions for future technical developments and clinical integration of CPath. For updated information on this survey review paper and accessing to the original model cards repository, please refer to GitHub. Updated version of this draft can also be found from arXiv.
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Affiliation(s)
- Mahdi S Hosseini
- Department of Computer Science and Software Engineering (CSSE), Concordia Univeristy, Montreal, QC H3H 2R9, Canada
| | | | - Vincent Quoc-Huy Trinh
- Institute for Research in Immunology and Cancer of the University of Montreal, Montreal, QC H3T 1J4, Canada
| | - Lyndon Chan
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Danial Hasan
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Xingwen Li
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Stephen Yang
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Taehyo Kim
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Haochen Zhang
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Theodore Wu
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Kajanan Chinniah
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Sina Maghsoudlou
- Department of Computer Science and Software Engineering (CSSE), Concordia Univeristy, Montreal, QC H3H 2R9, Canada
| | - Ryan Zhang
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Jiadai Zhu
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Samir Khaki
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Andrei Buin
- Huron Digitial Pathology, St. Jacobs, ON N0B 2N0, Canada
| | - Fatemeh Chaji
- Department of Computer Science and Software Engineering (CSSE), Concordia Univeristy, Montreal, QC H3H 2R9, Canada
| | - Ala Salehi
- Department of Electrical and Computer Engineering, University of New Brunswick, Fredericton, NB E3B 5A3, Canada
| | - Bich Ngoc Nguyen
- University of Montreal Hospital Center, Montreal, QC H2X 0C2, Canada
| | - Dimitris Samaras
- Department of Computer Science, Stony Brook University, Stony Brook, NY 11794, United States
| | - Konstantinos N Plataniotis
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
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3
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Gonzalez R, Saha A, Campbell CJ, Nejat P, Lokker C, Norgan AP. Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and opportunities. J Pathol Inform 2024; 15:100347. [PMID: 38162950 PMCID: PMC10755052 DOI: 10.1016/j.jpi.2023.100347] [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: 08/21/2023] [Revised: 10/06/2023] [Accepted: 11/01/2023] [Indexed: 01/03/2024] Open
Abstract
This paper discusses some overlooked challenges faced when working with machine learning models for histopathology and presents a novel opportunity to support "Learning Health Systems" with them. Initially, the authors elaborate on these challenges after separating them according to their mitigation strategies: those that need innovative approaches, time, or future technological capabilities and those that require a conceptual reappraisal from a critical perspective. Then, a novel opportunity to support "Learning Health Systems" by integrating hidden information extracted by ML models from digitalized histopathology slides with other healthcare big data is presented.
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Affiliation(s)
- Ricardo Gonzalez
- DeGroote School of Business, McMaster University, Hamilton, Ontario, Canada
- Division of Computational Pathology and Artificial Intelligence, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, United States
| | - Ashirbani Saha
- Department of Oncology, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada
- Escarpment Cancer Research Institute, McMaster University and Hamilton Health Sciences, Hamilton, Ontario, Canada
| | - Clinton J.V. Campbell
- William Osler Health System, Brampton, Ontario, Canada
- Department of Pathology and Molecular Medicine, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada
| | - Peyman Nejat
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, United States
| | - Cynthia Lokker
- Health Information Research Unit, Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Andrew P. Norgan
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, United States
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4
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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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5
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Campion TR, Craven CK, Dorr DA, Bernstam EV, Knosp BM. Understanding enterprise data warehouses to support clinical and translational research: impact, sustainability, demand management, and accessibility. J Am Med Inform Assoc 2024; 31:1522-1528. [PMID: 38777803 PMCID: PMC11187432 DOI: 10.1093/jamia/ocae111] [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: 02/16/2024] [Revised: 04/10/2024] [Accepted: 05/05/2024] [Indexed: 05/25/2024] Open
Abstract
OBJECTIVES Healthcare organizations, including Clinical and Translational Science Awards (CTSA) hubs funded by the National Institutes of Health, seek to enable secondary use of electronic health record (EHR) data through an enterprise data warehouse for research (EDW4R), but optimal approaches are unknown. In this qualitative study, our goal was to understand EDW4R impact, sustainability, demand management, and accessibility. MATERIALS AND METHODS We engaged a convenience sample of informatics leaders from CTSA hubs (n = 21) for semi-structured interviews and completed a directed content analysis of interview transcripts. RESULTS EDW4R have created institutional capacity for single- and multi-center studies, democratized access to EHR data for investigators from multiple disciplines, and enabled the learning health system. Bibliometrics have been challenging due to investigator non-compliance, but one hub's requirement to link all study protocols with funding records enabled quantifying an EDW4R's multi-million dollar impact. Sustainability of EDW4R has relied on multiple funding sources with a general shift away from the CTSA grant toward institutional and industry support. To address EDW4R demand, institutions have expanded staff, used different governance approaches, and provided investigator self-service tools. EDW4R accessibility can benefit from improved tools incorporating user-centered design, increased data literacy among scientists, expansion of informaticians in the workforce, and growth of team science. DISCUSSION As investigator demand for EDW4R has increased, approaches to tracking impact, ensuring sustainability, and improving accessibility of EDW4R resources have varied. CONCLUSION This study adds to understanding of how informatics leaders seek to support investigators using EDW4R across the CTSA consortium and potentially elsewhere.
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Affiliation(s)
- Thomas R Campion
- Clinical & Translational Science Center, Weill Cornell Medicine, New York, NY 10022, United States
| | - Catherine K Craven
- Division of Clinical Research Informatics, Department of Population Health Sciences, The University of Texas Health San Antonio, San Antonio, TX 78229, United States
| | - David A Dorr
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR 97239, United States
- Department of Medicine, Oregon Health & Science University, Portland, OR 97239, United States
| | - Elmer V Bernstam
- D. Bradley McWilliams School of Biomedical Informatics, The University of Texas Health Science Center, Houston, TX 77030, United States
- Division of General Internal Medicine, McGovern Medical School and Center for Clinical and Translational Sciences, The University of Texas Health Science Center, Houston, TX 77030, United States
| | - Boyd M Knosp
- Roy J. and Lucille A. Carver College of Medicine and the Institute for Clinical & Translational Science, University of Iowa, Iowa City, IA 52242, United States
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6
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Browning L, Jesus C, Malacrino S, Guan Y, White K, Puddle A, Alham NK, Haghighat M, Colling R, Birks J, Rittscher J, Verrill C. Artificial Intelligence-Based Quality Assessment of Histopathology Whole-Slide Images within a Clinical Workflow: Assessment of 'PathProfiler' in a Diagnostic Pathology Setting. Diagnostics (Basel) 2024; 14:990. [PMID: 38786288 PMCID: PMC11120465 DOI: 10.3390/diagnostics14100990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 04/17/2024] [Accepted: 04/28/2024] [Indexed: 05/25/2024] Open
Abstract
Digital pathology continues to gain momentum, with the promise of artificial intelligence to aid diagnosis and for assessment of features which may impact prognosis and clinical management. Successful adoption of these technologies depends upon the quality of digitised whole-slide images (WSI); however, current quality control largely depends upon manual assessment, which is inefficient and subjective. We previously developed PathProfiler, an automated image quality assessment tool, and in this feasibility study we investigate its potential for incorporation into a diagnostic clinical pathology setting in real-time. A total of 1254 genitourinary WSI were analysed by PathProfiler. PathProfiler was developed and trained on prostate tissue and, of the prostate biopsy WSI, representing 46% of the WSI analysed, 4.5% were flagged as potentially being of suboptimal quality for diagnosis. All had concordant subjective issues, mainly focus-related, 54% severe enough to warrant remedial action which resulted in improved image quality. PathProfiler was less reliable in assessment of non-prostate surgical resection-type cases, on which it had not been trained. PathProfiler shows potential for incorporation into a digitised clinical pathology workflow, with opportunity for image quality improvement. Whilst its reliability in the current form appears greatest for assessment of prostate specimens, other specimen types, particularly biopsies, also showed benefit.
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Affiliation(s)
- Lisa Browning
- Department of Cellular Pathology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
| | - Christine Jesus
- Department of Cellular Pathology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
| | - Stefano Malacrino
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford OX3 9DU, UK
| | - Yue Guan
- Department of Cellular Pathology, Royal Berkshire Hospital, Royal Berkshire NHS Foundation Trust, Reading RG1 5AN, UK
| | - Kieron White
- Department of Cellular Pathology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
| | - Alison Puddle
- Department of Cellular Pathology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
| | | | - Maryam Haghighat
- School of Electrical Engineering and Robotics, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | - Richard Colling
- Department of Cellular Pathology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford OX3 9DU, UK
| | - Jacqueline Birks
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
| | - Jens Rittscher
- Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
| | - Clare Verrill
- Department of Cellular Pathology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford OX3 9DU, UK
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
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7
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Schüffler P, Steiger K, Mogler C. [Artificial intelligence for pathology-how, where, and why?]. PATHOLOGIE (HEIDELBERG, GERMANY) 2024; 45:198-202. [PMID: 38472382 PMCID: PMC11045628 DOI: 10.1007/s00292-024-01314-9] [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: 02/16/2024] [Indexed: 03/14/2024]
Abstract
Artificial intelligence promises many innovations and simplifications in pathology, but also raises just as many questions and uncertainties. In this article, we provide a brief overview of the current status, the goals already achieved by existing algorithms, and the remaining challenges.
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Affiliation(s)
- Peter Schüffler
- Institut für Pathologie, TUM School of Medicine and Health, Technische Universität München, München, Deutschland.
- TUM School of Computation, Information and Technology, Technische Universität München, München, Deutschland.
- Munich Center for Machine Learning (MCML), München, Deutschland.
| | - Katja Steiger
- Institut für Pathologie, TUM School of Medicine and Health, Technische Universität München, München, Deutschland
| | - Carolin Mogler
- Institut für Pathologie, TUM School of Medicine and Health, Technische Universität München, München, Deutschland
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8
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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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9
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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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10
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Chiang S, Tessier-Cloutier B, Klein E, Ardon O, Mueller JJ, Leitao MM, Abu-Rustum NR, Ellenson LH. Establishing guidelines for sentinel lymph node ultrastaging in endometrial cancer. Int J Gynecol Cancer 2024:ijgc-2023-005157. [PMID: 38388180 DOI: 10.1136/ijgc-2023-005157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2024] Open
Abstract
BACKGROUND Many sentinel lymph node (SLN) ultrastaging protocols for endometrial cancer exist, but there is no consensus method. OBJECTIVE This study aims to develop guidelines for size criteria in SLN evaluation for endometrial cancer, to determine whether a single cytokeratin AE1:AE3 immunohistochemical slide provides sufficient data for diagnosis, and to compare cost efficiency between current and limited ultrastaging protocols at a large tertiary care institution. METHODS Our current SLN ultrastaging protocol consists of cutting two adjacent paraffin block sections at two levels (L1 and L2), 50 μm apart, with two slides at each level stained with hematoxylin and eosin and cytokeratin AE1:AE3 immunohistochemistry. We retrospectively reviewed digitized L1 and L2 slides of all positive ultrastaged SLNs from patients treated for endometrial cancer between January 2013 and January 2020. SLN diagnosis was defined by measuring the largest cluster of contiguous tumor cells in a single cross section: macrometastasis (>2.0 mm), micrometastasis (>0.2 to ≤2.0 mm or >200 cells), or isolated tumor cells (≤0.2 mm or ≤200 cells). Concordance between L1 and L2 results was evaluated. Cost efficiency between current (two immunohistochemical slides per block) and proposed limited (one immunohistochemical slide per block) protocols was compared. RESULTS Digitized slides of 147 positive SLNs from 109 patients were reviewed; 4.1% of SLNs were reclassified based on refined size criteria. Complete concordance between L1 and L2 interpretations was seen in 91.8% of SLNs. A false-negative rate of 0%-0.9% in detecting micrometastasis and macrometastasis using a limited protocol was observed. Estimated charge-level savings of a limited protocol were 50% per patient. CONCLUSION High diagnostic accuracy in SLN interpretation may be achieved using a limited ultrastaging protocol of one immunohistochemical slide per block and linear measurement of the largest cluster of contiguous tumor cells. Implementation of the proposed limited ultrastaging protocol may result in laboratory cost savings with minimal impact on health outcomes.
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Affiliation(s)
- Sarah Chiang
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Basile Tessier-Cloutier
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Eric Klein
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Orly Ardon
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Jennifer J Mueller
- Department of Surgery, Gynecology Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Mario M Leitao
- Department of Surgery, Gynecology Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Nadeem R Abu-Rustum
- Department of Surgery, Gynecology Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Lora H Ellenson
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, USA
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11
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Samueli B, Aizenberg N, Shaco-Levy R, Katzav A, Kezerle Y, Krausz J, Mazareb S, Niv-Drori H, Peled HB, Sabo E, Tobar A, Asa SL. Complete digital pathology transition: A large multi-center experience. Pathol Res Pract 2024; 253:155028. [PMID: 38142526 DOI: 10.1016/j.prp.2023.155028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 12/08/2023] [Indexed: 12/26/2023]
Abstract
INTRODUCTION Transitioning from glass slide pathology to digital pathology for primary diagnostics requires an appropriate laboratory information system, an image management system, and slide scanners; it also reinforces the need for sophisticated pathology informatics including synoptic reporting. Previous reports have discussed the transition itself and relevant considerations for it, but not the selection criteria and considerations for the infrastructure. OBJECTIVE To describe the process used to evaluate slide scanners, image management systems, and synoptic reporting systems for a large multisite institution. METHODS Six network hospitals evaluated six slide scanners, three image management systems, and three synoptic reporting systems. Scanners were evaluated based on the quality of image, speed, ease of operation, and special capabilities (including z-stacking, fluorescence and others). Image management and synoptic reporting systems were evaluated for their ease of use and capacity. RESULTS Among the scanners evaluated, the Leica GT450 produced the highest quality images, while the 3DHistech Pannoramic provided fluorescence and superior z-stacking. The newest generation of scanners, released relatively recently, performed better than slightly older scanners from major manufacturers Although the Olympus VS200 was not fully vetted due to not meeting all inclusion criteria, it is discussed herein due to its exceptional versatility. For Image Management Software, the authors believe that Sectra is, at the time of writing the best developed option, but this could change in the very near future as other systems improve their capabilities. All synoptic reporting systems performed impressively. CONCLUSIONS Specifics regarding quality and abilities of different components will change rapidly with time, but large pathology practices considering such a transition should be aware of the issues discussed and evaluate the most current generation to arrive at appropriate conclusions.
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Affiliation(s)
- Benzion Samueli
- Department of Pathology, Soroka University Medical Center, P.O. Box 151, Be'er Sheva 8410101, Israel; Faculty of Health Sciences, Ben Gurion University of the Negev, P.O. Box 653, Be'er Sheva 8410501, Israel.
| | - Natalie Aizenberg
- Department of Pathology, Soroka University Medical Center, P.O. Box 151, Be'er Sheva 8410101, Israel; Faculty of Health Sciences, Ben Gurion University of the Negev, P.O. Box 653, Be'er Sheva 8410501, Israel
| | - Ruthy Shaco-Levy
- Department of Pathology, Soroka University Medical Center, P.O. Box 151, Be'er Sheva 8410101, Israel; Faculty of Health Sciences, Ben Gurion University of the Negev, P.O. Box 653, Be'er Sheva 8410501, Israel; Department of Pathology, Barzilai Medical Center, 2 Ha-Histadrut St, Ashkelon 7830604, Israel
| | - Aviva Katzav
- Pathology Institute, Meir Medical Center, Kfar Saba 4428164, Israel
| | - Yarden Kezerle
- Department of Pathology, Soroka University Medical Center, P.O. Box 151, Be'er Sheva 8410101, Israel; Faculty of Health Sciences, Ben Gurion University of the Negev, P.O. Box 653, Be'er Sheva 8410501, Israel
| | - Judit Krausz
- Department of Pathology, HaEmek Medical Center, 21 Yitzhak Rabin Ave, Afula 183411, Israel
| | - Salam Mazareb
- Department of Pathology, Carmel Medical Center, 7 Michal Street, Haifa 3436212, Israel
| | - Hagit Niv-Drori
- Department of Pathology, Rabin Medical Center, 39 Jabotinsky St, Petah Tikva 4941492, Israel; Faculty of Medicine, Tel Aviv University, P.O. Box 39040, Tel Aviv 6139001, Israel
| | - Hila Belhanes Peled
- Department of Pathology, HaEmek Medical Center, 21 Yitzhak Rabin Ave, Afula 183411, Israel
| | - Edmond Sabo
- Department of Pathology, Carmel Medical Center, 7 Michal Street, Haifa 3436212, Israel; Rappaport Faculty of Medicine, Technion Israel Institute of Technology, Haifa 3525433, Israel
| | - Ana Tobar
- Department of Pathology, Rabin Medical Center, 39 Jabotinsky St, Petah Tikva 4941492, Israel; Faculty of Medicine, Tel Aviv University, P.O. Box 39040, Tel Aviv 6139001, Israel
| | - Sylvia L Asa
- Institute of Pathology, University Hospitals Cleveland Medical Center, Case Western Reserve University, 11100 Euclid Avenue, Room 204, Cleveland, OH 44106, USA
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12
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Schwen LO, Kiehl TR, Carvalho R, Zerbe N, Homeyer A. Digitization of Pathology Labs: A Review of Lessons Learned. J Transl Med 2023; 103:100244. [PMID: 37657651 DOI: 10.1016/j.labinv.2023.100244] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 07/18/2023] [Accepted: 08/23/2023] [Indexed: 09/03/2023] Open
Abstract
Pathology laboratories are increasingly using digital workflows. This has the potential of increasing laboratory efficiency, but the digitization process also involves major challenges. Several reports have been published describing the individual experiences of specific laboratories with the digitization process. However, a comprehensive overview of the lessons learned is still lacking. We provide an overview of the lessons learned for different aspects of the digitization process, including digital case management, digital slide reading, and computer-aided slide reading. We also cover metrics used for monitoring performance and pitfalls and corresponding values observed in practice. The overview is intended to help pathologists, information technology decision makers, and administrators to benefit from the experiences of others and to implement the digitization process in an optimal way to make their own laboratory future-proof.
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Affiliation(s)
- Lars Ole Schwen
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany.
| | - Tim-Rasmus Kiehl
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Institute of Pathology, Berlin, Germany
| | - Rita Carvalho
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Institute of Pathology, Berlin, Germany
| | - Norman Zerbe
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Institute of Pathology, Berlin, Germany
| | - André Homeyer
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
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13
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Ardon O, Labasin M, Friedlander M, Manzo A, Corsale L, Ntiamoah P, Wright J, Elenitoba-Johnson K, Reuter VE, Hameed MR, Hanna MG. Quality Management System in Clinical Digital Pathology Operations at a Tertiary Cancer Center. J Transl Med 2023; 103:100246. [PMID: 37659445 PMCID: PMC10841911 DOI: 10.1016/j.labinv.2023.100246] [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: 05/19/2023] [Revised: 08/11/2023] [Accepted: 08/28/2023] [Indexed: 09/04/2023] Open
Abstract
Digital pathology workflows can improve pathology operations by allowing reliable and fast retrieval of digital images, digitally reviewing pathology slides, enabling remote work and telepathology, use of computer-aided tools, and sharing of digital images for research and educational purposes. The need for quality systems is a prerequisite for successful clinical-grade digital pathology adoption and patient safety. In this article, we describe the development of a structured digital pathology laboratory quality management system (QMS) for clinical digital pathology operations at Memorial Sloan Kettering Cancer Center (MSK). This digital pathology-specific QMS development stemmed from the gaps that were identified when MSK integrated digital pathology into its clinical practice. The digital scan team in conjunction with the Department of Pathology and Laboratory Medicine quality team developed a QMS tailored to the scanning operation to support departmental and institutional needs. As a first step, systemic mapping of the digital pathology operations identified the prescan, scan, and postscan processes; instrumentation; and staffing involved in the digital pathology operation. Next, gaps identified in quality control and quality assurance measures led to the development of standard operating procedures and training material for the different roles and workflows in the process. All digital pathology-related documents were subject to regulatory review and approval by departmental leadership. The quality essentials were developed into an extensive Digital Pathology Quality Essentials framework to specifically address the needs of the growing clinical use of digital pathology technologies. Using the unique digital experience gained at MSK, we present our recommendations for QMS for large-scale digital pathology operations in clinical settings.
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Affiliation(s)
- Orly Ardon
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York.
| | - Marc Labasin
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Maria Friedlander
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Allyne Manzo
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Lorraine Corsale
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Peter Ntiamoah
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Jeninne Wright
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Kojo Elenitoba-Johnson
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Victor E Reuter
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Meera R Hameed
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Matthew G Hanna
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
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14
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Raciti P, Sue J, Retamero JA, Ceballos R, Godrich R, Kunz JD, Casson A, Thiagarajan D, Ebrahimzadeh Z, Viret J, Lee D, Schüffler PJ, DeMuth G, Gulturk E, Kanan C, Rothrock B, Reis-Filho J, Klimstra DS, Reuter V, Fuchs TJ. Clinical Validation of Artificial Intelligence-Augmented Pathology Diagnosis Demonstrates Significant Gains in Diagnostic Accuracy in Prostate Cancer Detection. Arch Pathol Lab Med 2023; 147:1178-1185. [PMID: 36538386 DOI: 10.5858/arpa.2022-0066-oa] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/28/2022] [Indexed: 09/29/2023]
Abstract
CONTEXT.— Prostate cancer diagnosis rests on accurate assessment of tissue by a pathologist. The application of artificial intelligence (AI) to digitized whole slide images (WSIs) can aid pathologists in cancer diagnosis, but robust, diverse evidence in a simulated clinical setting is lacking. OBJECTIVE.— To compare the diagnostic accuracy of pathologists reading WSIs of prostatic biopsy specimens with and without AI assistance. DESIGN.— Eighteen pathologists, 2 of whom were genitourinary subspecialists, evaluated 610 prostate needle core biopsy WSIs prepared at 218 institutions, with the option for deferral. Two evaluations were performed sequentially for each WSI: initially without assistance, and immediately thereafter aided by Paige Prostate (PaPr), a deep learning-based system that provides a WSI-level binary classification of suspicious for cancer or benign and pinpoints the location that has the greatest probability of harboring cancer on suspicious WSIs. Pathologists' changes in sensitivity and specificity between the assisted and unassisted modalities were assessed, together with the impact of PaPr output on the assisted reads. RESULTS.— Using PaPr, pathologists improved their sensitivity and specificity across all histologic grades and tumor sizes. Accuracy gains on both benign and cancerous WSIs could be attributed to PaPr, which correctly classified 100% of the WSIs showing corrected diagnoses in the PaPr-assisted phase. CONCLUSIONS.— This study demonstrates the effectiveness and safety of an AI tool for pathologists in simulated diagnostic practice, bridging the gap between computational pathology research and its clinical application, and resulted in the first US Food and Drug Administration authorization of an AI system in pathology.
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Affiliation(s)
- Patricia Raciti
- From Paige (Raciti, Sue, Retamero, Ceballos, Godrich, Kunz, Casson, Thiagarajan, Ebrahimzadeh, Viret, Lee, Schüffler, Gulturk, Kanan, Rothrock, Klimstra, Fuchs), New York, New York
| | - Jillian Sue
- From Paige (Raciti, Sue, Retamero, Ceballos, Godrich, Kunz, Casson, Thiagarajan, Ebrahimzadeh, Viret, Lee, Schüffler, Gulturk, Kanan, Rothrock, Klimstra, Fuchs), New York, New York
| | - Juan A Retamero
- From Paige (Raciti, Sue, Retamero, Ceballos, Godrich, Kunz, Casson, Thiagarajan, Ebrahimzadeh, Viret, Lee, Schüffler, Gulturk, Kanan, Rothrock, Klimstra, Fuchs), New York, New York
| | - Rodrigo Ceballos
- From Paige (Raciti, Sue, Retamero, Ceballos, Godrich, Kunz, Casson, Thiagarajan, Ebrahimzadeh, Viret, Lee, Schüffler, Gulturk, Kanan, Rothrock, Klimstra, Fuchs), New York, New York
| | - Ran Godrich
- From Paige (Raciti, Sue, Retamero, Ceballos, Godrich, Kunz, Casson, Thiagarajan, Ebrahimzadeh, Viret, Lee, Schüffler, Gulturk, Kanan, Rothrock, Klimstra, Fuchs), New York, New York
| | - Jeremy D Kunz
- From Paige (Raciti, Sue, Retamero, Ceballos, Godrich, Kunz, Casson, Thiagarajan, Ebrahimzadeh, Viret, Lee, Schüffler, Gulturk, Kanan, Rothrock, Klimstra, Fuchs), New York, New York
| | - Adam Casson
- From Paige (Raciti, Sue, Retamero, Ceballos, Godrich, Kunz, Casson, Thiagarajan, Ebrahimzadeh, Viret, Lee, Schüffler, Gulturk, Kanan, Rothrock, Klimstra, Fuchs), New York, New York
| | - Dilip Thiagarajan
- From Paige (Raciti, Sue, Retamero, Ceballos, Godrich, Kunz, Casson, Thiagarajan, Ebrahimzadeh, Viret, Lee, Schüffler, Gulturk, Kanan, Rothrock, Klimstra, Fuchs), New York, New York
| | - Zahra Ebrahimzadeh
- From Paige (Raciti, Sue, Retamero, Ceballos, Godrich, Kunz, Casson, Thiagarajan, Ebrahimzadeh, Viret, Lee, Schüffler, Gulturk, Kanan, Rothrock, Klimstra, Fuchs), New York, New York
| | - Julian Viret
- From Paige (Raciti, Sue, Retamero, Ceballos, Godrich, Kunz, Casson, Thiagarajan, Ebrahimzadeh, Viret, Lee, Schüffler, Gulturk, Kanan, Rothrock, Klimstra, Fuchs), New York, New York
| | - Donghun Lee
- From Paige (Raciti, Sue, Retamero, Ceballos, Godrich, Kunz, Casson, Thiagarajan, Ebrahimzadeh, Viret, Lee, Schüffler, Gulturk, Kanan, Rothrock, Klimstra, Fuchs), New York, New York
| | - Peter J Schüffler
- From Paige (Raciti, Sue, Retamero, Ceballos, Godrich, Kunz, Casson, Thiagarajan, Ebrahimzadeh, Viret, Lee, Schüffler, Gulturk, Kanan, Rothrock, Klimstra, Fuchs), New York, New York
| | | | - Emre Gulturk
- From Paige (Raciti, Sue, Retamero, Ceballos, Godrich, Kunz, Casson, Thiagarajan, Ebrahimzadeh, Viret, Lee, Schüffler, Gulturk, Kanan, Rothrock, Klimstra, Fuchs), New York, New York
| | - Christopher Kanan
- From Paige (Raciti, Sue, Retamero, Ceballos, Godrich, Kunz, Casson, Thiagarajan, Ebrahimzadeh, Viret, Lee, Schüffler, Gulturk, Kanan, Rothrock, Klimstra, Fuchs), New York, New York
| | - Brandon Rothrock
- From Paige (Raciti, Sue, Retamero, Ceballos, Godrich, Kunz, Casson, Thiagarajan, Ebrahimzadeh, Viret, Lee, Schüffler, Gulturk, Kanan, Rothrock, Klimstra, Fuchs), New York, New York
| | - Jorge Reis-Filho
- The Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York (Reis-Filho, Reuter)
| | - David S Klimstra
- From Paige (Raciti, Sue, Retamero, Ceballos, Godrich, Kunz, Casson, Thiagarajan, Ebrahimzadeh, Viret, Lee, Schüffler, Gulturk, Kanan, Rothrock, Klimstra, Fuchs), New York, New York
| | - Victor Reuter
- The Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York (Reis-Filho, Reuter)
| | - Thomas J Fuchs
- From Paige (Raciti, Sue, Retamero, Ceballos, Godrich, Kunz, Casson, Thiagarajan, Ebrahimzadeh, Viret, Lee, Schüffler, Gulturk, Kanan, Rothrock, Klimstra, Fuchs), New York, New York
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15
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Schüffler P, Steiger K, Weichert W. How to use AI in pathology. Genes Chromosomes Cancer 2023; 62:564-567. [PMID: 37254901 DOI: 10.1002/gcc.23178] [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: 04/19/2023] [Revised: 05/15/2023] [Accepted: 05/20/2023] [Indexed: 06/01/2023] Open
Abstract
AI plays an important role in pathology, both in clinical practice supporting pathologists in their daily work, and in research discovering novel biomarkers for improved patient care. Still, AI is in its starting phase, and many pathology labs still need to transition to a digital workflow to be able to enjoy the benefits of AI. In this perspective, we explain the major benefits of AI in pathology, highlight key requirements that need to be met and example how to use it in a typical workflow.
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Affiliation(s)
- Peter Schüffler
- Institute of Pathology, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
- TUM School of Computing, Information and Technology, Technical University of Munich, Munich, Germany
- Munich Data Science Institute, Technical University of Munich, Munich, Germany
| | - Katja Steiger
- Institute of Pathology, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Wilko Weichert
- Institute of Pathology, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
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16
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Han C, Pan Y, Liu C, Yang X, Li J, Wang K, Sun Z, Liu H, Jin G, Fang F, Pan X, Tang T, Chen X, Pang S, Ma L, Wang X, Ren Y, Liu M, Liu F, Jiang M, Zhao J, Lu C, Lu Z, Gao D, Jiang Z, Pei J. Assessing the decision quality of artificial intelligence and oncologists of different experience in different regions in breast cancer treatment. Front Oncol 2023; 13:1152013. [PMID: 37361565 PMCID: PMC10289408 DOI: 10.3389/fonc.2023.1152013] [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: 01/27/2023] [Accepted: 04/26/2023] [Indexed: 06/28/2023] Open
Abstract
Background AI-based clinical decision support system (CDSS) has important prospects in overcoming the current informational challenges that cancer diseases faced, promoting the homogeneous development of standardized treatment among different geographical regions, and reforming the medical model. However, there are still a lack of relevant indicators to comprehensively assess its decision-making quality and clinical impact, which greatly limits the development of its clinical research and clinical application. This study aims to develop and application an assessment system that can comprehensively assess the decision-making quality and clinical impacts of physicians and CDSS. Methods Enrolled adjuvant treatment decision stage early breast cancer cases were randomly assigned to different decision-making physician panels (each panel consisted of three different seniority physicians in different grades hospitals), each physician made an independent "Initial Decision" and then reviewed the CDSS report online and made a "Final Decision". In addition, the CDSS and guideline expert groups independently review all cases and generate "CDSS Recommendations" and "Guideline Recommendations" respectively. Based on the design framework, a multi-level multi-indicator system including "Decision Concordance", "Calibrated Concordance", " Decision Concordance with High-level Physician", "Consensus Rate", "Decision Stability", "Guideline Conformity", and "Calibrated Conformity" were constructed. Results 531 cases containing 2124 decision points were enrolled; 27 different seniority physicians from 10 different grades hospitals have generated 6372 decision opinions before and after referring to the "CDSS Recommendations" report respectively. Overall, the calibrated decision concordance was significantly higher for CDSS and provincial-senior physicians (80.9%) than other physicians. At the same time, CDSS has a higher " decision concordance with high-level physician" (76.3%-91.5%) than all physicians. The CDSS had significantly higher guideline conformity than all decision-making physicians and less internal variation, with an overall guideline conformity variance of 17.5% (97.5% vs. 80.0%), a standard deviation variance of 6.6% (1.3% vs. 7.9%), and a mean difference variance of 7.8% (1.5% vs. 9.3%). In addition, provincial-middle seniority physicians had the highest decision stability (54.5%). The overall consensus rate among physicians was 64.2%. Conclusions There are significant internal variation in the standardization treatment level of different seniority physicians in different geographical regions in the adjuvant treatment of early breast cancer. CDSS has a higher standardization treatment level than all physicians and has the potential to provide immediate decision support to physicians and have a positive impact on standardizing physicians' treatment behaviors.
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Affiliation(s)
- Chunguang Han
- Department of Pediatric Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Yubo Pan
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Department of Breast Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Chang Liu
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Department of Breast Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Xiaowei Yang
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Department of Breast Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Jianbin Li
- Department of Breast Cancer, Fifth Medical Center, Chinese People’s Liberation Army General Hospital, Beijing, China
| | - Kun Wang
- Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital and Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Zhengkui Sun
- Department of Breast Oncology Surgery, Jiangxi Cancer Hospital (The Second People's Hospital of Jiangxi Province), Nanchang, China
| | - Hui Liu
- Department of Breast Surgery, Henan Provincial People's Hospital, Zhengzhou, China
| | - Gongsheng Jin
- Department of Oncological Surgery, the First Affiliated Hospital of Bengbu Medical College, Bengbu, China
| | - Fang Fang
- Department of Thyroid and Breast surgery, the First Affiliated Hospital of Wannan Medical College (Yijishan Hospital), Wuhhu, China
| | - Xiaofeng Pan
- Department of Thyroid and Breast surgery, the First Affiliated Hospital of Wannan Medical College (Yijishan Hospital), Wuhhu, China
| | - Tong Tang
- Department of General Surgury, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Xiao Chen
- Department of General Surgury, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Shiyong Pang
- Department of General Surgery, Lu'an People's Hospital of Anhui Province (Lu'an Hospital of Anhui Medical University), Lu'an, China
| | - Li Ma
- Department of Thyroid and Breast Surgery, Anqing Municipal Hospital (Anqing Hospital Affiliated to Anhui Medical University), Anqing, China
| | - Xiaodong Wang
- Department of Thyroid and Breast Surgery, The people's hospital of Bozhou (Bozhou Hospital Affiliated to Anhui Medical University), Bozhou, China
| | - Yun Ren
- Department of Thyroid and Breast surgery, Department of Oncological Surgery, Taihe county people's hospital (The Taihe hospital of Wannan Medical College), Fuyang, China
| | - Mengyou Liu
- Department of Thyroid and Breast surgery, Lixin County People's Hospital, Bozhou, China
| | - Feng Liu
- Department of Breast Surgery, Fuyang Cancer Hospital, Fuyang, China
| | - Mengxue Jiang
- Department of Breast Surgery, Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Jiqi Zhao
- Department of Breast Surgery, Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Chenyang Lu
- Department of Breast Surgery, Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Zhengdong Lu
- Department of Breast Surgery, Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Dongjing Gao
- Department of Breast Surgery, Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Zefei Jiang
- Department of Breast Cancer, Fifth Medical Center, Chinese People’s Liberation Army General Hospital, Beijing, China
| | - Jing Pei
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Department of Breast Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
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Ardon O, Klein E, Manzo A, Corsale L, England C, Mazzella A, Geneslaw L, Philip J, Ntiamoah P, Wright J, Sirintrapun SJ, Lin O, Elenitoba-Johnson K, Reuter VE, Hameed MR, Hanna MG. Digital pathology operations at a tertiary cancer center: Infrastructure requirements and operational cost. J Pathol Inform 2023; 14:100318. [PMID: 37811334 PMCID: PMC10550754 DOI: 10.1016/j.jpi.2023.100318] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 05/12/2023] [Accepted: 05/12/2023] [Indexed: 10/10/2023] Open
Abstract
Whole slide imaging is revolutionizing the field of pathology and is currently being used for clinical, educational, and research initiatives by an increasing number of institutions. Pathology departments have distinct needs for digital pathology systems, yet the cost of digital workflows is cited as a major barrier for widespread adoption by many organizations. Memorial Sloan Kettering Cancer Center (MSK) is an early adopter of whole slide imaging with incremental investments in resources that started more than 15 years ago. This experience and the large-scale scan operations led to the identification of required framework components of digital pathology operations. The cost of these components for the 2021 digital pathology operations at MSK were studied and calculated to enable an understanding of the operation and benchmark the accompanying costs. This paper describes the unique infrastructure cost and the costs associated with the digital pathology clinical operation use cases in a large, tertiary cancer center. These calculations can serve as a blueprint for other institutions to provide the necessary concepts and offer insights towards the financial requirements for digital pathology adoption by other institutions.
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Affiliation(s)
- Orly Ardon
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Eric Klein
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Allyne Manzo
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Lorraine Corsale
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Christine England
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Allix Mazzella
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Luke Geneslaw
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - John Philip
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Peter Ntiamoah
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jeninne Wright
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Oscar Lin
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Kojo Elenitoba-Johnson
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Victor E. Reuter
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Meera R. Hameed
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Matthew G. Hanna
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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18
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Clunie DA, Flanders A, Taylor A, Erickson B, Bialecki B, Brundage D, Gutman D, Prior F, Seibert JA, Perry J, Gichoya JW, Kirby J, Andriole K, Geneslaw L, Moore S, Fitzgerald TJ, Tellis W, Xiao Y, Farahani K, Luo J, Rosenthal A, Kandarpa K, Rosen R, Goetz K, Babcock D, Xu B, Hsiao J. Report of the Medical Image De-Identification (MIDI) Task Group - Best Practices and Recommendations. ARXIV 2023:arXiv:2303.10473v2. [PMID: 37033463 PMCID: PMC10081345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Subscribe] [Scholar Register] [Indexed: 04/11/2023]
Affiliation(s)
| | | | | | | | | | | | | | - Fred Prior
- University of Arkansas for Medical Sciences
| | | | | | | | - Justin Kirby
- Frederick National Laboratory for Cancer Research
| | | | | | | | | | | | - Ying Xiao
- University of Pennsylvania Health System
| | | | - James Luo
- National Heart, Lung, and Blood Institute (NHLBI)
| | - Alex Rosenthal
- National Institute of Allergy and Infectious Diseases (NIAID)
| | - Kris Kandarpa
- National Institute of Biomedical Imaging and Bioengineering (NIBIB)
| | - Rebecca Rosen
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)
| | | | - Debra Babcock
- National Institute of Neurological Disorders and Stroke (NINDS)
| | - Ben Xu
- National Institute on Alcohol Abuse and Alcoholism (NIAAA)
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19
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Gorman C, Punzo D, Octaviano I, Pieper S, Longabaugh WJR, Clunie DA, Kikinis R, Fedorov AY, Herrmann MD. Interoperable slide microscopy viewer and annotation tool for imaging data science and computational pathology. Nat Commun 2023; 14:1572. [PMID: 36949078 PMCID: PMC10033920 DOI: 10.1038/s41467-023-37224-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 03/08/2023] [Indexed: 03/24/2023] Open
Abstract
The exchange of large and complex slide microscopy imaging data in biomedical research and pathology practice is impeded by a lack of data standardization and interoperability, which is detrimental to the reproducibility of scientific findings and clinical integration of technological innovations. We introduce Slim, an open-source, web-based slide microscopy viewer that implements the internationally accepted Digital Imaging and Communications in Medicine (DICOM) standard to achieve interoperability with a multitude of existing medical imaging systems. We showcase the capabilities of Slim as the slide microscopy viewer of the NCI Imaging Data Commons and demonstrate how the viewer enables interactive visualization of traditional brightfield microscopy and highly-multiplexed immunofluorescence microscopy images from The Cancer Genome Atlas and Human Tissue Atlas Network, respectively, using standard DICOMweb services. We further show how Slim enables the collection of standardized image annotations for the development or validation of machine learning models and the visual interpretation of model inference results in the form of segmentation masks, spatial heat maps, or image-derived measurements.
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Affiliation(s)
- Chris Gorman
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | | | | | | | | | | | - Ron Kikinis
- Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Andrey Y Fedorov
- Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
| | - Markus D Herrmann
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
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20
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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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21
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Ho DJ, Agaram NP, Jean MH, Suser SD, Chu C, Vanderbilt CM, Meyers PA, Wexler LH, Healey JH, Fuchs TJ, Hameed MR. Deep Learning-Based Objective and Reproducible Osteosarcoma Chemotherapy Response Assessment and Outcome Prediction. THE AMERICAN JOURNAL OF PATHOLOGY 2023; 193:341-349. [PMID: 36563747 PMCID: PMC10013034 DOI: 10.1016/j.ajpath.2022.12.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Revised: 11/21/2022] [Accepted: 12/05/2022] [Indexed: 12/24/2022]
Abstract
Osteosarcoma is the most common primary bone cancer, whose standard treatment includes pre-operative chemotherapy followed by resection. Chemotherapy response is used for prognosis and management of patients. Necrosis is routinely assessed after chemotherapy from histology slides on resection specimens, where necrosis ratio is defined as the ratio of necrotic tumor/overall tumor. Patients with necrosis ratio ≥90% are known to have a better outcome. Manual microscopic review of necrosis ratio from multiple glass slides is semiquantitative and can have intraobserver and interobserver variability. In this study, an objective and reproducible deep learning-based approach was proposed to estimate necrosis ratio with outcome prediction from scanned hematoxylin and eosin whole slide images (WSIs). To conduct the study, 103 osteosarcoma cases with 3134 WSIs were collected. Deep Multi-Magnification Network was trained to segment multiple tissue subtypes, including viable tumor and necrotic tumor at a pixel level and to calculate case-level necrosis ratio from multiple WSIs. Necrosis ratio estimated by the segmentation model highly correlates with necrosis ratio from pathology reports manually assessed by experts. Furthermore, patients were successfully stratified to predict overall survival with P = 2.4 × 10-6 and progression-free survival with P = 0.016. This study indicates that deep learning can support pathologists as an objective tool to analyze osteosarcoma from histology for assessing treatment response and predicting patient outcome.
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Affiliation(s)
- David J Ho
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Narasimhan P Agaram
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Marc-Henri Jean
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Stephanie D Suser
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Cynthia Chu
- DataLine, Technology Division, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Chad M Vanderbilt
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Paul A Meyers
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Leonard H Wexler
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - John H Healey
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Thomas J Fuchs
- Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Meera R Hameed
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York.
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22
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Bisson T, Franz M, Dogan O I, Romberg D, Jansen C, Hufnagl P, Zerbe N. Anonymization of whole slide images in histopathology for research and education. Digit Health 2023; 9:20552076231171475. [PMID: 37205164 PMCID: PMC10185865 DOI: 10.1177/20552076231171475] [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: 11/04/2022] [Accepted: 04/06/2023] [Indexed: 05/21/2023] Open
Abstract
Objective The exchange of health-related data is subject to regional laws and regulations, such as the General Data Protection Regulation (GDPR) in the EU or the Health Insurance Portability and Accountability Act (HIPAA) in the United States, resulting in non-trivial challenges for researchers and educators when working with these data. In pathology, the digitization of diagnostic tissue samples inevitably generates identifying data that can consist of sensitive but also acquisition-related information stored in vendor-specific file formats. Distribution and off-clinical use of these Whole Slide Images (WSIs) are usually done in these formats, as an industry-wide standardization such as DICOM is yet only tentatively adopted and slide scanner vendors currently do not provide anonymization functionality. Methods We developed a guideline for the proper handling of histopathological image data particularly for research and education with regard to the GDPR. In this context, we evaluated existing anonymization methods and examined proprietary format specifications to identify all sensitive information for the most common WSI formats. This work results in a software library that enables GDPR-compliant anonymization of WSIs while preserving the native formats. Results Based on the analysis of proprietary formats, all occurrences of sensitive information were identified for file formats frequently used in clinical routine, and finally, an open-source programming library with an executable CLI tool and wrappers for different programming languages was developed. Conclusions Our analysis showed that there is no straightforward software solution to anonymize WSIs in a GDPR-compliant way while maintaining the data format. We closed this gap with our extensible open-source library that works instantaneously and offline.
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Affiliation(s)
- Tom Bisson
- Institute of Pathology, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Michael Franz
- Institute of Pathology, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Isil Dogan O
- Institute of Pathology, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Daniel Romberg
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
| | - Christoph Jansen
- Institute of Pathology, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Peter Hufnagl
- Institute of Pathology, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Centrum für Biomedizinische Bild- und Informationsverarbeitung (CBMI), University of Applied Sciences (HTW) Berlin, Berlin, Germany
| | - Norman Zerbe
- Institute of Pathology, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
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23
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King H, Williams B, Treanor D, Randell R. How, for whom, and in what contexts will artificial intelligence be adopted in pathology? A realist interview study. J Am Med Inform Assoc 2022; 30:529-538. [PMID: 36565465 PMCID: PMC9933065 DOI: 10.1093/jamia/ocac254] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 11/14/2022] [Accepted: 12/09/2022] [Indexed: 12/25/2022] Open
Abstract
OBJECTIVE There is increasing interest in using artificial intelligence (AI) in pathology to improve accuracy and efficiency. Studies of clinicians' perceptions of AI have found only moderate acceptability, suggesting further research is needed regarding integration into clinical practice. This study aimed to explore stakeholders' theories concerning how and in what contexts AI is likely to become integrated into pathology. MATERIALS AND METHODS A literature review provided tentative theories that were revised through a realist interview study with 20 pathologists and 5 pathology trainees. Questions sought to elicit whether, and in what ways, the tentative theories fitted with interviewees' perceptions and experiences. Analysis focused on identifying the contextual factors that may support or constrain uptake of AI in pathology. RESULTS Interviews highlighted the importance of trust in AI, with interviewees emphasizing evaluation and the opportunity for pathologists to become familiar with AI as means for establishing trust. Interviewees expressed a desire to be involved in design and implementation of AI tools, to ensure such tools address pressing needs, but needs vary by subspecialty. Workflow integration is desired but whether AI tools should work automatically will vary according to the task and the context. CONCLUSIONS It must not be assumed that AI tools that provide benefit in one subspecialty will provide benefit in others. Pathologists should be involved in the decision to introduce AI, with opportunity to assess strengths and weaknesses. Further research is needed concerning the evidence required to satisfy pathologists regarding the benefits of AI.
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Affiliation(s)
- Henry King
- School of Medicine, University of Leeds, Leeds, UK
| | - Bethany Williams
- Department of Pathology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Darren Treanor
- School of Medicine, University of Leeds, Leeds, UK,Department of Pathology, Leeds Teaching Hospitals NHS Trust, Leeds, UK,Department of Clinical Pathology, Linköping University, Linköping, Sweden,Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden,Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | - Rebecca Randell
- Corresponding Author: Rebecca Randell, PhD, Faculty of Health Studies, University of Bradford, Richmond Road, Bradford BD7 1DP, UK;
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24
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Ho DJ, Chui MH, Vanderbilt CM, Jung J, Robson ME, Park CS, Roh J, Fuchs TJ. Deep Interactive Learning-based ovarian cancer segmentation of H&E-stained whole slide images to study morphological patterns of BRCA mutation. J Pathol Inform 2022; 14:100160. [PMID: 36536772 PMCID: PMC9758515 DOI: 10.1016/j.jpi.2022.100160] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 11/22/2022] [Accepted: 11/22/2022] [Indexed: 11/27/2022] Open
Abstract
Deep learning has been widely used to analyze digitized hematoxylin and eosin (H&E)-stained histopathology whole slide images. Automated cancer segmentation using deep learning can be used to diagnose malignancy and to find novel morphological patterns to predict molecular subtypes. To train pixel-wise cancer segmentation models, manual annotation from pathologists is generally a bottleneck due to its time-consuming nature. In this paper, we propose Deep Interactive Learning with a pretrained segmentation model from a different cancer type to reduce manual annotation time. Instead of annotating all pixels from cancer and non-cancer regions on giga-pixel whole slide images, an iterative process of annotating mislabeled regions from a segmentation model and training/finetuning the model with the additional annotation can reduce the time. Especially, employing a pretrained segmentation model can further reduce the time than starting annotation from scratch. We trained an accurate ovarian cancer segmentation model with a pretrained breast segmentation model by 3.5 hours of manual annotation which achieved intersection-over-union of 0.74, recall of 0.86, and precision of 0.84. With automatically extracted high-grade serous ovarian cancer patches, we attempted to train an additional classification deep learning model to predict BRCA mutation. The segmentation model and code have been released at https://github.com/MSKCC-Computational-Pathology/DMMN-ovary.
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Affiliation(s)
- David Joon Ho
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - M. Herman Chui
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Chad M. Vanderbilt
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jiwon Jung
- Department of Pathology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Mark E. Robson
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Chan-Sik Park
- Department of Pathology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Jin Roh
- Department of Pathology, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Thomas J. Fuchs
- Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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25
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Schüffler PJ, Stamelos E, Ahmed I, Yarlagadda DVK, Ardon O, Hanna MG, Reuter VE, Klimstra DS, Hameed M. Efficient Visualization of Whole Slide Images in Web-based Viewers for Digital Pathology. Arch Pathol Lab Med 2022; 146:1273-1280. [PMID: 34979569 PMCID: PMC10060618 DOI: 10.5858/arpa.2021-0197-oa] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/07/2021] [Indexed: 11/06/2022]
Abstract
CONTEXT.— Wide adoption of digital pathology requires efficient visualization and navigation in Web-based digital slide viewers, which is poorly defined. OBJECTIVE.— To define and quantify relevant performance metrics for efficient visualization of cases and slides in digital slide viewers. DESIGN.— With a universal slide viewer used in clinical routine diagnostics, we evaluated the impact of slide caching, compression type, tile, and block size of whole slide images generated from Philips, Leica, and 3DHistech scanners on streaming performance on case, slide, and field of view levels. RESULTS.— Two hundred thirty-nine pathologists routinely reviewed 60 080 whole slide images over 3 months. The median time to open a case's slides from the laboratory information system was less than 4 seconds, the time to change to a slide within the case was less than 1 second, and the time to render the adjacent field of view when navigating the slide was less than one-quarter of a second. A whole slide image's block size and a viewer tile size of 1024 pixels showed best performance to display a field of view and was preferrable over smaller tiles due to fewer mosaic effects. For Philips, fastest median slide streaming pace was 238 ms per field of view and for 3DHistech, 125 ms. For Leica, the fastest pace of 108 ms per field of view was established with block serving without decompression. CONCLUSIONS.— This is the first study to systematically assess user-centric slide visualization performance metrics for digital viewers, including time to open a case, time to change a slide, and time to change a field of view. These metrics help to improve the viewer's configuration, leading to an efficient visualization baseline that is widely accepted among pathologists using routine digital pathology.
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Affiliation(s)
- Peter J Schüffler
- From the Institute of Pathology, Technical University of Munich, Munich, Germany (Schüffler)
| | - Evangelos Stamelos
- From the Department of Pathology, Memorial Sloan Kettering Cancer Center, New York City, New York (Schüffler, Stamelos, Ahmed, Yarlagadda, Ardon, Hanna, Reuter, Klimstra, Hameed)
| | - Ishtiaque Ahmed
- From the Department of Pathology, Memorial Sloan Kettering Cancer Center, New York City, New York (Schüffler, Stamelos, Ahmed, Yarlagadda, Ardon, Hanna, Reuter, Klimstra, Hameed)
| | - D Vijay K Yarlagadda
- From the Department of Pathology, Memorial Sloan Kettering Cancer Center, New York City, New York (Schüffler, Stamelos, Ahmed, Yarlagadda, Ardon, Hanna, Reuter, Klimstra, Hameed)
| | - Orly Ardon
- From the Department of Pathology, Memorial Sloan Kettering Cancer Center, New York City, New York (Schüffler, Stamelos, Ahmed, Yarlagadda, Ardon, Hanna, Reuter, Klimstra, Hameed)
| | - Matthew G Hanna
- From the Department of Pathology, Memorial Sloan Kettering Cancer Center, New York City, New York (Schüffler, Stamelos, Ahmed, Yarlagadda, Ardon, Hanna, Reuter, Klimstra, Hameed)
| | - Victor E Reuter
- From the Department of Pathology, Memorial Sloan Kettering Cancer Center, New York City, New York (Schüffler, Stamelos, Ahmed, Yarlagadda, Ardon, Hanna, Reuter, Klimstra, Hameed)
| | - David S Klimstra
- From the Department of Pathology, Memorial Sloan Kettering Cancer Center, New York City, New York (Schüffler, Stamelos, Ahmed, Yarlagadda, Ardon, Hanna, Reuter, Klimstra, Hameed)
| | - Meera Hameed
- From the Department of Pathology, Memorial Sloan Kettering Cancer Center, New York City, New York (Schüffler, Stamelos, Ahmed, Yarlagadda, Ardon, Hanna, Reuter, Klimstra, Hameed)
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Ashman K, Zhuge H, Shanley E, Fox S, Halat S, Sholl A, Summa B, Brown JQ. Whole slide image data utilization informed by digital diagnosis patterns. J Pathol Inform 2022; 13:100113. [PMID: 36268057 PMCID: PMC9577055 DOI: 10.1016/j.jpi.2022.100113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/28/2021] [Indexed: 12/24/2022] Open
Abstract
Context Despite the benefits of digital pathology, data storage and management of digital whole slide images introduces new logistical and infrastructure challenges to traditionally analog pathology labs. Aims Our goal was to analyze pathologist slide diagnosis patterns to determine the minimum number of pixels required during the diagnosis. Methods We developed a method of using pathologist viewing patterns to vary digital image resolution across virtual slides, which we call variable resolution images. An additional pathologist reviewed the variable resolution images to determine if diagnoses could still be rendered. Results Across all slides, the pathologists rarely zoomed in to the full resolution level. As a result, the variable resolution images are significantly smaller than the original whole slide images. Despite the reduction in image sizes, the final pathologist reviewer could still proide diagnoses on the variable resolution slide images. Conclusions Future studies will be conducted to understand variability in resolution requirements between and within pathologists. These findings have the potential to dramatically reduce the data storage requirements of high-resolution whole slide images.
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Affiliation(s)
- Kimberly Ashman
- Tulane University, Department of Biomedical Engineering, New Orleans, LA 70118, USA
| | - Huimin Zhuge
- Tulane University, Department of Biomedical Engineering, New Orleans, LA 70118, USA
| | - Erin Shanley
- Tulane University, Department of Biomedical Engineering, New Orleans, LA 70118, USA
| | - Sharon Fox
- LSU Health Sciences Center, Department of Pathology, New Orleans, LA 70112, USA
| | - Shams Halat
- Tulane School of Medicine, Tulane University Department of Pathology and Lab Medicine, New Orleans, LA 70112, USA
| | - Andrew Sholl
- Delta Pathology Group, Touro Infirmary, New Orleans, LA 70115, USA
| | - Brian Summa
- Tulane University, Department of Computer Science, New Orleans, LA 70118, USA
| | - J. Quincy Brown
- Tulane University, Department of Biomedical Engineering, New Orleans, LA 70118, USA
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Abstract
Artificial intelligence (AI) powered by the accumulating clinical and molecular data about cancer has fueled the expectation that a transformation in cancer treatments towards significant improvement of patient outcomes is at hand. However, such transformation has been so far elusive. The opacity of AI algorithms and the lack of quality annotated data being available at population scale are among the challenges to the application of AI in oncology. Fundamentally however, the heterogeneity of cancer and its evolutionary dynamics make every tumor response to therapy sufficiently different from the population, machine-learned statistical models, challenging hence the capacity of these models to yield reliable inferences about treatment recommendations that can improve patient outcomes. This article reviews the nominal elements of clinical decision-making for precision oncology and frames the utility of AI to cancer treatment improvements in light of cancer unique challenges.
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Affiliation(s)
- Youcef Derbal
- Ted Rogers School of Information Technology Management, 7984Ryerson University, Toronto, ON, Canada
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28
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Temprana-Salvador J, López-García P, Castellví Vives J, de Haro L, Ballesta E, Rojas Abusleme M, Arrufat M, Marques F, Casas JR, Gallego C, Pons L, Mate JL, Fernández PL, López-Bonet E, Bosch R, Martínez S, Ramón y Cajal S, Matias-Guiu X. DigiPatICS: Digital Pathology Transformation of the Catalan Health Institute Network of 8 Hospitals—Planification, Implementation, and Preliminary Results. Diagnostics (Basel) 2022; 12:diagnostics12040852. [PMID: 35453900 PMCID: PMC9025604 DOI: 10.3390/diagnostics12040852] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Revised: 03/17/2022] [Accepted: 03/28/2022] [Indexed: 11/21/2022] Open
Abstract
Complete digital pathology transformation for primary histopathological diagnosis is a challenging yet rewarding endeavor. Its advantages are clear with more efficient workflows, but there are many technical and functional difficulties to be faced. The Catalan Health Institute (ICS) has started its DigiPatICS project, aiming to deploy digital pathology in an integrative, holistic, and comprehensive way within a network of 8 hospitals, over 168 pathologists, and over 1 million slides each year. We describe the bidding process and the careful planning that was required, followed by swift implementation in stages. The purpose of the DigiPatICS project is to increase patient safety and quality of care, improving diagnosis and the efficiency of processes in the pathological anatomy departments of the ICS through process improvement, digital pathology, and artificial intelligence tools.
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Affiliation(s)
- Jordi Temprana-Salvador
- Department of Pathology, Vall d’Hebron University Hospital, CIBERONC, 08035 Barcelona, Spain; (J.C.V.); (S.R.y.C.)
- Correspondence: ; Tel.: +34-93-274-68-09
| | - Pablo López-García
- Functional Competence Center, Information Systems, Catalan Health Institute (Institut Català de la Salut), 08006 Barcelona, Spain; (P.L.-G.); (L.d.H.); (E.B.)
| | - Josep Castellví Vives
- Department of Pathology, Vall d’Hebron University Hospital, CIBERONC, 08035 Barcelona, Spain; (J.C.V.); (S.R.y.C.)
| | - Lluís de Haro
- Functional Competence Center, Information Systems, Catalan Health Institute (Institut Català de la Salut), 08006 Barcelona, Spain; (P.L.-G.); (L.d.H.); (E.B.)
| | - Eudald Ballesta
- Functional Competence Center, Information Systems, Catalan Health Institute (Institut Català de la Salut), 08006 Barcelona, Spain; (P.L.-G.); (L.d.H.); (E.B.)
| | - Matias Rojas Abusleme
- Center for Telecommunications and Information Technology (Centre de Telecomunicacions i Tecnologies de la Informació, CTTI), Catalan Health Institute (Institut Català de la Salut), 08006 Barcelona, Spain;
| | - Miquel Arrufat
- Economic and Financial Management, Catalan Health Institute (Institut Català de la Salut), 08006 Barcelona, Spain;
| | - Ferran Marques
- Image Processing Group, Technical University of Catalonia (UPC), 08034 Barcelona, Spain; (F.M.); (J.R.C.)
| | - Josep R. Casas
- Image Processing Group, Technical University of Catalonia (UPC), 08034 Barcelona, Spain; (F.M.); (J.R.C.)
| | - Carlos Gallego
- Digital Medical Imaging System of Catalonia (SIMDCAT), TIC Salut, 08005 Barcelona, Spain;
| | - Laura Pons
- Department of Pathology, Germans Trias i Pujol University Hospital, 08916 Badalona, Spain; (L.P.); (J.L.M.); (P.L.F.)
| | - José Luis Mate
- Department of Pathology, Germans Trias i Pujol University Hospital, 08916 Badalona, Spain; (L.P.); (J.L.M.); (P.L.F.)
| | - Pedro Luis Fernández
- Department of Pathology, Germans Trias i Pujol University Hospital, 08916 Badalona, Spain; (L.P.); (J.L.M.); (P.L.F.)
| | - Eugeni López-Bonet
- Department of Pathology, Doctor Josep Trueta Hospital of Girona, 17007 Girona, Spain;
| | - Ramon Bosch
- Department of Pathology, Verge de la Cinta Hospital of Tortosa, 43500 Tarragona, Spain;
| | - Salomé Martínez
- Department of Pathology, Joan XXIII University Hospital of Tarragona, 43005 Tarragona, Spain;
| | - Santiago Ramón y Cajal
- Department of Pathology, Vall d’Hebron University Hospital, CIBERONC, 08035 Barcelona, Spain; (J.C.V.); (S.R.y.C.)
| | - Xavier Matias-Guiu
- Department of Pathology, Arnau de Vilanova University Hospital, 25198 Lleida, Spain;
- Department of Pathology, Bellvitge University Hospital, CIBERONC, 08907 Barcelona, Spain
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29
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The Role of Artificial Intelligence in Early Cancer Diagnosis. Cancers (Basel) 2022; 14:cancers14061524. [PMID: 35326674 PMCID: PMC8946688 DOI: 10.3390/cancers14061524] [Citation(s) in RCA: 54] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 03/08/2022] [Accepted: 03/10/2022] [Indexed: 02/01/2023] Open
Abstract
Improving the proportion of patients diagnosed with early-stage cancer is a key priority of the World Health Organisation. In many tumour groups, screening programmes have led to improvements in survival, but patient selection and risk stratification are key challenges. In addition, there are concerns about limited diagnostic workforces, particularly in light of the COVID-19 pandemic, placing a strain on pathology and radiology services. In this review, we discuss how artificial intelligence algorithms could assist clinicians in (1) screening asymptomatic patients at risk of cancer, (2) investigating and triaging symptomatic patients, and (3) more effectively diagnosing cancer recurrence. We provide an overview of the main artificial intelligence approaches, including historical models such as logistic regression, as well as deep learning and neural networks, and highlight their early diagnosis applications. Many data types are suitable for computational analysis, including electronic healthcare records, diagnostic images, pathology slides and peripheral blood, and we provide examples of how these data can be utilised to diagnose cancer. We also discuss the potential clinical implications for artificial intelligence algorithms, including an overview of models currently used in clinical practice. Finally, we discuss the potential limitations and pitfalls, including ethical concerns, resource demands, data security and reporting standards.
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30
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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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31
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Vizcarra JC, Burlingame EA, Hug CB, Goltsev Y, White BS, Tyson DR, Sokolov A. A community-based approach to image analysis of cells, tissues and tumors. Comput Med Imaging Graph 2022; 95:102013. [PMID: 34864359 PMCID: PMC8761177 DOI: 10.1016/j.compmedimag.2021.102013] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 11/09/2021] [Accepted: 11/09/2021] [Indexed: 01/03/2023]
Abstract
Emerging multiplexed imaging platforms provide an unprecedented view of an increasing number of molecular markers at subcellular resolution and the dynamic evolution of tumor cellular composition. As such, they are capable of elucidating cell-to-cell interactions within the tumor microenvironment that impact clinical outcome and therapeutic response. However, the rapid development of these platforms has far outpaced the computational methods for processing and analyzing the data they generate. While being technologically disparate, all imaging assays share many computational requirements for post-collection data processing. As such, our Image Analysis Working Group (IAWG), composed of researchers in the Cancer Systems Biology Consortium (CSBC) and the Physical Sciences - Oncology Network (PS-ON), convened a workshop on "Computational Challenges Shared by Diverse Imaging Platforms" to characterize these common issues and a follow-up hackathon to implement solutions for a selected subset of them. Here, we delineate these areas that reflect major axes of research within the field, including image registration, segmentation of cells and subcellular structures, and identification of cell types from their morphology. We further describe the logistical organization of these events, believing our lessons learned can aid others in uniting the imaging community around self-identified topics of mutual interest, in designing and implementing operational procedures to address those topics and in mitigating issues inherent in image analysis (e.g., sharing exemplar images of large datasets and disseminating baseline solutions to hackathon challenges through open-source code repositories).
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Affiliation(s)
- Juan Carlos Vizcarra
- Department of Biomedical Engineering, Georgia Institute of Technology & Emory University, Atlanta, GA, USA
| | - Erik A Burlingame
- Computational Biology Program, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Clemens B Hug
- Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Boston, MA, USA
| | - Yury Goltsev
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA, USA
| | - Brian S White
- Computational Oncology, Sage Bionetworks, Seattle, WA, USA
| | - Darren R Tyson
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Artem Sokolov
- Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Boston, MA, USA; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
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32
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Rosenthal J, Carelli R, Omar M, Brundage D, Halbert E, Nyman J, Hari SN, Van Allen EM, Marchionni L, Umeton R, Loda M. Building tools for machine learning and artificial intelligence in cancer research: best practices and a case study with the PathML toolkit for computational pathology. Mol Cancer Res 2021; 20:202-206. [PMID: 34880124 DOI: 10.1158/1541-7786.mcr-21-0665] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 10/25/2021] [Accepted: 12/01/2021] [Indexed: 11/16/2022]
Abstract
Imaging datasets in cancer research are growing exponentially in both quantity and information density. These massive datasets may enable derivation of insights for cancer research and clinical care, but only if researchers are equipped with the tools to leverage advanced computational analysis approaches such as machine learning and artificial intelligence. In this work, we highlight three themes to guide development of such computational tools: scalability, standardization, and ease of use. We then apply these principles to develop PathML, a general-purpose research toolkit for computational pathology. We describe the design of the PathML framework and demonstrate applications in diverse use-cases. PathML is publicly available at www.pathml.com.
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Affiliation(s)
| | | | - Mohamed Omar
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine
| | - David Brundage
- Pathology and Laboratory Medicine, Weill Cornell Medicine
| | | | - Jackson Nyman
- Department of Medical Oncology, Dana-Farber Cancer Institute
| | - Surya N Hari
- Department of Medical Oncology, Dana-Farber Cancer Institute
| | | | | | - Renato Umeton
- Informatics and Analytics, Dana-Farber Cancer Institute
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33
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Schüffler PJ, Ozcan GG, Al-Ahmadie H, Fuchs TJ. FlexTileSource: An OpenSeadragon Extension for Efficient Whole-Slide Image Visualization. J Pathol Inform 2021; 12:31. [PMID: 34760328 PMCID: PMC8529343 DOI: 10.4103/jpi.jpi_13_21] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 05/03/2021] [Accepted: 07/01/2021] [Indexed: 11/08/2022] Open
Abstract
BACKGROUND Web-based digital slide viewers for pathology commonly use OpenSlide and OpenSeadragon (OSD) to access, visualize, and navigate whole-slide images (WSI). Their standard settings represent WSI as deep zoom images (DZI), a generic image pyramid structure that differs from the proprietary pyramid structure in the WSI files. The transformation from WSI to DZI is an additional, time-consuming step when rendering digital slides in the viewer, and inefficiency of digital slide viewers is a major criticism for digital pathology. AIMS To increase efficiency of digital slide visualization by serving tiles directly from the native WSI pyramid, making the transformation from WSI to DZI obsolete. METHODS We implemented a new flexible tile source for OSD that accepts arbitrary native pyramid structures instead of DZI levels. We measured its performance on a data set of 8104 WSI reviewed by 207 pathologists over 40 days in a web-based digital slide viewer used for routine diagnostics. RESULTS The new FlexTileSource accelerates the display of a field of view in general by 67 ms and even by 117 ms if the block size of the WSI and the tile size of the viewer is increased to 1024 px. We provide the code of our open-source library freely on https://github.com/schuefflerlab/openseadragon. CONCLUSIONS This is the first study to quantify visualization performance on a web-based slide viewer at scale, taking block size and tile size of digital slides into account. Quantifying performance will enable to compare and improve web-based viewers and therewith facilitate the adoption of digital pathology.
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Affiliation(s)
- Peter J. Schüffler
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York City, NY, USA
- TUM School of Medicine, Technical University of Munich, Munich, Germany
| | - Gamze Gokturk Ozcan
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York City, NY, USA
| | - Hikmat Al-Ahmadie
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York City, NY, USA
| | - Thomas J. Fuchs
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York City, NY, USA
- Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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