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Schukow CP, Macknis JK. Remote Placental Sign-Out: What Digital Pathology Can Offer for Pediatric Pathologists. Pediatr Dev Pathol 2024:10935266231225799. [PMID: 38468487 DOI: 10.1177/10935266231225799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Affiliation(s)
- Casey P Schukow
- Department of Pathology, Corewell Health's Beaumont Hospital, Royal Oak, MI, USA
| | - Jacqueline K Macknis
- Department of Pathology, Corewell Health's Beaumont Hospital, Royal Oak, MI, USA
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2
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Malik S, Zaheer S. ChatGPT as an aid for pathological diagnosis of cancer. Pathol Res Pract 2024; 253:154989. [PMID: 38056135 DOI: 10.1016/j.prp.2023.154989] [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: 08/23/2023] [Revised: 11/26/2023] [Accepted: 11/27/2023] [Indexed: 12/08/2023]
Abstract
Diagnostic workup of cancer patients is highly reliant on the science of pathology using cytopathology, histopathology, and other ancillary techniques like immunohistochemistry and molecular cytogenetics. Data processing and learning by means of artificial intelligence (AI) has become a spearhead for the advancement of medicine, with pathology and laboratory medicine being no exceptions. ChatGPT, an artificial intelligence (AI)-based chatbot, that was recently launched by OpenAI, is currently a talk of the town, and its role in cancer diagnosis is also being explored meticulously. Pathology workflow by integration of digital slides, implementation of advanced algorithms, and computer-aided diagnostic techniques extend the frontiers of the pathologist's view beyond a microscopic slide and enables effective integration, assimilation, and utilization of knowledge that is beyond human limits and boundaries. Despite of it's numerous advantages in the pathological diagnosis of cancer, it comes with several challenges like integration of digital slides with input language parameters, problems of bias, and legal issues which have to be addressed and worked up soon so that we as a pathologists diagnosing malignancies are on the same band wagon and don't miss the train.
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Affiliation(s)
- Shaivy Malik
- Department of Pathology, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, India
| | - Sufian Zaheer
- Department of Pathology, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, India.
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3
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Janowczyk A, Zlobec I, Walker C, Berezowska S, Huschauer V, Tinguely M, Kupferschmid J, Mallet T, Merkler D, Kreutzfeldt M, Gasic R, Rau TT, Mazzucchelli L, Eyberg I, Cathomas G, Mertz KD, Koelzer VH, Soldini D, Jochum W, Rössle M, Henkel M, Grobholz R. Swiss digital pathology recommendations: results from a Delphi process conducted by the Swiss Digital Pathology Consortium of the Swiss Society of Pathology. Virchows Arch 2023:10.1007/s00428-023-03712-5. [PMID: 38112792 DOI: 10.1007/s00428-023-03712-5] [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: 06/16/2023] [Revised: 10/23/2023] [Accepted: 11/04/2023] [Indexed: 12/21/2023]
Abstract
Integration of digital pathology (DP) into clinical diagnostic workflows is increasingly receiving attention as new hardware and software become available. To facilitate the adoption of DP, the Swiss Digital Pathology Consortium (SDiPath) organized a Delphi process to produce a series of recommendations for DP integration within Swiss clinical environments. This process saw the creation of 4 working groups, focusing on the various components of a DP system (1) scanners, quality assurance and validation of scans, (2) integration of Whole Slide Image (WSI)-scanners and DP systems into the Pathology Laboratory Information System, (3) digital workflow-compliance with general quality guidelines, and (4) image analysis (IA)/artificial intelligence (AI), with topic experts for each recruited for discussion and statement generation. The work product of the Delphi process is 83 consensus statements presented here, forming the basis for "SDiPath Recommendations for Digital Pathology". They represent an up-to-date resource for national and international hospitals, researchers, device manufacturers, algorithm developers, and all supporting fields, with the intent of providing expectations and best practices to help ensure safe and efficient DP usage.
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Affiliation(s)
- Andrew Janowczyk
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, USA.
- Department of Oncology, Division of Precision Oncology, Geneva University Hospitals, Geneva, Switzerland.
- Department of Diagnostics, Division of Clinical Pathology, Geneva University Hospitals, Geneva, Switzerland.
| | - Inti Zlobec
- Institute for Tissue Medicine and Pathology, University of Bern, Bern, Switzerland
| | - Cedric Walker
- Institute of Animal Pathology, Vetsuisse Faculty, University of Bern, Bern, Switzerland
| | - Sabina Berezowska
- Institute of Pathology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | | | - Marianne Tinguely
- Institute of Pathology Enge, Zurich, Switzerland
- Medical Faculty, University of Zürich, Zurich, Switzerland
| | | | - Thomas Mallet
- Division of Clinical Pathology, Geneva University Hospital, Geneva, Switzerland
| | - Doron Merkler
- Division of Clinical Pathology, Geneva University Hospital, Geneva, Switzerland
- Department of Pathology and Immunology, University of Geneva, Geneva, Switzerland
| | - Mario Kreutzfeldt
- Division of Clinical Pathology, Geneva University Hospital, Geneva, Switzerland
- Department of Pathology and Immunology, University of Geneva, Geneva, Switzerland
| | | | - Tilman T Rau
- Institute of Pathology, University Hospital and Heinrich-Heine University, Düsseldorf, Germany
- Institute of Pathology, University of Bern, Bern, Switzerland
| | | | - Isgard Eyberg
- Institute of Pathology, Cantonal Hospital Aarau, Aarau, Switzerland
| | - Gieri Cathomas
- Institute of Tissue Medicine and Pathology, University of Bern, Bern, Switzerland
| | - Kirsten D Mertz
- Institute of Pathology, Cantonal Hospital Baselland, Liestal, Switzerland
| | - Viktor H Koelzer
- Department of Pathology and Molecular Pathology, University and University Hospital of Zürich, Zurich, Switzerland
| | | | - Wolfram Jochum
- Institute of Pathology, Cantonal Hospital St. Gallen, St. Gallen, Switzerland
| | - Matthias Rössle
- Pathologie Luzerner Kantonsspital (Pathology Cantonal Hospital Lucerne), Spitalstrasse, Switzerland
| | - Maurice Henkel
- Research & Analytic Services University Hospital Basel, Basel, Switzerland
- Institute of Radiology, University Hospital Basel, Basel, Switzerland
- University of Basel, Basel, Switzerland
| | - Rainer Grobholz
- Medical Faculty, University of Zürich, Zurich, Switzerland
- Institute of Pathology, Cantonal Hospital Aarau, Aarau, Switzerland
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Grobholz R, Janowczyk A, Frei AL, Kreutzfeldt M, Koelzer VH, Zlobec I. National digital pathology projects in Switzerland: A 2023 update. PATHOLOGIE (HEIDELBERG, GERMANY) 2023; 44:225-228. [PMID: 37987815 PMCID: PMC10739407 DOI: 10.1007/s00292-023-01259-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 10/19/2023] [Indexed: 11/22/2023]
Abstract
The Swiss Digital Pathology Consortium (SDiPath) was founded in 2018 as a working group of the Swiss Society for Pathology with the aim of networking, training, and promoting digital pathology (DP) at a national level. Since then, two national surveys have been carried out on the level of knowledge, dissemination, use, and needs in DP, which have resulted in clear fields of action. In addition to organizing symposia and workshops, national guidelines were drawn up and an initiative for a national DP platform actively codesigned. With the growing use of digital image processing and artificial intelligence tools, continuous monitoring, evaluation, and exchange of experiences will be pursued, along with best practices.
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Affiliation(s)
- Rainer Grobholz
- Medical Faculty, University of Zurich, Zurich, Switzerland.
- Institute of Pathology, Kantonsspital Aarau, Tellstr. 25, 5001, Aarau, Switzerland.
| | - Andrew Janowczyk
- Department of Biomedical Engineering, Emory University, Atlanta, GA, USA
- Department of Oncology, Division of Precision Oncology, University Hospital of Geneva, Geneva, Switzerland
- Department of Diagnostics, Division of Clinical Pathology, University Hospital of Geneva, Geneva, Switzerland
| | - Ana Leni Frei
- Institute for Tissue Medicine and Pathology, University Bern, Bern, Switzerland
| | - Mario Kreutzfeldt
- Department of Pathology and Immunology, Division of Clinical Pathology, University & University Hospitals of Geneva, Geneva, Switzerland
| | - Viktor H Koelzer
- Department of Pathology und Molecular Pathology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Inti Zlobec
- Institute for Tissue Medicine and Pathology, University Bern, Bern, Switzerland
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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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Chong Y, Hong SA, Oh HK, Jung SJ, Kim BS, Jeong JY, Lee HC, Gong G. Diagnostic proficiency test using digital cytopathology and comparative assessment of whole slide images of cytologic samples for quality assurance program in Korea. J Pathol Transl Med 2023; 57:251-264. [PMID: 37608552 PMCID: PMC10518242 DOI: 10.4132/jptm.2023.07.17] [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: 05/24/2023] [Revised: 07/11/2023] [Accepted: 07/17/2023] [Indexed: 08/24/2023] Open
Abstract
BACKGROUND The Korean Society for Cytopathology introduced a digital proficiency test (PT) in 2021. However, many doubtful opinions remain on whether digitally scanned images can satisfactorily present subtle differences in the nuclear features and chromatin patterns of cytological samples. METHODS We prepared 30 whole-slide images (WSIs) from the conventional PT archive by a selection process for digital PT. Digital and conventional PT were performed in parallel for volunteer institutes, and the results were compared using feedback. To assess the quality of cytological assessment WSIs, 12 slides were collected and scanned using five different scanners, with four cytopathologists evaluating image quality through a questionnaire. RESULTS Among the 215 institutes, 108 and 107 participated in glass and digital PT, respectively. No significant difference was noted in category C (major discordance), although the number of discordant cases was slightly higher in the digital PT group. Leica, 3DHistech Pannoramic 250 Flash, and Hamamatsu NanoZoomer 360 systems showed comparable results in terms of image quality, feature presentation, and error rates for most cytological samples. Overall satisfaction was observed with the general convenience and image quality of digital PT. CONCLUSIONS As three-dimensional clusters are common and nuclear/chromatin features are critical for cytological interpretation, careful selection of scanners and optimal conditions are mandatory for the successful establishment of digital quality assurance programs in cytology.
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Affiliation(s)
- Yosep Chong
- Department of Hospital Pathology, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Soon Auck Hong
- Department of Pathology, Chung-Ang University College of Medicine, Seoul, Korea
| | - Hoon Kyu Oh
- Department of Pathology, Daegu Catholic University School of Medicine, Daegu, Korea
| | - Soo Jin Jung
- Department of Pathology, Inje University Busan Paik Hospital, Busan, Korea
| | - Bo-Sung Kim
- Department of Pathology, Green Cross Laboratories, Yongin, Korea
| | - Ji Yun Jeong
- Department of Pathology, Kyungpook National University Chilgok Hospital, School of Medicine, Kyungpook National University, Daegu, Korea
| | - Ho-Chang Lee
- Department of Pathology, Chungbuk National University College of Medicine, Cheongju, Korea
| | - Gyungyub Gong
- Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - The Committee of Quality Improvement of Korean Society for Cytopathology
- Department of Hospital Pathology, College of Medicine, The Catholic University of Korea, Seoul, Korea
- Department of Pathology, Chung-Ang University College of Medicine, Seoul, Korea
- Department of Pathology, Daegu Catholic University School of Medicine, Daegu, Korea
- Department of Pathology, Inje University Busan Paik Hospital, Busan, Korea
- Department of Pathology, Green Cross Laboratories, Yongin, Korea
- Department of Pathology, Kyungpook National University Chilgok Hospital, School of Medicine, Kyungpook National University, Daegu, Korea
- Department of Pathology, Chungbuk National University College of Medicine, Cheongju, Korea
- Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
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Alam MR, Seo KJ, Abdul-Ghafar J, Yim K, Lee SH, Jang HJ, Jung CK, Chong Y. Recent application of artificial intelligence on histopathologic image-based prediction of gene mutation in solid cancers. Brief Bioinform 2023; 24:bbad151. [PMID: 37114657 DOI: 10.1093/bib/bbad151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 03/24/2023] [Accepted: 03/24/2023] [Indexed: 04/29/2023] Open
Abstract
PURPOSE Evaluation of genetic mutations in cancers is important because distinct mutational profiles help determine individualized drug therapy. However, molecular analyses are not routinely performed in all cancers because they are expensive, time-consuming and not universally available. Artificial intelligence (AI) has shown the potential to determine a wide range of genetic mutations on histologic image analysis. Here, we assessed the status of mutation prediction AI models on histologic images by a systematic review. METHODS A literature search using the MEDLINE, Embase and Cochrane databases was conducted in August 2021. The articles were shortlisted by titles and abstracts. After a full-text review, publication trends, study characteristic analysis and comparison of performance metrics were performed. RESULTS Twenty-four studies were found mostly from developed countries, and their number is increasing. The major targets were gastrointestinal, genitourinary, gynecological, lung and head and neck cancers. Most studies used the Cancer Genome Atlas, with a few using an in-house dataset. The area under the curve of some of the cancer driver gene mutations in particular organs was satisfactory, such as 0.92 of BRAF in thyroid cancers and 0.79 of EGFR in lung cancers, whereas the average of all gene mutations was 0.64, which is still suboptimal. CONCLUSION AI has the potential to predict gene mutations on histologic images with appropriate caution. Further validation with larger datasets is still required before AI models can be used in clinical practice to predict gene mutations.
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Affiliation(s)
- Mohammad Rizwan Alam
- Department of Hospital Pathology, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Kyung Jin Seo
- Department of Hospital Pathology, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Jamshid Abdul-Ghafar
- Department of Hospital Pathology, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Kwangil Yim
- Department of Hospital Pathology, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Sung Hak Lee
- Department of Hospital Pathology, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Hyun-Jong Jang
- Catholic Big Data Integration Center, Department of Physiology, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Chan Kwon Jung
- Department of Hospital Pathology, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Yosep Chong
- Department of Hospital Pathology, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
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Piya S, Lennerz JK. Sustainable development goals applied to digital pathology and artificial intelligence applications in low- to middle-income countries. Front Med (Lausanne) 2023; 10:1146075. [PMID: 37256085 PMCID: PMC10225661 DOI: 10.3389/fmed.2023.1146075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 04/27/2023] [Indexed: 06/01/2023] Open
Abstract
Digital Pathology (DP) and Artificial Intelligence (AI) can be useful in low- and middle-income countries; however, many challenges exist. The United Nations developed sustainable development goals that aim to overcome some of these challenges. The sustainable development goals have not been applied to DP/AI applications in low- to middle income countries. We established a framework to align the 17 sustainable development goals with a 27-indicator list for low- and middle-income countries (World Bank/WHO) and a list of 21 essential elements for DP/AI. After categorization into three domains (human factors, IT/electronics, and materials + reagents), we permutated these layers into 153 concatenated statements for prioritization on a four-tiered scale. The two authors tested the subjective ranking framework and endpoints included ranked sum scores and visualization across the three layers. The authors assigned 364 points with 1.1-1.3 points per statement. We noted the prioritization of human factors (43%) at the indicator layer whereas IT/electronic (36%) and human factors (35%) scored highest at the essential elements layer. The authors considered goal 9 (industry, innovation, and infrastructure; average points 2.33; sum 42), goal 4 (quality education; 2.17; 39), and goal 8 (decent work and economic growth; 2.11; 38) most relevant; intra-/inter-rater variability assessment after a 3-month-washout period confirmed these findings. The established framework allows individual stakeholders to capture the relative importance of sustainable development goals for overcoming limitations to a specific problem. The framework can be used to raise awareness and help identify synergies between large-scale global objectives and solutions in resource-limited settings.
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Affiliation(s)
- Sumi Piya
- Nepal Medical College and Teaching Hospital (NMCTH), Kathmandu, Nepal
- Nepal Cancer Hospital and Research Center, Lalitpur, Nepal
- Department of Pathology, Center for Integrated Diagnostics, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Jochen K. Lennerz
- Department of Pathology, Center for Integrated Diagnostics, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
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Hortsch M, Koney NKK, Oommen AM, Yohannan DG, Li Y, de Melo Leite ACR, Girão-Carmona VCC. Virtual Microscopy Goes Global: The Images Are Virtual and the Problems Are Real. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2023; 1421:79-124. [PMID: 37524985 DOI: 10.1007/978-3-031-30379-1_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/02/2023]
Abstract
For the last two centuries, the scholarly education of histology and pathology has been based on technology, initially on the availability of low-cost, high-quality light microscopes, and more recently on the introduction of computers and e-learning approaches to biomedical education. Consequently, virtual microscopy (VM) is replacing glass slides and the traditional light microscope as the main instruments of instruction in histology and pathology laboratories. However, as with most educational changes, there are advantages and disadvantages associated with a new technology. The use of VM for the teaching of histology and pathology requires an extensive infrastructure and the availability of computing devices to all learners, both posing a considerable financial strain on schools and students. Furthermore, there may be valid reasons for practicing healthcare professionals to maintain competency in using light microscopes. In addition, some educators may be reluctant to embrace new technologies. These are some of the reasons why the introduction of VM as an integral part of histology and pathology instruction has been globally uneven. This paper compares the teaching of histology and pathology using traditional or VM in five different countries and their adjacent regions, representing developed, as well as developing areas of the globe. We identify general and local roadblocks to the introduction of this still-emerging didactic technology and outline solutions for overcoming these barriers.
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Affiliation(s)
- Michael Hortsch
- Departments of Cell and Developmental Biology and of Learning Health Sciences, University of Michigan, Ann Arbor, MI, USA.
| | - Nii Koney-Kwaku Koney
- Department of Anatomy, University of Ghana Medical School, University of Ghana, Korle Bu, Accra, Ghana
| | - Aswathy Maria Oommen
- Government Medical College Thiruvananthapuram, Thiruvananthapuram, Kerala, India
- Kerala University of Health Sciences, Thrissur, Kerala, India
| | - Doris George Yohannan
- Government Medical College Thiruvananthapuram, Thiruvananthapuram, Kerala, India
- Kerala University of Health Sciences, Thrissur, Kerala, India
| | - Yan Li
- Department of Anatomy, Histology and Embryology, Fudan University, Shanghai, China
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Ko YS, Choi YM, Kim M, Park Y, Ashraf M, Quiñones Robles WR, Kim MJ, Jang J, Yun S, Hwang Y, Jang H, Yi MY. Improving quality control in the routine practice for histopathological interpretation of gastrointestinal endoscopic biopsies using artificial intelligence. PLoS One 2022; 17:e0278542. [PMID: 36520777 PMCID: PMC9754254 DOI: 10.1371/journal.pone.0278542] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 11/18/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Colorectal and gastric cancer are major causes of cancer-related deaths. In Korea, gastrointestinal (GI) endoscopic biopsy specimens account for a high percentage of histopathologic examinations. Lack of a sufficient pathologist workforce can cause an increase in human errors, threatening patient safety. Therefore, we developed a digital pathology total solution combining artificial intelligence (AI) classifier models and pathology laboratory information system for GI endoscopic biopsy specimens to establish a post-analytic daily fast quality control (QC) system, which was applied in clinical practice for a 3-month trial run by four pathologists. METHODS AND FINDINGS Our whole slide image (WSI) classification framework comprised patch-generator, patch-level classifier, and WSI-level classifier. The classifiers were both based on DenseNet (Dense Convolutional Network). In laboratory tests, the WSI classifier achieved accuracy rates of 95.8% and 96.0% in classifying histopathological WSIs of colorectal and gastric endoscopic biopsy specimens, respectively, into three classes (Negative for dysplasia, Dysplasia, and Malignant). Classification by pathologic diagnosis and AI prediction were compared and daily reviews were conducted, focusing on discordant cases for early detection of potential human errors by the pathologists, allowing immediate correction, before the pathology report error is conveyed to the patients. During the 3-month AI-assisted daily QC trial run period, approximately 7-10 times the number of slides compared to that in the conventional monthly QC (33 months) were reviewed by pathologists; nearly 100% of GI endoscopy biopsy slides were double-checked by the AI models. Further, approximately 17-30 times the number of potential human errors were detected within an average of 1.2 days. CONCLUSIONS The AI-assisted daily QC system that we developed and established demonstrated notable improvements in QC, in quantitative, qualitative, and time utility aspects. Ultimately, we developed an independent AI-assisted post-analytic daily fast QC system that was clinically applicable and influential, which could enhance patient safety.
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Affiliation(s)
- Young Sin Ko
- Pathology Center, Seegene Medical Foundation, Seoul, Republic of Korea
- * E-mail: (YSK); (MYY)
| | - Yoo Mi Choi
- Pathology Center, Seegene Medical Foundation, Seoul, Republic of Korea
| | - Mujin Kim
- Graduate School of Data Science, Department of Industrial & Systems Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Youngjin Park
- Graduate School of Data Science, Department of Industrial & Systems Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Murtaza Ashraf
- Graduate School of Data Science, Department of Industrial & Systems Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Willmer Rafell Quiñones Robles
- Graduate School of Data Science, Department of Industrial & Systems Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Min-Ju Kim
- Department of Pathology, Incheon Sejong Hospital, Incheon, Republic of Korea
| | - Jiwook Jang
- AI Research Team, Digital Innovation Sector, Seegene Medical Foundation, Seoul, Republic of Korea
| | - Seokju Yun
- AI Research Team, Digital Innovation Sector, Seegene Medical Foundation, Seoul, Republic of Korea
| | - Yuri Hwang
- AI Research Team, Digital Innovation Sector, Seegene Medical Foundation, Seoul, Republic of Korea
| | - Hani Jang
- AI Research Team, Digital Innovation Sector, Seegene Medical Foundation, Seoul, Republic of Korea
| | - Mun Yong Yi
- Graduate School of Data Science, Department of Industrial & Systems Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
- * E-mail: (YSK); (MYY)
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Chong Y, Bae JM, Kang DW, Kim G, Han HS. Development of quality assurance program for digital pathology by the Korean Society of Pathologists. J Pathol Transl Med 2022; 56:370-382. [DOI: 10.4132/jptm.2022.09.30] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 09/29/2022] [Indexed: 11/19/2022] Open
Abstract
Background: Digital pathology (DP) using whole slide imaging is a recently emerging game changer technology that can fundamentally change the way of working in pathology. The Digital Pathology Study Group (DPSG) of the Korean Society of Pathologists (KSP) published a consensus report on the recommendations for pathologic practice using DP. Accordingly, the need for the development and implementation of a quality assurance program (QAP) for DP has been raised.Methods: To provide a standard baseline reference for internal and external QAP for DP, the members of the Committee of Quality Assurance of the KSP developed a checklist for the Redbook and a QAP trial for DP based on the prior DPSG consensus report. Four leading institutes participated in the QAP trial in the first year, and we gathered feedback from these institutes afterwards.Results: The newly developed checklists of QAP for DP contain 39 items (216 score): eight items for quality control of DP systems; three for DP personnel; nine for hardware and software requirements for DP systems; 15 for validation, operation, and management of DP systems; and four for data security and personal information protection. Most participants in the QAP trial replied that continuous education on unfamiliar terminology and more practical experience is demanding.Conclusions: The QAP for DP is essential for the safe implementation of DP in pathologic practice. Each laboratory should prepare an institutional QAP according to this checklist, and consecutive revision of the checklist with feedback from the QAP trial for DP needs to follow.
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Recent Applications of Artificial Intelligence from Histopathologic Image-Based Prediction of Microsatellite Instability in Solid Cancers: A Systematic Review. Cancers (Basel) 2022; 14:cancers14112590. [PMID: 35681570 PMCID: PMC9179592 DOI: 10.3390/cancers14112590] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 05/07/2022] [Accepted: 05/22/2022] [Indexed: 12/11/2022] Open
Abstract
Cancers with high microsatellite instability (MSI-H) have a better prognosis and respond well to immunotherapy. However, MSI is not tested in all cancers because of the additional costs and time of diagnosis. Therefore, artificial intelligence (AI)-based models have been recently developed to evaluate MSI from whole slide images (WSIs). Here, we aimed to assess the current state of AI application to predict MSI based on WSIs analysis in MSI-related cancers and suggest a better study design for future studies. Studies were searched in online databases and screened by reference type, and only the full texts of eligible studies were reviewed. The included 14 studies were published between 2018 and 2021, and most of the publications were from developed countries. The commonly used dataset is The Cancer Genome Atlas dataset. Colorectal cancer (CRC) was the most common type of cancer studied, followed by endometrial, gastric, and ovarian cancers. The AI models have shown the potential to predict MSI with the highest AUC of 0.93 in the case of CRC. The relatively limited scale of datasets and lack of external validation were the limitations of most studies. Future studies with larger datasets are required to implicate AI models in routine diagnostic practice for MSI prediction.
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Ailia MJ, Thakur N, Abdul-Ghafar J, Jung CK, Yim K, Chong Y. Current Trend of Artificial Intelligence Patents in Digital Pathology: A Systematic Evaluation of the Patent Landscape. Cancers (Basel) 2022; 14:cancers14102400. [PMID: 35626006 PMCID: PMC9139645 DOI: 10.3390/cancers14102400] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 05/04/2022] [Accepted: 05/10/2022] [Indexed: 12/12/2022] Open
Abstract
Simple Summary The combination of digital pathology (DP) with artificial intelligence (AI) offers faster, more accurate, and more comprehensive diagnoses, resulting in more precise individualized treatment. As this technology is constantly evolving, it is critical to understand the current state of AI applications in DP. Thus, it is necessary to analyze AI patent applications, assignees, and leaders in the field. In this study, five major patent databases, namely, those of the USPTO, EPO, KIPO, JPO, and CNIPA, were searched using key phrases, such as DP, AI, machine learning, and deep learning, and 523 patents were shortlisted based on the inclusion criteria. Our data demonstrated that the key areas of the patents were whole-slide imaging, segmentation, classification, and detection. In the past five years, an increasing trend in patent filing has been observed, mainly in a few prominent countries, with a focus on the digitization of pathological images and AI technologies that support the critical role of pathologists. Abstract The integration of digital pathology (DP) with artificial intelligence (AI) enables faster, more accurate, and thorough diagnoses, leading to more precise personalized treatment. As technology is advancing rapidly, it is critical to understand the current state of AI applications in DP. Therefore, a patent analysis of AI in DP is required to assess the application and publication trends, major assignees, and leaders in the field. We searched five major patent databases, namely, those of the USPTO, EPO, KIPO, JPO, and CNIPA, from 1974 to 2021, using keywords such as DP, AI, machine learning, and deep learning. We discovered 6284 patents, 523 of which were used for trend analyses on time series, international distribution, top assignees; word cloud analysis; and subject category analyses. Patent filing and publication have increased exponentially over the past five years. The United States has published the most patents, followed by China and South Korea (248, 117, and 48, respectively). The top assignees were Paige.AI, Inc. (New York City, NY, USA) and Siemens, Inc. (Munich, Germany) The primary areas were whole-slide imaging, segmentation, classification, and detection. Based on these findings, we expect a surge in DP and AI patent applications focusing on the digitalization of pathological images and AI technologies that support the vital role of pathologists.
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Affiliation(s)
| | | | | | | | | | - Yosep Chong
- Correspondence: ; Tel.: +82-2-2258-1620; Fax: +82-2-783-6648
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Coulter C, McKay F, Hallowell N, Browning L, Colling R, Macklin P, Sorell T, Aslam M, Bryson G, Treanor D, Verrill C. Understanding the ethical and legal considerations of Digital Pathology. JOURNAL OF PATHOLOGY CLINICAL RESEARCH 2022; 8:101-115. [PMID: 34796679 PMCID: PMC8822384 DOI: 10.1002/cjp2.251] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 09/12/2021] [Accepted: 10/12/2021] [Indexed: 12/21/2022]
Abstract
Digital Pathology (DP) is a platform which has the potential to develop a truly integrated and global pathology community. The generation of DP data at scale creates novel challenges for the histopathology community in managing, processing, and governing the use of these data. The current understanding of, and confidence in, the legal and ethical aspects of DP by pathologists is unknown. We developed an electronic survey (e-survey), comprising 22 questions, with input from the Royal College of Pathologists (RCPath) Digital Pathology Working Group. The e-survey was circulated via e-mail and social media (Twitter) through the RCPath Digital Pathology Working Group network, RCPath Trainee Committee network, the Pathology image data Lake for Analytics, Knowledge and Education (PathLAKE) digital pathology consortium, National Pathology Imaging Co-operative (NPIC), local contacts, and to the membership of both The Pathological Society of Great Britain and Ireland and the British Division of the International Academy of Pathology (BDIAP). Between 14 July 2020 and 6 September 2020, we collected 198 responses representing a cross section of histopathologists, including individuals with experience of DP research. We ascertained that, in the UK, DP is being used for diagnosis, research, and teaching, and that the platform is enabling data sharing. Our survey demonstrated that there is often a lack of confidence and understanding of the key issues of consent, legislation, and ethical guidelines. Of 198 respondents, 82 (41%) did not know when the use of digital scanned slide images would fall under the relevant legislation and 93 (47%) were 'Not confident at all' in their interpretation of consent for scanned slide images in research. With increasing uptake of DP, a working knowledge of these areas is essential but histopathologists often express a lack of confidence in these topics. The need for specific training in these areas is highlighted by the findings of this study.
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Affiliation(s)
- Cheryl Coulter
- Department of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK.,Nuffield Division of Clinical Laboratory Sciences, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Francis McKay
- The Wellcome Centre for Ethics and Humanities and the Ethox Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Nina Hallowell
- The Wellcome Centre for Ethics and Humanities and the Ethox Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Lisa Browning
- Department of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK.,NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | - Richard Colling
- Department of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK.,Nuffield Department of Surgical Sciences, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Philip Macklin
- Department of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | - Tom Sorell
- Department of Politics and International Studies, University of Warwick, Coventry, UK
| | - Muhammad Aslam
- Department of Histopathology, Glangwilli Hospital, Hywel Dda University Health Board, Carmarthen, Wales, UK
| | - Gareth Bryson
- Department of Pathology, Queen Elizabeth University Hospital, NHS Greater Glasgow and Clyde, Glasgow, Scotland, UK
| | - Darren Treanor
- Department of Pathology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Clare Verrill
- Department of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK.,NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK.,Nuffield Department of Surgical Sciences, University of Oxford, John Radcliffe Hospital, Oxford, UK
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L'Imperio V, Gibilisco F, Fraggetta F. What is Essential is (No More) Invisible to the Eyes: The Introduction of BlocDoc in the Digital Pathology Workflow. J Pathol Inform 2021; 12:32. [PMID: 34760329 PMCID: PMC8529340 DOI: 10.4103/jpi.jpi_35_21] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 07/07/2021] [Accepted: 07/13/2021] [Indexed: 11/12/2022] Open
Abstract
Background: The implementation of a fully digital workflow in any anatomic pathology department requires a complete conversion to a tracked system. Ensuring the strict correspondence of the material submitted for the analysis, from the accessioning to the reporting phase, is mandatory in the anatomic pathology laboratory, especially when implementing the digital pathology for primary histological diagnosis. The proposed solutions, up to now, rely on the verification that all the materials present in the glass slide are also present in the whole slide images (WSIs). Although different methods have already been implemented for this purpose (e.g., the “macroimage” of the digital slide, representing the overview of the glass slide), the recent introduction of a device to capture the cut surface of paraffin blocks put the quality control of the digital workflow a step forward, allowing to match the digitized slide with the corresponding block. This system may represent a reliable, easy-to-use alternative to further reduce tissue inconsistencies between material sent to the lab and the final glass slides or WSIs. Methods: The Anatomic Pathology of the Gravina Hospital in Caltagirone, Sicily, Italy, has implemented the application of the BlocDoc devices (SPOT Imaging, Sterling Heights, USA) in its digital workflow. The instruments were positioned next to every microtome/sectioning station, with the possibility to capture the “normal” and the polarized image of the cut surface of the blocks directly by the technician. The presence of a monitor in the BlocDoc device allowed the technician to check the concordance between the cut surface of the block and the material on the corresponding slide. The link between BlocDoc and the laboratory information system, through the presence of the 2D barcode, allowed the pathologists to access the captured image of the cut surface of the block at the pathologist workstation, thus enabling the direct comparison between this image and the WSI (thumbnail and “macroimage”). Results: During the implementation period, more than 10.000 (11.248) blocks were routinely captured using the BlocDoc. The employment of this approach allowed a drastic reduction of the discordances and tissue inconsistencies. The implementation of the BlocDoc in the routine allowed the detection of two different types of “errors,” the so-called “systematic” and “occasional” ones. The first type was intrinsic of some specific specimens (e.g., transurethral resection of the prostate, nasal polypectomies, and piecemeal uterine myomectomies) characterized by the three-dimensional nature of the fragments and affected almost 100% of these samples. On the other hand, the “occasional” errors, mainly due to inexperience or extreme caution of the technicians in handling tiny specimens, affected 98 blocks (0.9%) of these samples and progressively reduced with the rising confidence with the BlocDoc. One of these cases was clinically relevant. No problems in the recognition of the 2D barcodes were encountered using a laser cassette printer. Finally, rare failures have been recorded during the period, accounting for <0.1% of all the cases, mainly due to network connection issues. Conclusions: The implementation of BlocDoc can further improve the effectiveness of the digital workflow, demonstrating its safety and robustness as a valid alternative to the traditional, nontracked analogic workflow.
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Affiliation(s)
- Vincenzo L'Imperio
- Department of Medicine and Surgery, Pathology, ASST Monza, University of Milano-Bicocca, Monza, Italy
| | - Fabio Gibilisco
- Department of Medical and Surgical Sciences and Advanced Technologies, "G.F. Ingrassia", Anatomic Pathology, University of Catania, Catania, Italy
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