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Coudry RA, Assis EA, Frassetto FP, Jansen AM, da Silva LM, Parra-Medina R, Saieg M. Crossing the Andes: Challenges and opportunities for digital pathology in Latin America. J Pathol Inform 2024; 15:100369. [PMID: 38638195 PMCID: PMC11025004 DOI: 10.1016/j.jpi.2024.100369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 02/05/2024] [Accepted: 02/17/2024] [Indexed: 04/20/2024] Open
Abstract
The most widely accepted and used type of digital pathology (DP) is whole-slide imaging (WSI). The USFDA granted two WSI system approvals for primary diagnosis, the first in 2017. In Latin America, DP has the potential to reshape healthcare by enhancing diagnostic capabilities through artificial intelligence (AI) and standardizing pathology reports. Yet, we must tackle regulatory hurdles, training, resource availability, and unique challenges to the region. Collectively addressing these hurdles can enable the region to harness DP's advantages-enhancing disease diagnosis, medical research, and healthcare accessibility for its population. Americas Health Foundation assembled a panel of Latin American pathologists who are experts in DP to assess the hurdles to implementing it into pathologists' workflows in the region and provide recommendations for overcoming them. Some key steps recommended include creating a Latin American Society of Digital Pathology to provide continuing education, developing AI models trained on the Latin American population, establishing national regulatory frameworks for protecting the data, and standardizing formats for DP images to ensure that pathologists can collaborate and validate specimens across the various DP platforms.
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Affiliation(s)
| | | | | | | | | | - Rafael Parra-Medina
- National Cancer Institute (INC), Bogotá, Colombia
- Fundación Universitaria de Ciencias de la Salud (FUCS), Bogotá, Colombia
| | - Mauro Saieg
- Grupo Fleury, São Paulo, Brazil
- Santa Casa Medical School, São Paulo, SP, Brazil
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2
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Li BC, Hammond S, Parwani AV, Shen R. Artificial intelligence algorithm accurately assesses oestrogen receptor immunohistochemistry in metastatic breast cancer cytology specimens: A pilot study. Cytopathology 2024; 35:464-472. [PMID: 38519745 DOI: 10.1111/cyt.13373] [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: 12/24/2023] [Revised: 02/09/2024] [Accepted: 03/02/2024] [Indexed: 03/25/2024]
Abstract
OBJECTIVE The Visiopharm artificial intelligence (AI) algorithm for oestrogen receptor (ER) immunohistochemistry (IHC) in whole slide images (WSIs) has been successfully validated in surgical pathology. This study aimed to assess its efficacy in cytology specimens. METHODS The study cohort comprised 105 consecutive cytology specimens with metastatic breast carcinoma. ER IHC WSIs were seamlessly integrated into the Visiopharm platform from the Image Management System (IMS) during our routine digital workflow, and an AI algorithm was employed for analysis. ER AI scores were compared with pathologists' manual consensus scores. Optimization steps were implemented and evaluated to reduce discordance. RESULTS The overall concordance between pathologists' scores and AI scores was excellent (99/105, 94.3%). Six cases exhibited discordant results, including two false-negative (FN) cases due to abundant histiocytes incorrectly counted as negatively stained tumour cells by AI, two FN cases owing to weak staining, and two false-positive (FP) cases where pigmented macrophages were erroneously counted as positively stained tumour cells by AI. The Pearson correlation coefficient of ER-positive percentages between pathologists' and AI scores was 0.8483. Optimization steps, such as lowering the cut-off threshold and additional training using higher input magnification, significantly improved accuracy. CONCLUSIONS The automated ER AI algorithm demonstrated excellent concordance with pathologists' assessments and accurately differentiated ER-positive from ER-negative metastatic breast carcinoma cytology cases. However, precision in identifying tumour cells in cytology specimens requires further enhancement.
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Affiliation(s)
- Brenna C Li
- Dublin Jerome High School, Dublin, Ohio, USA
| | - Scott Hammond
- Department of Pathology, Wexner Medical Center, The Ohio State University, Columbus, Ohio, USA
| | - Anil V Parwani
- Department of Pathology, Wexner Medical Center, The Ohio State University, Columbus, Ohio, USA
| | - Rulong Shen
- Department of Pathology, Wexner Medical Center, The Ohio State University, Columbus, Ohio, USA
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3
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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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4
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Ivanova M, Pescia C, Trapani D, Venetis K, Frascarelli C, Mane E, Cursano G, Sajjadi E, Scatena C, Cerbelli B, d’Amati G, Porta FM, Guerini-Rocco E, Criscitiello C, Curigliano G, Fusco N. Early Breast Cancer Risk Assessment: Integrating Histopathology with Artificial Intelligence. Cancers (Basel) 2024; 16:1981. [PMID: 38893102 PMCID: PMC11171409 DOI: 10.3390/cancers16111981] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Revised: 05/13/2024] [Accepted: 05/17/2024] [Indexed: 06/21/2024] Open
Abstract
Effective risk assessment in early breast cancer is essential for informed clinical decision-making, yet consensus on defining risk categories remains challenging. This paper explores evolving approaches in risk stratification, encompassing histopathological, immunohistochemical, and molecular biomarkers alongside cutting-edge artificial intelligence (AI) techniques. Leveraging machine learning, deep learning, and convolutional neural networks, AI is reshaping predictive algorithms for recurrence risk, thereby revolutionizing diagnostic accuracy and treatment planning. Beyond detection, AI applications extend to histological subtyping, grading, lymph node assessment, and molecular feature identification, fostering personalized therapy decisions. With rising cancer rates, it is crucial to implement AI to accelerate breakthroughs in clinical practice, benefiting both patients and healthcare providers. However, it is important to recognize that while AI offers powerful automation and analysis tools, it lacks the nuanced understanding, clinical context, and ethical considerations inherent to human pathologists in patient care. Hence, the successful integration of AI into clinical practice demands collaborative efforts between medical experts and computational pathologists to optimize patient outcomes.
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Affiliation(s)
- Mariia Ivanova
- Division of Pathology, European Institute of Oncology IRCCS, 20141 Milan, Italy; (M.I.); (C.P.); (K.V.); (C.F.); (E.M.); (G.C.); (E.S.); (F.M.P.); (E.G.-R.)
| | - Carlo Pescia
- Division of Pathology, European Institute of Oncology IRCCS, 20141 Milan, Italy; (M.I.); (C.P.); (K.V.); (C.F.); (E.M.); (G.C.); (E.S.); (F.M.P.); (E.G.-R.)
| | - Dario Trapani
- Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology IRCCS, 20141 Milan, Italy; (D.T.); (C.C.); (G.C.)
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Konstantinos Venetis
- Division of Pathology, European Institute of Oncology IRCCS, 20141 Milan, Italy; (M.I.); (C.P.); (K.V.); (C.F.); (E.M.); (G.C.); (E.S.); (F.M.P.); (E.G.-R.)
| | - Chiara Frascarelli
- Division of Pathology, European Institute of Oncology IRCCS, 20141 Milan, Italy; (M.I.); (C.P.); (K.V.); (C.F.); (E.M.); (G.C.); (E.S.); (F.M.P.); (E.G.-R.)
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Eltjona Mane
- Division of Pathology, European Institute of Oncology IRCCS, 20141 Milan, Italy; (M.I.); (C.P.); (K.V.); (C.F.); (E.M.); (G.C.); (E.S.); (F.M.P.); (E.G.-R.)
| | - Giulia Cursano
- Division of Pathology, European Institute of Oncology IRCCS, 20141 Milan, Italy; (M.I.); (C.P.); (K.V.); (C.F.); (E.M.); (G.C.); (E.S.); (F.M.P.); (E.G.-R.)
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Elham Sajjadi
- Division of Pathology, European Institute of Oncology IRCCS, 20141 Milan, Italy; (M.I.); (C.P.); (K.V.); (C.F.); (E.M.); (G.C.); (E.S.); (F.M.P.); (E.G.-R.)
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Cristian Scatena
- Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, 56126 Pisa, Italy;
| | - Bruna Cerbelli
- Department of Medical-Surgical Sciences and Biotechnologies, Sapienza University of Rome, 00185 Rome, Italy;
| | - Giulia d’Amati
- Department of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, 00185 Rome, Italy;
| | - Francesca Maria Porta
- Division of Pathology, European Institute of Oncology IRCCS, 20141 Milan, Italy; (M.I.); (C.P.); (K.V.); (C.F.); (E.M.); (G.C.); (E.S.); (F.M.P.); (E.G.-R.)
| | - Elena Guerini-Rocco
- Division of Pathology, European Institute of Oncology IRCCS, 20141 Milan, Italy; (M.I.); (C.P.); (K.V.); (C.F.); (E.M.); (G.C.); (E.S.); (F.M.P.); (E.G.-R.)
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Carmen Criscitiello
- Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology IRCCS, 20141 Milan, Italy; (D.T.); (C.C.); (G.C.)
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Giuseppe Curigliano
- Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology IRCCS, 20141 Milan, Italy; (D.T.); (C.C.); (G.C.)
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Nicola Fusco
- Division of Pathology, European Institute of Oncology IRCCS, 20141 Milan, Italy; (M.I.); (C.P.); (K.V.); (C.F.); (E.M.); (G.C.); (E.S.); (F.M.P.); (E.G.-R.)
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
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Borazjani FM, Sarkhuni MR, Nahvijou A. Challenges and benefits of telepathology in education: lessons learned from COVID-19-a systematic review. J Public Health (Oxf) 2024:fdae063. [PMID: 38704152 DOI: 10.1093/pubmed/fdae063] [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: 11/06/2023] [Revised: 03/11/2024] [Accepted: 04/16/2024] [Indexed: 05/06/2024] Open
Abstract
BACKGROUND The COVID-19 pandemic in 2020 posed significant communication challenges, especially in the healthcare sector. Telepathology provides a valuable means for healthcare providers to communicate. This study investigated the key challenges and benefits of telepathology in education through a systematic review of relevant studies conducted during this period. METHODS This systematic review was conducted in 2022. We utilized databases, including PubMed, Google Scholar and ScienceDirect. Our search was performed from 7 February 2022 to 13 February 2022. We selected articles based on inclusion criteria and used the Critical Appraisal Skills Program checklist to assess study strengths and limitations. We extracted data using a checklist and synthesized the results narratively. RESULTS We initially identified 125 articles, and after screening, 15 were included in the study. These studies reported various challenges, including cost, technology, communication problems, educational difficulties, time wasting, legal issues and family distraction problems. Conversely, studies mentioned benefits, such as care improvement, better education, time efficiency, proper communication, cost and technology advancement. CONCLUSIONS The results of this study will help future efforts and investigations to implement and set up telepathology. Based on our review, despite the challenges, the benefits of telepathology in education are greater than these obstacles, indicating its potential for future use.
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Affiliation(s)
- Fariba Moalem Borazjani
- Health Information Management Department, School of Allied Medical Sciences, Tehran University of Medical Sciences, Master of Medical Informatics, Tehran 1439957181, Iran
| | - Mahsa Raeisi Sarkhuni
- School of nursing and midwifery, Hormozgan University of Medical Sciences, Bachelor of Surgical Technology, Bandar Abbas 7933144192, Iran
| | - Azin Nahvijou
- Cancer Research Center of Cancer Institute, Tehran University of Medical Sciences, Associate Professor of Healthcare Services Management, Tehran 1419733141, Iran
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6
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Shaker N, Shen R, Limbach AL, Satturwar S, Kobalka P, Ahmadian S, Sun S, Chen W, Lujan G, Esnakula A, Parwani A, Li Z. Automated imaging analysis of Ki-67 immunohistochemistry on whole slide images of cell blocks from pancreatic neuroendocrine neoplasms. J Am Soc Cytopathol 2024; 13:205-212. [PMID: 38433072 DOI: 10.1016/j.jasc.2024.02.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 02/06/2024] [Accepted: 02/06/2024] [Indexed: 03/05/2024]
Abstract
INTRODUCTION Accurate grading of pancreatic neuroendocrine tumors (PanNETs) relies on the assessment of Ki-67 immunohistochemistry (IHC). While digital imaging analysis (DIA) has been employed for Ki-67 IHC assessment in surgical specimens, its applicability to cytologic specimens remains underexplored. This study aimed to evaluate an automated DIA for assessing Ki-67 IHC on PanNET cell blocks. MATERIALS AND METHODS The study included 61 consecutive PanNETs and 5 pancreatic neuroendocrine carcinomas. Ki-67 IHC slides from cell blocks were digitally scanned into whole slide images using Philips IntelliSite Scanners and analyzed in batches using the Visiopharm Ki-67 App in a digital workflow. Ki-67 scores obtained through DIA were compared to pathologists' manual scores. RESULTS The Pearson correlation coefficient of the percentage of Ki-67-stained nuclei between DIA reads and the originally reported reads was 0.9681. Concordance between DIA Ki-67 grades and pathologists' Ki-67 grades was observed in 92.4% (61/66) of cases with the calculated Cohen's Kappa coefficient of 0.862 (almost perfect agreement). Discordance between DIA and pathologists' consensus reads occurred in 5 PanNET cases which were upgraded from G1 to G2 by DIA due to contaminated Ki-67-stained inflammatory cells. CONCLUSIONS DIA demonstrated excellent concordance with pathologists' assessments, with only minor grading discrepancies. However, the essential role of pathologists in confirming results is emphasized to enhance overall accuracy.
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Affiliation(s)
- Nada Shaker
- Department of Pathology, The Ohio State University, Columbus, Ohio
| | - Rulong Shen
- Department of Pathology, The Ohio State University, Columbus, Ohio
| | | | - Swati Satturwar
- Department of Pathology, The Ohio State University, Columbus, Ohio
| | - Peter Kobalka
- Department of Pathology, The Ohio State University, Columbus, Ohio
| | - Saman Ahmadian
- Department of Pathology, The Ohio State University, Columbus, Ohio
| | - Shaoli Sun
- Department of Pathology, The Ohio State University, Columbus, Ohio
| | - Wei Chen
- Department of Pathology, The Ohio State University, Columbus, Ohio
| | - Giovanni Lujan
- Department of Pathology, The Ohio State University, Columbus, Ohio
| | - Ashwini Esnakula
- Department of Pathology, The Ohio State University, Columbus, Ohio
| | - Anil Parwani
- Department of Pathology, The Ohio State University, Columbus, Ohio
| | - Zaibo Li
- Department of Pathology, The Ohio State University, Columbus, Ohio.
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7
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Roth CJ, Petersilge C, Clunie D, Towbin AJ, Cram D, Primo R, Li X, Berkowitz SJ, Barnosky V, Krupinski EA. HIMSS-SIIM Enterprise Imaging Community White Papers: Reflections and Future Directions. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:429-443. [PMID: 38336948 PMCID: PMC11031499 DOI: 10.1007/s10278-024-00992-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 01/23/2024] [Indexed: 02/12/2024]
Affiliation(s)
| | - Cheryl Petersilge
- Vidagos University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | | | - Alexander J Towbin
- Department of Radiology, Cincinnati Children's Hospital, Cincinnati, OH, USA.
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA.
| | - Dawn Cram
- PaxeraHealth, 85 Wells Ave Suite 120, Newton, MA, 02459, USA
| | - Rik Primo
- Primo Medical Imaging Informatics Inc, Chicago, IL, USA
| | - Xin Li
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Seth J Berkowitz
- Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02115, USA
| | - Victoria Barnosky
- Robert Morris University, Moon Township, Suazio, Philadelphia, PA, USA
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8
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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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Kim D, Sundling KE, Virk R, Thrall MJ, Alperstein S, Bui MM, Chen-Yost H, Donnelly AD, Lin O, Liu X, Madrigal E, Michelow P, Schmitt FC, Vielh PR, Zakowski MF, Parwani AV, Jenkins E, Siddiqui MT, Pantanowitz L, Li Z. Digital cytology part 1: digital cytology implementation for practice: a concept paper with review and recommendations from the American Society of Cytopathology Digital Cytology Task Force. J Am Soc Cytopathol 2024; 13:86-96. [PMID: 38158316 DOI: 10.1016/j.jasc.2023.11.006] [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: 11/06/2023] [Revised: 11/28/2023] [Accepted: 11/29/2023] [Indexed: 01/03/2024]
Abstract
Digital cytology and artificial intelligence (AI) are gaining greater adoption in the cytopathology laboratory. However, peer-reviewed real-world data and literature are lacking regarding the current clinical landscape. The American Society of Cytopathology in conjunction with the International Academy of Cytology and the Digital Pathology Association established a special task force comprising 20 members with expertise and/or interest in digital cytology. The aim of the group was to investigate the feasibility of incorporating digital cytology, specifically cytology whole slide scanning and AI applications, into the workflow of the laboratory. In turn, the impact on cytopathologists, cytologists (cytotechnologists), and cytology departments were also assessed. The task force reviewed existing literature on digital cytology, conducted a worldwide survey, and held a virtual roundtable discussion on digital cytology and AI with multiple industry corporate representatives. This white paper, presented in 2 parts, summarizes the current state of digital cytology and AI practice in global cytology practice. Part 1 of the white paper presented herein is a review and offers best practice recommendations for incorporating digital cytology into practice. Part 2 of the white paper provides a comprehensive review of AI in cytology practice along with best practice recommendations and legal considerations. Additionally, the results of a global survey regarding digital cytology are highlighted.
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Affiliation(s)
- David Kim
- Department of Pathology & Laboratory Medicine, Memorial Sloan-Kettering Cancer Center, New York, New York
| | - Kaitlin E Sundling
- The Wisconsin State Laboratory of Hygiene and Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, Madison, Wisconsin
| | - Renu Virk
- Department of Pathology and Cell Biology, Columbia University, New York, New York
| | - Michael J Thrall
- Department of Pathology and Genomic Medicine, Houston Methodist Hospital, Houston, Texas
| | - Susan Alperstein
- Department of Pathology and Laboratory Medicine, New York Presbyterian-Weill Cornell Medicine, New York, New York
| | - Marilyn M Bui
- The Department of Pathology, Moffitt Cancer Center & Research Institute, Tampa, Florida
| | | | - Amber D Donnelly
- Diagnostic Cytology Education, University of Nebraska Medical Center, College of Allied Health Professions, Omaha, Nebraska
| | - Oscar Lin
- Department of Pathology & Laboratory Medicine, Memorial Sloan-Kettering Cancer Center, New York, New York
| | - Xiaoying Liu
- Department of Pathology and Laboratory Medicine, Dartmouth Hitchcock Medical Center, Lebanon, New Hampshire
| | - Emilio Madrigal
- Department of Pathology, Massachusetts General Hospital, Boston, Massachusetts
| | - Pamela Michelow
- Division of Anatomical Pathology, School of Pathology, University of the Witwatersrand, Johannesburg, South Africa; Department of Pathology, National Health Laboratory Services, Johannesburg, South Africa
| | - Fernando C Schmitt
- Department of Pathology, Medical Faculty of Porto University, Porto, Portugal
| | - Philippe R Vielh
- Department of Pathology, Medipath and American Hospital of Paris, Paris, France
| | | | - Anil V Parwani
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | | | - Momin T Siddiqui
- Department of Pathology and Laboratory Medicine, New York Presbyterian-Weill Cornell Medicine, New York, New York
| | - Liron Pantanowitz
- Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania.
| | - Zaibo Li
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio.
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10
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Chen W, Ziebell J, Arole V, Parkinson B, Yu L, Dai H, Frankel WL, Yearsley M, Esnakula A, Sun S, Gamble D, Vazzano J, Mishra M, Schoenfield L, Kneile J, Reuss S, Schumacher M, Satturwar S, Li Z, Parwani A, Lujan G. Comparing Accuracy of Helicobacter pylori Identification Using Traditional Hematoxylin and Eosin-Stained Glass Slides With Digital Whole Slide Imaging. J Transl Med 2024; 104:100262. [PMID: 37839639 DOI: 10.1016/j.labinv.2023.100262] [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: 04/24/2022] [Revised: 10/05/2023] [Accepted: 10/06/2023] [Indexed: 10/17/2023] Open
Abstract
With advancements in the field of digital pathology, there has been a growing need to compare the diagnostic abilities of pathologists using digitized whole slide images against those when using traditional hematoxylin and eosin (H&E)-stained glass slides for primary diagnosis. One of the most common specimens received in pathology practices is an endoscopic gastric biopsy with a request to rule out Helicobacter pylori (H. pylori) infection. The current standard of care is the identification of the organisms on H&E-stained slides. Immunohistochemical or histochemical stains are used selectively. However, due to their small size (2-4 μm in length by 0.5-1 μm in width), visualization of the organisms can present a diagnostic challenge. The goal of the study was to compare the ability of pathologists to identify H. pylori on H&E slides using a digital platform against the gold standard of H&E glass slides using routine light microscopy. Diagnostic accuracy rates using glass slides vs digital slides were 81% vs 72% (P = .0142) based on H&E slides alone. When H. pylori immunohistochemical slides were provided, the diagnostic accuracy was significantly improved to comparable rates (96% glass vs 99% digital, P = 0.2199). Furthermore, differences in practice settings (academic/subspecialized vs community/general) and the duration of sign-out experience did not significantly impact the accuracy of detecting H. pylori on digital slides. We concluded that digital whole slide images, although amenable in different practice settings and teaching environments, does present some shortcomings in accuracy and precision, especially in certain circumstances and thus is not yet fully capable of completely replacing glass slide review for identification of H. pylori. We specifically recommend reviewing glass slides and/or performing ancillary stains, especially when there is a discrepancy between the degree of inflammation and the presence of microorganisms on digital images.
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Affiliation(s)
- Wei Chen
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Jennifer Ziebell
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Vidya Arole
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Bryce Parkinson
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Lianbo Yu
- Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio
| | - Harrison Dai
- Eastern Virginia Medical School, Norfolk, Virginia
| | - Wendy L Frankel
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Martha Yearsley
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Ashwini Esnakula
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Shaoli Sun
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Denise Gamble
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Jennifer Vazzano
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Manisha Mishra
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Lynn Schoenfield
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Jeffrey Kneile
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Sarah Reuss
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Melinda Schumacher
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Swati Satturwar
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Zaibo Li
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Anil Parwani
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Giovanni Lujan
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio.
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11
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Schmidt RL, White SK, Timme KH, McFarland MM, Lomo LC. Graduate Medical Education in Pathology: A Scoping Review. Arch Pathol Lab Med 2024; 148:117-127. [PMID: 37014974 DOI: 10.5858/arpa.2022-0365-ra] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/10/2023] [Indexed: 04/06/2023]
Abstract
CONTEXT.— Pathologists have produced a substantial body of literature on graduate medical education (GME). However, this body of literature is diverse and has not yet been characterized. OBJECTIVE.— To chart the concepts, research methods, and publication patterns of studies on GME in pathology. DATA SOURCES.— This was a systematic scoping review covering all literature produced since 1980 in the PubMed and Embase databases. CONCLUSIONS.— Research on GME in pathology is evenly dispersed across educational topics. This body of literature would benefit from research based on theory, stronger study designs, and studies that can provide evidence to support decisions on educational policies.
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Affiliation(s)
- Robert L Schmidt
- From the Department of Pathology (Schmidt, White, Lomo) and Eccles Health Sciences Library (McFarland), University of Utah, Salt Lake City
| | - Sandra K White
- From the Department of Pathology (Schmidt, White, Lomo) and Eccles Health Sciences Library (McFarland), University of Utah, Salt Lake City
| | - Kathleen H Timme
- the Department of Endocrinology, Eccles Primary Children's Hospital, Salt Lake City, Utah (Timme)
| | - Mary M McFarland
- From the Department of Pathology (Schmidt, White, Lomo) and Eccles Health Sciences Library (McFarland), University of Utah, Salt Lake City
| | - Lesley C Lomo
- From the Department of Pathology (Schmidt, White, Lomo) and Eccles Health Sciences Library (McFarland), University of Utah, Salt Lake City
- ARUP Laboratories, Salt Lake City, Utah (Schmidt, Lomo)
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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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Shafi S, Parwani AV. Artificial intelligence in diagnostic pathology. Diagn Pathol 2023; 18:109. [PMID: 37784122 PMCID: PMC10546747 DOI: 10.1186/s13000-023-01375-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 07/21/2023] [Indexed: 10/04/2023] Open
Abstract
Digital pathology (DP) is being increasingly employed in cancer diagnostics, providing additional tools for faster, higher-quality, accurate diagnosis. The practice of diagnostic pathology has gone through a staggering transformation wherein new tools such as digital imaging, advanced artificial intelligence (AI) algorithms, and computer-aided diagnostic techniques are being used for assisting, augmenting and empowering the computational histopathology and AI-enabled diagnostics. This is paving the way for advancement in precision medicine in cancer. Automated whole slide imaging (WSI) scanners are now rendering diagnostic quality, high-resolution images of entire glass slides and combining these images with innovative digital pathology tools is making it possible to integrate imaging into all aspects of pathology reporting including anatomical, clinical, and molecular pathology. The recent approvals of WSI scanners for primary diagnosis by the FDA as well as the approval of prostate AI algorithm has paved the way for starting to incorporate this exciting technology for use in primary diagnosis. AI tools can provide a unique platform for innovations and advances in anatomical and clinical pathology workflows. In this review, we describe the milestones and landmark trials in the use of AI in clinical pathology with emphasis on future directions.
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Affiliation(s)
- Saba Shafi
- Department of Pathology, The Ohio State University Wexner Medical Center, E409 Doan Hall, 410 West 10th Ave, Columbus, OH, 43210, USA
| | - Anil V Parwani
- Department of Pathology, The Ohio State University Wexner Medical Center, E409 Doan Hall, 410 West 10th Ave, Columbus, OH, 43210, USA.
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14
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Browning L, Winter L, Cooper RA, Ghosh A, Dytor T, Colling R, Fryer E, Rittscher J, Verrill C. Impact of the transition to digital pathology in a clinical setting on histopathologists in training: experiences and perceived challenges within a UK training region. J Clin Pathol 2023; 76:712-718. [PMID: 35906044 PMCID: PMC10511979 DOI: 10.1136/jcp-2022-208416] [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: 05/24/2022] [Accepted: 07/08/2022] [Indexed: 11/03/2022]
Abstract
AIMS With increasing utility of digital pathology (DP), it is important to consider the experiences of histopathologists in training, particularly in view of the varied access to DP across a training region and the consequent need to remain competent in reporting on glass slides (GS), which is also relevant for the Fellowship of the Royal College of Pathologists part 2 examination. Understanding the impact of DP on training is limited but could aid development of guidance to support the transition. We sought to investigate the perceptions of histopathologists in training around the introduction of DP for clinical diagnosis within a training region, and the potential training benefits and challenges. METHODS An anonymous online survey was circulated to 24 histopathologists in training within a UK training region, including a hospital which has been fully digitised since summer 2020. RESULTS 19 of 24 histopathologists in training responded (79%). The results indicate that DP offers many benefits to training, including ease of access to cases to enhance individual learning and teaching in general. Utilisation of DP for diagnosis appears variable; almost half of the (10 of 19) respondents with DP experience using it only for ancillary purposes such as measurements, reporting varying levels of confidence in using DP clinically. For those yet to undergo the transition, there was a perceived anxiety regarding digital reporting despite experience with DP in other contexts. CONCLUSIONS The survey evidences the need for provision of training and support for histopathologists in training during the transition to DP, and for consideration of their need to maintain competence and confidence with GS reporting.
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Affiliation(s)
- Lisa Browning
- NIHR Oxford Biomedical Research Centre, Oxford, UK
- Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Lucinda Winter
- Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | | | - Abhisek Ghosh
- Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- Nuffield Department of Clinical Laboratory Sciences, University of Oxford, Oxford, UK
| | - Thomas Dytor
- Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Richard Colling
- Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Eve Fryer
- Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Jens Rittscher
- NIHR Oxford Biomedical Research Centre, Oxford, UK
- Department of Engineering Science, University of Oxford, Oxford, UK
- Big Data Institute, University of Oxford, Oxford, UK
| | - Clare Verrill
- NIHR Oxford Biomedical Research Centre, Oxford, UK
- Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
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15
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Zehra T, Jaffar N, Shams M, Chundriger Q, Ahmed A, Anum F, Alsubaie N, Ahmad Z. Use of a Novel Deep Learning Open-Source Model for Quantification of Ki-67 in Breast Cancer Patients in Pakistan: A Comparative Study between the Manual and Automated Methods. Diagnostics (Basel) 2023; 13:3105. [PMID: 37835848 PMCID: PMC10572449 DOI: 10.3390/diagnostics13193105] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 09/01/2023] [Accepted: 09/05/2023] [Indexed: 10/15/2023] Open
Abstract
Introduction: Breast cancer is the most common cancer in women; its early detection plays a crucial role in improving patient outcomes. Ki-67 is a biomarker commonly used for evaluating the proliferation of cancer cells in breast cancer patients. The quantification of Ki-67 has traditionally been performed by pathologists through a manual examination of tissue samples, which can be time-consuming and subject to inter- and intra-observer variability. In this study, we used a novel deep learning model to quantify Ki-67 in breast cancer in digital images prepared by a microscope-attached camera. Objective: To compare the automated detection of Ki-67 with the manual eyeball/hotspot method. Place and duration of study: This descriptive, cross-sectional study was conducted at the Jinnah Sindh Medical University. Glass slides of diagnosed cases of breast cancer were obtained from the Aga Khan University Hospital after receiving ethical approval. The duration of the study was one month. Methodology: We prepared 140 digital images stained with the Ki-67 antibody using a microscope-attached camera at 10×. An expert pathologist (P1) evaluated the Ki-67 index of the hotspot fields using the eyeball method. The images were uploaded to the DeepLiif software to detect the exact percentage of Ki-67 positive cells. SPSS version 24 was used for data analysis. Diagnostic accuracy was also calculated by other pathologists (P2, P3) and by AI using a Ki-67 cut-off score of 20 and taking P1 as the gold standard. Results: The manual and automated scoring methods showed a strong positive correlation as the kappa coefficient was significant. The p value was <0.001. The highest diagnostic accuracy, i.e., 95%, taking P1 as gold standard, was found for AI, compared to pathologists P2 and P3. Conclusions: Use of quantification-based deep learning models can make the work of pathologists easier and more reproducible. Our study is one of the earliest studies in this field. More studies with larger sample sizes are needed in future to develop a cohort.
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Affiliation(s)
- Talat Zehra
- Department of Pathology, Jinnah Sindh Medical University, Karachi 75001, Pakistan; (T.Z.); (N.J.)
| | - Nazish Jaffar
- Department of Pathology, Jinnah Sindh Medical University, Karachi 75001, Pakistan; (T.Z.); (N.J.)
| | - Mahin Shams
- Department of Pathology, United Medical and Dental College, Karachi 71500, Pakistan;
| | - Qurratulain Chundriger
- Department of Pathology and Laboratory Medicine, Section of Histopathology, Aga Khan University Hospital, Karachi 3500, Pakistan; (Q.C.); (A.A.)
| | - Arsalan Ahmed
- Department of Pathology and Laboratory Medicine, Section of Histopathology, Aga Khan University Hospital, Karachi 3500, Pakistan; (Q.C.); (A.A.)
| | - Fariha Anum
- Research Department, Ziauddin University, Karachi 75600, Pakistan;
| | - Najah Alsubaie
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University (PNU), P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Zubair Ahmad
- Consultant Histopathologist, Sultan Qaboos Comprehensive Cancer Care and Research Centre, Seeb P.O. Box 556, Oman;
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16
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Liu Y, Han D, Parwani AV, Li Z. Applications of Artificial Intelligence in Breast Pathology. Arch Pathol Lab Med 2023; 147:1003-1013. [PMID: 36800539 DOI: 10.5858/arpa.2022-0457-ra] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/15/2022] [Indexed: 02/19/2023]
Abstract
CONTEXT.— Increasing implementation of whole slide imaging together with digital workflow and advances in computing capacity enable the use of artificial intelligence (AI) in pathology, including breast pathology. Breast pathologists often face a significant workload, with diagnosis complexity, tedious repetitive tasks, and semiquantitative evaluation of biomarkers. Recent advances in developing AI algorithms have provided promising approaches to meet the demand in breast pathology. OBJECTIVE.— To provide an updated review of AI in breast pathology. We examined the success and challenges of current and potential AI applications in diagnosing and grading breast carcinomas and other pathologic changes, detecting lymph node metastasis, quantifying breast cancer biomarkers, predicting prognosis and therapy response, and predicting potential molecular changes. DATA SOURCES.— We obtained data and information by searching and reviewing literature on AI in breast pathology from PubMed and based our own experience. CONCLUSIONS.— With the increasing application in breast pathology, AI not only assists in pathology diagnosis to improve accuracy and reduce pathologists' workload, but also provides new information in predicting prognosis and therapy response.
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Affiliation(s)
- Yueping Liu
- From the Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China (Liu, Han)
| | - Dandan Han
- From the Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China (Liu, Han)
| | - Anil V Parwani
- The Department of Pathology, The Ohio State University, Columbus (Parwani, Li)
| | - Zaibo Li
- From the Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China (Liu, Han)
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17
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Girolami I, Eccher A. Digital diagnostic cytopathology: Has the pandemic brought us closer? Cytopathology 2023; 34:419-422. [PMID: 36721906 DOI: 10.1111/cyt.13214] [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: 11/26/2022] [Revised: 01/17/2023] [Accepted: 01/27/2023] [Indexed: 02/02/2023]
Abstract
The COVID-19 pandemic has acted as a powerful change driver in the field of pathology and has had relevant consequences on the practice of cytopathology, in terms of changes in workload, rates of malignancy, and the performance of cytology. At the same time, regulatory authorities have relaxed their requirements for the deployment of digital pathology for remote diagnostic reporting. However, most of these improvements have concerned digital histopathology. Data from a literature search show that experiences in digital cytopathology during the pandemic have concerned mainly educational and academic activities. From a broader point of view, when searching for all published literature on digital pathology, only a minority of papers deal with cytopathology, but a noticeable increase in publications has been seen in the last 10 years, with an upward trend toward a maximum of papers in 2021. Indeed, the pandemic has led to greater awareness of the possibility of digital for cytopathology as well.
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Affiliation(s)
- Ilaria Girolami
- Department of Pathology, Provincial Hospital of Bolzano (SABES-ASDAA), Bolzano-Bozen, Italy
- Lehrkrankenhaus der Paracelsus Medizinischen Privatuniversität, Salzburg, Austria
- Department of Diagnostics and Public Health, University of Verona, Verona, Italy
| | - Albino Eccher
- Department of Pathology and Diagnostics, University and Hospital Trust of Verona, Verona, Italy
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18
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Challa B, Tahir M, Hu Y, Kellough D, Lujan G, Sun S, Parwani AV, Li Z. Artificial Intelligence-Aided Diagnosis of Breast Cancer Lymph Node Metastasis on Histologic Slides in a Digital Workflow. Mod Pathol 2023; 36:100216. [PMID: 37178923 DOI: 10.1016/j.modpat.2023.100216] [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: 03/01/2023] [Revised: 04/03/2023] [Accepted: 05/05/2023] [Indexed: 05/15/2023]
Abstract
Identifying lymph node (LN) metastasis in invasive breast carcinoma can be tedious and time-consuming. We investigated an artificial intelligence (AI) algorithm to detect LN metastasis by screening hematoxylin and eosin (H&E) slides in a clinical digital workflow. The study included 2 sentinel LN (SLN) cohorts (a validation cohort with 234 SLNs and a consensus cohort with 102 SLNs) and 1 nonsentinel LN cohort (258 LNs enriched with lobular carcinoma and postneoadjuvant therapy cases). All H&E slides were scanned into whole slide images in a clinical digital workflow, and whole slide images were automatically batch-analyzed using the Visiopharm Integrator System (VIS) metastasis AI algorithm. For the SLN validation cohort, the VIS metastasis AI algorithm detected all 46 metastases, including 19 macrometastases, 26 micrometastases, and 1 with isolated tumor cells with a sensitivity of 100%, specificity of 41.5%, positive predictive value of 29.5%, and negative predictive value (NPV) of 100%. The false positivity was caused by histiocytes (52.7%), crushed lymphocytes (18.2%), and others (29.1%), which were readily recognized during pathologists' reviews. For the SLN consensus cohort, 3 pathologists examined all VIS AI annotated H&E slides and cytokeratin immunohistochemistry slides with similar average concordance rates (99% for both modalities). However, the average time consumed by pathologists using VIS AI annotated slides was significantly less than using immunohistochemistry slides (0.6 vs 1.0 minutes, P = .0377). For the nonsentinel LN cohort, the AI algorithm detected all 81 metastases, including 23 from lobular carcinoma and 31 from postneoadjuvant chemotherapy cases, with a sensitivity of 100%, specificity of 78.5%, positive predictive value of 68.1%, and NPV of 100%. The VIS AI algorithm showed perfect sensitivity and NPV in detecting LN metastasis and less time consumed, suggesting its potential utility as a screening modality in routine clinical digital pathology workflow to improve efficiency.
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Affiliation(s)
- Bindu Challa
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Maryam Tahir
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Yan Hu
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - David Kellough
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Giovani Lujan
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Shaoli Sun
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Anil V Parwani
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Zaibo Li
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio.
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19
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Chahar S, Choudhary L, Ahuja R, Choudhary A. Impact of COVID-19 Pandemic on Pathology Residents/Trainees in North America: A Survey-Based Study. Cureus 2023; 15:e40967. [PMID: 37503469 PMCID: PMC10370283 DOI: 10.7759/cureus.40967] [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] [Accepted: 06/25/2023] [Indexed: 07/29/2023] Open
Abstract
Background The COVID-19 pandemic has had a significant impact on resident training and education in the field of Pathology. This study aims to identify the tangible effects and resultant changes in education for Pathology trainees that have resulted from the pandemic. Design An electronic survey regarding Pathology trainee perceptions and experiences in relation to COVID-19 was created via Google Forms. The questionnaire was distributed to the pathology trainees via Twitter and email. The survey was also shared with all Pathology residency program coordinators across the USA and Canada. Results One hundred forty-five trainees responded to the questionnaire. 37.6% reported a significant decrease in specimen volume, whereas 43.3% reported a slight decrease in specimen volume. 18.3% reported the cancellation of educational lectures before shifting to a virtual platform for didactic purposes. However, 74.6% reported shifting all educational activities to virtual platforms. 35% cited cancellations of grand rounds, whereas 18.2% reported cancellations of grand rounds led by guest speakers. 53.5% took COVID-19 tests, and 22.7% were quarantined. 100% reported a change in sign-out culture. Conclusions This pandemic has significantly impacted pathology training in various aspects, including training, education, and well-being. Residents harbored anxiety and stress regarding board exam delays or uncertainties, inadequate exam preparation time, family separation, and compromised safety. The exact quantification of educational loss varied from program to program. A significant decrease in specimen volume and detrimental changes in sign-out culture are indicators of compromised resident education due to the pandemic. This pandemic has extended the use of digital pathology and virtual platforms to a higher extent. Free virtual educational resources provided by various pathology organizations were critically important interventions during this pandemic, contributing to resident education. The pandemic has shown that developing a comprehensive infrastructure to overcome the loss of educational opportunities is of paramount importance to alleviate stress and anxiety among trainees.
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Affiliation(s)
- Satyapal Chahar
- Pathology/Gastrointestinal and Liver Pathology, University of Mississippi Medical Center, Jackson, USA
| | | | - Ram Ahuja
- Biomedical Sciences, York University, Toronto, CAN
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20
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Toksöz Yıldırım AN, Zenginkinet T, Okay E, Celik A, Tarcan ZC, Esen MF, Onay T, Turhan Y, Özkan K, Akyurek M. The Impact of COVID-19 Pandemic Restrictions on Musculoskeletal Pathology Services. Cureus 2023; 15:e39493. [PMID: 37362477 PMCID: PMC10290541 DOI: 10.7759/cureus.39493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/25/2023] [Indexed: 06/28/2023] Open
Abstract
BACKGROUND AND OBJECTIVES The COVID-19 pandemic has had a negative impact on healthcare in musculoskeletal pathology. There is no standard protocol for pathology services during a pandemic. The study aimed to assess the impact of COVID-19 restrictions on the workload of the musculoskeletal pathology service and the hurdles faced in collaboration with the orthopedic oncology unit in a tertiary reference center in a developing country. MATERIALS AND METHODS The pathology reports from mid-March to mid-June 2019, 2020, and 2021 were retrospectively reviewed. RESULTS Significant differences were found between the pandemic period (2020) and the non-pandemic periods (2019-2021) in benign bone and soft tissue lesions, resection surgeries, and soft tissue tumors, which were more prevalent in the non-pandemic periods. However, there was no significant decrease in biopsy procedures. Conclusion: During the pandemic period, the biopsy procedure appears to be feasible for bone and soft tissue lesions without the need for anesthesia.
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Affiliation(s)
| | - Tulay Zenginkinet
- Pathology, Istanbul Medeniyet University Goztepe Prof. Dr. Suleyman Yalcin City Hospital, Istanbul, TUR
| | - Erhan Okay
- Orthopaedics, Istanbul Medeniyet University Goztepe Prof. Dr. Suleyman Yalcin City Hospital, Istanbul, TUR
| | - Aykut Celik
- Orthopaedics, Istanbul Medeniyet University Goztepe Prof. Dr. Suleyman Yalcin City Hospital, Istanbul, TUR
| | - Zeynep Cagla Tarcan
- Orthopaedics, Istanbul Medeniyet University Goztepe Prof. Dr. Suleyman Yalcin City Hospital, Istanbul, TUR
| | - Muhammed Fevzi Esen
- Health Information Systems, Institution of Hamidiye Medical Sciences, University of Health Sciences Turkey, Istanbul, TUR
| | - Tolga Onay
- Orthopaedic Surgery and Traumatology, Dr. Lütfi Kırdar Kartal Training and Research Hospital, İstanbul, TUR
| | - Yalçın Turhan
- Orthopaedics and Traumatology, Duzce University Medical Faculty, Duzce, TUR
| | - Korhan Özkan
- Orthopaedics and Traumatology, Medeniyet University Göztepe Training and Reseach Hospital, Istanbul, TUR
| | - Muhlik Akyurek
- Orthopaedics, Maria-Josef-Hospital Greven, Klinik für Orthopadie, Unfall-und Handchirurgie Deutschland, Greven, DEU
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21
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Wong CM, Kezlarian BE, Lin O. Current status of machine learning in thyroid cytopathology. J Pathol Inform 2023; 14:100309. [PMID: 37077698 PMCID: PMC10106504 DOI: 10.1016/j.jpi.2023.100309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 03/24/2023] [Accepted: 03/27/2023] [Indexed: 04/03/2023] Open
Abstract
The implementation of Digital Pathology has allowed the development of computational Pathology. Digital image-based applications that have received FDA Breakthrough Device Designation have been primarily focused on tissue specimens. The development of Artificial Intelligence-assisted algorithms using Cytology digital images has been much more limited due to technical challenges and a lack of optimized scanners for Cytology specimens. Despite the challenges in scanning whole slide images of cytology specimens, there have been many studies evaluating CP to create decision-support tools in Cytopathology. Among different Cytology specimens, thyroid fine needle aspiration biopsy (FNAB) specimens have one of the greatest potentials to benefit from machine learning algorithms (MLA) derived from digital images. Several authors have evaluated different machine learning algorithms focused on thyroid cytology in the past few years. The results are promising. The algorithms have mostly shown increased accuracy in the diagnosis and classification of thyroid cytology specimens. They have brought new insights and demonstrated the potential for improving future cytopathology workflow efficiency and accuracy. However, many issues still need to be addressed to further build on and improve current MLA models and their applications. To optimally train and validate MLA for thyroid cytology specimens, larger datasets obtained from multiple institutions are needed. MLAs hold great potential in improving thyroid cancer diagnostic speed and accuracy that will lead to improvements in patient management.
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Affiliation(s)
| | | | - Oscar Lin
- Corresponding author at: Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA.
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22
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Maity S, Nauhria S, Nayak N, Nauhria S, Coffin T, Wray J, Haerianardakani S, Sah R, Spruce A, Jeong Y, Maj MC, Sharma A, Okpara N, Ike CJ, Nath R, Nelson J, Parwani AV. Virtual Versus Light Microscopy Usage among Students: A Systematic Review and Meta-Analytic Evidence in Medical Education. Diagnostics (Basel) 2023; 13:diagnostics13030558. [PMID: 36766660 PMCID: PMC9914930 DOI: 10.3390/diagnostics13030558] [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: 07/19/2022] [Revised: 01/26/2023] [Accepted: 01/30/2023] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND The usage of whole-slide images has recently been gaining a foothold in medical education, training, and diagnosis. OBJECTIVES The first objective of the current study was to compare academic performance on virtual microscopy (VM) and light microscopy (LM) for learning pathology, anatomy, and histology in medical and dental students during the COVID-19 period. The second objective was to gather insight into various applications and usage of such technology for medical education. MATERIALS AND METHODS Using the keywords "virtual microscopy" or "light microscopy" or "digital microscopy" and "medical" and "dental" students, databases (PubMed, Embase, Scopus, Cochrane, CINAHL, and Google Scholar) were searched. Hand searching and snowballing were also employed for article searching. After extracting the relevant data based on inclusion and execution criteria, the qualitative data were used for the systematic review and quantitative data were used for meta-analysis. The Newcastle Ottawa Scale (NOS) scale was used to assess the quality of the included studies. Additionally, we registered our systematic review protocol in the prospective register of systematic reviews (PROSPERO) with registration number CRD42020205583. RESULTS A total of 39 studies met the criteria to be included in the systematic review. Overall, results indicated a preference for this technology and better academic scores. Qualitative analyses reported improved academic scores, ease of use, and enhanced collaboration amongst students as the top advantages, whereas technical issues were a disadvantage. The performance comparison of virtual versus light microscopy meta-analysis included 19 studies. Most (10/39) studies were from medical universities in the USA. VM was mainly used for teaching pathology courses (25/39) at medical schools (30/39). Dental schools (10/39) have also reported using VM for teaching microscopy. The COVID-19 pandemic was responsible for the transition to VM use in 17/39 studies. The pooled effect size of 19 studies significantly demonstrated higher exam performance (SMD: 1.36 [95% CI: 0.75, 1.96], p < 0.001) among the students who used VM for their learning. Students in the VM group demonstrated significantly higher exam performance than LM in pathology (SMD: 0.85 [95% CI: 0.26, 1.44], p < 0.01) and histopathology (SMD: 1.25 [95% CI: 0.71, 1.78], p < 0.001). For histology (SMD: 1.67 [95% CI: -0.05, 3.40], p = 0.06), the result was insignificant. The overall analysis of 15 studies assessing exam performance showed significantly higher performance for both medical (SMD: 1.42 [95% CI: 0.59, 2.25], p < 0.001) and dental students (SMD: 0.58 [95% CI: 0.58, 0.79], p < 0.001). CONCLUSIONS The results of qualitative and quantitative analyses show that VM technology and digitization of glass slides enhance the teaching and learning of microscopic aspects of disease. Additionally, the COVID-19 global health crisis has produced many challenges to overcome from a macroscopic to microscopic scale, for which modern virtual technology is the solution. Therefore, medical educators worldwide should incorporate newer teaching technologies in the curriculum for the success of the coming generation of health-care professionals.
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Affiliation(s)
- Sabyasachi Maity
- Department of Physiology, Neuroscience, and Behavioral Sciences, St. George’s University School of Medicine, St. George’s, Grenada
| | - Samal Nauhria
- Department of Pathology, St. Matthews University School of Medicine, Georgetown P.O. Box 30992, Cayman Islands
- Correspondence:
| | - Narendra Nayak
- Department of Microbiology, St. Matthews University School of Medicine, Georgetown P.O. Box 30992, Cayman Islands
| | - Shreya Nauhria
- Department of Psychology, University of Leicester, Leicester LE1 7RH, UK
| | - Tamara Coffin
- Medical Student Research Institute, St. George’s University School of Medicine, St. George’s, Grenada
| | - Jadzia Wray
- Medical Student Research Institute, St. George’s University School of Medicine, St. George’s, Grenada
| | - Sepehr Haerianardakani
- Medical Student Research Institute, St. George’s University School of Medicine, St. George’s, Grenada
| | - Ramsagar Sah
- Department of Public Health, Torrens University, Ultimo, Sydney, NSW 2007, Australia
| | - Andrew Spruce
- Department of Pathology, St. Matthews University School of Medicine, Georgetown P.O. Box 30992, Cayman Islands
| | - Yujin Jeong
- Department of Clinical Medicine, American University of Antigua, St. John’s, Antigua and Barbuda
| | - Mary C. Maj
- Department of Biochemistry, St. George’s University School of Medicine, St. George’s, Grenada
| | - Abhimanyu Sharma
- Department of Pathology, Government Medical College, Jammu 180001, India
| | - Nicole Okpara
- Department of Pathology, St. Matthews University School of Medicine, Georgetown P.O. Box 30992, Cayman Islands
| | - Chidubem J. Ike
- Department of Clinical Medicine, American University of Antigua, St. John’s, Antigua and Barbuda
| | - Reetuparna Nath
- Department of Education Service, St. George’s University, St. George’s, Grenada
| | - Jack Nelson
- Medical Illustrator, The Centre for Biomedical Visualization, St. George’s University, St. George’s, Grenada
| | - Anil V. Parwani
- Department of Pathology, Wexner Medical Center, The Ohio State University, Cooperative Human Tissue Network (CHTN) Midwestern Division, Columbus, OH 43210, USA
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Parwani AV, Patel A, Zhou M, Cheville JC, Tizhoosh H, Humphrey P, Reuter VE, True LD. An update on computational pathology tools for genitourinary pathology practice: A review paper from the Genitourinary Pathology Society (GUPS). J Pathol Inform 2023; 14:100177. [PMID: 36654741 PMCID: PMC9841212 DOI: 10.1016/j.jpi.2022.100177] [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: 03/02/2022] [Revised: 12/20/2022] [Accepted: 12/20/2022] [Indexed: 12/31/2022] Open
Abstract
Machine learning has been leveraged for image analysis applications throughout a multitude of subspecialties. This position paper provides a perspective on the evolutionary trajectory of practical deep learning tools for genitourinary pathology through evaluating the most recent iterations of such algorithmic devices. Deep learning tools for genitourinary pathology demonstrate potential to enhance prognostic and predictive capacity for tumor assessment including grading, staging, and subtype identification, yet limitations in data availability, regulation, and standardization have stymied their implementation.
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Affiliation(s)
- Anil V. Parwani
- The Ohio State University, Columbus, Ohio, USA
- Corresponding author.
| | - Ankush Patel
- The Ohio State University, 2441 60th Ave SE, Mercer Island, Washington 98040, USA
| | - Ming Zhou
- Tufts University, Medford, Massachusetts, USA
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Frank SJ. Accurate diagnostic tissue segmentation and concurrent disease subtyping with small datasets. J Pathol Inform 2022; 14:100174. [PMID: 36687530 PMCID: PMC9852683 DOI: 10.1016/j.jpi.2022.100174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 09/01/2022] [Accepted: 12/15/2022] [Indexed: 12/24/2022] Open
Abstract
Purpose To provide a flexible, end-to-end platform for visually distinguishing diseased from undiseased tissue in a medical image, in particular pathology slides, and classifying diseased regions by subtype. Highly accurate results are obtained using small training datasets and reduced-scale source images that can be easily shared. Approach An ensemble of lightweight convolutional neural networks (CNNs) is trained on different subsets of images derived from a relatively small number of annotated whole-slide histopathology images (WSIs). The WSIs are first reduced in scale in a manner that preserves anatomic features critical to analysis while also facilitating convenient handling and storage. The segmentation and subtyping tasks are performed sequentially on the reduced-scale images using the same basic workflow: generating and sifting tiles from the image, then classifying each tile with an ensemble of appropriately trained CNNs. For segmentation, the CNN predictions are combined using a function to favor a selected similarity metric, and a mask or map for a a candidate image is produced from tiles whose combined predictions exceed a decision boundary. For subtyping, the resulting mask is applied to the candidate image, and new tiles are derived from the unoccluded regions. These are classified by the subtyping CNNs to produce an overall subtype prediction. Results and conclusion This approach was applied successfully to two very different datasets of large WSIs, one (PAIP2020) involving multiple subtypes of colorectal cancer and the other (CAMELYON16) single-type breast cancer metastases. Scored using standard similarity metrics, the segmentations outperformed more complex models typifying the state of the art.
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Michelow PM, De Wet DR, Fainman GM. Evaluation of the pivot to digital teaching and learning in pathology: Sentiments from a low-middle-income country. Cancer Cytopathol 2022; 130:920-926. [PMID: 35679144 DOI: 10.1002/cncy.22608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Revised: 05/10/2022] [Accepted: 05/11/2022] [Indexed: 01/04/2023]
Affiliation(s)
- Pamela M Michelow
- Department of Anatomical Pathology, Faculty of Health Sciences, University of the Witwatersrand and National Health Laboratory Service, Johannesburg, South Africa
| | - Daniel R De Wet
- Department of Pathology, Faculty of Medicine and Health Sciences, Stellenbosch University and National Health Laboratory Service, Cape Town, South Africa
| | - Gina M Fainman
- Department of Speech and Hearing, Faculty of Humanities, University of the Witwatersrand, Johannesburg, South Africa
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26
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Liscia DS, D’Andrea M, Biletta E, Bellis D, Demo K, Ferrero F, Petti A, Butinar R, D’Andrea E, Davini G. Use of digital pathology and artificial intelligence for the diagnosis of Helicobacter pylori in gastric biopsies. Pathologica 2022; 114:295-303. [DOI: 10.32074/1591-951x-751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Accepted: 01/22/2022] [Indexed: 12/20/2022] Open
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27
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Browning L, White K, Siiankoski D, Colling R, Roskell D, Fryer E, Hemsworth H, Roberts-Gant S, Roelofsen R, Rittscher J, Verrill C. RFID analysis of the complexity of cellular pathology workflow—An opportunity for digital pathology. Front Med (Lausanne) 2022; 9:933933. [PMID: 35979219 PMCID: PMC9377528 DOI: 10.3389/fmed.2022.933933] [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] [Received: 05/01/2022] [Accepted: 07/07/2022] [Indexed: 12/02/2022] Open
Abstract
Digital pathology (DP) offers potential for time efficiency gains over an analog workflow however, to date, evidence supporting this claim is relatively lacking. Studies available concentrate on specific workflow points such as diagnostic reporting time, rather than overall efficiencies in slide logistics that might be expected. This is in part a result of the complexity and variation in analog working, and the challenge therefore in capturing this. We have utilized RFID technology to conduct a novel study capturing the movement of diagnostic cases within the analog pathway in a large teaching hospital setting, thus providing benchmark data for potential efficiency gains with DP. This technology overcomes the need to manually record data items and has facilitated the capture of both the physical journey of a case and the time associated with relevant components of the analog pathway predicted to be redundant in the digital setting. RFID tracking of 1,173 surgical pathology cases and over 30 staff in an analog cellular pathology workflow illustrates the complexity of the physical movement of slides within the department, which impacts on case traceability within the system. Detailed analysis of over 400 case journeys highlights redundant periods created by batching of slides at workflow points, including potentially 2–3 h for a case to become available for reporting after release from the lab, and variable lag-times prior to collection for reporting, and provides an illustration of patterns of lab and pathologist working within the analog setting. This study supports the challenge in evidencing efficiency gains to be anticipated with DP in the context of the variation and complexity of the analog pathway, but also evidences the efficiency gains that may be expected through a greater understanding of patterns of working and movement of cases. Such data may benefit other departments building a business case for DP.
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Affiliation(s)
- Lisa Browning
- Department of Cellular Pathology, Oxford University Hospitals NHS Trust, John Radcliffe Hospital, Oxford, United Kingdom
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
- *Correspondence: Lisa Browning
| | - Kieron White
- Department of Cellular Pathology, Oxford University Hospitals NHS Trust, John Radcliffe Hospital, Oxford, United Kingdom
| | - Darrin Siiankoski
- Department of Cellular Pathology, Oxford University Hospitals NHS Trust, John Radcliffe Hospital, Oxford, United Kingdom
| | - Richard Colling
- Department of Cellular Pathology, Oxford University Hospitals NHS Trust, John Radcliffe Hospital, Oxford, United Kingdom
- Nuffield Department of Surgical Sciences, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
| | - Derek Roskell
- Department of Cellular Pathology, Oxford University Hospitals NHS Trust, John Radcliffe Hospital, Oxford, United Kingdom
| | - Eve Fryer
- Department of Cellular Pathology, Oxford University Hospitals NHS Trust, John Radcliffe Hospital, Oxford, United Kingdom
| | - Helen Hemsworth
- Department of Cellular Pathology, Oxford University Hospitals NHS Trust, John Radcliffe Hospital, Oxford, United Kingdom
| | - Sharon Roberts-Gant
- Department of Cellular Pathology, Oxford University Hospitals NHS Trust, John Radcliffe Hospital, Oxford, United Kingdom
| | - Ruud Roelofsen
- Philips Digital and Computational Pathology, Precision Diagnosis Solutions, Best, Netherlands
| | - Jens Rittscher
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
- Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, United Kingdom
| | - Clare Verrill
- Department of Cellular Pathology, Oxford University Hospitals NHS Trust, John Radcliffe Hospital, Oxford, United Kingdom
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
- Nuffield Department of Surgical Sciences, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
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28
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Dawson H. Digital pathology – Rising to the challenge. Front Med (Lausanne) 2022; 9:888896. [PMID: 35935788 PMCID: PMC9354827 DOI: 10.3389/fmed.2022.888896] [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: 03/03/2022] [Accepted: 06/28/2022] [Indexed: 11/13/2022] Open
Abstract
Digital pathology has gone through considerable technical advances during the past few years and certain aspects of digital diagnostics have been widely and swiftly adopted in many centers, catalyzed by the COVID-19 pandemic. However, analysis of requirements, careful planning, and structured implementation should to be considered in order to reap the full benefits of a digital workflow. The aim of this review is to provide a practical, concise and hands-on summary of issues relevant to implementing and developing digital diagnostics in the pathology laboratory. These include important initial considerations, possible approaches to overcome common challenges, potential diagnostic pitfalls, validation and regulatory issues and an introduction to the emerging field of image analysis in routine.
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Patel AU, Shaker N, Mohanty S, Sharma S, Gangal S, Eloy C, Parwani AV. Cultivating Clinical Clarity through Computer Vision: A Current Perspective on Whole Slide Imaging and Artificial Intelligence. Diagnostics (Basel) 2022; 12:diagnostics12081778. [PMID: 35892487 PMCID: PMC9332710 DOI: 10.3390/diagnostics12081778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Revised: 07/10/2022] [Accepted: 07/11/2022] [Indexed: 11/17/2022] Open
Abstract
Diagnostic devices, methodological approaches, and traditional constructs of clinical pathology practice, cultivated throughout centuries, have transformed radically in the wake of explosive technological growth and other, e.g., environmental, catalysts of change. Ushered into the fray of modern laboratory medicine are digital imaging devices and machine-learning (ML) software fashioned to mitigate challenges, e.g., practitioner shortage while preparing clinicians for emerging interconnectivity of environments and diagnostic information in the era of big data. As computer vision shapes new constructs for the modern world and intertwines with clinical medicine, cultivating clarity of our new terrain through examining the trajectory and current scope of computational pathology and its pertinence to clinical practice is vital. Through review of numerous studies, we find developmental efforts for ML migrating from research to standardized clinical frameworks while overcoming obstacles that have formerly curtailed adoption of these tools, e.g., generalizability, data availability, and user-friendly accessibility. Groundbreaking validatory efforts have facilitated the clinical deployment of ML tools demonstrating the capacity to effectively aid in distinguishing tumor subtype and grade, classify early vs. advanced cancer stages, and assist in quality control and primary diagnosis applications. Case studies have demonstrated the benefits of streamlined, digitized workflows for practitioners alleviated by decreased burdens.
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Affiliation(s)
- Ankush U. Patel
- Mayo Clinic Department of Laboratory Medicine and Pathology, Rochester, MN 55905, USA
- Correspondence: ; Tel.: +1-206-451-3519
| | - Nada Shaker
- Department of Pathology, Wexner Medical Center, The Ohio State University, Columbus, OH 43210, USA; (N.S.); (S.G.); (A.V.P.)
| | - Sambit Mohanty
- CORE Diagnostics, Gurugram 122016, India; (S.M.); (S.S.)
- Advanced Medical Research Institute, Bareilly 243001, India
| | - Shivani Sharma
- CORE Diagnostics, Gurugram 122016, India; (S.M.); (S.S.)
| | - Shivam Gangal
- Department of Pathology, Wexner Medical Center, The Ohio State University, Columbus, OH 43210, USA; (N.S.); (S.G.); (A.V.P.)
- College of Engineering, Biomedical Engineering, The Ohio State University, Columbus, OH 43210, USA
| | - Catarina Eloy
- Institute of Molecular Pathology and Immunology of the University of Porto (IPATIMUP), Rua Júlio Amaral de Carvalho, 45, 4200-135 Porto, Portugal;
- Institute for Research and Innovation in Health (I3S Consortium), Rua Alfredo Allen, 208, 4200-135 Porto, Portugal
| | - Anil V. Parwani
- Department of Pathology, Wexner Medical Center, The Ohio State University, Columbus, OH 43210, USA; (N.S.); (S.G.); (A.V.P.)
- Cooperative Human Tissue Network (CHTN) Midwestern Division, Columbus, OH 43240, USA
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Chang S, Sadimin E, Yao K, Hamilton S, Aoun P, Pillai R, Muirhead D, Schmolze D. Establishment of a whole slide imaging-based frozen section service at a cancer center. J Pathol Inform 2022; 13:100106. [PMID: 36268067 PMCID: PMC9577038 DOI: 10.1016/j.jpi.2022.100106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 05/20/2022] [Accepted: 05/21/2022] [Indexed: 12/03/2022] Open
Abstract
Background In recent years, there has been a surge of interest in clinical digital pathology (DP). Hardware and software platforms have matured and become more affordable, and advances in artificial intelligence promise to transform the practice of pathology. At our institution, we are launching a stepwise process of DP adoption which will eventually encompass our entire workflow. Out of necessity, we began by establishing a whole slide imaging (WSI)-based frozen section service. Methods We proceeded in a systematic manner by first assembling a team of key stakeholders. We carefully evaluated the various options for digitizing frozen sections before deciding that a WSI-based solution made the most sense for us. We used a formalized evaluation system to quantify performance metrics that were relevant to us. After deciding on a WSI-based system, we likewise carefully considered the various whole slide scanners and digital slide management systems available before making decisions. Results During formal evaluation by pathologists, the WSI-based system outperformed competing platforms. Although implementation was relatively complex, we have been happy with the results and have noticed significant improvements in our frozen section turnaround time. Our users have been happy with the slide management system, which we plan on utilizing in future DP efforts. Conclusions There are various options for digitizing frozen section slides. Although WSI-based systems are more complex and expensive than some alternatives, they perform well and may make sense for institutions with a pre-existing or planned larger DP infrastructure.
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Shafi S, Kellough DA, Lujan G, Satturwar S, Parwani AV, Li Z. Integrating and validating automated digital imaging analysis of estrogen receptor immunohistochemistry in a fully digital workflow for clinical use. J Pathol Inform 2022; 13:100122. [PMID: 36268080 PMCID: PMC9577060 DOI: 10.1016/j.jpi.2022.100122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 06/22/2022] [Accepted: 06/24/2022] [Indexed: 11/01/2022] Open
Abstract
Background Design Results Conclusions
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32
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Lam AK, Bai A, Leung M. Whole-Slide Imaging: Updates and Applications in Papillary Thyroid Carcinoma. Methods Mol Biol 2022; 2534:197-213. [PMID: 35670977 DOI: 10.1007/978-1-0716-2505-7_14] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Whole-slide imaging (WSI) has wide spectrum of application in histopathology, especially in the study of cancer including papillary thyroid carcinoma. The main applications of WSI system include research, teaching, and assessment and recently pathology practices. The other major advantages of WSI over histological sections on glass slides are easier storage and sharing of information as well as adaptation of use in artificial intelligence. The applications of WSI depend on factors such as volume of services requiring WSI, physical factors (computer server, bandwidth limitation of networks, storages requirements for data), adaption of the WSI images with the laboratory workflow, personnel (IT expert, pathologist, technicians) adaptation to the WSI workflow, validation studies, ethics, and cost efficiency of the application(s).
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Affiliation(s)
- Alfred K Lam
- Cancer Molecular Pathology of School of Medicine and Dentistry, Menzies Health Institute Queensland, Griffith University, Gold Coast, QLD, Australia.
- Pathology Queensland, Gold Coast University Hospital, Southport, QLD, Australia.
- Faculty of Medicine, University of Queensland, Herston, QLD, Australia.
| | - Alfa Bai
- ACT GENOMICS (HONG KONG) LTD, Hong Kong Science Park, Pak Shek Kok, Hong Kong
| | - Melissa Leung
- Cancer Molecular Pathology of School of Medicine and Dentistry, Menzies Health Institute Queensland, Griffith University, Gold Coast, QLD, Australia
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Digital Pathology Transformation in a Supraregional Germ Cell Tumour Network. Diagnostics (Basel) 2021; 11:diagnostics11122191. [PMID: 34943429 PMCID: PMC8700654 DOI: 10.3390/diagnostics11122191] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 11/16/2021] [Accepted: 11/23/2021] [Indexed: 01/21/2023] Open
Abstract
Background: In this article we share our experience of creating a digital pathology (DP) supraregional germ cell tumour service, including full digitisation of the central laboratory. Methods: DP infrastructure (Philips) was deployed across our hospital network to allow full central digitisation with partial digitisation of two peripheral sites in the supraregional testis germ cell tumour network. We used a survey-based approach to capture the quantitative and qualitative experiences of the multidisciplinary teams involved. Results: The deployment enabled case sharing for the purposes of diagnostic reporting, second opinion, and supraregional review. DP was seen as a positive step forward for the departments involved, and for the wider germ cell tumour network, and was completed without significant issues. Whilst there were challenges, the transition to DP was regarded as worthwhile, and examples of benefits to patients are already recognised. Conclusion: Pathology networks, including highly specialised services, such as in this study, are ideally suited to be digitised. We highlight many of the benefits but also the challenges that must be overcome for such clinical transformation. Overall, from the survey, the change was seen as universally positive for our service and highlights the importance of engagement of the whole team to achieve success.
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Marble HD, Bard AZ, Mizrachi MM, Lennerz JK. Temporary Regulatory Deviations and the Coronavirus Disease 2019 (COVID-19) PCR Labeling Update Study Indicate What Laboratory-Developed Test Regulation by the US Food and Drug Administration (FDA) Could Look Like. J Mol Diagn 2021; 23:1207-1217. [PMID: 34538703 PMCID: PMC8444018 DOI: 10.1016/j.jmoldx.2021.07.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 06/20/2021] [Accepted: 07/01/2021] [Indexed: 12/23/2022] Open
Abstract
The coronavirus disease 2019 (COVID-19) response necessitated innovations and a series of regulatory deviations that also affected laboratory-developed tests (LDTs). To examine real-world consequences and specify regulatory paradigm shifts, legislative proposals were aligned on a common timeline with Emergency Use Authorization (EUA) of LDTs and the US Food and Drug Administration (FDA)-orchestrated severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) labeling update study. The initial EUA adoption by LDT developers shows that the FDA can have oversight over LDTs. We used efficiency-corrected microcosting of our EUA PCR assay to estimate the national cost of the labeling update study to $0.3 to $1.4 million US dollars. Labeling update study performance data showed lower average detection limits in commercial in vitro diagnostic (IVD) assays versus LDTs (32,000 ± 75,000 versus 71,000 ± 147,000 nucleic acid amplification tests/mL; P = 0.04); however, comparison also shows that FDA review of IVD assays and LDTs did not prevent differences between initial and labeling update performance (IVD assay, P < 0.0001; LDT, P = 0.003). The regulatory shifts re-emphasized that both commercial tests and LDTs rely heavily on laboratory competence and procedures; however, lack of performance data on authorized tests, when clinically implemented, precludes assessment of the benefit related to regulatory review. Temporary regulatory deviations during the pandemic and regulatory science tools (ie, reference material) have generated valuable real-world evidence to inform pending legislation regarding LDT regulation.
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Affiliation(s)
- Hetal D Marble
- Center for Integrated Diagnostics, Department of Pathology, Massachusetts General Hospital/Harvard Medical School, Boston, Massachusetts
| | - Adam Z Bard
- Center for Integrated Diagnostics, Department of Pathology, Massachusetts General Hospital/Harvard Medical School, Boston, Massachusetts
| | - Mark M Mizrachi
- Center for Autoimmune Musculoskeletal and Hematopoietic Diseases, Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, Manhasset, New York; Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York
| | - Jochen K Lennerz
- Center for Integrated Diagnostics, Department of Pathology, Massachusetts General Hospital/Harvard Medical School, Boston, Massachusetts.
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Cho WC, Gill P, Aung PP, Gu J, Nagarajan P, Ivan D, Curry JL, Prieto VG, Torres-Cabala CA. The utility of digital pathology in improving the diagnostic skills of pathology trainees in commonly encountered pigmented cutaneous lesions during the COVID-19 pandemic: A single academic institution experience. Ann Diagn Pathol 2021; 54:151807. [PMID: 34418768 PMCID: PMC8450757 DOI: 10.1016/j.anndiagpath.2021.151807] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 08/10/2021] [Indexed: 11/18/2022]
Abstract
Digital pathology has become an integral part of pathology education in recent years, particularly during the COVID-19 pandemic, for its potential utility as a teaching tool that augments the traditional 1-to-1 sign-out experience. Herein, we evaluate the utility of whole slide imaging (WSI) in reducing diagnostic errors in pigmented cutaneous lesions by pathology fellows without subspecialty training in dermatopathology. Ten cases of 4 pigmented cutaneous lesions commonly encountered by general pathologists were selected. Corresponding whole slide images were distributed to our fellows, along with two sets of online surveys, each composed of 10 multiple-choice questions with 4 answers. Identical cases were used for both surveys to minimize variability in trainees' scores depending on the perceived level of difficulty, with the second set being distributed after random shuffling. Brief image-based teaching slides as self-assessment tool were provided to trainees between each survey. Pre- and post-self-assessment scores were analyzed. 61% (17/28) and 39% (11/28) of fellows completed the first and second surveys, respectively. The mean score in the first survey was 5.2/10. The mean score in the second survey following self-assessment increased to 7.2/10. 64% (7/11) of trainees showed an improvement in their scores, with 1 trainee improving his/her score by 8 points. No fellow scored less post-self-assessment than on the initial assessment. The difference in individual scores between two surveys was statistically significant (p = 0.003). Our study demonstrates the utility of WSI-based self-assessment learning as a source of improving diagnostic skills of pathology trainees in a short period of time.
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Affiliation(s)
- Woo Cheal Cho
- Department of Pathology, University of Texas, MD Anderson Cancer Center, United States of America
| | - Pavandeep Gill
- Department of Pathology, University of Texas, MD Anderson Cancer Center, United States of America
| | - Phyu P Aung
- Department of Pathology, University of Texas, MD Anderson Cancer Center, United States of America
| | - Jun Gu
- School of Health Professions, University of Texas, MD Anderson Cancer Center, United States of America
| | - Priyadharsini Nagarajan
- Department of Pathology, University of Texas, MD Anderson Cancer Center, United States of America
| | - Doina Ivan
- Department of Pathology, University of Texas, MD Anderson Cancer Center, United States of America
| | - Jonathan L Curry
- Department of Pathology, University of Texas, MD Anderson Cancer Center, United States of America
| | - Victor G Prieto
- Department of Pathology, University of Texas, MD Anderson Cancer Center, United States of America
| | - Carlos A Torres-Cabala
- Department of Pathology, University of Texas, MD Anderson Cancer Center, United States of America.
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36
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Wharton KA, Wood D, Manesse M, Maclean KH, Leiss F, Zuraw A. Tissue Multiplex Analyte Detection in Anatomic Pathology - Pathways to Clinical Implementation. Front Mol Biosci 2021; 8:672531. [PMID: 34386519 PMCID: PMC8353449 DOI: 10.3389/fmolb.2021.672531] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Accepted: 07/14/2021] [Indexed: 12/12/2022] Open
Abstract
Background: Multiplex tissue analysis has revolutionized our understanding of the tumor microenvironment (TME) with implications for biomarker development and diagnostic testing. Multiplex labeling is used for specific clinical situations, but there remain barriers to expanded use in anatomic pathology practice. Methods: We review immunohistochemistry (IHC) and related assays used to localize molecules in tissues, with reference to United States regulatory and practice landscapes. We review multiplex methods and strategies used in clinical diagnosis and in research, particularly in immuno-oncology. Within the framework of assay design and testing phases, we examine the suitability of multiplex immunofluorescence (mIF) for clinical diagnostic workflows, considering its advantages and challenges to implementation. Results: Multiplex labeling is poised to radically transform pathologic diagnosis because it can answer questions about tissue-level biology and single-cell phenotypes that cannot be addressed with traditional IHC biomarker panels. Widespread implementation will require improved detection chemistry, illustrated by InSituPlex technology (Ultivue, Inc., Cambridge, MA) that allows coregistration of hematoxylin and eosin (H&E) and mIF images, greater standardization and interoperability of workflow and data pipelines to facilitate consistent interpretation by pathologists, and integration of multichannel images into digital pathology whole slide imaging (WSI) systems, including interpretation aided by artificial intelligence (AI). Adoption will also be facilitated by evidence that justifies incorporation into clinical practice, an ability to navigate regulatory pathways, and adequate health care budgets and reimbursement. We expand the brightfield WSI system “pixel pathway” concept to multiplex workflows, suggesting that adoption might be accelerated by data standardization centered on cell phenotypes defined by coexpression of multiple molecules. Conclusion: Multiplex labeling has the potential to complement next generation sequencing in cancer diagnosis by allowing pathologists to visualize and understand every cell in a tissue biopsy slide. Until mIF reagents, digital pathology systems including fluorescence scanners, and data pipelines are standardized, we propose that diagnostic labs will play a crucial role in driving adoption of multiplex tissue diagnostics by using retrospective data from tissue collections as a foundation for laboratory-developed test (LDT) implementation and use in prospective trials as companion diagnostics (CDx).
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