Reference Citation Analysis: Find an Article, Find a Category, Find a Journal, Find a Scholar

For: Abels E, Pantanowitz L. Current State of the Regulatory Trajectory for Whole Slide Imaging Devices in the USA. J Pathol Inform 2017;8:23. [PMID: 28584684 PMCID: PMC5450449 DOI: 10.4103/jpi.jpi_11_17] [Citation(s) in RCA: 67] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2017] [Accepted: 03/08/2017] [Indexed: 11/10/2022] Open

For:	Abels E, Pantanowitz L. Current State of the Regulatory Trajectory for Whole Slide Imaging Devices in the USA. J Pathol Inform 2017;8:23. [PMID: 28584684 PMCID: PMC5450449 DOI: 10.4103/jpi.jpi_11_17] [Citation(s) in RCA: 67] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2017] [Accepted: 03/08/2017] [Indexed: 11/10/2022] Open

Number

Cited by Other Article(s)

Ahsan M, Jahangir F, Rathore S, Mumtaz M, Zaman S. Evaluation of whole-slide imaging for diagnosing frozen sections. Ann Diagn Pathol 2025;75:152431. [PMID: 39705800 DOI: 10.1016/j.anndiagpath.2024.152431] [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: 12/13/2024] [Accepted: 12/16/2024] [Indexed: 12/23/2024]

Eloy C, Fraggetta F, van Diest PJ, Polónia A, Curado M, Temprana-Salvador J, Zlobec I, Purqueras E, Weis CA, Matias-Guiu X, Schirmacher P, Ryška A. Digital transformation of pathology - the European Society of Pathology expert opinion paper. Virchows Arch 2025:10.1007/s00428-025-04090-w. [PMID: 40164935 DOI: 10.1007/s00428-025-04090-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2025] [Revised: 03/18/2025] [Accepted: 03/20/2025] [Indexed: 04/02/2025]

Marletta S, Caputo A, Guidi G, Pantanowitz L, Pagni F, Bavieri I, L'Imperio V, Brunelli M, Dei Tos AP, Eccher A. Digital Pathology Displays Under Pressure: Benchmarking Performance Across Market Grades. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2025:10.1007/s10278-025-01452-3. [PMID: 40011344 DOI: 10.1007/s10278-025-01452-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2024] [Revised: 01/20/2025] [Accepted: 02/11/2025] [Indexed: 02/28/2025]

Jennings C, Treanor D, Brettle D. Pathologists light level preferences using the microscope-study to guide digital pathology display use. J Pathol Inform 2024;15:100379. [PMID: 38846642 PMCID: PMC11153930 DOI: 10.1016/j.jpi.2024.100379] [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: 02/20/2024] [Revised: 04/05/2024] [Accepted: 04/26/2024] [Indexed: 06/09/2024] Open

Abstract

Background

Currently, there is a paucity of guidelines relating to displays used for digital pathology making procurement decisions, and optimal display configuration, challenging.Experience suggests pathologists have personal preferences for brightness when using a conventional microscope which we hypothesized could be used as a predictor for display setup.

Methods

We conducted an online survey across six NHS hospitals, totalling 108 practicing pathologists, to capture brightness adjustment habits on both microscopes and displays.A convenience subsample of respondents was then invited to take part in a practical task to determine microscope brightness and display luminance preferences in the normal working environment. A novel adaptation for a lightmeter was developed to directly measure the light output from the microscope eyepiece.

Results

The survey (response rate 59% n=64) indicates 81% of respondents adjust the brightness on their microscope. In comparison, only 11% report adjusting their digital display. Display adjustments were more likely to be for visual comfort and ambient light compensation rather than for tissue factors, common for microscope adjustments. Part of this discrepancy relates to lack of knowledge of how to adjust displays and lack of guidance on whether this is safe; But, 66% felt that the ability to adjust the light on the display was important.Twenty consultants took part in the practical brightness assessment. Light preferences on the microscope showed no correlation with display preferences, except where a pathologist has a markedly brighter microscope light preference. All of the preferences in this cohort were for a display luminance of <500 cd/m2, with 90% preferring 350 cd/m2 or less. There was no correlation between these preferences and the ambient lighting in the room.

Conclusions

We conclude that microscope preferences can only be used to predict display luminance requirements where the microscope is being used at very high brightness levels. A display capable of a brightness of 500 cd/m2 should be suitable for almost all pathologists with 300 cd/m2 suitable for the majority. Although display luminance is not frequently changed by users, the ability to do so was felt to be important by the majority of respondents.Further work needs to be undertaken to establish the relationship between diagnostic performance, luminance preferences, and ambient lighting levels.

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Hosseini MS, Bejnordi BE, Trinh VQH, Chan L, Hasan D, Li X, Yang S, Kim T, Zhang H, Wu T, Chinniah K, Maghsoudlou S, Zhang R, Zhu J, Khaki S, Buin A, Chaji F, Salehi A, Nguyen BN, Samaras D, Plataniotis KN. Computational pathology: A survey review and the way forward. J Pathol Inform 2024;15:100357. [PMID: 38420608 PMCID: PMC10900832 DOI: 10.1016/j.jpi.2023.100357] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 12/21/2023] [Accepted: 12/23/2023] [Indexed: 03/02/2024] Open

Abstract

Computational Pathology (CPath) is an interdisciplinary science that augments developments of computational approaches to analyze and model medical histopathology images. The main objective for CPath is to develop infrastructure and workflows of digital diagnostics as an assistive CAD system for clinical pathology, facilitating transformational changes in the diagnosis and treatment of cancer that are mainly address by CPath tools. With evergrowing developments in deep learning and computer vision algorithms, and the ease of the data flow from digital pathology, currently CPath is witnessing a paradigm shift. Despite the sheer volume of engineering and scientific works being introduced for cancer image analysis, there is still a considerable gap of adopting and integrating these algorithms in clinical practice. This raises a significant question regarding the direction and trends that are undertaken in CPath. In this article we provide a comprehensive review of more than 800 papers to address the challenges faced in problem design all-the-way to the application and implementation viewpoints. We have catalogued each paper into a model-card by examining the key works and challenges faced to layout the current landscape in CPath. We hope this helps the community to locate relevant works and facilitate understanding of the field's future directions. In a nutshell, we oversee the CPath developments in cycle of stages which are required to be cohesively linked together to address the challenges associated with such multidisciplinary science. We overview this cycle from different perspectives of data-centric, model-centric, and application-centric problems. We finally sketch remaining challenges and provide directions for future technical developments and clinical integration of CPath. For updated information on this survey review paper and accessing to the original model cards repository, please refer to GitHub. Updated version of this draft can also be found from arXiv.

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Affiliation(s)

Mahdi S. Hosseini Department of Computer Science and Software Engineering (CSSE), Concordia Univeristy, Montreal, QC H3H 2R9, Canada
Babak Ehteshami Bejnordi Qualcomm AI Research, Qualcomm Technologies Netherlands B.V., Amsterdam, The Netherlands
Vincent Quoc-Huy Trinh Institute for Research in Immunology and Cancer of the University of Montreal, Montreal, QC H3T 1J4, Canada
Lyndon Chan The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
Danial Hasan The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
Xingwen Li The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
Stephen Yang The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
Taehyo Kim The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
Haochen Zhang The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
Theodore Wu The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
Kajanan Chinniah The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
Sina Maghsoudlou Department of Computer Science and Software Engineering (CSSE), Concordia Univeristy, Montreal, QC H3H 2R9, Canada
Ryan Zhang The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
Jiadai Zhu The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
Samir Khaki The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
Andrei Buin Huron Digitial Pathology, St. Jacobs, ON N0B 2N0, Canada
Fatemeh Chaji Department of Computer Science and Software Engineering (CSSE), Concordia Univeristy, Montreal, QC H3H 2R9, Canada
Ala Salehi Department of Electrical and Computer Engineering, University of New Brunswick, Fredericton, NB E3B 5A3, Canada
Bich Ngoc Nguyen University of Montreal Hospital Center, Montreal, QC H2X 0C2, Canada
Dimitris Samaras Department of Computer Science, Stony Brook University, Stony Brook, NY 11794, United States
Konstantinos N. Plataniotis The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada

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Mubarak M, Rashid R, Sapna F, Shakeel S. Expanding role and scope of artificial intelligence in the field of gastrointestinal pathology. Artif Intell Gastroenterol 2024;5:91550. [DOI: 10.35712/aig.v5.i2.91550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 07/06/2024] [Accepted: 07/29/2024] [Indexed: 08/08/2024] Open

Lococo F, Ghaly G, Chiappetta M, Flamini S, Evangelista J, Bria E, Stefani A, Vita E, Martino A, Boldrini L, Sassorossi C, Campanella A, Margaritora S, Mohammed A. Implementation of Artificial Intelligence in Personalized Prognostic Assessment of Lung Cancer: A Narrative Review. Cancers (Basel) 2024;16:1832. [PMID: 38791910 PMCID: PMC11119930 DOI: 10.3390/cancers16101832] [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: 03/26/2024] [Revised: 05/02/2024] [Accepted: 05/08/2024] [Indexed: 05/26/2024] Open

Abstract

Artificial Intelligence (AI) has revolutionized the management of non-small-cell lung cancer (NSCLC) by enhancing different aspects, including staging, prognosis assessment, treatment prediction, response evaluation, recurrence/prognosis prediction, and personalized prognostic assessment. AI algorithms may accurately classify NSCLC stages using machine learning techniques and deep imaging data analysis. This could potentially improve precision and efficiency in staging, facilitating personalized treatment decisions. Furthermore, there are data suggesting the potential application of AI-based models in predicting prognosis in terms of survival rates and disease progression by integrating clinical, imaging and molecular data. In the present narrative review, we will analyze the preliminary studies reporting on how AI algorithms could predict responses to various treatment modalities, such as surgery, radiotherapy, chemotherapy, targeted therapy, and immunotherapy. There is robust evidence suggesting that AI also plays a crucial role in predicting the likelihood of tumor recurrence after surgery and the pattern of failure, which has significant implications for tailoring adjuvant treatments. The successful implementation of AI in personalized prognostic assessment requires the integration of different data sources, including clinical, molecular, and imaging data. Machine learning (ML) and deep learning (DL) techniques enable AI models to analyze these data and generate personalized prognostic predictions, allowing for a precise and individualized approach to patient care. However, challenges relating to data quality, interpretability, and the ability of AI models to generalize need to be addressed. Collaboration among clinicians, data scientists, and regulators is critical for the responsible implementation of AI and for maximizing its benefits in providing a more personalized prognostic assessment. Continued research, validation, and collaboration are essential to fully exploit the potential of AI in NSCLC management and improve patient outcomes. Herein, we have summarized the state of the art of applications of AI in lung cancer for predicting staging, prognosis, and pattern of recurrence after treatment in order to provide to the readers a large comprehensive overview of this challenging issue.

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Affiliation(s)

Filippo Lococo Faculty of Medicine and Surgery, Catholic University of Sacred Heart, 00168 Rome, Italy; (M.C.); (J.E.); (E.B.); (A.S.); (E.V.); (A.M.); (L.B.); (C.S.); (S.M.) Thoracic Surgery, A. Gemelli University Hospital Foundation IRCCS, 00168 Rome, Italy; (S.F.); (A.C.)
Galal Ghaly Faculty of Medicine and Surgery, Thoracic Surgery Unit, Cairo University, Giza 12613, Egypt; (G.G.); (A.M.)
Marco Chiappetta Faculty of Medicine and Surgery, Catholic University of Sacred Heart, 00168 Rome, Italy; (M.C.); (J.E.); (E.B.); (A.S.); (E.V.); (A.M.); (L.B.); (C.S.); (S.M.) Thoracic Surgery, A. Gemelli University Hospital Foundation IRCCS, 00168 Rome, Italy; (S.F.); (A.C.)
Sara Flamini Thoracic Surgery, A. Gemelli University Hospital Foundation IRCCS, 00168 Rome, Italy; (S.F.); (A.C.)
Jessica Evangelista Faculty of Medicine and Surgery, Catholic University of Sacred Heart, 00168 Rome, Italy; (M.C.); (J.E.); (E.B.); (A.S.); (E.V.); (A.M.); (L.B.); (C.S.); (S.M.) Thoracic Surgery, A. Gemelli University Hospital Foundation IRCCS, 00168 Rome, Italy; (S.F.); (A.C.)
Emilio Bria Faculty of Medicine and Surgery, Catholic University of Sacred Heart, 00168 Rome, Italy; (M.C.); (J.E.); (E.B.); (A.S.); (E.V.); (A.M.); (L.B.); (C.S.); (S.M.) Medical Oncology, A. Gemelli University Hospital Foundation IRCCS, 00168 Rome, Italy
Alessio Stefani Faculty of Medicine and Surgery, Catholic University of Sacred Heart, 00168 Rome, Italy; (M.C.); (J.E.); (E.B.); (A.S.); (E.V.); (A.M.); (L.B.); (C.S.); (S.M.) Medical Oncology, A. Gemelli University Hospital Foundation IRCCS, 00168 Rome, Italy
Emanuele Vita Faculty of Medicine and Surgery, Catholic University of Sacred Heart, 00168 Rome, Italy; (M.C.); (J.E.); (E.B.); (A.S.); (E.V.); (A.M.); (L.B.); (C.S.); (S.M.) Medical Oncology, A. Gemelli University Hospital Foundation IRCCS, 00168 Rome, Italy
Antonella Martino Faculty of Medicine and Surgery, Catholic University of Sacred Heart, 00168 Rome, Italy; (M.C.); (J.E.); (E.B.); (A.S.); (E.V.); (A.M.); (L.B.); (C.S.); (S.M.) Radiotherapy Unit, A. Gemelli University Hospital Foundation IRCCS, 00168 Rome, Italy
Luca Boldrini Faculty of Medicine and Surgery, Catholic University of Sacred Heart, 00168 Rome, Italy; (M.C.); (J.E.); (E.B.); (A.S.); (E.V.); (A.M.); (L.B.); (C.S.); (S.M.) Radiotherapy Unit, A. Gemelli University Hospital Foundation IRCCS, 00168 Rome, Italy
Carolina Sassorossi Faculty of Medicine and Surgery, Catholic University of Sacred Heart, 00168 Rome, Italy; (M.C.); (J.E.); (E.B.); (A.S.); (E.V.); (A.M.); (L.B.); (C.S.); (S.M.) Thoracic Surgery, A. Gemelli University Hospital Foundation IRCCS, 00168 Rome, Italy; (S.F.); (A.C.)
Annalisa Campanella Thoracic Surgery, A. Gemelli University Hospital Foundation IRCCS, 00168 Rome, Italy; (S.F.); (A.C.)
Stefano Margaritora Faculty of Medicine and Surgery, Catholic University of Sacred Heart, 00168 Rome, Italy; (M.C.); (J.E.); (E.B.); (A.S.); (E.V.); (A.M.); (L.B.); (C.S.); (S.M.) Thoracic Surgery, A. Gemelli University Hospital Foundation IRCCS, 00168 Rome, Italy; (S.F.); (A.C.)
Abdelrahman Mohammed Faculty of Medicine and Surgery, Thoracic Surgery Unit, Cairo University, Giza 12613, Egypt; (G.G.); (A.M.)

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Jain E, Patel A, Parwani AV, Shafi S, Brar Z, Sharma S, Mohanty SK. Whole Slide Imaging Technology and Its Applications: Current and Emerging Perspectives. Int J Surg Pathol 2024;32:433-448. [PMID: 37437093 DOI: 10.1177/10668969231185089] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/14/2023]

Abstract

Background. Whole slide imaging (WSI) represents a paradigm shift in pathology, serving as a necessary first step for a wide array of digital tools to enter the field. It utilizes virtual microscopy wherein glass slides are converted into digital slides and are viewed by pathologists by automated image analysis. Its impact on pathology workflow, reproducibility, dissemination of educational material, expansion of service to underprivileged areas, and institutional collaboration exemplifies a significant innovative movement. The recent US Food and Drug Administration approval to WSI for its use in primary surgical pathology diagnosis has opened opportunities for wider application of this technology in routine practice. Main Text. The ongoing technological advances in digital scanners, image visualization methods, and the integration of artificial intelligence-derived algorithms with these systems provide avenues to exploit its applications. Its benefits are innumerable such as ease of access through the internet, avoidance of physical storage space, and no risk of deterioration of staining quality or breakage of slides to name a few. Although the benefits of WSI to pathology practices are many, the complexities of implementation remain an obstacle to widespread adoption. Some barriers including the high cost, technical glitches, and most importantly professional hesitation to adopt a new technology have hindered its use in routine pathology. Conclusions. In this review, we summarize the technical aspects of WSI, its applications in diagnostic pathology, training, and research along with future perspectives. It also highlights improved understanding of the current challenges to implementation, as well as the benefits and successes of the technology. WSI provides a golden opportunity for pathologists to guide its evolution, standardization, and implementation to better acquaint them with the key aspects of this technology and its judicial use. Also, implementation of routine digital pathology is an extra step requiring resources which (currently) does not usually result increased efficiency or payment.

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Islam A, Banerjee A, Wati SM, Roy B, Chatterjee K, Singhania KN. Whole-Slide Imaging (WSI) Versus Traditional Microscopy (TM) Through Evaluation of Parameters in Oral Histopathology: A Pilot Study. JOURNAL OF PHARMACY AND BIOALLIED SCIENCES 2024;16:S1685-S1689. [PMID: 38882897 PMCID: PMC11174336 DOI: 10.4103/jpbs.jpbs_1042_23] [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: 10/13/2023] [Revised: 11/17/2023] [Accepted: 11/25/2023] [Indexed: 06/18/2024] Open

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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Samueli B, Aizenberg N, Shaco-Levy R, Katzav A, Kezerle Y, Krausz J, Mazareb S, Niv-Drori H, Peled HB, Sabo E, Tobar A, Asa SL. Complete digital pathology transition: A large multi-center experience. Pathol Res Pract 2024;253:155028. [PMID: 38142526 DOI: 10.1016/j.prp.2023.155028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 12/08/2023] [Indexed: 12/26/2023]

Abstract

INTRODUCTION

Transitioning from glass slide pathology to digital pathology for primary diagnostics requires an appropriate laboratory information system, an image management system, and slide scanners; it also reinforces the need for sophisticated pathology informatics including synoptic reporting. Previous reports have discussed the transition itself and relevant considerations for it, but not the selection criteria and considerations for the infrastructure.

OBJECTIVE

To describe the process used to evaluate slide scanners, image management systems, and synoptic reporting systems for a large multisite institution.

METHODS

Six network hospitals evaluated six slide scanners, three image management systems, and three synoptic reporting systems. Scanners were evaluated based on the quality of image, speed, ease of operation, and special capabilities (including z-stacking, fluorescence and others). Image management and synoptic reporting systems were evaluated for their ease of use and capacity.

RESULTS

Among the scanners evaluated, the Leica GT450 produced the highest quality images, while the 3DHistech Pannoramic provided fluorescence and superior z-stacking. The newest generation of scanners, released relatively recently, performed better than slightly older scanners from major manufacturers Although the Olympus VS200 was not fully vetted due to not meeting all inclusion criteria, it is discussed herein due to its exceptional versatility. For Image Management Software, the authors believe that Sectra is, at the time of writing the best developed option, but this could change in the very near future as other systems improve their capabilities. All synoptic reporting systems performed impressively.

CONCLUSIONS

Specifics regarding quality and abilities of different components will change rapidly with time, but large pathology practices considering such a transition should be aware of the issues discussed and evaluate the most current generation to arrive at appropriate conclusions.

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Affiliation(s)

Benzion Samueli Department of Pathology, Soroka University Medical Center, P.O. Box 151, Be'er Sheva 8410101, Israel; Faculty of Health Sciences, Ben Gurion University of the Negev, P.O. Box 653, Be'er Sheva 8410501, Israel.
Natalie Aizenberg Department of Pathology, Soroka University Medical Center, P.O. Box 151, Be'er Sheva 8410101, Israel; Faculty of Health Sciences, Ben Gurion University of the Negev, P.O. Box 653, Be'er Sheva 8410501, Israel
Ruthy Shaco-Levy Department of Pathology, Soroka University Medical Center, P.O. Box 151, Be'er Sheva 8410101, Israel; Faculty of Health Sciences, Ben Gurion University of the Negev, P.O. Box 653, Be'er Sheva 8410501, Israel; Department of Pathology, Barzilai Medical Center, 2 Ha-Histadrut St, Ashkelon 7830604, Israel
Aviva Katzav Pathology Institute, Meir Medical Center, Kfar Saba 4428164, Israel
Yarden Kezerle Department of Pathology, Soroka University Medical Center, P.O. Box 151, Be'er Sheva 8410101, Israel; Faculty of Health Sciences, Ben Gurion University of the Negev, P.O. Box 653, Be'er Sheva 8410501, Israel
Judit Krausz Department of Pathology, HaEmek Medical Center, 21 Yitzhak Rabin Ave, Afula 183411, Israel
Salam Mazareb Department of Pathology, Carmel Medical Center, 7 Michal Street, Haifa 3436212, Israel
Hagit Niv-Drori Department of Pathology, Rabin Medical Center, 39 Jabotinsky St, Petah Tikva 4941492, Israel; Faculty of Medicine, Tel Aviv University, P.O. Box 39040, Tel Aviv 6139001, Israel
Hila Belhanes Peled Department of Pathology, HaEmek Medical Center, 21 Yitzhak Rabin Ave, Afula 183411, Israel
Edmond Sabo Department of Pathology, Carmel Medical Center, 7 Michal Street, Haifa 3436212, Israel; Rappaport Faculty of Medicine, Technion Israel Institute of Technology, Haifa 3525433, Israel
Ana Tobar Department of Pathology, Rabin Medical Center, 39 Jabotinsky St, Petah Tikva 4941492, Israel; Faculty of Medicine, Tel Aviv University, P.O. Box 39040, Tel Aviv 6139001, Israel
Sylvia L Asa Institute of Pathology, University Hospitals Cleveland Medical Center, Case Western Reserve University, 11100 Euclid Avenue, Room 204, Cleveland, OH 44106, USA

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Turashvili G, Gjeorgjievski SG, Wang Q, Ewaz A, Ai D, Li X, Badve SS. Intraoperative assessment of axillary sentinel lymph nodes by telepathology. Breast Cancer Res Treat 2023;202:423-434. [PMID: 37688667 DOI: 10.1007/s10549-023-07101-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Accepted: 08/17/2023] [Indexed: 09/11/2023]

Hanna MG, Ardon O. Digital pathology systems enabling quality patient care. Genes Chromosomes Cancer 2023;62:685-697. [PMID: 37458325 PMCID: PMC11265285 DOI: 10.1002/gcc.23192] [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: 04/13/2023] [Revised: 06/27/2023] [Accepted: 07/06/2023] [Indexed: 09/20/2023] Open

Marletta S, Salatiello M, Pantanowitz L, Bellevicine C, Bongiovanni M, Bonoldi E, De Rezende G, Fadda G, Incardona P, Munari E, Pagni F, Rossi ED, Tallini G, Troncone G, Ugolini C, Vigliar E, Eccher A. Delphi expert consensus for whole slide imaging in thyroid cytopathology. Cytopathology 2023;34:581-589. [PMID: 37530465 DOI: 10.1111/cyt.13279] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 07/05/2023] [Accepted: 07/14/2023] [Indexed: 08/03/2023]

Affiliation(s)

Stefano Marletta Department of Diagnostics and Public Health, Section of Pathology, University of Verona, Verona, Italy Department of Pathology, Pederzoli Hospital, Peschiera del Garda, Italy
Maria Salatiello Department of Public Health, University of Naples Federico II, Naples, Italy
Liron Pantanowitz Department of Pathology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
Claudio Bellevicine Department of Public Health, University of Naples Federico II, Naples, Italy
Massimo Bongiovanni Cytopathology, SYNLAB Suisse SA, Lausanne, Switzerland
Emanuela Bonoldi Niguarda Cancer Center, Milan, Italy
Gisele De Rezende Niguarda Cancer Center, Milan, Italy
Guido Fadda Department of Human Pathology of the Adulthood and of the Developing Age "Gaetano Barresi", Faculty of Medicine and Surgery, University of Messina, Messina, Italy
Paolo Incardona Complex Structure of Anatomic Pathology, ASST Spedali Civili of Brescia, Brescia, Italy
Enrico Munari Pathology Unit, Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
Fabio Pagni Department of Medicine and Surgery, Pathology, University of Milano-Bicocca, IRCCS (Scientific Institute for Research, Hospitalization and Healthcare) Fondazione San Gerardo dei Tintori, Monza, Italy
Esther Diana Rossi Division of Anatomic Pathology and Histology, Fondazione Policlinico Universitario A.Gemelli-IRCCS, Rome, Italy
Giovanni Tallini Dipartimento di Scienze Mediche e Chirurgiche (DIMEC), University of Bologna, Bologna, Italy Solid Tumor Molecular Pathology Laboratory, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
Giancarlo Troncone Department of Public Health, University of Naples Federico II, Naples, Italy
Clara Ugolini Department of Surgical, Medical, Molecular Pathology and Critical Area, University of Pisa, Pisa, Italy
Elena Vigliar Department of Public Health, University of Naples Federico II, Naples, Italy
Albino Eccher Department of Pathology and Diagnostics, University and Hospital Trust of Verona, Verona, Italy

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Palazzi X, Barale-Thomas E, Bawa B, Carter J, Janardhan K, Marxfeld H, Nyska A, Saravanan C, Schaudien D, Schumacher VL, Spaet RH, Tangermann S, Turner OC, Vezzali E. Results of the European Society of Toxicologic Pathology Survey on the Use of Artificial Intelligence in Toxicologic Pathology. Toxicol Pathol 2023;51:216-224. [PMID: 37732701 DOI: 10.1177/01926233231182115] [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: 09/22/2023]

Jiang S, Song P, Wang T, Yang L, Wang R, Guo C, Feng B, Maiden A, Zheng G. Spatial- and Fourier-domain ptychography for high-throughput bio-imaging. Nat Protoc 2023:10.1038/s41596-023-00829-4. [PMID: 37248392 DOI: 10.1038/s41596-023-00829-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 03/03/2023] [Indexed: 05/31/2023]

Wang T, Jiang S, Song P, Wang R, Yang L, Zhang T, Zheng G. Optical ptychography for biomedical imaging: recent progress and future directions [Invited]. BIOMEDICAL OPTICS EXPRESS 2023;14:489-532. [PMID: 36874495 PMCID: PMC9979669 DOI: 10.1364/boe.480685] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 12/10/2022] [Accepted: 12/10/2022] [Indexed: 05/25/2023]

Marletta S, Pantanowitz L, Santonicco N, Caputo A, Bragantini E, Brunelli M, Girolami I, Eccher A. Application of Digital Imaging and Artificial Intelligence to Pathology of the Placenta. Pediatr Dev Pathol 2023;26:5-12. [PMID: 36448447 DOI: 10.1177/10935266221137953] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]

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

Zhang Z, Chan RKY, Wong KKY. Quantized spiral-phase-modulation based deep learning for real-time defocusing distance prediction. OPTICS EXPRESS 2022;30:26931-26940. [PMID: 36236875 DOI: 10.1364/oe.460858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Accepted: 04/30/2022] [Indexed: 06/16/2023]

Jiang S, Guo C, Song P, Wang T, Wang R, Zhang T, Wu Q, Pandey R, Zheng G. High-throughput digital pathology via a handheld, multiplexed, and AI-powered ptychographic whole slide scanner. LAB ON A CHIP 2022;22:2657-2670. [PMID: 35583207 DOI: 10.1039/d2lc00084a] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]

Li Q, Liu X, Han K, Guo C, Jiang J, Ji X, Wu X. Learning to autofocus in whole slide imaging via physics-guided deep cascade networks. OPTICS EXPRESS 2022;30:14319-14340. [PMID: 35473178 DOI: 10.1364/oe.416824] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Accepted: 01/08/2021] [Indexed: 06/14/2023]

Chiu HY, Chao HS, Chen YM. Application of Artificial Intelligence in Lung Cancer. Cancers (Basel) 2022;14:1370. [PMID: 35326521 PMCID: PMC8946647 DOI: 10.3390/cancers14061370] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 03/07/2022] [Indexed: 12/12/2022] Open

Mehrvar S, Himmel LE, Babburi P, Goldberg AL, Guffroy M, Janardhan K, Krempley AL, Bawa B. Deep Learning Approaches and Applications in Toxicologic Histopathology: Current Status and Future Perspectives. J Pathol Inform 2021;12:42. [PMID: 34881097 PMCID: PMC8609289 DOI: 10.4103/jpi.jpi_36_21] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 07/18/2021] [Indexed: 12/13/2022] Open

Patel A, Balis UGJ, Cheng J, Li Z, Lujan G, McClintock DS, Pantanowitz L, Parwani A. Contemporary Whole Slide Imaging Devices and Their Applications within the Modern Pathology Department: A Selected Hardware Review. J Pathol Inform 2021;12:50. [PMID: 35070479 PMCID: PMC8721869 DOI: 10.4103/jpi.jpi_66_21] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 09/22/2021] [Indexed: 12/21/2022] Open

Guo C, Jiang S, Yang L, Song P, Wang T, Shao X, Zhang Z, Murphy M, Zheng G. Deep learning-enabled whole slide imaging (DeepWSI): oil-immersion quality using dry objectives, longer depth of field, higher system throughput, and better functionality. OPTICS EXPRESS 2021;29:39669-39684. [PMID: 34809325 DOI: 10.1364/oe.441892] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 11/04/2021] [Indexed: 05/18/2023]

Lemaillet P, Takeda K, Lamont AC, Agrawal A. Colorimetrical uncertainty estimation for the performance assessment of whole slide imaging scanners. J Med Imaging (Bellingham) 2021;8:057501. [PMID: 34660844 DOI: 10.1117/1.jmi.8.5.057501] [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: 05/10/2021] [Accepted: 09/16/2021] [Indexed: 11/14/2022] Open

Abstract

Purpose: Whole slide imaging (WSI) scanners produce tissue slide images with a large field of view and a high resolution for pathologists to use in diagnoses. Color performance tests of these color imaging devices are necessary and can use stained tissue slides if the color truth is established using a hyperspectral imaging microscopy system (HIMS). The purpose of this study was to estimate the reproducibility uncertainty of CIELAB coordinates for a reference tissue slide measured by both the HIMS and a WSI scanner. Approach: We compared the color performances of the WSI scanner to those of the reference established by the HIMS using the International Commission on Illumination (Commission Internationale de l'Éclairage, or CIE) 1976 Δ E a b * color difference with the just noticeable color difference (JNCD, Δ E a b * ≤ 2 ), and the results from the overlap of the CIELAB coordinates' uncertainty within the error bar, with a coverage factor k = 2 . The reported uncertainty results from measurements and image registration uncertainties. Results: For the blank area common to the HIMS and the WSI average images, the color agreement was higher using the JNCD condition versus the CIELAB uncertainty overlap criterion (82% and 20% of the pixels in the images, respectively). This difference is explained by the fact that numerous pixels have CIELAB coordinates near one another but corresponding to CIELAB uncertainty values small enough not to overlap. In the colored area of the images, the JNCD condition was met for 0.19% of the pixels in the images, compared with 4.3% for the CIELAB uncertainty overlap criterion. Conclusions: The distribution of uncertainties on the CIELAB coordinates was broader for the HIMS compared with the WSI scanner. The WSI scanner had a systemic error in the color reproduction, which pointed to a potential inadequate color calibration of this device.

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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: 1.0] [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]

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: 4.3] [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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Samuelson MI, Chen SJ, Boukhar SA, Schnieders EM, Walhof ML, Bellizzi AM, Robinson RA, Rajan K D A. Rapid Validation of Whole-Slide Imaging for Primary Histopathology Diagnosis. Am J Clin Pathol 2021;155:638-648. [PMID: 33511392 PMCID: PMC7929400 DOI: 10.1093/ajcp/aqaa280] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open

Lujan G, Quigley JC, Hartman D, Parwani A, Roehmholdt B, Meter BV, Ardon O, Hanna MG, Kelly D, Sowards C, Montalto M, Bui M, Zarella MD, LaRosa V, Slootweg G, Retamero JA, Lloyd MC, Madory J, Bowman D. Dissecting the Business Case for Adoption and Implementation of Digital Pathology: A White Paper from the Digital Pathology Association. J Pathol Inform 2021;12:17. [PMID: 34221633 PMCID: PMC8240548 DOI: 10.4103/jpi.jpi_67_20] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Revised: 09/20/2020] [Accepted: 11/02/2020] [Indexed: 12/13/2022] Open

Schumacher VL, Aeffner F, Barale-Thomas E, Botteron C, Carter J, Elies L, Engelhardt JA, Fant P, Forest T, Hall P, Hildebrand D, Klopfleisch R, Lucotte T, Marxfeld H, Mckinney L, Moulin P, Neyens E, Palazzi X, Piton A, Riccardi E, Roth DR, Rousselle S, Vidal JD, Williams B. The Application, Challenges, and Advancement Toward Regulatory Acceptance of Digital Toxicologic Pathology: Results of the 7th ESTP International Expert Workshop (September 20-21, 2019). Toxicol Pathol 2020;49:720-737. [PMID: 33297858 DOI: 10.1177/0192623320975841] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]

Bian Z, Guo C, Jiang S, Zhu J, Wang R, Song P, Zhang Z, Hoshino K, Zheng G. Autofocusing technologies for whole slide imaging and automated microscopy. JOURNAL OF BIOPHOTONICS 2020;13:e202000227. [PMID: 32844560 DOI: 10.1002/jbio.202000227] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Revised: 08/14/2020] [Accepted: 08/20/2020] [Indexed: 06/11/2023]

Jahn SW, Plass M, Moinfar F. Digital Pathology: Advantages, Limitations and Emerging Perspectives. J Clin Med 2020;9:E3697. [PMID: 33217963 PMCID: PMC7698715 DOI: 10.3390/jcm9113697] [Citation(s) in RCA: 119] [Impact Index Per Article: 23.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 10/27/2020] [Accepted: 11/13/2020] [Indexed: 12/11/2022] Open

Elmore JG, Shucard H, Lee AC, Wang PC, Kerr KF, Carney PA, Drew T, Brunyé TT, Weaver DL. Pathology Trainees' Experience and Attitudes on Use of Digital Whole Slide Images. Acad Pathol 2020;7:2374289520951922. [PMID: 33088907 PMCID: PMC7545516 DOI: 10.1177/2374289520951922] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Revised: 06/22/2020] [Accepted: 06/30/2020] [Indexed: 11/15/2022] Open

Abstract

Digital whole slide images are Food and Drug Administration approved for clinical diagnostic use in pathology; however, integration is nascent. Trainees from 9 pathology training programs completed an online survey to ascertain attitudes toward and experiences with whole slide images for pathological interpretations. Respondents (n = 76) reported attending 63 unique medical schools (45 United States, 18 international). While 63% reported medical school exposure to whole slide images, most reported ≤ 5 hours. Those who began training more recently were more likely to report at least some exposure to digital whole slide image training in medical school compared to those who began training earlier: 75% of respondents beginning training in 2017 or 2018 reported exposure to whole slide images compared to 54% for trainees beginning earlier. Trainees exposed to whole slide images in medical school were more likely to agree they were comfortable using whole slide images for interpretation compared to those not exposed (29% vs 12%; P = .06). Most trainees agreed that accurate diagnoses can be made using whole slide images for primary diagnosis (92%; 95% CI: 86-98) and that whole slide images are useful for obtaining second opinions (93%; 95% CI: 88-99). Trainees reporting whole slide image experience during training, compared to those with no experience, were more likely to agree they would use whole slide images in 5 years for primary diagnosis (64% vs 50%; P = .3) and second opinions (86% vs 76%; P = .4). In conclusion, although exposure to whole slide images in medical school has increased, overall exposure is limited. Positive attitudes toward future whole slide image diagnostic use were associated with exposure to this technology during medical training. Curricular integration may promote adoption.

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Abel JT, Ouillette P, Williams CL, Blau J, Cheng J, Yao K, Lee WY, Cornish TC, Balis UGJ, McClintock DS. Display Characteristics and Their Impact on Digital Pathology: A Current Review of Pathologists' Future "Microscope". J Pathol Inform 2020;11:23. [PMID: 33042602 PMCID: PMC7518209 DOI: 10.4103/jpi.jpi_38_20] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Revised: 05/22/2020] [Accepted: 05/28/2020] [Indexed: 01/04/2023] Open

Marble HD, Huang R, Dudgeon SN, Lowe A, Herrmann MD, Blakely S, Leavitt MO, Isaacs M, Hanna MG, Sharma A, Veetil J, Goldberg P, Schmid JH, Lasiter L, Gallas BD, Abels E, Lennerz JK. A Regulatory Science Initiative to Harmonize and Standardize Digital Pathology and Machine Learning Processes to Speed up Clinical Innovation to Patients. J Pathol Inform 2020;11:22. [PMID: 33042601 PMCID: PMC7518200 DOI: 10.4103/jpi.jpi_27_20] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Revised: 04/20/2020] [Accepted: 06/16/2020] [Indexed: 12/13/2022] Open

Abstract

Unlocking the full potential of pathology data by gaining computational access to histological pixel data and metadata (digital pathology) is one of the key promises of computational pathology. Despite scientific progress and several regulatory approvals for primary diagnosis using whole-slide imaging, true clinical adoption at scale is slower than anticipated. In the U.S., advances in digital pathology are often siloed pursuits by individual stakeholders, and to our knowledge, there has not been a systematic approach to advance the field through a regulatory science initiative. The Alliance for Digital Pathology (the Alliance) is a recently established, volunteer, collaborative, regulatory science initiative to standardize digital pathology processes to speed up innovation to patients. The purpose is: (1) to account for the patient perspective by including patient advocacy; (2) to investigate and develop methods and tools for the evaluation of effectiveness, safety, and quality to specify risks and benefits in the precompetitive phase; (3) to help strategize the sequence of clinically meaningful deliverables; (4) to encourage and streamline the development of ground-truth data sets for machine learning model development and validation; and (5) to clarify regulatory pathways by investigating relevant regulatory science questions. The Alliance accepts participation from all stakeholders, and we solicit clinically relevant proposals that will benefit the field at large. The initiative will dissolve once a clinical, interoperable, modularized, integrated solution (from tissue acquisition to diagnostic algorithm) has been implemented. In times of rapidly evolving discoveries, scientific input from subject-matter experts is one essential element to inform regulatory guidance and decision-making. The Alliance aims to establish and promote synergistic regulatory science efforts that will leverage diverse inputs to move digital pathology forward and ultimately improve patient care.

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van der Graaff L, van Leenders GJLH, Boyaval F, Stallinga S. Computational imaging modalities for multi-focal whole-slide imaging systems. APPLIED OPTICS 2020;59:5967-5982. [PMID: 32672740 DOI: 10.1364/ao.394290] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Accepted: 06/08/2020] [Indexed: 06/11/2023]

Convergence of Digital Pathology and Artificial Intelligence Tools in Anatomic Pathology Practice: Current Landscape and Future Directions. Adv Anat Pathol 2020;27:221-226. [PMID: 32541593 DOI: 10.1097/pap.0000000000000271] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]

Rahman A, Jahangir C, Lynch SM, Alattar N, Aura C, Russell N, Lanigan F, Gallagher WM. Advances in tissue-based imaging: impact on oncology research and clinical practice. Expert Rev Mol Diagn 2020;20:1027-1037. [PMID: 32510287 DOI: 10.1080/14737159.2020.1770599] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]

“Teledermatopathology: A Review”. CURRENT DERMATOLOGY REPORTS 2020. [DOI: 10.1007/s13671-020-00299-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]

Rana A, Lowe A, Lithgow M, Horback K, Janovitz T, Da Silva A, Tsai H, Shanmugam V, Bayat A, Shah P. Use of Deep Learning to Develop and Analyze Computational Hematoxylin and Eosin Staining of Prostate Core Biopsy Images for Tumor Diagnosis. JAMA Netw Open 2020;3:e205111. [PMID: 32432709 PMCID: PMC7240356 DOI: 10.1001/jamanetworkopen.2020.5111] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open

Abstract

IMPORTANCE

Histopathological diagnoses of tumors from tissue biopsy after hematoxylin and eosin (H&E) dye staining is the criterion standard for oncological care, but H&E staining requires trained operators, dyes and reagents, and precious tissue samples that cannot be reused.

OBJECTIVES

To use deep learning algorithms to develop models that perform accurate computational H&E staining of native nonstained prostate core biopsy images and to develop methods for interpretation of H&E staining deep learning models and analysis of computationally stained images by computer vision and clinical approaches.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study used hundreds of thousands of native nonstained RGB (red, green, and blue channel) whole slide image (WSI) patches of prostate core tissue biopsies obtained from excess tissue material from prostate core biopsies performed in the course of routine clinical care between January 7, 2014, and January 7, 2017, at Brigham and Women's Hospital, Boston, Massachusetts. Biopsies were registered with their H&E-stained versions. Conditional generative adversarial neural networks (cGANs) that automate conversion of native nonstained RGB WSI to computational H&E-stained images were then trained. Deidentified whole slide images of prostate core biopsy and medical record data were transferred to Massachusetts Institute of Technology, Cambridge, for computational research. Results were shared with physicians for clinical evaluations. Data were analyzed from July 2018 to February 2019.

MAIN OUTCOMES AND MEASURES

Methods for detailed computer vision image analytics, visualization of trained cGAN model outputs, and clinical evaluation of virtually stained images were developed. The main outcome was interpretable deep learning models and computational H&E-stained images that achieved high performance in these metrics.

RESULTS

Among 38 patients who provided samples, single core biopsy images were extracted from each whole slide, resulting in 102 individual nonstained and H&E dye-stained image pairs that were compared with matched computationally stained and unstained images. Calculations showed high similarities between computationally and H&E dye-stained images, with a mean (SD) structural similarity index (SSIM) of 0.902 (0.026), Pearson correlation coefficient (PCC) of 0.962 (0.096), and peak signal to noise ratio (PSNR) of 22.821 (1.232) dB. A second cGAN performed accurate computational destaining of H&E-stained images back to their native nonstained form, with a mean (SD) SSIM of 0.900 (0.030), PCC of 0.963 (0.011), and PSNR of 25.646 (1.943) dB compared with native nonstained images. A single blind prospective study computed approximately 95% pixel-by-pixel overlap among prostate tumor annotations provided by 5 board certified pathologists on computationally stained images, compared with those on H&E dye-stained images. This study also used the first visualization and explanation of neural network kernel activation maps during H&E staining and destaining of RGB images by cGANs. High similarities between kernel activation maps of computationally and H&E-stained images (mean-squared errors <0.0005) provide additional mathematical and mechanistic validation of the staining system.

CONCLUSIONS AND RELEVANCE

These findings suggest that computational H&E staining of native unlabeled RGB images of prostate core biopsy could reproduce Gleason grade tumor signatures that were easily assessed and validated by clinicians. Methods for benchmarking, visualization, and clinical validation of deep learning models and virtually H&E-stained images communicated in this study have wide applications in clinical informatics and oncology research. Clinical researchers may use these systems for early indications of possible abnormalities in native nonstained tissue biopsies prior to histopathological workflows.

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van der Wel MJ, Coleman HG, Bergman JJGHM, Jansen M, Meijer SL. Histopathologist features predictive of diagnostic concordance at expert level among a large international sample of pathologists diagnosing Barrett's dysplasia using digital pathology. Gut 2020;69:811-822. [PMID: 31852770 DOI: 10.1136/gutjnl-2019-318985] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2019] [Revised: 08/13/2019] [Accepted: 08/18/2019] [Indexed: 12/16/2022]

Abstract

OBJECTIVE

Guidelines mandate expert pathology review of Barrett's oesophagus (BO) biopsies that reveal dysplasia, but there are no evidence-based standards to corroborate expert reviewer status. We investigated BO concordance rates and pathologist features predictive of diagnostic discordance.

DESIGN

Pathologists (n=51) from over 20 countries assessed 55 digitised BO biopsies from across the diagnostic spectrum, before and after viewing matched p53 labelling. Extensive demographic and clinical experience data were obtained via online questionnaire. Reference diagnoses were obtained from a review panel (n=4) of experienced Barrett's pathologists.

RESULTS

We recorded over 6000 case diagnoses with matched demographic data. Of 2805 H&E diagnoses, we found excellent concordance (>70%) for non-dysplastic BO and high-grade dysplasia, and intermediate concordance for low-grade dysplasia (42%) and indefinite for dysplasia (23%). Major diagnostic errors were found in 248 diagnoses (8.8%), which reduced to 232 (8.3%) after viewing p53 labelled slides. Demographic variables correlating with diagnostic proficiency were analysed in multivariate analysis, which revealed that at least 5 years of professional experience was protective against major diagnostic error for H&E slide review (OR 0.48, 95% CI 0.31 to 0.74). Working in a non-teaching hospital was associated with increased odds of major diagnostic error (OR 1.76, 95% CI 1.15 to 2.69); however, this was neutralised when pathologists viewed p53 labelled slides. Notably, neither case volume nor self-identifying as an expert predicted diagnostic proficiency. Extrapolating our data to real-world case prevalence suggests that 92.3% of major diagnostic errors are due to overinterpreting non-dysplastic BO.

CONCLUSION

Our data provide evidence-based criteria for diagnostic proficiency in Barrett's histopathology.

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Lee TB, Lee J, Jun JH. Three-Dimensional Approaches in Histopathological Tissue Clearing System. KOREAN JOURNAL OF CLINICAL LABORATORY SCIENCE 2020. [DOI: 10.15324/kjcls.2020.52.1.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open

Yoshikawa T, Horai Y, Asaoka Y, Sakurai T, Kikuchi S, Yamaoka M, Tanaka M. Current status of pathological image analysis technology in pharmaceutical companies: a questionnaire survey of the Japan Pharmaceutical Manufacturers Association. J Toxicol Pathol 2020;33:131-139. [PMID: 32425346 PMCID: PMC7218240 DOI: 10.1293/tox.2019-0056] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Accepted: 12/24/2019] [Indexed: 12/20/2022] Open

Affiliation(s)

Tsuyoshi Yoshikawa Japan Pharmaceutical Manufacturers Association, R&D subcommittee, 2-3-22 Nihonbashi Honcho, Chuo-ku, Tokyo 103-0023, Japan.,Department of Drug Safety Research, Nonclinical Research Center, Tokushima Research Institute, Otsuka Pharmaceutical Co., Ltd., 463-10 Kagasuno, Kawauchi-cho, Tokushima-shi, Tokushima 771-0192, Japan
Yasushi Horai Japan Pharmaceutical Manufacturers Association, R&D subcommittee, 2-3-22 Nihonbashi Honcho, Chuo-ku, Tokyo 103-0023, Japan.,Sohyaku. Innovative Research Division, Mitsubishi Tanabe Pharma Corporation, 2-26-1 Muraoka-Higashi, Fujisawa-shi, Kanagawa 251-8555, Japan
Yoshiji Asaoka Japan Pharmaceutical Manufacturers Association, R&D subcommittee, 2-3-22 Nihonbashi Honcho, Chuo-ku, Tokyo 103-0023, Japan.,Drug Research Evaluation, Research Laboratory for Development, Shionogi Pharmaceutical Research Center, Shionogi & Co., Ltd., 3-3-1 Futaba-cho, Toyonaka-shi, Osaka 561-0825, Japan
Takanobu Sakurai Japan Pharmaceutical Manufacturers Association, R&D subcommittee, 2-3-22 Nihonbashi Honcho, Chuo-ku, Tokyo 103-0023, Japan
Satomi Kikuchi Japan Pharmaceutical Manufacturers Association, R&D subcommittee, 2-3-22 Nihonbashi Honcho, Chuo-ku, Tokyo 103-0023, Japan.,DMPK and Safety Assessment, Research Center, Mochida Pharmaceutical Co., Ltd., 772 Uenohara, Jimba, Gotemba-shi, Shizuoka, 412-8524, Japan
Makiko Yamaoka Japan Pharmaceutical Manufacturers Association, R&D subcommittee, 2-3-22 Nihonbashi Honcho, Chuo-ku, Tokyo 103-0023, Japan.,Toxicology Research Laboratory, Watarase Research Center, Discovery Research Headquarters, Kyorin Pharmaceutical Co., Ltd., 1848 Nogi, Nogi-machi, Shimotsuga-gun, Tochigi 329-0114, Japan
Masaharu Tanaka Japan Pharmaceutical Manufacturers Association, R&D subcommittee, 2-3-22 Nihonbashi Honcho, Chuo-ku, Tokyo 103-0023, Japan.,Sohyaku. Innovative Research Division, Mitsubishi Tanabe Pharma Corporation, 2-26-1 Muraoka-Higashi, Fujisawa-shi, Kanagawa 251-8555, Japan.,Research & Development Department, Japan Bioindustry Association, 2-26-9 Hachobori, Chuo-ku, Tokyo 104-0032, Japan

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Rai Dastidar T, Ethirajan R. Whole slide imaging system using deep learning-based automated focusing. BIOMEDICAL OPTICS EXPRESS 2020;11:480-491. [PMID: 32010529 PMCID: PMC6968754 DOI: 10.1364/boe.379780] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Revised: 12/14/2019] [Accepted: 12/16/2019] [Indexed: 05/20/2023]

Aeffner F, Adissu HA, Boyle MC, Cardiff RD, Hagendorn E, Hoenerhoff MJ, Klopfleisch R, Newbigging S, Schaudien D, Turner O, Wilson K. Digital Microscopy, Image Analysis, and Virtual Slide Repository. ILAR J 2019;59:66-79. [PMID: 30535284 DOI: 10.1093/ilar/ily007] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2017] [Revised: 05/03/2018] [Indexed: 02/07/2023] Open

Affiliation(s)

Famke Aeffner Famke Aeffner, DVM PhD DACVP, is a principal pathologist in the Comparative Biology and Safety Sciences Department at Amgen Inc. in South San Francisco, California. Hibret Adissu, DVM PhD DVSc DACVP, is an investigative pathologist in the Laboratory of Cancer Biology and Genetics, Center for Cancer Research, at the National Cancer Institute in Bethesda, Maryland. Michael C. Boyle, DVM PhD DACVP DABT, is a principal pathologist in the Comparative Biology and Safety Sciences at Amgen Inc. in Thousand Oaks, California. Robert D. Cardiff, MD PhD, is a distinguished professor of pathology (emeritus) at the Center for Comparative Medicine at the University of California in Davis, California. Erik Hagendorn is a senior scientist of informatics at AbbVie Bioresearch in Worcester, Massachusetts. Mark J. Hoenerhoff, DVM PhD DACVP, is an associate professor and veterinary pathologist at the In Vivo Animal Core, Unit for Laboratory Animal Medicine, at the University of Michigan in Ann Arbor, Michigan. Robert Klopfleisch, DVM PhD DACVP, is an associate professor at the Institute of Veterinary Pathology of the Freie Universitaet Berlin, in Berlin, Germany. Susan Newbigging, BSc MSc DVM DVSc, is a pathologist and Director of The Pathology Core at the Toronto Center of Phenogenomics in Toronto, Ontario, Canada. Dirk Schaudien, DVM PhD DACVP, is a veterinary pathologist at the Fraunhofer Institute for Toxicology and Experimental Medicine, in Hannover, Germany. Oliver Turner, BSC(Hons), BVSc MRCVS PhD DACVP DABT, is a senior pathologist in the Preclinical Safety department of Novartis Pharmaceuticals in East Hanover, New Jersey. Kristin Wilson, DVM PhD DACVP, is a pathologist at Flagship Biosciences Inc. in Westminster, Colorado
Hibret A Adissu Famke Aeffner, DVM PhD DACVP, is a principal pathologist in the Comparative Biology and Safety Sciences Department at Amgen Inc. in South San Francisco, California. Hibret Adissu, DVM PhD DVSc DACVP, is an investigative pathologist in the Laboratory of Cancer Biology and Genetics, Center for Cancer Research, at the National Cancer Institute in Bethesda, Maryland. Michael C. Boyle, DVM PhD DACVP DABT, is a principal pathologist in the Comparative Biology and Safety Sciences at Amgen Inc. in Thousand Oaks, California. Robert D. Cardiff, MD PhD, is a distinguished professor of pathology (emeritus) at the Center for Comparative Medicine at the University of California in Davis, California. Erik Hagendorn is a senior scientist of informatics at AbbVie Bioresearch in Worcester, Massachusetts. Mark J. Hoenerhoff, DVM PhD DACVP, is an associate professor and veterinary pathologist at the In Vivo Animal Core, Unit for Laboratory Animal Medicine, at the University of Michigan in Ann Arbor, Michigan. Robert Klopfleisch, DVM PhD DACVP, is an associate professor at the Institute of Veterinary Pathology of the Freie Universitaet Berlin, in Berlin, Germany. Susan Newbigging, BSc MSc DVM DVSc, is a pathologist and Director of The Pathology Core at the Toronto Center of Phenogenomics in Toronto, Ontario, Canada. Dirk Schaudien, DVM PhD DACVP, is a veterinary pathologist at the Fraunhofer Institute for Toxicology and Experimental Medicine, in Hannover, Germany. Oliver Turner, BSC(Hons), BVSc MRCVS PhD DACVP DABT, is a senior pathologist in the Preclinical Safety department of Novartis Pharmaceuticals in East Hanover, New Jersey. Kristin Wilson, DVM PhD DACVP, is a pathologist at Flagship Biosciences Inc. in Westminster, Colorado
Michael C Boyle Famke Aeffner, DVM PhD DACVP, is a principal pathologist in the Comparative Biology and Safety Sciences Department at Amgen Inc. in South San Francisco, California. Hibret Adissu, DVM PhD DVSc DACVP, is an investigative pathologist in the Laboratory of Cancer Biology and Genetics, Center for Cancer Research, at the National Cancer Institute in Bethesda, Maryland. Michael C. Boyle, DVM PhD DACVP DABT, is a principal pathologist in the Comparative Biology and Safety Sciences at Amgen Inc. in Thousand Oaks, California. Robert D. Cardiff, MD PhD, is a distinguished professor of pathology (emeritus) at the Center for Comparative Medicine at the University of California in Davis, California. Erik Hagendorn is a senior scientist of informatics at AbbVie Bioresearch in Worcester, Massachusetts. Mark J. Hoenerhoff, DVM PhD DACVP, is an associate professor and veterinary pathologist at the In Vivo Animal Core, Unit for Laboratory Animal Medicine, at the University of Michigan in Ann Arbor, Michigan. Robert Klopfleisch, DVM PhD DACVP, is an associate professor at the Institute of Veterinary Pathology of the Freie Universitaet Berlin, in Berlin, Germany. Susan Newbigging, BSc MSc DVM DVSc, is a pathologist and Director of The Pathology Core at the Toronto Center of Phenogenomics in Toronto, Ontario, Canada. Dirk Schaudien, DVM PhD DACVP, is a veterinary pathologist at the Fraunhofer Institute for Toxicology and Experimental Medicine, in Hannover, Germany. Oliver Turner, BSC(Hons), BVSc MRCVS PhD DACVP DABT, is a senior pathologist in the Preclinical Safety department of Novartis Pharmaceuticals in East Hanover, New Jersey. Kristin Wilson, DVM PhD DACVP, is a pathologist at Flagship Biosciences Inc. in Westminster, Colorado
Robert D Cardiff Famke Aeffner, DVM PhD DACVP, is a principal pathologist in the Comparative Biology and Safety Sciences Department at Amgen Inc. in South San Francisco, California. Hibret Adissu, DVM PhD DVSc DACVP, is an investigative pathologist in the Laboratory of Cancer Biology and Genetics, Center for Cancer Research, at the National Cancer Institute in Bethesda, Maryland. Michael C. Boyle, DVM PhD DACVP DABT, is a principal pathologist in the Comparative Biology and Safety Sciences at Amgen Inc. in Thousand Oaks, California. Robert D. Cardiff, MD PhD, is a distinguished professor of pathology (emeritus) at the Center for Comparative Medicine at the University of California in Davis, California. Erik Hagendorn is a senior scientist of informatics at AbbVie Bioresearch in Worcester, Massachusetts. Mark J. Hoenerhoff, DVM PhD DACVP, is an associate professor and veterinary pathologist at the In Vivo Animal Core, Unit for Laboratory Animal Medicine, at the University of Michigan in Ann Arbor, Michigan. Robert Klopfleisch, DVM PhD DACVP, is an associate professor at the Institute of Veterinary Pathology of the Freie Universitaet Berlin, in Berlin, Germany. Susan Newbigging, BSc MSc DVM DVSc, is a pathologist and Director of The Pathology Core at the Toronto Center of Phenogenomics in Toronto, Ontario, Canada. Dirk Schaudien, DVM PhD DACVP, is a veterinary pathologist at the Fraunhofer Institute for Toxicology and Experimental Medicine, in Hannover, Germany. Oliver Turner, BSC(Hons), BVSc MRCVS PhD DACVP DABT, is a senior pathologist in the Preclinical Safety department of Novartis Pharmaceuticals in East Hanover, New Jersey. Kristin Wilson, DVM PhD DACVP, is a pathologist at Flagship Biosciences Inc. in Westminster, Colorado
Erik Hagendorn Famke Aeffner, DVM PhD DACVP, is a principal pathologist in the Comparative Biology and Safety Sciences Department at Amgen Inc. in South San Francisco, California. Hibret Adissu, DVM PhD DVSc DACVP, is an investigative pathologist in the Laboratory of Cancer Biology and Genetics, Center for Cancer Research, at the National Cancer Institute in Bethesda, Maryland. Michael C. Boyle, DVM PhD DACVP DABT, is a principal pathologist in the Comparative Biology and Safety Sciences at Amgen Inc. in Thousand Oaks, California. Robert D. Cardiff, MD PhD, is a distinguished professor of pathology (emeritus) at the Center for Comparative Medicine at the University of California in Davis, California. Erik Hagendorn is a senior scientist of informatics at AbbVie Bioresearch in Worcester, Massachusetts. Mark J. Hoenerhoff, DVM PhD DACVP, is an associate professor and veterinary pathologist at the In Vivo Animal Core, Unit for Laboratory Animal Medicine, at the University of Michigan in Ann Arbor, Michigan. Robert Klopfleisch, DVM PhD DACVP, is an associate professor at the Institute of Veterinary Pathology of the Freie Universitaet Berlin, in Berlin, Germany. Susan Newbigging, BSc MSc DVM DVSc, is a pathologist and Director of The Pathology Core at the Toronto Center of Phenogenomics in Toronto, Ontario, Canada. Dirk Schaudien, DVM PhD DACVP, is a veterinary pathologist at the Fraunhofer Institute for Toxicology and Experimental Medicine, in Hannover, Germany. Oliver Turner, BSC(Hons), BVSc MRCVS PhD DACVP DABT, is a senior pathologist in the Preclinical Safety department of Novartis Pharmaceuticals in East Hanover, New Jersey. Kristin Wilson, DVM PhD DACVP, is a pathologist at Flagship Biosciences Inc. in Westminster, Colorado
Mark J Hoenerhoff Famke Aeffner, DVM PhD DACVP, is a principal pathologist in the Comparative Biology and Safety Sciences Department at Amgen Inc. in South San Francisco, California. Hibret Adissu, DVM PhD DVSc DACVP, is an investigative pathologist in the Laboratory of Cancer Biology and Genetics, Center for Cancer Research, at the National Cancer Institute in Bethesda, Maryland. Michael C. Boyle, DVM PhD DACVP DABT, is a principal pathologist in the Comparative Biology and Safety Sciences at Amgen Inc. in Thousand Oaks, California. Robert D. Cardiff, MD PhD, is a distinguished professor of pathology (emeritus) at the Center for Comparative Medicine at the University of California in Davis, California. Erik Hagendorn is a senior scientist of informatics at AbbVie Bioresearch in Worcester, Massachusetts. Mark J. Hoenerhoff, DVM PhD DACVP, is an associate professor and veterinary pathologist at the In Vivo Animal Core, Unit for Laboratory Animal Medicine, at the University of Michigan in Ann Arbor, Michigan. Robert Klopfleisch, DVM PhD DACVP, is an associate professor at the Institute of Veterinary Pathology of the Freie Universitaet Berlin, in Berlin, Germany. Susan Newbigging, BSc MSc DVM DVSc, is a pathologist and Director of The Pathology Core at the Toronto Center of Phenogenomics in Toronto, Ontario, Canada. Dirk Schaudien, DVM PhD DACVP, is a veterinary pathologist at the Fraunhofer Institute for Toxicology and Experimental Medicine, in Hannover, Germany. Oliver Turner, BSC(Hons), BVSc MRCVS PhD DACVP DABT, is a senior pathologist in the Preclinical Safety department of Novartis Pharmaceuticals in East Hanover, New Jersey. Kristin Wilson, DVM PhD DACVP, is a pathologist at Flagship Biosciences Inc. in Westminster, Colorado
Robert Klopfleisch Famke Aeffner, DVM PhD DACVP, is a principal pathologist in the Comparative Biology and Safety Sciences Department at Amgen Inc. in South San Francisco, California. Hibret Adissu, DVM PhD DVSc DACVP, is an investigative pathologist in the Laboratory of Cancer Biology and Genetics, Center for Cancer Research, at the National Cancer Institute in Bethesda, Maryland. Michael C. Boyle, DVM PhD DACVP DABT, is a principal pathologist in the Comparative Biology and Safety Sciences at Amgen Inc. in Thousand Oaks, California. Robert D. Cardiff, MD PhD, is a distinguished professor of pathology (emeritus) at the Center for Comparative Medicine at the University of California in Davis, California. Erik Hagendorn is a senior scientist of informatics at AbbVie Bioresearch in Worcester, Massachusetts. Mark J. Hoenerhoff, DVM PhD DACVP, is an associate professor and veterinary pathologist at the In Vivo Animal Core, Unit for Laboratory Animal Medicine, at the University of Michigan in Ann Arbor, Michigan. Robert Klopfleisch, DVM PhD DACVP, is an associate professor at the Institute of Veterinary Pathology of the Freie Universitaet Berlin, in Berlin, Germany. Susan Newbigging, BSc MSc DVM DVSc, is a pathologist and Director of The Pathology Core at the Toronto Center of Phenogenomics in Toronto, Ontario, Canada. Dirk Schaudien, DVM PhD DACVP, is a veterinary pathologist at the Fraunhofer Institute for Toxicology and Experimental Medicine, in Hannover, Germany. Oliver Turner, BSC(Hons), BVSc MRCVS PhD DACVP DABT, is a senior pathologist in the Preclinical Safety department of Novartis Pharmaceuticals in East Hanover, New Jersey. Kristin Wilson, DVM PhD DACVP, is a pathologist at Flagship Biosciences Inc. in Westminster, Colorado
Susan Newbigging Famke Aeffner, DVM PhD DACVP, is a principal pathologist in the Comparative Biology and Safety Sciences Department at Amgen Inc. in South San Francisco, California. Hibret Adissu, DVM PhD DVSc DACVP, is an investigative pathologist in the Laboratory of Cancer Biology and Genetics, Center for Cancer Research, at the National Cancer Institute in Bethesda, Maryland. Michael C. Boyle, DVM PhD DACVP DABT, is a principal pathologist in the Comparative Biology and Safety Sciences at Amgen Inc. in Thousand Oaks, California. Robert D. Cardiff, MD PhD, is a distinguished professor of pathology (emeritus) at the Center for Comparative Medicine at the University of California in Davis, California. Erik Hagendorn is a senior scientist of informatics at AbbVie Bioresearch in Worcester, Massachusetts. Mark J. Hoenerhoff, DVM PhD DACVP, is an associate professor and veterinary pathologist at the In Vivo Animal Core, Unit for Laboratory Animal Medicine, at the University of Michigan in Ann Arbor, Michigan. Robert Klopfleisch, DVM PhD DACVP, is an associate professor at the Institute of Veterinary Pathology of the Freie Universitaet Berlin, in Berlin, Germany. Susan Newbigging, BSc MSc DVM DVSc, is a pathologist and Director of The Pathology Core at the Toronto Center of Phenogenomics in Toronto, Ontario, Canada. Dirk Schaudien, DVM PhD DACVP, is a veterinary pathologist at the Fraunhofer Institute for Toxicology and Experimental Medicine, in Hannover, Germany. Oliver Turner, BSC(Hons), BVSc MRCVS PhD DACVP DABT, is a senior pathologist in the Preclinical Safety department of Novartis Pharmaceuticals in East Hanover, New Jersey. Kristin Wilson, DVM PhD DACVP, is a pathologist at Flagship Biosciences Inc. in Westminster, Colorado
Dirk Schaudien Famke Aeffner, DVM PhD DACVP, is a principal pathologist in the Comparative Biology and Safety Sciences Department at Amgen Inc. in South San Francisco, California. Hibret Adissu, DVM PhD DVSc DACVP, is an investigative pathologist in the Laboratory of Cancer Biology and Genetics, Center for Cancer Research, at the National Cancer Institute in Bethesda, Maryland. Michael C. Boyle, DVM PhD DACVP DABT, is a principal pathologist in the Comparative Biology and Safety Sciences at Amgen Inc. in Thousand Oaks, California. Robert D. Cardiff, MD PhD, is a distinguished professor of pathology (emeritus) at the Center for Comparative Medicine at the University of California in Davis, California. Erik Hagendorn is a senior scientist of informatics at AbbVie Bioresearch in Worcester, Massachusetts. Mark J. Hoenerhoff, DVM PhD DACVP, is an associate professor and veterinary pathologist at the In Vivo Animal Core, Unit for Laboratory Animal Medicine, at the University of Michigan in Ann Arbor, Michigan. Robert Klopfleisch, DVM PhD DACVP, is an associate professor at the Institute of Veterinary Pathology of the Freie Universitaet Berlin, in Berlin, Germany. Susan Newbigging, BSc MSc DVM DVSc, is a pathologist and Director of The Pathology Core at the Toronto Center of Phenogenomics in Toronto, Ontario, Canada. Dirk Schaudien, DVM PhD DACVP, is a veterinary pathologist at the Fraunhofer Institute for Toxicology and Experimental Medicine, in Hannover, Germany. Oliver Turner, BSC(Hons), BVSc MRCVS PhD DACVP DABT, is a senior pathologist in the Preclinical Safety department of Novartis Pharmaceuticals in East Hanover, New Jersey. Kristin Wilson, DVM PhD DACVP, is a pathologist at Flagship Biosciences Inc. in Westminster, Colorado
Oliver Turner Famke Aeffner, DVM PhD DACVP, is a principal pathologist in the Comparative Biology and Safety Sciences Department at Amgen Inc. in South San Francisco, California. Hibret Adissu, DVM PhD DVSc DACVP, is an investigative pathologist in the Laboratory of Cancer Biology and Genetics, Center for Cancer Research, at the National Cancer Institute in Bethesda, Maryland. Michael C. Boyle, DVM PhD DACVP DABT, is a principal pathologist in the Comparative Biology and Safety Sciences at Amgen Inc. in Thousand Oaks, California. Robert D. Cardiff, MD PhD, is a distinguished professor of pathology (emeritus) at the Center for Comparative Medicine at the University of California in Davis, California. Erik Hagendorn is a senior scientist of informatics at AbbVie Bioresearch in Worcester, Massachusetts. Mark J. Hoenerhoff, DVM PhD DACVP, is an associate professor and veterinary pathologist at the In Vivo Animal Core, Unit for Laboratory Animal Medicine, at the University of Michigan in Ann Arbor, Michigan. Robert Klopfleisch, DVM PhD DACVP, is an associate professor at the Institute of Veterinary Pathology of the Freie Universitaet Berlin, in Berlin, Germany. Susan Newbigging, BSc MSc DVM DVSc, is a pathologist and Director of The Pathology Core at the Toronto Center of Phenogenomics in Toronto, Ontario, Canada. Dirk Schaudien, DVM PhD DACVP, is a veterinary pathologist at the Fraunhofer Institute for Toxicology and Experimental Medicine, in Hannover, Germany. Oliver Turner, BSC(Hons), BVSc MRCVS PhD DACVP DABT, is a senior pathologist in the Preclinical Safety department of Novartis Pharmaceuticals in East Hanover, New Jersey. Kristin Wilson, DVM PhD DACVP, is a pathologist at Flagship Biosciences Inc. in Westminster, Colorado
Kristin Wilson Famke Aeffner, DVM PhD DACVP, is a principal pathologist in the Comparative Biology and Safety Sciences Department at Amgen Inc. in South San Francisco, California. Hibret Adissu, DVM PhD DVSc DACVP, is an investigative pathologist in the Laboratory of Cancer Biology and Genetics, Center for Cancer Research, at the National Cancer Institute in Bethesda, Maryland. Michael C. Boyle, DVM PhD DACVP DABT, is a principal pathologist in the Comparative Biology and Safety Sciences at Amgen Inc. in Thousand Oaks, California. Robert D. Cardiff, MD PhD, is a distinguished professor of pathology (emeritus) at the Center for Comparative Medicine at the University of California in Davis, California. Erik Hagendorn is a senior scientist of informatics at AbbVie Bioresearch in Worcester, Massachusetts. Mark J. Hoenerhoff, DVM PhD DACVP, is an associate professor and veterinary pathologist at the In Vivo Animal Core, Unit for Laboratory Animal Medicine, at the University of Michigan in Ann Arbor, Michigan. Robert Klopfleisch, DVM PhD DACVP, is an associate professor at the Institute of Veterinary Pathology of the Freie Universitaet Berlin, in Berlin, Germany. Susan Newbigging, BSc MSc DVM DVSc, is a pathologist and Director of The Pathology Core at the Toronto Center of Phenogenomics in Toronto, Ontario, Canada. Dirk Schaudien, DVM PhD DACVP, is a veterinary pathologist at the Fraunhofer Institute for Toxicology and Experimental Medicine, in Hannover, Germany. Oliver Turner, BSC(Hons), BVSc MRCVS PhD DACVP DABT, is a senior pathologist in the Preclinical Safety department of Novartis Pharmaceuticals in East Hanover, New Jersey. Kristin Wilson, DVM PhD DACVP, is a pathologist at Flagship Biosciences Inc. in Westminster, Colorado

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Zhou Y, Zhang J, Huang J, Deng K, Zhang J, Qin Z, Wang Z, Zhang X, Tuo Y, Chen L, Chen Y, Huang P. Digital whole-slide image analysis for automated diatom test in forensic cases of drowning using a convolutional neural network algorithm. Forensic Sci Int 2019;302:109922. [DOI: 10.1016/j.forsciint.2019.109922] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2019] [Revised: 08/05/2019] [Accepted: 08/06/2019] [Indexed: 01/01/2023]

Colling R, Pitman H, Oien K, Rajpoot N, Macklin P, Snead D, Sackville T, Verrill C. Artificial intelligence in digital pathology: a roadmap to routine use in clinical practice. J Pathol 2019;249:143-150. [PMID: 31144302 DOI: 10.1002/path.5310] [Citation(s) in RCA: 137] [Impact Index Per Article: 22.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Revised: 05/02/2019] [Accepted: 05/10/2019] [Indexed: 12/28/2022]

Hanna MG, Pantanowitz L. Feasibility of using the Omnyx digital pathology system for cytology practice. J Am Soc Cytopathol 2019;8:182-189. [PMID: 31272601 DOI: 10.1016/j.jasc.2019.01.003] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2018] [Revised: 01/14/2019] [Accepted: 01/14/2019] [Indexed: 11/16/2022]

Abstract

INTRODUCTION

Whole slide imaging systems have focused mostly on surgical pathologic evaluation. However, for pathology laboratories to become fully digital, cytology slides will also need to be digitized, managed, viewed, and analyzed. Our aim was to determine the feasibility of using the Omnyx whole slide imaging system for various purposes in cytology.

MATERIALS AND METHODS

Our institution implemented the Omnyx digital pathology system, which was tested for feasibility and not implemented for clinical use in cytology. Glass slides (n = 18), scanned using various whole slide scanners, were uploaded into the Omnyx system. The system was evaluated for its feasibility with cytology case management, digital slide navigation and annotation, telecytology, and cytologic-histologic correlation.

RESULTS

The Omnyx software was able to manage cases similar to a laboratory information system. Users were able to electronically distribute, search, and sort the clinical cases. A graphic dashboard approach and virtual slide tray is available for end users to evaluate cases. The ability to create custom folders and drag-and-drop images into these folders fulfilled clinical, quality assurance, education, and research needs. Innovative tools for digital slide navigation and annotation (eg, auto-pan, adding text to annotation, hiding annotations, image coregistration) offered innovative methods to work with slides. Omnyx also provided a mechanism for sharing images with others to perform teleconsultation.

CONCLUSIONS

We have demonstrated the feasibility of using the Omnyx whole slide imaging system for various purposes in cytology practice. The application supported, not only case management, but also the ability to perform cytologic-histologic correlation and telecytology. The viewer offered many features that improved digital slide navigation and annotation.

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