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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] [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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Hartman DJ. Applications of Artificial Intelligence in Lung Pathology. Surg Pathol Clin 2024; 17:321-328. [PMID: 38692814 DOI: 10.1016/j.path.2023.11.013] [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] [Indexed: 05/03/2024]
Abstract
Artificial intelligence/machine learning tools are being created for use in pathology. Some examples related to lung pathology include acid-fast stain evaluation, programmed death ligand-1 (PDL-1) interpretation, evaluating histologic patterns of non-small-cell lung carcinoma, evaluating histologic features in mesothelioma associated with adverse outcomes, predicting response to anti-PDL-1 therapy from hematoxylin and eosin-stained slides, evaluation of tumor microenvironment, evaluating patterns of interstitial lung disease, nondestructive methods for tissue evaluation, and others. There are still some frameworks (regulatory, workflow, and payment) that need to be established for these tools to be integrated into pathology.
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Affiliation(s)
- Douglas J Hartman
- University of Pittsburgh Medical Center, 200 Lothrop Street C-620, Pittsburgh, PA 15213, USA.
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Neto PC, Montezuma D, Oliveira SP, Oliveira D, Fraga J, Monteiro A, Monteiro J, Ribeiro L, Gonçalves S, Reinhard S, Zlobec I, Pinto IM, Cardoso JS. An interpretable machine learning system for colorectal cancer diagnosis from pathology slides. NPJ Precis Oncol 2024; 8:56. [PMID: 38443695 PMCID: PMC10914836 DOI: 10.1038/s41698-024-00539-4] [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: 07/18/2023] [Accepted: 02/08/2024] [Indexed: 03/07/2024] Open
Abstract
Considering the profound transformation affecting pathology practice, we aimed to develop a scalable artificial intelligence (AI) system to diagnose colorectal cancer from whole-slide images (WSI). For this, we propose a deep learning (DL) system that learns from weak labels, a sampling strategy that reduces the number of training samples by a factor of six without compromising performance, an approach to leverage a small subset of fully annotated samples, and a prototype with explainable predictions, active learning features and parallelisation. Noting some problems in the literature, this study is conducted with one of the largest WSI colorectal samples dataset with approximately 10,500 WSIs. Of these samples, 900 are testing samples. Furthermore, the robustness of the proposed method is assessed with two additional external datasets (TCGA and PAIP) and a dataset of samples collected directly from the proposed prototype. Our proposed method predicts, for the patch-based tiles, a class based on the severity of the dysplasia and uses that information to classify the whole slide. It is trained with an interpretable mixed-supervision scheme to leverage the domain knowledge introduced by pathologists through spatial annotations. The mixed-supervision scheme allowed for an intelligent sampling strategy effectively evaluated in several different scenarios without compromising the performance. On the internal dataset, the method shows an accuracy of 93.44% and a sensitivity between positive (low-grade and high-grade dysplasia) and non-neoplastic samples of 0.996. On the external test samples varied with TCGA being the most challenging dataset with an overall accuracy of 84.91% and a sensitivity of 0.996.
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Affiliation(s)
- Pedro C Neto
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), R. Dr. Roberto Frias, Porto, 4200-465, Porto, Portugal.
- Faculty of Engineering, University of Porto (FEUP), R. Dr. Roberto Frias, Porto, 4200-465, Porto, Portugal.
| | - Diana Montezuma
- IMP Diagnostics, Praça do Bom Sucesso, 61, sala 808, Porto, 4150-146, Porto, Portugal.
- Cancer Biology and Epigenetics Group, Research Center of IPO Porto (CI-IPOP) / RISE@CI-IPOP (Health Research Network), Portuguese Oncology Institute of Porto (IPO Porto) / Porto Comprehensive Cancer Center (Porto.CCC), R. Dr. António Bernardino de Almeida 865, Porto, 4200-072, Porto, Portugal.
- Doctoral Programme in Medical Sciences, School of Medicine and Biomedical Sciences - University of Porto (ICBAS-UP), R. Jorge de Viterbo Ferreira 228, Porto, 4050-313, Porto, Portugal.
| | - Sara P Oliveira
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), R. Dr. Roberto Frias, Porto, 4200-465, Porto, Portugal.
- Faculty of Engineering, University of Porto (FEUP), R. Dr. Roberto Frias, Porto, 4200-465, Porto, Portugal.
| | - Domingos Oliveira
- IMP Diagnostics, Praça do Bom Sucesso, 61, sala 808, Porto, 4150-146, Porto, Portugal
| | - João Fraga
- Department of Pathology, IPO-Porto, R. Dr. António Bernardino de Almeida 865, Porto, 4200-072, Porto, Portugal
| | - Ana Monteiro
- IMP Diagnostics, Praça do Bom Sucesso, 61, sala 808, Porto, 4150-146, Porto, Portugal
| | - João Monteiro
- IMP Diagnostics, Praça do Bom Sucesso, 61, sala 808, Porto, 4150-146, Porto, Portugal
| | - Liliana Ribeiro
- IMP Diagnostics, Praça do Bom Sucesso, 61, sala 808, Porto, 4150-146, Porto, Portugal
| | - Sofia Gonçalves
- IMP Diagnostics, Praça do Bom Sucesso, 61, sala 808, Porto, 4150-146, Porto, Portugal
| | - Stefan Reinhard
- Institute of Pathology, University of Bern, Uni Bern, Murtenstrasse 31, Bern, 3008, Bern, Switzerland
| | - Inti Zlobec
- Institute of Pathology, University of Bern, Uni Bern, Murtenstrasse 31, Bern, 3008, Bern, Switzerland
| | - Isabel M Pinto
- IMP Diagnostics, Praça do Bom Sucesso, 61, sala 808, Porto, 4150-146, Porto, Portugal
| | - Jaime S Cardoso
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), R. Dr. Roberto Frias, Porto, 4200-465, Porto, Portugal
- Faculty of Engineering, University of Porto (FEUP), R. Dr. Roberto Frias, Porto, 4200-465, Porto, Portugal
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Magalhães G, Calisto R, Freire C, Silva R, Montezuma D, Canberk S, Schmitt F. Invisible for a few but essential for many: the role of Histotechnologists in the establishment of digital pathology. J Histotechnol 2024; 47:39-52. [PMID: 37869882 DOI: 10.1080/01478885.2023.2268297] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 10/03/2023] [Indexed: 10/24/2023]
Abstract
Digital pathology (DP) is indisputably the future for histopathology laboratories. The process of digital implementation requires deep workflow reorganisation which involves an interdisciplinary team. This transformation may have the greatest impact on the Histotechnologist (HTL) profession. Our review of the literature has clearly revealed that the role of HTLs in the establishment of DP is being unnoticed and guidance is limited. This article aims to bring HTLs from behind-the-scenes into the spotlight. Our objective is to provide them guidance and practical recommendations to successfully contribute to the implementation of a new digital workflow. Furthermore, it also intends to contribute for improvement of study programs, ensuring the role of HTL in DP is addressed as part of graduate and post-graduate education. In our review, we report on the differences encountered between workflow schemes and the limitations observed in this process. The authors propose a digital workflow to achieve its limitless potential, focusing on the HTL's role. This article explores the novel responsibilities of HTLs during specimen gross dissection, embedding, microtomy, staining, digital scanning, and whole slide image quality control. Furthermore, we highlight the benefits and challenges that DP implementation might bring the HTLs career. HTLs have an important role in the digital workflow: the responsibility of achieving the perfect glass slide.
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Affiliation(s)
- Gisela Magalhães
- Histopathology Department, Portsmouth Hospital University NHS Trust, Portsmouth, UK
- Department of Pathological Anatomy, School of Health Polytechnic of Porto (ESS|P.PORTO), Porto, Portugal
| | - Rita Calisto
- Department of Pathological Anatomy, School of Health Polytechnic of Porto (ESS|P.PORTO), Porto, Portugal
- Department of Pathological Anatomy, Hospital do Divino Espírito Santo, Ponta Delgada, Portugal
| | - Catarina Freire
- Department of Pathological Anatomy, School of Health Polytechnic of Porto (ESS|P.PORTO), Porto, Portugal
- Department of Pathological Anatomy, Hospital do Divino Espírito Santo, Ponta Delgada, Portugal
| | - Regina Silva
- Department of Pathological Anatomy, School of Health Polytechnic of Porto (ESS|P.PORTO), Porto, Portugal
- Centro de Investigação em Saúde e Ambiente, ESS,P.PORTO, Porto, Portugal
| | - Diana Montezuma
- Research & Development Unit, IMP Diagnostics, Porto, Portugal
- School of Medicine and Biomedical Sciences, University of Porto (ICBAS-UP), Porto, Portugal
| | - Sule Canberk
- Institute for Research and Innovation in Health (i3S), University of Porto, Porto, Portugal
- Cancer Signalling & Metabolism, Institute of Molecular Pathology and Immunology of the University of Porto (Ipatimup), Porto, Portugal
- Faculty of Medicine of the University of Porto (FMUP), Porto, Portugal
| | - Fernando Schmitt
- Department of Pathology, Faculty of Medicine of University of Porto, Porto, Portugal
- CINTESIS@RISE, Health Research Network, Alameda Prof. Hernâni Monteiro, Portugal
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Iwuajoku V, Haas A, Ekici K, Khan MZ, Stögbauer F, Steiger K, Mogler C, Schüffler PJ. [Digital transformation of a routine histopathology lab : Dos and don'ts!]. PATHOLOGIE (HEIDELBERG, GERMANY) 2024; 45:98-105. [PMID: 38189845 PMCID: PMC10902067 DOI: 10.1007/s00292-023-01291-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 11/15/2023] [Indexed: 01/09/2024]
Abstract
The implementation of digital histopathology in the laboratory marks a crucial milestone in the overall digital transformation of pathology. This shift offers a range of new possibilities, including access to extensive datasets for AI-assisted analyses, the flexibility of remote work and home office arrangements for specialists, and the expedited and simplified sharing of images and data for research, conferences, and tumor boards. However, the transition to a fully digital workflow involves significant technological and personnel-related efforts. It necessitates careful and adaptable change management to minimize disruptions, particularly in the personnel domain, and to prevent the loss of valuable potential from employees who may be resistant to change. This article consolidates our institute's experiences, highlighting technical and personnel-related challenges encountered during the transition to digital pathology. It also presents a comprehensive overview of potential difficulties at various interfaces when converting routine operations to a digital workflow.
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Affiliation(s)
- Viola Iwuajoku
- Institut für Pathologie, TUM School of Medicine and Health, Technische Universität München, Trogerstraße 18, 81675, München, Deutschland
| | - Anette Haas
- Institut für Pathologie, TUM School of Medicine and Health, Technische Universität München, Trogerstraße 18, 81675, München, Deutschland
| | - Kübra Ekici
- Institut für Pathologie, TUM School of Medicine and Health, Technische Universität München, Trogerstraße 18, 81675, München, Deutschland
| | - Mohammad Zaid Khan
- Institut für Pathologie, TUM School of Medicine and Health, Technische Universität München, Trogerstraße 18, 81675, München, Deutschland
| | - Fabian Stögbauer
- Institut für Pathologie, TUM School of Medicine and Health, Technische Universität München, Trogerstraße 18, 81675, München, Deutschland
| | - Katja Steiger
- Institut für Pathologie, TUM School of Medicine and Health, Technische Universität München, Trogerstraße 18, 81675, München, Deutschland
| | - Carolin Mogler
- Institut für Pathologie, TUM School of Medicine and Health, Technische Universität München, Trogerstraße 18, 81675, München, Deutschland
| | - Peter J Schüffler
- Institut für Pathologie, TUM School of Medicine and Health, Technische Universität München, Trogerstraße 18, 81675, München, Deutschland.
- TUM School of Computational Information and Technology, Technische Universität München, München, Deutschland.
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6
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Jensen CL, Thomsen LK, Zeuthen M, Johnsen S, El Jashi R, Nielsen MFB, Hemstra LE, Smith J. Biomedical laboratory scientists and technicians in digital pathology - Is there a need for professional development? Digit Health 2024; 10:20552076241237392. [PMID: 38495864 PMCID: PMC10943708 DOI: 10.1177/20552076241237392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/19/2024] [Indexed: 03/19/2024] Open
Abstract
Objective Digital pathology (DP) is moving into Danish pathology departments at high pace. Conventionally, biomedical laboratory scientists (BLS) and technicians have prepared tissue sections for light microscopy, but workflow alterations are required for the new digital era with whole slide imaging (WSI); digitally assisted image analysis (DAIA) and artificial intelligence (AI). We aim to explore the role of BLS in DP and assess a potential need for professional development. Methods We investigated the roles of BLS in the new digital era through qualitative interviews at Danish Pathology Departments in 2019/2020 before DP implementation (supported by a questionnaire); and in 2022 after DP implementation. Additionally, senior lecturers from three Danish University Colleges reported on how DP was integrated into the 2023 bachelor's degree educational curricula for BLS students. Results At some Danish pathology departments, BLS were involved in the implementation process of DP and their greatest concerns were lack of physical laboratory requirements (69%) and implementation strategies (63%). BLS were generally positive towards working with DP, however, some expressed concern about extended working hours for scanning. Work-task transfers from pathologists were generally greeted positively from both management and pathologists; however, at follow-up interviews after DP implementation, job transfers had not been effectuated. At Danish university colleges, DP had been integrated systematically in the curricula for BLS students, especially WSI. Conclusion Involving BLS in DP implementation and development may benefit the process, as BLS have a hands-on workflow perspective with a focus on quality assurance. Several new work opportunities for BLS may occur with DP including WSI, DAIA and AI, and therefore new qualifications are warranted, which must be considered in future undergraduate programmes for BLS students or postgraduate programmes for BLS.
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Affiliation(s)
- Charlotte Lerbech Jensen
- Center for Engineering and Science, Biomedical Laboratory Science, University College Absalon, Næstved, Denmark
| | - Lisbeth Koch Thomsen
- Center for Engineering and Science, Biomedical Laboratory Science, University College Absalon, Næstved, Denmark
| | - Mette Zeuthen
- Department of Technology, Faculty of Health, University College Copenhagen, Copenhagen, Denmark
| | - Sys Johnsen
- Department of Technology, Faculty of Health, University College Copenhagen, Copenhagen, Denmark
| | - Rima El Jashi
- Department of Biomedical Laboratory Science, Physiotherapy and Radiography, Biomedical Laboratory Science, UCL University College, Odense, Denmark
| | - Michael Friberg Bruun Nielsen
- Department of Biomedical Laboratory Science, Physiotherapy and Radiography, Biomedical Laboratory Science, UCL University College, Odense, Denmark
| | - Line E Hemstra
- Center for Engineering and Science, Biomedical Laboratory Science, University College Absalon, Næstved, Denmark
| | - Julie Smith
- Department of Technology, Faculty of Health, University College Copenhagen, Copenhagen, Denmark
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7
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Pinto DG, Bychkov A, Tsuyama N, Fukuoka J, Eloy C. Real-World Implementation of Digital Pathology: Results From an Intercontinental Survey. J Transl Med 2023; 103:100261. [PMID: 37839634 DOI: 10.1016/j.labinv.2023.100261] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Revised: 09/29/2023] [Accepted: 10/07/2023] [Indexed: 10/17/2023] Open
Abstract
The past 70 years have been characterized by rapid advancements in computer technology, and the health care system has not been immune to this trend. However, anatomical pathology has remained largely an analog discipline. In recent years, this has been changing with the growing adoption of digital pathology, partly driven by the potential of computer-aided diagnosis. As part of an international collaboration, we conducted a comprehensive survey to gain a deeper understanding of the status of digital pathology implementation in Europe and Asia. A total of 127 anatomical pathology laboratories participated in the survey, including 75 from Europe and 52 from Asia, with 72 laboratories having established digital pathology workflow and 55 without digital pathology. Laboratories using digital pathology for diagnostic (n = 29) and nondiagnostic (n = 43) purposes were thoroughly questioned about their implementation strategies and institutional experiences, including details on equipment, storage, integration with laboratory information system, computer-aided diagnosis, and the costs of going digital. The impact of the digital pathology workflow was also evaluated, focusing on turnaround time, specimen traceability, quality control, and overall satisfaction. Laboratories without access to digital pathology were asked to provide insights into their perceptions of the technology, expectations, barriers to adoption, and potential facilitators. Our findings indicate that although digital pathology is still the future for many, it is already the present for some. This decade may be a time when anatomical pathology finally embraces digital revolution on a larger scale.
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Affiliation(s)
- Daniel Gomes Pinto
- Serviço de Anatomia Patológica, Hospital Garcia de Orta, EPE, Almada, Portugal; NOVA Medical School, Lisboa, Portugal; IPATIMUP - Instituto de Patologia e Imunologia Molecular da Universidade do Porto, Porto, Portugal
| | - Andrey Bychkov
- Department of Pathology, Kameda Medical Center, Kamogawa, Chiba, Japan
| | - Naoko Tsuyama
- Division of Pathology, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Junya Fukuoka
- Department of Pathology, Kameda Medical Center, Kamogawa, Chiba, Japan; Department of Pathology Informatics, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan
| | - Catarina Eloy
- IPATIMUP - Instituto de Patologia e Imunologia Molecular da Universidade do Porto, Porto, Portugal; Instituto de Investigação e Inovação Em Saúde (i3S) and Faculty of Medicine, University of Porto (FMUP), Porto, Portugal.
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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: 0] [Impact Index Per Article: 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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9
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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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Caputo A, L’Imperio V, Merolla F, Girolami I, Leoni E, Mea VD, Pagni F, Fraggetta F. The slow-paced digital evolution of pathology: lights and shadows from a multifaceted board. Pathologica 2023; 115:127-136. [PMID: 37387439 PMCID: PMC10462988 DOI: 10.32074/1591-951x-868] [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: 04/03/2023] [Accepted: 04/04/2023] [Indexed: 07/01/2023] Open
Abstract
Objective The digital revolution in pathology represents an invaluable resource fto optimise costs, reduce the risk of error and improve patient care, even though it is still adopted in a minority of laboratories. Barriers include concerns about initial costs, lack of confidence in using whole slide images for primary diagnosis, and lack of guidance on transition. To address these challenges and develop a programme to facilitate the introduction of digital pathology (DP) in Italian pathology departments, a panel discussion was set up to identify the key points to be considered. Methods On 21 July 2022, an initial conference call was held on Zoom to identify the main issues to be discussed during the face-to-face meeting. The final summit was divided into four different sessions: (I) the definition of DP, (II) practical applications of DP, (III) the use of AI in DP, (IV) DP and education. Results Essential requirements for the implementation of DP are a fully tracked and automated workflow, selection of the appropriate scanner based on the specific needs of each department, and a strong commitment combined with coordinated teamwork (pathologists, technicians, biologists, IT service and industries). This could reduce human error, leading to the application of AI tools for diagnosis, prognosis and prediction. Open challenges are the lack of specific regulations for virtual slide storage and the optimal storage solution for large volumes of slides. Conclusion Teamwork is key to DP transition, including close collaboration with industry. This will ease the transition and help bridge the gap that currently exists between many labs and full digitisation. The ultimate goal is to improve patient care.
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Affiliation(s)
- Alessandro Caputo
- Department of Pathology, Ruggi University Hospital, Salerno, Italy
- Pathology Unit, Gravina Hospital Caltagirone ASP, Catania, Italy
| | - Vincenzo L’Imperio
- Department of Medicine and Surgery, Pathology, University of Milan-Bicocca, IRCCS Fondazione San Gerardo dei Tintori, Monza, Italy
| | - Francesco Merolla
- Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, Campobasso, Italy
| | - Ilaria Girolami
- Department of Pathology, Provincial Hospital of Bolzano (SABES-ASDAA), Bolzano-Bozen, Italy; Lehrkrankenhaus der Paracelsus Medizinischen Privatuniversität
| | - Eleonora Leoni
- Pathology Unit, Busto Arsizio Hospital, Busto Arsizio, Italy
| | - Vincenzo Della Mea
- Department of Mathematics, Computer Science and Physics, University of Udine, Udine, Italy
| | - Fabio Pagni
- Department of Medicine and Surgery, Pathology, University of Milan-Bicocca, IRCCS Fondazione San Gerardo dei Tintori, Monza, Italy
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Neto PC, Oliveira SP, Montezuma D, Fraga J, Monteiro A, Ribeiro L, Gonçalves S, Pinto IM, Cardoso JS. iMIL4PATH: A Semi-Supervised Interpretable Approach for Colorectal Whole-Slide Images. Cancers (Basel) 2022; 14:cancers14102489. [PMID: 35626093 PMCID: PMC9139905 DOI: 10.3390/cancers14102489] [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: 04/22/2022] [Revised: 05/13/2022] [Accepted: 05/17/2022] [Indexed: 02/04/2023] Open
Abstract
Simple Summary Nowadays, colorectal cancer is the third most incident cancer worldwide and, although it can be detected by imaging techniques, diagnosis is always based on biopsy samples. This assessment includes neoplasia grading, a subjective yet important task for pathologists. With the growing availability of digital slides, the development of robust and high-performance computer vision algorithms can help to tackle such a task. In this work, we propose an approach to automatically detect and grade lesions in colorectal biopsies with high sensitivity. The presented model attempts to support slide decision reasoning in terms of the spatial distribution of lesions, focusing the pathologist’s attention on key areas. Thus, it can be integrated into clinical practice as a second opinion or as a flag for details that may have been missed at first glance. Abstract Colorectal cancer (CRC) diagnosis is based on samples obtained from biopsies, assessed in pathology laboratories. Due to population growth and ageing, as well as better screening programs, the CRC incidence rate has been increasing, leading to a higher workload for pathologists. In this sense, the application of AI for automatic CRC diagnosis, particularly on whole-slide images (WSI), is of utmost relevance, in order to assist professionals in case triage and case review. In this work, we propose an interpretable semi-supervised approach to detect lesions in colorectal biopsies with high sensitivity, based on multiple-instance learning and feature aggregation methods. The model was developed on an extended version of the recent, publicly available CRC dataset (the CRC+ dataset with 4433 WSI), using 3424 slides for training and 1009 slides for evaluation. The proposed method attained 90.19% classification ACC, 98.8% sensitivity, 85.7% specificity, and a quadratic weighted kappa of 0.888 at slide-based evaluation. Its generalisation capabilities are also studied on two publicly available external datasets.
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Affiliation(s)
- Pedro C. Neto
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), 4200-465 Porto, Portugal; (S.P.O.); (J.S.C.)
- Faculty of Engineering, University of Porto (FEUP), 4200-465 Porto, Portugal
- Correspondence:
| | - Sara P. Oliveira
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), 4200-465 Porto, Portugal; (S.P.O.); (J.S.C.)
- Faculty of Engineering, University of Porto (FEUP), 4200-465 Porto, Portugal
| | - Diana Montezuma
- IMP Diagnostics, 4150-146 Porto, Portugal; (D.M.); (A.M.); (L.R.); (S.G.); (I.M.P.)
- School of Medicine and Biomedical Sciences, University of Porto (ICBAS), 4050-313 Porto, Portugal
- Cancer Biology and Epigenetics Group, IPO-Porto, 4200-072 Porto, Portugal
| | - João Fraga
- Department of Pathology, IPO-Porto, 4200-072 Porto, Portugal;
| | - Ana Monteiro
- IMP Diagnostics, 4150-146 Porto, Portugal; (D.M.); (A.M.); (L.R.); (S.G.); (I.M.P.)
| | - Liliana Ribeiro
- IMP Diagnostics, 4150-146 Porto, Portugal; (D.M.); (A.M.); (L.R.); (S.G.); (I.M.P.)
| | - Sofia Gonçalves
- IMP Diagnostics, 4150-146 Porto, Portugal; (D.M.); (A.M.); (L.R.); (S.G.); (I.M.P.)
| | - Isabel M. Pinto
- IMP Diagnostics, 4150-146 Porto, Portugal; (D.M.); (A.M.); (L.R.); (S.G.); (I.M.P.)
| | - Jaime S. Cardoso
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), 4200-465 Porto, Portugal; (S.P.O.); (J.S.C.)
- Faculty of Engineering, University of Porto (FEUP), 4200-465 Porto, Portugal
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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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Deep Learning on Histopathological Images for Colorectal Cancer Diagnosis: A Systematic Review. Diagnostics (Basel) 2022; 12:diagnostics12040837. [PMID: 35453885 PMCID: PMC9028395 DOI: 10.3390/diagnostics12040837] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 03/22/2022] [Accepted: 03/25/2022] [Indexed: 02/04/2023] Open
Abstract
Colorectal cancer (CRC) is the second most common cancer in women and the third most common in men, with an increasing incidence. Pathology diagnosis complemented with prognostic and predictive biomarker information is the first step for personalized treatment. The increased diagnostic load in the pathology laboratory, combined with the reported intra- and inter-variability in the assessment of biomarkers, has prompted the quest for reliable machine-based methods to be incorporated into the routine practice. Recently, Artificial Intelligence (AI) has made significant progress in the medical field, showing potential for clinical applications. Herein, we aim to systematically review the current research on AI in CRC image analysis. In histopathology, algorithms based on Deep Learning (DL) have the potential to assist in diagnosis, predict clinically relevant molecular phenotypes and microsatellite instability, identify histological features related to prognosis and correlated to metastasis, and assess the specific components of the tumor microenvironment.
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