1
|
Barcellona L, Nicolè L, Cappellesso R, Dei Tos AP, Ghidoni S. SlideTiler: A dataset creator software for boosting deep learning on histological whole slide images. J Pathol Inform 2024; 15:100356. [PMID: 38222323 PMCID: PMC10787253 DOI: 10.1016/j.jpi.2023.100356] [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: 10/07/2023] [Revised: 11/15/2023] [Accepted: 12/04/2023] [Indexed: 01/16/2024] Open
Abstract
The introduction of deep learning caused a significant breakthrough in digital pathology. Thanks to its capability of mining hidden data patterns in digitised histological slides to resolve diagnostic tasks and extract prognostic and predictive information. However, the high performance achieved in classification tasks depends on the availability of large datasets, whose collection and preprocessing are still time-consuming processes. Therefore, strategies to make these steps more efficient are worth investigation. This work introduces SlideTiler, an open-source software with a user-friendly graphical interface. SlideTiler can manage several image preprocessing phases through an intuitive workflow that does not require specific coding skills. The software was designed to provide direct access to virtual slides, allowing custom tiling of specific regions of interest drawn by the user, tile labelling, quality assessment, and direct export to dataset directories. To illustrate the functions and the scalability of SlideTiler, a deep learning-based classifier was implemented to classify 4 different tumour histotypes available in the TCGA repository. The results demonstrate the effectiveness of SlideTiler in facilitating data preprocessing and promoting accessibility to digitised pathology images for research purposes. Considering the increasing interest in deep learning applications of digital pathology, SlideTiler has a positive impact on this field. Moreover, SlideTiler has been conceived as a dynamic tool in constant evolution, and more updated and efficient versions will be released in the future.
Collapse
Affiliation(s)
- Leonardo Barcellona
- Department of Information Engineering, University of Padua, Padua, Italy
- Polytechnic University of Turin, Turin, Italy
| | - Lorenzo Nicolè
- Unit of Pathology and Cytopathology, Ospedale dell’Angelo, Mestre, Italy
- Department of Medicine, DIMED, University of Padua, Padua, Italy
| | | | - Angelo Paolo Dei Tos
- Department of Medicine, DIMED, University of Padua, Padua, Italy
- Department of Integrated diagnostics, Azienda Ospedale-Università, Padua, Italy
| | - Stefano Ghidoni
- Department of Information Engineering, University of Padua, Padua, Italy
| |
Collapse
|
2
|
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.
Collapse
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
| |
Collapse
|
3
|
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.
Collapse
Affiliation(s)
- Mahdi S Hosseini
- Department of Computer Science and Software Engineering (CSSE), Concordia Univeristy, Montreal, QC H3H 2R9, Canada
| | | | - Vincent Quoc-Huy Trinh
- Institute for Research in Immunology and Cancer of the University of Montreal, Montreal, QC H3T 1J4, Canada
| | - Lyndon Chan
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Danial Hasan
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Xingwen Li
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Stephen Yang
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Taehyo Kim
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Haochen Zhang
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Theodore Wu
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Kajanan Chinniah
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Sina Maghsoudlou
- Department of Computer Science and Software Engineering (CSSE), Concordia Univeristy, Montreal, QC H3H 2R9, Canada
| | - Ryan Zhang
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Jiadai Zhu
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Samir Khaki
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Andrei Buin
- Huron Digitial Pathology, St. Jacobs, ON N0B 2N0, Canada
| | - Fatemeh Chaji
- Department of Computer Science and Software Engineering (CSSE), Concordia Univeristy, Montreal, QC H3H 2R9, Canada
| | - Ala Salehi
- Department of Electrical and Computer Engineering, University of New Brunswick, Fredericton, NB E3B 5A3, Canada
| | - Bich Ngoc Nguyen
- University of Montreal Hospital Center, Montreal, QC H2X 0C2, Canada
| | - Dimitris Samaras
- Department of Computer Science, Stony Brook University, Stony Brook, NY 11794, United States
| | - Konstantinos N Plataniotis
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| |
Collapse
|
4
|
Lempp C, Arms S, Bertram CA, Klopfleisch R, Igl BW, Hezler L, Nolte T, Pohlmeyer-Esch G. A Minimal Approach to Demonstrate Concordance of Digital and Conventional Microscopy in Toxicologic Pathology. Toxicol Pathol 2024:1926233241255125. [PMID: 38829005 DOI: 10.1177/01926233241255125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/05/2024]
Abstract
Digitalization of pathology workflows has undergone a rapid evolution and has been widely established in the diagnostic field but remains a challenge in the nonclinical safety context due to lack of regulatory guidance and validation experience for good laboratory practice (GLP) use. One means to demonstrate that digital slides are fit for purpose, that is, provide sufficient quality for pathologists to reach a diagnosis, is conduction of comparison studies, which have been published both, for veterinary and human diagnostic pathology, but not for toxicologic pathology. Here, we present an approach that uses study material from nonclinical safety studies and that allows for the statistical comparison of concordance rates for glass and digital slide evaluation while minimizing time and effort for the involved personnel. Using a benchmark study design, we demonstrate that evaluation of digital slides fits the purpose of nonclinical safety evaluation. These results add to reports of successful workflow validations and support the full adaptation of digital pathology in the regulatory field.
Collapse
Affiliation(s)
- Charlotte Lempp
- Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riß, Germany
| | - Stefanie Arms
- Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riß, Germany
| | | | | | | | - Leonie Hezler
- Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riß, Germany
| | - Thomas Nolte
- Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riß, Germany
| | | |
Collapse
|
5
|
Eccher A, Marletta S, Sbaraglia M, Guerriero A, Rossi M, Gambaro G, Scarpa A, Dei Tos AP. Digital pathology structure and deployment in Veneto: a proof-of-concept study. Virchows Arch 2024:10.1007/s00428-024-03823-7. [PMID: 38744690 DOI: 10.1007/s00428-024-03823-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 04/16/2024] [Accepted: 05/08/2024] [Indexed: 05/16/2024]
Abstract
Nowadays pathology laboratories are worldwide facing a digital revolution, with an increasing number of institutions adopting digital pathology (DP) and whole slide imaging solutions. Despite indeed providing novel and helpful advantages, embracing a whole DP workflow is still challenging, especially for wide healthcare networks. The Azienda Zero of the Veneto Italian region has begun a process of a fully digital transformation of an integrated network of 12 hospitals producing nearly 3 million slides per year. In the present article, we describe the planning stages and the operative phases needed to support such a disruptive transition, along with the initial preliminary results emerging from the project. The ultimate goal of the DP program in the Veneto Italian region is to improve patients' clinical care through a safe and standardized process, encompassing a total digital management of pathology samples, easy file sharing with experienced colleagues, and automatic support by artificial intelligence tools.
Collapse
Affiliation(s)
- Albino Eccher
- Department of Medical and Sciences for Children and Adults, University of Modena and Reggio Emilia, University Hospital of Modena, Modena, Italy
| | - Stefano Marletta
- Department of Diagnostic and Public Health, Section of Pathology, University of Verona, P.Leee L.A. Scuro N. 10, 37134, Verona, Italy.
- Division of Pathology, Humanitas Istituto Clinico Catanese, Catania, Italy.
| | - Marta Sbaraglia
- Surgical Pathology and Cytopathology Unit, Department of Medicine-DIMED, University of Padua School of Medicine, Padua, Italy
| | - Angela Guerriero
- Surgical Pathology and Cytopathology Unit, Department of Medicine-DIMED, University of Padua School of Medicine, Padua, Italy
| | - Mattia Rossi
- Division of Nephrology, Department of Medicine, University of Verona, Verona, Italy
| | - Giovanni Gambaro
- Division of Nephrology, Department of Medicine, University of Verona, Verona, Italy
| | - Aldo Scarpa
- Department of Diagnostic and Public Health, Section of Pathology, University of Verona, P.Leee L.A. Scuro N. 10, 37134, Verona, Italy
| | - Angelo Paolo Dei Tos
- Surgical Pathology and Cytopathology Unit, Department of Medicine-DIMED, University of Padua School of Medicine, Padua, Italy
| |
Collapse
|
6
|
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: 5] [Impact Index Per Article: 5.0] [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.
Collapse
Affiliation(s)
- Ekta Jain
- Department of Pathology and Laboratory Medicine, CORE Diagnostics, Gurgaon, India
| | - Ankush Patel
- Department of Pathology, Wexner Medical Center, Columbus, OH, USA
| | - Anil V Parwani
- Department of Pathology, Wexner Medical Center, Columbus, OH, USA
| | - Saba Shafi
- Department of Pathology, Wexner Medical Center, Columbus, OH, USA
| | - Zoya Brar
- Department of Pathology and Laboratory Medicine, CORE Diagnostics, Gurgaon, India
| | - Shivani Sharma
- Department of Pathology and Laboratory Medicine, CORE Diagnostics, Gurgaon, India
| | - Sambit K Mohanty
- Department of Pathology and Laboratory Medicine, CORE Diagnostics, Gurgaon, India
| |
Collapse
|
7
|
McCaffrey C, Jahangir C, Murphy C, Burke C, Gallagher WM, Rahman A. Artificial intelligence in digital histopathology for predicting patient prognosis and treatment efficacy in breast cancer. Expert Rev Mol Diagn 2024; 24:363-377. [PMID: 38655907 DOI: 10.1080/14737159.2024.2346545] [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: 12/07/2023] [Accepted: 04/19/2024] [Indexed: 04/26/2024]
Abstract
INTRODUCTION Histological images contain phenotypic information predictive of patient outcomes. Due to the heavy workload of pathologists, the time-consuming nature of quantitatively assessing histological features, and human eye limitations to recognize spatial patterns, manually extracting prognostic information in routine pathological workflows remains challenging. Digital pathology has facilitated the mining and quantification of these features utilizing whole-slide image (WSI) scanners and artificial intelligence (AI) algorithms. AI algorithms to identify image-based biomarkers from the tumor microenvironment (TME) have the potential to revolutionize the field of oncology, reducing delays between diagnosis and prognosis determination, allowing for rapid stratification of patients and prescription of optimal treatment regimes, thereby improving patient outcomes. AREAS COVERED In this review, the authors discuss how AI algorithms and digital pathology can predict breast cancer patient prognosis and treatment outcomes using image-based biomarkers, along with the challenges of adopting this technology in clinical settings. EXPERT OPINION The integration of AI and digital pathology presents significant potential for analyzing the TME and its diagnostic, prognostic, and predictive value in breast cancer patients. Widespread clinical adoption of AI faces ethical, regulatory, and technical challenges, although prospective trials may offer reassurance and promote uptake, ultimately improving patient outcomes by reducing diagnosis-to-prognosis delivery delays.
Collapse
Affiliation(s)
- Christine McCaffrey
- UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - Chowdhury Jahangir
- UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - Clodagh Murphy
- UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - Caoimbhe Burke
- UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - William M Gallagher
- UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - Arman Rahman
- UCD School of Medicine, UCD Conway Institute, University College Dublin, Dublin, Ireland
| |
Collapse
|
8
|
Hijazi A, Bifulco C, Baldin P, Galon J. Digital Pathology for Better Clinical Practice. Cancers (Basel) 2024; 16:1686. [PMID: 38730638 PMCID: PMC11083211 DOI: 10.3390/cancers16091686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 04/24/2024] [Accepted: 04/25/2024] [Indexed: 05/13/2024] Open
Abstract
(1) Background: Digital pathology (DP) is transforming the landscape of clinical practice, offering a revolutionary approach to traditional pathology analysis and diagnosis. (2) Methods: This innovative technology involves the digitization of traditional glass slides which enables pathologists to access, analyze, and share high-resolution whole-slide images (WSI) of tissue specimens in a digital format. By integrating cutting-edge imaging technology with advanced software, DP promises to enhance clinical practice in numerous ways. DP not only improves quality assurance and standardization but also allows remote collaboration among experts for a more accurate diagnosis. Artificial intelligence (AI) in pathology significantly improves cancer diagnosis, classification, and prognosis by automating various tasks. It also enhances the spatial analysis of tumor microenvironment (TME) and enables the discovery of new biomarkers, advancing their translation for therapeutic applications. (3) Results: The AI-driven immune assays, Immunoscore (IS) and Immunoscore-Immune Checkpoint (IS-IC), have emerged as powerful tools for improving cancer diagnosis, prognosis, and treatment selection by assessing the tumor immune contexture in cancer patients. Digital IS quantitative assessment performed on hematoxylin-eosin (H&E) and CD3+/CD8+ stained slides from colon cancer patients has proven to be more reproducible, concordant, and reliable than expert pathologists' evaluation of immune response. Outperforming traditional staging systems, IS demonstrated robust potential to enhance treatment efficiency in clinical practice, ultimately advancing cancer patient care. Certainly, addressing the challenges DP has encountered is essential to ensure its successful integration into clinical guidelines and its implementation into clinical use. (4) Conclusion: The ongoing progress in DP holds the potential to revolutionize pathology practices, emphasizing the need to incorporate powerful AI technologies, including IS, into clinical settings to enhance personalized cancer therapy.
Collapse
Affiliation(s)
- Assia Hijazi
- The French National Institute of Health & Medical Research (INSERM), Laboratory of Integrative Cancer Immunology, F-75006 Paris, France;
- Equipe Labellisée Ligue Contre le Cancer, F-75006 Paris, France
- Centre de Recherche des Cordeliers, Sorbonne Université, Université Paris Cité, F-75006 Paris, France
| | - Carlo Bifulco
- Providence Genomics, Portland, OR 02912, USA;
- Earle A Chiles Research Institute, Portland, OR 97213, USA
| | - Pamela Baldin
- Department of Pathology, Cliniques Universitaires Saint Luc, UCLouvain, 1200 Brussels, Belgium;
| | - Jérôme Galon
- The French National Institute of Health & Medical Research (INSERM), Laboratory of Integrative Cancer Immunology, F-75006 Paris, France;
- Equipe Labellisée Ligue Contre le Cancer, F-75006 Paris, France
- Centre de Recherche des Cordeliers, Sorbonne Université, Université Paris Cité, F-75006 Paris, France
- Veracyte, 13009 Marseille, France
| |
Collapse
|
9
|
Azam AS, Tsang YW, Thirlwall J, Kimani PK, Sah S, Gopalakrishnan K, Boyd C, Loughrey MB, Kelly PJ, Boyle DP, Salto-Tellez M, Clark D, Ellis IO, Ilyas M, Rakha E, Bickers A, Roberts ISD, Soares MF, Neil DAH, Takyi A, Raveendran S, Hero E, Evans H, Osman R, Fatima K, Hughes RW, McIntosh SA, Moran GW, Ortiz-Fernandez-Sordo J, Rajpoot NM, Storey B, Ahmed I, Dunn JA, Hiller L, Snead DRJ. Digital pathology for reporting histopathology samples, including cancer screening samples - definitive evidence from a multisite study. Histopathology 2024; 84:847-862. [PMID: 38233108 DOI: 10.1111/his.15129] [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: 07/31/2023] [Revised: 11/28/2023] [Accepted: 12/13/2023] [Indexed: 01/19/2024]
Abstract
AIMS To conduct a definitive multicentre comparison of digital pathology (DP) with light microscopy (LM) for reporting histopathology slides including breast and bowel cancer screening samples. METHODS A total of 2024 cases (608 breast, 607 GI, 609 skin, 200 renal) were studied, including 207 breast and 250 bowel cancer screening samples. Cases were examined by four pathologists (16 study pathologists across the four speciality groups), using both LM and DP, with the order randomly assigned and 6 weeks between viewings. Reports were compared for clinical management concordance (CMC), meaning identical diagnoses plus differences which do not affect patient management. Percentage CMCs were computed using logistic regression models with crossed random-effects terms for case and pathologist. The obtained percentage CMCs were referenced to 98.3% calculated from previous studies. RESULTS For all cases LM versus DP comparisons showed the CMC rates were 99.95% [95% confidence interval (CI) = 99.90-99.97] and 98.96 (95% CI = 98.42-99.32) for cancer screening samples. In speciality groups CMC for LM versus DP showed: breast 99.40% (99.06-99.62) overall and 96.27% (94.63-97.43) for cancer screening samples; [gastrointestinal (GI) = 99.96% (99.89-99.99)] overall and 99.93% (99.68-99.98) for bowel cancer screening samples; skin 99.99% (99.92-100.0); renal 99.99% (99.57-100.0). Analysis of clinically significant differences revealed discrepancies in areas where interobserver variability is known to be high, in reads performed with both modalities and without apparent trends to either. CONCLUSIONS Comparing LM and DP CMC, overall rates exceed the reference 98.3%, providing compelling evidence that pathologists provide equivalent results for both routine and cancer screening samples irrespective of the modality used.
Collapse
Affiliation(s)
- Ayesha S Azam
- University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
- Warwick Medical School, University of Warwick, Coventry, UK
| | - Yee-Wah Tsang
- University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
| | | | - Peter K Kimani
- Warwick Medical School, University of Warwick, Coventry, UK
| | - Shatrughan Sah
- University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
| | | | - Clinton Boyd
- Belfast Health and Social Care Trust, Belfast, UK
| | - Maurice B Loughrey
- Belfast Health and Social Care Trust, Belfast, UK
- Queen's University, Belfast, UK
| | - Paul J Kelly
- Belfast Health and Social Care Trust, Belfast, UK
| | | | | | - David Clark
- Nottingham University Hospital NHS Trust, Nottingham, UK
| | - Ian O Ellis
- Nottingham University Hospital NHS Trust, Nottingham, UK
- University of Nottingham, Nottingham, UK
| | - Mohammad Ilyas
- Nottingham University Hospital NHS Trust, Nottingham, UK
- University of Nottingham, Nottingham, UK
| | - Emad Rakha
- Nottingham University Hospital NHS Trust, Nottingham, UK
- University of Nottingham, Nottingham, UK
| | - Adam Bickers
- Northern Lincolnshire and Goole NHS Foundation Trust, Scunthorpe, UK
| | - Ian S D Roberts
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Maria F Soares
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | | | - Abi Takyi
- University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
| | | | - Emily Hero
- University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
- University Hospitals of Leicester NHS Trust, Leicester, UK
| | - Harriet Evans
- University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
- Warwick Medical School, University of Warwick, Coventry, UK
| | - Rania Osman
- University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
| | - Khunsha Fatima
- Warwick Medical School, University of Warwick, Coventry, UK
| | - Rhian W Hughes
- University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
| | | | | | | | - Nasir M Rajpoot
- Computer Science Department, University of Warwick, Coventry, UK
| | - Ben Storey
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Imtiaz Ahmed
- University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
| | - Janet A Dunn
- Warwick Medical School, University of Warwick, Coventry, UK
| | - Louise Hiller
- Warwick Medical School, University of Warwick, Coventry, UK
| | - David R J Snead
- University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
- Warwick Medical School, University of Warwick, Coventry, UK
- Computer Science Department, University of Warwick, Coventry, UK
| |
Collapse
|
10
|
Sajithkumar A, Thomas J, Saji AM, Ali F, E K HH, Adampulan HAG, Sarathchand S. Artificial Intelligence in pathology: current applications, limitations, and future directions. Ir J Med Sci 2024; 193:1117-1121. [PMID: 37542634 DOI: 10.1007/s11845-023-03479-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 07/26/2023] [Indexed: 08/07/2023]
Abstract
PURPOSE Given AI's recent success in computer vision applications, majority of pathologists anticipate that it will be able to assist them with a variety of digital pathology activities. Massive improvements in deep learning have enabled a synergy between Artificial Intelligence (AI) and deep learning, enabling image-based diagnosis against the backdrop of digital pathology. AI-based solutions are being developed to eliminate errors and save pathologists time. AIMS In this paper, we will discuss the components that went into the use of Artificial Intelligence in Pathology, its use in the medical profession, the obstacles and constraints that it encounters, and the future possibilities of AI in the medical field. CONCLUSIONS Based on these factors, we elaborate upon the use of AI in medical pathology and provide future recommendations for its successful implementation in this field.
Collapse
Affiliation(s)
- Akhil Sajithkumar
- Department of Oral Pathology and Microbiology, Malabar Dental College and Research Centre, Manoor Chekanoor Road, Mudur PO, Edappal, Malappuram Dist, 679578, India.
| | - Jubin Thomas
- Department of Oral Pathology and Microbiology, Malabar Dental College and Research Centre, Manoor Chekanoor Road, Mudur PO, Edappal, Malappuram Dist, 679578, India
| | - Ajish Meprathumalil Saji
- Department of Oral Pathology and Microbiology, Malabar Dental College and Research Centre, Manoor Chekanoor Road, Mudur PO, Edappal, Malappuram Dist, 679578, India
| | - Fousiya Ali
- Department of Oral Pathology and Microbiology, Malabar Dental College and Research Centre, Manoor Chekanoor Road, Mudur PO, Edappal, Malappuram Dist, 679578, India
| | - Haneena Hasin E K
- Department of Oral Pathology and Microbiology, Malabar Dental College and Research Centre, Manoor Chekanoor Road, Mudur PO, Edappal, Malappuram Dist, 679578, India
| | - Hannan Abdul Gafoor Adampulan
- Department of Oral Pathology and Microbiology, Malabar Dental College and Research Centre, Manoor Chekanoor Road, Mudur PO, Edappal, Malappuram Dist, 679578, India
| | - Swathy Sarathchand
- Sree Narayana Institute of Medical Sciences, Chalakka - Kuthiathode Rd, North Kuthiathode, Kunnukara, Kerala, 683594, India
| |
Collapse
|
11
|
Bruce C, Prassas I, Mokhtar M, Clarke B, Youssef E, Wang C, Yousef GM. Transforming diagnostics: The implementation of digital pathology in clinical laboratories. Histopathology 2024. [PMID: 38516992 DOI: 10.1111/his.15178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 01/18/2024] [Accepted: 03/04/2024] [Indexed: 03/23/2024]
Abstract
Digital pathology (DP) has emerged as a cutting-edge technology that promises to revolutionise diagnostics in clinical laboratories. This perspective article explores the implementation planning and considerations of DP in a single multicentre institution in Canada, the University Health Network, discussing benefits, challenges, potential implications and considerations for future adopters. We examine the transition from traditional microscopy to digital slide scanning and its impact on pathology practice, patient care and medical research. Furthermore, we address the regulatory, infrastructure and change management considerations for successful integration into clinical laboratories. By highlighting the advantages and addressing concerns, we aim to shed light on the transformative potential of DP and its role in shaping the future of diagnostics.
Collapse
Affiliation(s)
- Christine Bruce
- Laboratory Medicine Program, University Health Network, Toronto, ON, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
| | - Ioannis Prassas
- Laboratory Medicine Program, University Health Network, Toronto, ON, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
| | - Mark Mokhtar
- Laboratory Medicine Program, University Health Network, Toronto, ON, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
| | - Blaise Clarke
- Laboratory Medicine Program, University Health Network, Toronto, ON, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
| | - Elaria Youssef
- Laboratory Medicine Program, University Health Network, Toronto, ON, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
| | - Catherine Wang
- Laboratory Medicine Program, University Health Network, Toronto, ON, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
| | - George M Yousef
- Laboratory Medicine Program, University Health Network, Toronto, ON, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
| |
Collapse
|
12
|
Magalhães G, Calisto R, Freire C, Silva R, Montezuma D, Canberk S, Schmitt F. Invisible for a few but essential for many: the role of Histotechnologists in the establishment of digital pathology. J Histotechnol 2024; 47:39-52. [PMID: 37869882 DOI: 10.1080/01478885.2023.2268297] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 10/03/2023] [Indexed: 10/24/2023]
Abstract
Digital pathology (DP) is indisputably the future for histopathology laboratories. The process of digital implementation requires deep workflow reorganisation which involves an interdisciplinary team. This transformation may have the greatest impact on the Histotechnologist (HTL) profession. Our review of the literature has clearly revealed that the role of HTLs in the establishment of DP is being unnoticed and guidance is limited. This article aims to bring HTLs from behind-the-scenes into the spotlight. Our objective is to provide them guidance and practical recommendations to successfully contribute to the implementation of a new digital workflow. Furthermore, it also intends to contribute for improvement of study programs, ensuring the role of HTL in DP is addressed as part of graduate and post-graduate education. In our review, we report on the differences encountered between workflow schemes and the limitations observed in this process. The authors propose a digital workflow to achieve its limitless potential, focusing on the HTL's role. This article explores the novel responsibilities of HTLs during specimen gross dissection, embedding, microtomy, staining, digital scanning, and whole slide image quality control. Furthermore, we highlight the benefits and challenges that DP implementation might bring the HTLs career. HTLs have an important role in the digital workflow: the responsibility of achieving the perfect glass slide.
Collapse
Affiliation(s)
- Gisela Magalhães
- Histopathology Department, Portsmouth Hospital University NHS Trust, Portsmouth, UK
- Department of Pathological Anatomy, School of Health Polytechnic of Porto (ESS|P.PORTO), Porto, Portugal
| | - Rita Calisto
- Department of Pathological Anatomy, School of Health Polytechnic of Porto (ESS|P.PORTO), Porto, Portugal
- Department of Pathological Anatomy, Hospital do Divino Espírito Santo, Ponta Delgada, Portugal
| | - Catarina Freire
- Department of Pathological Anatomy, School of Health Polytechnic of Porto (ESS|P.PORTO), Porto, Portugal
- Department of Pathological Anatomy, Hospital do Divino Espírito Santo, Ponta Delgada, Portugal
| | - Regina Silva
- Department of Pathological Anatomy, School of Health Polytechnic of Porto (ESS|P.PORTO), Porto, Portugal
- Centro de Investigação em Saúde e Ambiente, ESS,P.PORTO, Porto, Portugal
| | - Diana Montezuma
- Research & Development Unit, IMP Diagnostics, Porto, Portugal
- School of Medicine and Biomedical Sciences, University of Porto (ICBAS-UP), Porto, Portugal
| | - Sule Canberk
- Institute for Research and Innovation in Health (i3S), University of Porto, Porto, Portugal
- Cancer Signalling & Metabolism, Institute of Molecular Pathology and Immunology of the University of Porto (Ipatimup), Porto, Portugal
- Faculty of Medicine of the University of Porto (FMUP), Porto, Portugal
| | - Fernando Schmitt
- Department of Pathology, Faculty of Medicine of University of Porto, Porto, Portugal
- CINTESIS@RISE, Health Research Network, Alameda Prof. Hernâni Monteiro, Portugal
| |
Collapse
|
13
|
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 2: artificial intelligence in cytology: a concept paper with review and recommendations from the American Society of Cytopathology Digital Cytology Task Force. J Am Soc Cytopathol 2024; 13:97-110. [PMID: 38158317 DOI: 10.1016/j.jasc.2023.11.005] [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 cytology laboratory. However, peer-reviewed real-world data and literature are lacking in regard to 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 is presented as a separate paper which details a review and best practice recommendations for incorporating digital cytology into practice. Part 2 of the white paper presented here provides a comprehensive review of AI in cytology practice along with best practice recommendations and legal considerations. Additionally, the cytology global survey results highlighting current AI practices by various laboratories, as well as current attitudes, are reported.
Collapse
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.
| |
Collapse
|
14
|
Lin O, Alperstein S, Barkan GA, Cuda JM, Kezlarian B, Jhala D, Jin X, Mehrotra S, Monaco SE, Rao J, Saieg M, Thrall M, Pantanowitz L. American Society of Cytopathology Telecytology validation recommendations for rapid on-site evaluation (ROSE). J Am Soc Cytopathol 2024; 13:111-121. [PMID: 38310002 DOI: 10.1016/j.jasc.2023.12.001] [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: 10/13/2023] [Revised: 12/03/2023] [Accepted: 12/06/2023] [Indexed: 02/05/2024]
Abstract
Telecytology has multiple applications, including rapid onsite evaluation (ROSE) of fine-needle aspiration (FNA) specimens. It can enhance cytopathology practice by increasing productivity, reducing costs, and providing subspecialty expertise in areas with limited access to a cytopathologist. However, there are currently no specific validation guidelines to ensure safe practice and compliance with regulations. This initiative, promoted by the American Society of Cytopathology (ASC), intends to propose recommendations for telecytology implementation. These recommendations propose that the validation process should include testing of all hardware and software, both separately and as a whole; training of all individuals who will participate in telecytology with regular competency evaluations; a structured approach using retrospective slides with defined diagnoses for validation and prospective cases for verification and quality assurance. Telecytology processes must be integrated into the laboratory's quality management system and benchmarks for discrepancy rates between preliminary and final diagnoses should be established and monitored. Special attention should be paid to minimize discrepancies that downgrade malignant cases to benign (false positive on telecytology). Currently, billing and reimbursement codes for telecytology are not yet available. Once, they are, recommendation of the appropriate usage of these codes would be a part of the recommendations. These proposed guidelines are intended to be a resource for laboratories that are considering implementing telecytology. These recommendations can help to ensure the safe and effective use of telecytology and maximize its benefits for patients.
Collapse
Affiliation(s)
- Oscar Lin
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York.
| | - Susan Alperstein
- Department of Pathology and Laboratory Medicine, New York Presbyterian Hospital, New York, New York
| | - Güliz A Barkan
- Department of Pathology and Laboratory Medicine, Loyola University Medical Center, Maywood, Illinois
| | - Jacqueline M Cuda
- Department of Pathology and Laboratory Services, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - Brie Kezlarian
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Darshana Jhala
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Pittsburgh, Pennsylvania
| | - Xiaobing Jin
- Department of Pathology, University of Michigan, Ann Arbor, Michigan
| | - Swati Mehrotra
- Department of Pathology and Laboratory Medicine, Loyola University Medical Center, Maywood, Illinois
| | - Sara E Monaco
- Department of Pathology, Geisinger Medical Center, Danville, Pennsylvania
| | - Jianyu Rao
- Department of Pathology and Laboratory, UCLA Health, Los Angeles, California
| | - Mauro Saieg
- Department of Pathology, Santa Casa Medical School, Sao Paulo, Brazil
| | - Michael Thrall
- Department of Pathology, Houston Methodist Hospital, Houston, Texas
| | - Liron Pantanowitz
- Department of Pathology and Laboratory Services, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| |
Collapse
|
15
|
Levy J, Yao K. The future of digital cytology and artificial intelligence: an editorial commentary for Digital Cytology part 1 and 2. J Am Soc Cytopathol 2024; 13:81-85. [PMID: 38267293 DOI: 10.1016/j.jasc.2023.12.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 12/20/2023] [Indexed: 01/26/2024]
Affiliation(s)
- Joshua Levy
- Department of Pathology & Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, California
| | - Keluo Yao
- Department of Pathology & Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, California; Enterprise Information Services, Cedars-Sinai, Los Angeles, California.
| |
Collapse
|
16
|
Eccher A, Becker JU, Pagni F, Cazzaniga G, Rossi M, Gambaro G, L’Imperio V, Marletta S. The Puzzle of Preimplantation Kidney Biopsy Decision-Making Process: The Pathologist Perspective. Life (Basel) 2024; 14:254. [PMID: 38398762 PMCID: PMC10890315 DOI: 10.3390/life14020254] [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: 01/11/2024] [Revised: 02/11/2024] [Accepted: 02/13/2024] [Indexed: 02/25/2024] Open
Abstract
Kidney transplantation is the best treatment for end-stage renal disease since it offers the greatest survival benefit compared to dialysis. The gap between the number of renal transplants performed and the number of patients awaiting renal transplants leads to a steadily increasing pressure on the scientific community. Kidney preimplantation biopsy is used as a component of the evaluation of organ quality before acceptance for transplantation. However, the reliability and predictive value of biopsy data are controversial. Most of the previously proposed predictive models were not associated with graft survival, but what has to be reaffirmed is that histologic examination of kidney tissue can provide an objective window on the state of the organ that cannot be deduced from clinical records and renal functional studies. The balance of evidence indicates that reliable decisions about donor suitability must be made based on the overall picture. This work discusses recent trends that can reduce diagnostic timing and variability among players in the decision-making process that lead to kidney transplants, from the pathologist's perspective.
Collapse
Affiliation(s)
- Albino Eccher
- Department of Medical and Sciences for Children and Adults, University of Modena and Reggio Emilia, University Hospital of Modena, 41100 Modena, Italy
| | - Jan Ulrich Becker
- Institute of Pathology, University Hospital of Cologne, 50923 Cologne, Germany;
| | - Fabio Pagni
- Department of Medicine and Surgery, Pathology, IRCCS Fondazione San Gerardo dei Tintori, University of Milano-Bicocca, 20126 Milano, Italy; (F.P.); (G.C.); (V.L.)
| | - Giorgio Cazzaniga
- Department of Medicine and Surgery, Pathology, IRCCS Fondazione San Gerardo dei Tintori, University of Milano-Bicocca, 20126 Milano, Italy; (F.P.); (G.C.); (V.L.)
| | - Mattia Rossi
- Division of Nephrology, Department of Medicine, University of Verona, 37129 Verona, Italy; (M.R.); (G.G.)
| | - Giovanni Gambaro
- Division of Nephrology, Department of Medicine, University of Verona, 37129 Verona, Italy; (M.R.); (G.G.)
| | - Vincenzo L’Imperio
- Department of Medicine and Surgery, Pathology, IRCCS Fondazione San Gerardo dei Tintori, University of Milano-Bicocca, 20126 Milano, Italy; (F.P.); (G.C.); (V.L.)
| | - Stefano Marletta
- Department of Diagnostics and Public Health, Section of Pathology, University of Verona, 37129 Verona, Italy;
- Division of Pathology, Humanitas Istituto Clinico Catanese, 95045 Catania, Italy
| |
Collapse
|
17
|
Das K. COVID and cytopathology training: Impact and innovations. Diagn Cytopathol 2024. [PMID: 38323803 DOI: 10.1002/dc.25280] [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: 09/29/2023] [Revised: 01/02/2024] [Accepted: 01/18/2024] [Indexed: 02/08/2024]
Abstract
Graduate medical education and training in Cytopathology faced numerous unexpected challenges during the COVID-19 pandemic of 2020. It was caused by the SARS-Co-V2 coronavirus and transmitted by breathing droplets or aerosol particles containing the virus and less commonly by contact with infected surfaces and fomites. To mitigate the rapid spread of disease non-essential services were closed, surgical procedures were prioritized, and "social distancing" was implemented. These measures led to a marked decline in the volume of specimens, number of fine needle aspiration (FNA) and rapid on-site evaluation procedures performed. The trainees in Pathology were required to stay at home either entirely or partly during the early period of the pandemic. This led to re-designing of the cytopathology training program nationwide. Many innovative methods and protocols were put in place to overcome the challenges faced and adjustments made in creating the virtual training program in Cytopathology. On May 5th, 2023, the WHO declared that COVID-19 was no longer a global emergency. Regulations were lifted and healthcare services returned to pre-pandemic era. Graduate medical education and training returned to normal however many changes were incorporated into the training program moving forward. Herein the impacts and innovations that COVID-19 had on Cytopathology training are described.
Collapse
Affiliation(s)
- Kasturi Das
- Pathology and Laboratory Medicine, Donald and Barbara Zucker School of Medicine at Hofstra Northwell, Hempstead, New York, USA
- Division of Cytopathology, Northwell Health Laboratories, Greenvale, New York, USA
| |
Collapse
|
18
|
Brancato V, Esposito G, Coppola L, Cavaliere C, Mirabelli P, Scapicchio C, Borgheresi R, Neri E, Salvatore M, Aiello M. Standardizing digital biobanks: integrating imaging, genomic, and clinical data for precision medicine. J Transl Med 2024; 22:136. [PMID: 38317237 PMCID: PMC10845786 DOI: 10.1186/s12967-024-04891-8] [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: 11/28/2023] [Accepted: 01/14/2024] [Indexed: 02/07/2024] Open
Abstract
Advancements in data acquisition and computational methods are generating a large amount of heterogeneous biomedical data from diagnostic domains such as clinical imaging, pathology, and next-generation sequencing (NGS), which help characterize individual differences in patients. However, this information needs to be available and suitable to promote and support scientific research and technological development, supporting the effective adoption of the precision medicine approach in clinical practice. Digital biobanks can catalyze this process, facilitating the sharing of curated and standardized imaging data, clinical, pathological and molecular data, crucial to enable the development of a comprehensive and personalized data-driven diagnostic approach in disease management and fostering the development of computational predictive models. This work aims to frame this perspective, first by evaluating the state of standardization of individual diagnostic domains and then by identifying challenges and proposing a possible solution towards an integrative approach that can guarantee the suitability of information that can be shared through a digital biobank. Our analysis of the state of the art shows the presence and use of reference standards in biobanks and, generally, digital repositories for each specific domain. Despite this, standardization to guarantee the integration and reproducibility of the numerical descriptors generated by each domain, e.g. radiomic, pathomic and -omic features, is still an open challenge. Based on specific use cases and scenarios, an integration model, based on the JSON format, is proposed that can help address this problem. Ultimately, this work shows how, with specific standardization and promotion efforts, the digital biobank model can become an enabling technology for the comprehensive study of diseases and the effective development of data-driven technologies at the service of precision medicine.
Collapse
Affiliation(s)
| | - Giuseppina Esposito
- Bio Check Up S.R.L, 80121, Naples, Italy
- Department of Advanced Biomedical Sciences, University of Naples Federico II, 80131, Naples, Italy
| | | | | | - Peppino Mirabelli
- UOS Laboratori di Ricerca e Biobanca, AORN Santobono-Pausilipon, Via Teresa Ravaschieri, 8, 80122, Naples, Italy
| | - Camilla Scapicchio
- Academic Radiology, Department of Translational Research, University of Pisa, via Roma, 67, 56126, Pisa, Italy
| | - Rita Borgheresi
- Academic Radiology, Department of Translational Research, University of Pisa, via Roma, 67, 56126, Pisa, Italy
| | - Emanuele Neri
- Academic Radiology, Department of Translational Research, University of Pisa, via Roma, 67, 56126, Pisa, Italy
| | | | | |
Collapse
|
19
|
Stegmüller T, Abbet C, Bozorgtabar B, Clarke H, Petignat P, Vassilakos P, Thiran JP. Self-supervised learning-based cervical cytology for the triage of HPV-positive women in resource-limited settings and low-data regime. Comput Biol Med 2024; 169:107809. [PMID: 38113684 DOI: 10.1016/j.compbiomed.2023.107809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 11/24/2023] [Accepted: 12/01/2023] [Indexed: 12/21/2023]
Abstract
Screening Papanicolaou test samples has proven to be highly effective in reducing cervical cancer-related mortality. However, the lack of trained cytopathologists hinders its widespread implementation in low-resource settings. Deep learning-assisted telecytology diagnosis emerges as an appealing alternative, but it requires the collection of large annotated training datasets, which is costly and time-consuming. In this paper, we demonstrate that the abundance of unlabeled images that can be extracted from Pap smear test whole slide images presents a fertile ground for self-supervised learning methods, yielding performance improvements compared to off-the-shelf pre-trained models for various downstream tasks. In particular, we propose Cervical Cell Copy-Pasting (C3P) as an effective augmentation method, which enables knowledge transfer from public and labeled single-cell datasets to unlabeled tiles. Not only does C3P outperforms naive transfer from single-cell images, but we also demonstrate its advantageous integration into multiple instance learning methods. Importantly, all our experiments are conducted on our introduced in-house dataset comprising liquid-based cytology Pap smear images obtained using low-cost technologies. This aligns with our long-term objective of deep learning-assisted telecytology for diagnosis in low-resource settings.
Collapse
Affiliation(s)
- Thomas Stegmüller
- Ecole Polytechnique Fédérale de Lausanne, Lausanne, 1015, Switzerland.
| | - Christian Abbet
- Ecole Polytechnique Fédérale de Lausanne, Lausanne, 1015, Switzerland
| | - Behzad Bozorgtabar
- Ecole Polytechnique Fédérale de Lausanne, Lausanne, 1015, Switzerland; Centre Hospitalier Universitaire Vaudois, Lausanne, 1011, Switzerland
| | - Holly Clarke
- Hôpitaux Universitaires de Genève, Genève, 1205, Switzerland
| | | | | | - Jean-Philippe Thiran
- Ecole Polytechnique Fédérale de Lausanne, Lausanne, 1015, Switzerland; Centre Hospitalier Universitaire Vaudois, Lausanne, 1011, Switzerland
| |
Collapse
|
20
|
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.
Collapse
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
| |
Collapse
|
21
|
Alajaji SA, Khoury ZH, Elgharib M, Saeed M, Ahmed ARH, Khan MB, Tavares T, Jessri M, Puche AC, Hoorfar H, Stojanov I, Sciubba JJ, Sultan AS. Generative Adversarial Networks in Digital Histopathology: Current Applications, Limitations, Ethical Considerations, and Future Directions. Mod Pathol 2024; 37:100369. [PMID: 37890670 DOI: 10.1016/j.modpat.2023.100369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 10/04/2023] [Accepted: 10/19/2023] [Indexed: 10/29/2023]
Abstract
Generative adversarial networks (GANs) have gained significant attention in the field of image synthesis, particularly in computer vision. GANs consist of a generative model and a discriminative model trained in an adversarial setting to generate realistic and novel data. In the context of image synthesis, the generator produces synthetic images, whereas the discriminator determines their authenticity by comparing them with real examples. Through iterative training, the generator allows the creation of images that are indistinguishable from real ones, leading to high-quality image generation. Considering their success in computer vision, GANs hold great potential for medical diagnostic applications. In the medical field, GANs can generate images of rare diseases, aid in learning, and be used as visualization tools. GANs can leverage unlabeled medical images, which are large in size, numerous in quantity, and challenging to annotate manually. GANs have demonstrated remarkable capabilities in image synthesis and have the potential to significantly impact digital histopathology. This review article focuses on the emerging use of GANs in digital histopathology, examining their applications and potential challenges. Histopathology plays a crucial role in disease diagnosis, and GANs can contribute by generating realistic microscopic images. However, ethical considerations arise because of the reliance on synthetic or pseudogenerated images. Therefore, the manuscript also explores the current limitations and highlights the ethical considerations associated with the use of this technology. In conclusion, digital histopathology has seen an emerging use of GANs for image enhancement, such as color (stain) normalization, virtual staining, and ink/marker removal. GANs offer significant potential in transforming digital pathology when applied to specific and narrow tasks (preprocessing enhancements). Evaluating data quality, addressing biases, protecting privacy, ensuring accountability and transparency, and developing regulation are imperative to ensure the ethical application of GANs.
Collapse
Affiliation(s)
- Shahd A Alajaji
- Department of Oncology and Diagnostic Sciences, University of Maryland School of Dentistry, Baltimore, Maryland; Department of Oral Medicine and Diagnostic Sciences, College of Dentistry, King Saud University, Riyadh, Saudi Arabia; Division of Artificial Intelligence Research, University of Maryland School of Dentistry, Baltimore, Maryland
| | - Zaid H Khoury
- Department of Oral Diagnostic Sciences and Research, School of Dentistry, Meharry Medical College, Nashville, Tennessee
| | | | | | | | | | - Tiffany Tavares
- Department of Comprehensive Dentistry, UT Health San Antonio, School of Dentistry, San Antonio, Texas
| | - Maryam Jessri
- Oral Medicine and Pathology Department, School of Dentistry, University of Queensland, Herston, Queensland, Australia; Oral Medicine Department, Metro North Hospital and Health Services, Queensland Health, Queensland, Australia
| | - Adam C Puche
- Department of Neurobiology, University of Maryland School of Medicine, Baltimore, Maryland
| | - Hamid Hoorfar
- Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland
| | - Ivan Stojanov
- Department of Pathology, Robert J. Tomsich Pathology and Laboratory Medicine Institute, Cleveland Clinic, Cleveland, Ohio
| | - James J Sciubba
- Department of Otolaryngology, Head and Neck Surgery, The Johns Hopkins University, Baltimore, Maryland
| | - Ahmed S Sultan
- Department of Oncology and Diagnostic Sciences, University of Maryland School of Dentistry, Baltimore, Maryland; Division of Artificial Intelligence Research, University of Maryland School of Dentistry, Baltimore, Maryland; University of Maryland Marlene and Stewart Greenebaum Comprehensive Cancer Center, Baltimore, Maryland.
| |
Collapse
|
22
|
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]
Abstract
PURPOSE Although axillary dissection is no longer indicated for many breast cancer patients with 1-2 positive axillary sentinel lymph nodes (ASLN), intraoperative ASLN assessment is still performed in many institutions for patients undergoing mastectomy or neoadjuvant therapy. With recent advancements in digital pathology, pathologists increasingly evaluate ASLN via remote telepathology. We aimed to compare the performance characteristics of remote telepathology and conventional on-site intraoperative ASLN assessment. METHODS Data from ASLN evaluation for breast cancer patients performed at two sites between April 2021 and October 2022 was collated. Remote telepathology consultation was conducted via the Aperio eSlideManager system. RESULTS A total of 385 patients were identified during the study period (83 telepathology, 302 on-site evaluations). Although not statistically significant (P = 0.20), the overall discrepancy rate between intraoperative and final diagnoses was slightly higher at 9.6% (8/83) for telepathology compared with 5.3% (16/302) for on-site assessment. Further comparison of performance characteristics of ASLN assessment between telepathology and conventional on-site evaluation revealed no statistically significant differences between deferral rates, discrepancy rates, interpretive or sampling errors, major or minor disagreements, false negative or false positive results as well as clinical impact and turn-around time (P ≥ 0.12). CONCLUSION ASLN assessment via telepathology is not significantly different from conventional on-site evaluation, although it shows a slightly higher overall discrepancy rate between intraoperative and final diagnoses (9.6% vs. 5.3%). Further studies are warranted to ensure accuracy of ASLN assessment via telepathology.
Collapse
Affiliation(s)
- Gulisa Turashvili
- Department of Pathology and Laboratory Medicine, Emory University Hospital, 1364 Clifton Road NE, Atlanta, GA, 30322, USA.
| | - Sandra Gjorgova Gjeorgjievski
- Department of Pathology and Laboratory Medicine, Emory University Hospital, 1364 Clifton Road NE, Atlanta, GA, 30322, USA
| | - Qun Wang
- Department of Pathology and Laboratory Medicine, Emory University Hospital, 1364 Clifton Road NE, Atlanta, GA, 30322, USA
| | - Abdulwahab Ewaz
- Department of Pathology and Laboratory Medicine, Emory University Hospital, 1364 Clifton Road NE, Atlanta, GA, 30322, USA
| | - Di Ai
- Department of Pathology and Laboratory Medicine, Emory University Hospital, 1364 Clifton Road NE, Atlanta, GA, 30322, USA
| | - Xiaoxian Li
- Department of Pathology and Laboratory Medicine, Emory University Hospital, 1364 Clifton Road NE, Atlanta, GA, 30322, USA
| | - Sunil S Badve
- Department of Pathology and Laboratory Medicine, Emory University Hospital, 1364 Clifton Road NE, Atlanta, GA, 30322, USA
| |
Collapse
|
23
|
Erion Barner LA, Gao G, Reddi DM, Lan L, Burke W, Mahmood F, Grady WM, Liu JTC. Artificial Intelligence-Triaged 3-Dimensional Pathology to Improve Detection of Esophageal Neoplasia While Reducing Pathologist Workloads. Mod Pathol 2023; 36:100322. [PMID: 37657711 DOI: 10.1016/j.modpat.2023.100322] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Revised: 07/25/2023] [Accepted: 08/25/2023] [Indexed: 09/03/2023]
Abstract
Early detection of esophageal neoplasia via evaluation of endoscopic surveillance biopsies is the key to maximizing survival for patients with Barrett's esophagus, but it is hampered by the sampling limitations of conventional slide-based histopathology. Comprehensive evaluation of whole biopsies with 3-dimensional (3D) pathology may improve early detection of malignancies, but large 3D pathology data sets are tedious for pathologists to analyze. Here, we present a deep learning-based method to automatically identify the most critical 2-dimensional (2D) image sections within 3D pathology data sets for pathologists to review. Our method first generates a 3D heatmap of neoplastic risk for each biopsy, then classifies all 2D image sections within the 3D data set in order of neoplastic risk. In a clinical validation study, we diagnose esophageal biopsies with artificial intelligence-triaged 3D pathology (3 images per biopsy) vs standard slide-based histopathology (16 images per biopsy) and show that our method improves detection sensitivity while reducing pathologist workloads.
Collapse
Affiliation(s)
| | - Gan Gao
- Department of Mechanical Engineering, University of Washington, Seattle, Washington
| | - Deepti M Reddi
- Department of Laboratory Medicine & Pathology, University of Washington School of Medicine, Seattle, Washington
| | - Lydia Lan
- Department of Mechanical Engineering, University of Washington, Seattle, Washington; Department of Biology, University of Washington, Seattle, Washington
| | - Wynn Burke
- Department of Laboratory Medicine & Pathology, University of Washington School of Medicine, Seattle, Washington; Department of Medicine (Gastroenterology Division), University of Washington School of Medicine, Seattle, Washington
| | - Faisal Mahmood
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts; Cancer Program, Broad Institute of Harvard and MIT, Cambridge, Massachusetts; Harvard Data Science Initiative, Harvard University, Cambridge, Massachusetts
| | - William M Grady
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Jonathan T C Liu
- Department of Mechanical Engineering, University of Washington, Seattle, Washington; Department of Laboratory Medicine & Pathology, University of Washington School of Medicine, Seattle, Washington; Department of Bioengineering, University of Washington, Seattle, Washington.
| |
Collapse
|
24
|
Pinto DG, Bychkov A, Tsuyama N, Fukuoka J, Eloy C. Real-World Implementation of Digital Pathology: Results From an Intercontinental Survey. J Transl Med 2023; 103:100261. [PMID: 37839634 DOI: 10.1016/j.labinv.2023.100261] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Revised: 09/29/2023] [Accepted: 10/07/2023] [Indexed: 10/17/2023] Open
Abstract
The past 70 years have been characterized by rapid advancements in computer technology, and the health care system has not been immune to this trend. However, anatomical pathology has remained largely an analog discipline. In recent years, this has been changing with the growing adoption of digital pathology, partly driven by the potential of computer-aided diagnosis. As part of an international collaboration, we conducted a comprehensive survey to gain a deeper understanding of the status of digital pathology implementation in Europe and Asia. A total of 127 anatomical pathology laboratories participated in the survey, including 75 from Europe and 52 from Asia, with 72 laboratories having established digital pathology workflow and 55 without digital pathology. Laboratories using digital pathology for diagnostic (n = 29) and nondiagnostic (n = 43) purposes were thoroughly questioned about their implementation strategies and institutional experiences, including details on equipment, storage, integration with laboratory information system, computer-aided diagnosis, and the costs of going digital. The impact of the digital pathology workflow was also evaluated, focusing on turnaround time, specimen traceability, quality control, and overall satisfaction. Laboratories without access to digital pathology were asked to provide insights into their perceptions of the technology, expectations, barriers to adoption, and potential facilitators. Our findings indicate that although digital pathology is still the future for many, it is already the present for some. This decade may be a time when anatomical pathology finally embraces digital revolution on a larger scale.
Collapse
Affiliation(s)
- Daniel Gomes Pinto
- Serviço de Anatomia Patológica, Hospital Garcia de Orta, EPE, Almada, Portugal; NOVA Medical School, Lisboa, Portugal; IPATIMUP - Instituto de Patologia e Imunologia Molecular da Universidade do Porto, Porto, Portugal
| | - Andrey Bychkov
- Department of Pathology, Kameda Medical Center, Kamogawa, Chiba, Japan
| | - Naoko Tsuyama
- Division of Pathology, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Junya Fukuoka
- Department of Pathology, Kameda Medical Center, Kamogawa, Chiba, Japan; Department of Pathology Informatics, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan
| | - Catarina Eloy
- IPATIMUP - Instituto de Patologia e Imunologia Molecular da Universidade do Porto, Porto, Portugal; Instituto de Investigação e Inovação Em Saúde (i3S) and Faculty of Medicine, University of Porto (FMUP), Porto, Portugal.
| |
Collapse
|
25
|
de Velozo G, Cordeiro J, Sousa J, Holanda AC, Pessoa G, Porfírio M, Távora F. Comparison of glass and digital slides for cervical cytopathology screening and interpretation. Diagn Cytopathol 2023; 51:735-743. [PMID: 37587842 DOI: 10.1002/dc.25209] [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: 05/22/2023] [Revised: 07/21/2023] [Accepted: 07/25/2023] [Indexed: 08/18/2023]
Abstract
Cervical cancer is the second most common form of cancer and a leading cause of premature death among women aged 15 to 44 worldwide. In Brazil, there is a high prevalence of infection by the human papillomavirus - HPV. Digital pathology optimizes time and space for reading cervicovaginal cytology slides. We evaluated the feasibility of using whole slide images (WSI) for the routine interpretation of cytology exams. A total of 99 cases of vaginal cytology were selected from a reference laboratory in Northeastern Brazil. Three cytotechnicians participated in the study. Cellular atypia was the one that most presented concordance values. Two observers almost perfectly agreed (k = 0.86 and k = 0.84, respectively) on the negative diagnoses. The performance of the evaluators for NILM (negative for intraepithelial lesion and malignancy) showed high reproducibility and sensitivity in the digital slides, mainly between evaluators A and C. In contrast, the microbiology group showed disagreement between the diagnoses by digital slides and the standard- gold. The concordance between the digital diagnoses and the gold standard for ASCUS was 89%. In the inflammatory category, Spearman's test showed similar results between raters A, B, and C (rs = 0.47, rs = 0.41, and rs = 0.47, respectively). This study reports the diagnostic validation using digital slides in view of the need to optimize the cytology visualization process. Our experience shows good diagnostic agreement between digital and optical microscopy in several analyzed categories, but mainly in relation to cellular atypia and inflammatory processes.
Collapse
Affiliation(s)
| | - Juliana Cordeiro
- Federal University of Ceará, Argos Patologia Laboratory, Fortaleza, Brazil
| | | | | | | | - Mônica Porfírio
- Federal University of Ceará, Argos Patologia Laboratory, Fortaleza, Brazil
| | - Fábio Távora
- Federal University of Ceará, Argos Patologia Laboratory, Fortaleza, Brazil
| |
Collapse
|
26
|
Shaker N, Shilo K, Esnakula AK, Shafi S, Challa B, Patel A, Kellough DA, Hammond S, Javaid S, Satturwar S, Yearsley MM, Li Z, Limbach AL, Lujan G, Parwani AV. Comparison of four different displays for identification of select pathologic features extracted from whole slide images of surgical pathology cases. Pathol Res Pract 2023; 251:154843. [PMID: 37826873 DOI: 10.1016/j.prp.2023.154843] [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: 07/21/2023] [Accepted: 09/30/2023] [Indexed: 10/14/2023]
Abstract
BACKGROUND The establishment of minimum standards for display selection for the whole slide image (WSI) interpretation has not been fully defined. Recently, pathologists have increasingly preferred using remote displays for clinical diagnostics. Our study aims to assess and compare the performance of three fixed work displays and one remote personal display in accurately identifying ten selected pathologic features integrated into WSIs. DESIGN Hematoxylin and eosin-stained glass slides were digitized using Philips scanners. Seven practicing pathologists and three residents reviewed ninety WSIs to identify ten pathologic features using the LG, Dell, and Samsung and an optional consumer-grade display. Ten pathologic features included eosinophils, neutrophils, plasma cells, granulomas, necrosis, mucin, hemosiderin, crystals, nucleoli, and mitoses. RESULTS The accuracy of the identification of ten features on different types of displays did not significantly differ among the three types of "fixed" workplace displays. The highest accuracy was observed for the identification of neutrophils, eosinophils, plasma cells, granuloma, and mucin. On the other hand, a lower accuracy was observed for the identification of crystals, mitoses, necrosis, hemosiderin, and nucleoli. Participant pathologists and residents preferred the use of larger displays (>30″) with a higher pixel count, resolution, and luminance. CONCLUSION Most features can be identified using any display. However, certain features posed more challenges across the three fixed display types. Furthermore, the use of a remote personal consumer-grade display chosen according to the pathologists' preference showed similar feature identification accuracy. Several factors of display characteristics seemed to influence pathologists' display preferences such as the display size, color, contrast ratio, pixel count, and luminance calibration. This study supports the use of standard "unlocked" vendor-agnostic displays for clinical digital pathology workflow rather than purchasing "locked" and more expensive displays that are part of a digital pathology system.
Collapse
Affiliation(s)
- Nada Shaker
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, OH, USA.
| | - Konstantin Shilo
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Ashwini K Esnakula
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Saba Shafi
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Bindu Challa
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Ankush Patel
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - David A Kellough
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Scott Hammond
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Sehrish Javaid
- Woody L. Hunt School of Dental Medicine, Texas Tech University Health Science Center, El Paso, TX, USA
| | - Swati Satturwar
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Martha M Yearsley
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Zaibo Li
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Abberly Lott Limbach
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Giovanni Lujan
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Anil V Parwani
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, OH, USA
| |
Collapse
|
27
|
Schwen LO, Kiehl TR, Carvalho R, Zerbe N, Homeyer A. Digitization of Pathology Labs: A Review of Lessons Learned. J Transl Med 2023; 103:100244. [PMID: 37657651 DOI: 10.1016/j.labinv.2023.100244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 07/18/2023] [Accepted: 08/23/2023] [Indexed: 09/03/2023] Open
Abstract
Pathology laboratories are increasingly using digital workflows. This has the potential of increasing laboratory efficiency, but the digitization process also involves major challenges. Several reports have been published describing the individual experiences of specific laboratories with the digitization process. However, a comprehensive overview of the lessons learned is still lacking. We provide an overview of the lessons learned for different aspects of the digitization process, including digital case management, digital slide reading, and computer-aided slide reading. We also cover metrics used for monitoring performance and pitfalls and corresponding values observed in practice. The overview is intended to help pathologists, information technology decision makers, and administrators to benefit from the experiences of others and to implement the digitization process in an optimal way to make their own laboratory future-proof.
Collapse
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
| |
Collapse
|
28
|
Hanna MG, Brogi E. Future Practices of Breast Pathology Using Digital and Computational Pathology. Adv Anat Pathol 2023; 30:421-433. [PMID: 37737690 DOI: 10.1097/pap.0000000000000414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/23/2023]
Abstract
Pathology clinical practice has evolved by adopting technological advancements initially regarded as potentially disruptive, such as electron microscopy, immunohistochemistry, and genomic sequencing. Breast pathology has a critical role as a medical domain, where the patient's pathology diagnosis has significant implications for prognostication and treatment of diseases. The advent of digital and computational pathology has brought about significant advancements in the field, offering new possibilities for enhancing diagnostic accuracy and improving patient care. Digital slide scanning enables to conversion of glass slides into high-fidelity digital images, supporting the review of cases in a digital workflow. Digitization offers the capability to render specimen diagnoses, digital archival of patient specimens, collaboration, and telepathology. Integration of image analysis and machine learning-based systems layered atop the high-resolution digital images offers novel workflows to assist breast pathologists in their clinical, educational, and research endeavors. Decision support tools may improve the detection and classification of breast lesions and the quantification of immunohistochemical studies. Computational biomarkers may help to contribute to patient management or outcomes. Furthermore, using digital and computational pathology may increase standardization and quality assurance, especially in areas with high interobserver variability. This review explores the current landscape and possible future applications of digital and computational techniques in the field of breast pathology.
Collapse
Affiliation(s)
- Matthew G Hanna
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY
| | | |
Collapse
|
29
|
Ardon O, Labasin M, Friedlander M, Manzo A, Corsale L, Ntiamoah P, Wright J, Elenitoba-Johnson K, Reuter VE, Hameed MR, Hanna MG. Quality Management System in Clinical Digital Pathology Operations at a Tertiary Cancer Center. J Transl Med 2023; 103:100246. [PMID: 37659445 PMCID: PMC10841911 DOI: 10.1016/j.labinv.2023.100246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 08/11/2023] [Accepted: 08/28/2023] [Indexed: 09/04/2023] Open
Abstract
Digital pathology workflows can improve pathology operations by allowing reliable and fast retrieval of digital images, digitally reviewing pathology slides, enabling remote work and telepathology, use of computer-aided tools, and sharing of digital images for research and educational purposes. The need for quality systems is a prerequisite for successful clinical-grade digital pathology adoption and patient safety. In this article, we describe the development of a structured digital pathology laboratory quality management system (QMS) for clinical digital pathology operations at Memorial Sloan Kettering Cancer Center (MSK). This digital pathology-specific QMS development stemmed from the gaps that were identified when MSK integrated digital pathology into its clinical practice. The digital scan team in conjunction with the Department of Pathology and Laboratory Medicine quality team developed a QMS tailored to the scanning operation to support departmental and institutional needs. As a first step, systemic mapping of the digital pathology operations identified the prescan, scan, and postscan processes; instrumentation; and staffing involved in the digital pathology operation. Next, gaps identified in quality control and quality assurance measures led to the development of standard operating procedures and training material for the different roles and workflows in the process. All digital pathology-related documents were subject to regulatory review and approval by departmental leadership. The quality essentials were developed into an extensive Digital Pathology Quality Essentials framework to specifically address the needs of the growing clinical use of digital pathology technologies. Using the unique digital experience gained at MSK, we present our recommendations for QMS for large-scale digital pathology operations in clinical settings.
Collapse
Affiliation(s)
- Orly Ardon
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York.
| | - Marc Labasin
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Maria Friedlander
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Allyne Manzo
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Lorraine Corsale
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Peter Ntiamoah
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Jeninne Wright
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Kojo Elenitoba-Johnson
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Victor E Reuter
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Meera R Hameed
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Matthew G Hanna
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| |
Collapse
|
30
|
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.
Collapse
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
| |
Collapse
|
31
|
Fusco N, Ivanova M, Frascarelli C, Criscitiello C, Cerbelli B, Pignataro MG, Pernazza A, Sajjadi E, Venetis K, Cursano G, Pagni F, Di Bella C, Accardo M, Amato M, Amico P, Bartoli C, Bogina G, Bortesi L, Boldorini R, Bruno S, Cabibi D, Caruana P, Dainese E, De Camilli E, Dell'Anna V, Duda L, Emmanuele C, Fanelli GN, Fernandes B, Ferrara G, Gnetti L, Gurrera A, Leone G, Lucci R, Mancini C, Marangi G, Mastropasqua MG, Nibid L, Orrù S, Pastena M, Peresi M, Perracchio L, Santoro A, Vezzosi V, Zambelli C, Zuccalà V, Rizzo A, Costarelli L, Pietribiasi F, Santinelli A, Scatena C, Curigliano G, Guerini-Rocco E, Martini M, Graziano P, Castellano I, d'Amati G. Advancing the PD-L1 CPS test in metastatic TNBC: Insights from pathologists and findings from a nationwide survey. Crit Rev Oncol Hematol 2023; 190:104103. [PMID: 37595344 DOI: 10.1016/j.critrevonc.2023.104103] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Revised: 08/11/2023] [Accepted: 08/12/2023] [Indexed: 08/20/2023] Open
Abstract
Pembrolizumab has received approval as a first-line treatment for unresectable/metastatic triple-negative breast cancer (mTNBC) with a PD-L1 combined positive score (CPS) of ≥ 10. However, assessing CPS in mTNBC poses challenges. Firstly, it represents a novel analysis for breast pathologists. Secondly, the heterogeneity of PD-L1 expression in mTNBC further complicates the assessment. Lastly, the lack of standardized assays and staining platforms adds to the complexity. In KEYNOTE trials, PD-L1 expression was evaluated using the IHC 22C3 pharmDx kit as a companion diagnostic test. However, both the 22C3 pharmDx and VENTANA PD-L1 (SP263) assays are validated for CPS assessment. Consequently, assay-platform choice, staining conditions, and scoring methods can significantly impact the testing outcomes. This consensus paper aims to discuss the intricacies of PD-L1 CPS testing in mTNBC and provide practical recommendations for pathologists. Additionally, we present findings from a nationwide Italian survey elucidating the state-of-the-art in PD-L1 CPS testing in mTNBC.
Collapse
Affiliation(s)
- Nicola Fusco
- Division of Pathology, IEO, European Institute of Oncology IRCCS, Milan, Italy; Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy.
| | - Mariia Ivanova
- Division of Pathology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Chiara Frascarelli
- Division of Pathology, IEO, European Institute of Oncology IRCCS, Milan, Italy; Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Carmen Criscitiello
- Division of Pathology, IEO, European Institute of Oncology IRCCS, Milan, Italy; Division of New Drugs and Early Drug Development for Innovative Therapies, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Bruna Cerbelli
- Department of Medical-Surgical Sciences and Biotechnologies Sapienza University of Rome, Rome, Italy
| | - Maria Gemma Pignataro
- Department of Medical-Surgical Sciences and Biotechnologies Sapienza University of Rome, Rome, Italy
| | - Angelina Pernazza
- Department of Medical-Surgical Sciences and Biotechnologies Sapienza University of Rome, Rome, Italy
| | - Elham Sajjadi
- Division of Pathology, IEO, European Institute of Oncology IRCCS, Milan, Italy; Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | | | - Giulia Cursano
- Division of Pathology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Fabio Pagni
- Department of Medicine and Surgery, University Milan Bicocca, Monza (MB), Italy; Department of Pathology, IRCCS San Gerardo Hospital, Monza (MB), Italy
| | - Camillo Di Bella
- Department of Pathology, IRCCS San Gerardo Hospital, Monza (MB), Italy
| | - Marina Accardo
- Pathology Unit, Department of Mental and Physical Health and Preventive Medicine, Università degli Studi della Campania "L. Vanvitelli", Naples, Italy
| | - Michelina Amato
- Department of Pathology, San Giovanni-Addolorata Hospital, Rome Italy
| | - Paolo Amico
- Department of Pathology, Ospedale Maria Paternò Arezzo, Ragusa, Italy
| | - Caterina Bartoli
- Morphological Diagnostic and Biomolecular Characterization Area, Complex Unit of Pathological Anatomy Empoli-Prato, Oncological Department Azienda USL Toscana Centro, Italy
| | - Giuseppe Bogina
- Pathology Unit, IRCCS Ospedale Sacro Cuore Don Calabria, Negrar di Valpolicella, Italy
| | - Laura Bortesi
- Pathology Unit, IRCCS Ospedale Sacro Cuore Don Calabria, Negrar di Valpolicella, Italy
| | - Renzo Boldorini
- Pathology Unit, University of Eastern Piedmont, Novara, Italy
| | - Sara Bruno
- Division of Pathology, ASL2 Savona, Liguria, Italy
| | - Daniela Cabibi
- Department of Sciences for the Promotion of Health and Mother and Child Care, Anatomic Pathology, University of Palermo, Palermo, Italy
| | - Pietro Caruana
- Pathology Unit, Department of Medicine and Surgery, University Hospital of Parma, Parma, Italy
| | - Emanuele Dainese
- Surgical Pathology Division, Department of Oncology, ASST Lecco, "A. Manzoni" Hospital, Lecco, Italy
| | - Elisa De Camilli
- Division of Pathology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | | | - Loren Duda
- Department of Clinical and Experimental Medicine, Pathology Unit, University of Foggia, Foggia, Italy
| | - Carmela Emmanuele
- Division of Pathology, Umberto I Hospital Presidium, Enna Provincial Health Department (ASP), Enna, Italy
| | - Giuseppe Nicolò Fanelli
- Division of Pathology, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | | | - Gerardo Ferrara
- Department of Anatomic Pathology and Cytopathology, G. Pascale National Cancer Institute Foundation (IRCCS) Naples, Italy
| | - Letizia Gnetti
- Division of Pathology, Umberto I Hospital Presidium, Enna Provincial Health Department (ASP), Enna, Italy
| | | | - Giorgia Leone
- Division of Pathology, Clinical Institute Humanitas Catania Cubba, Misterbianco (Catania), Italy
| | - Raffaella Lucci
- Pathology Unit, Monaldi Hospital, A.O. dei Colli of Naples, Naples, Italy
| | - Cristina Mancini
- Division of Pathology, Umberto I Hospital Presidium, Enna Provincial Health Department (ASP), Enna, Italy
| | - Grazia Marangi
- Anatomic Pathology Unit, SS. Annunziata Hospital, Taranto, Italy
| | - Mauro G Mastropasqua
- Department of Precision and Regenerative Medicine and Jonian Area, School of Medicine, University of Bari "Aldo Moro", Bari, Italy
| | - Lorenzo Nibid
- Research Unit of Anatomical Pathology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Rome, Italy; Anatomical Pathology Operative Research Unit, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, Rome, Italy
| | - Sandra Orrù
- Businco Oncologic Hospital, ARNAS Brotzu, Cagliari, Italy
| | - Maria Pastena
- IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | - Monica Peresi
- Pathology and Cytopathology Diagnostic Unit, Ospedale Villa Scassi di Genova, Genoa, Italy
| | - Letizia Perracchio
- Department of Pathology, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Angela Santoro
- General Pathology Unit, Department of Woman and Child Health and Public Health, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Vania Vezzosi
- Histopathology and Molecular Diagnostics Unit, Careggi Hospital, Firenze, Italy
| | | | - Valeria Zuccalà
- Pathology Unit, Pugliese-Ciaccio Hospital Catanzaro, Catanzaro, Italy
| | - Antonio Rizzo
- Division of Pathology, Clinical Institute Humanitas Catania Cubba, Misterbianco (Catania), Italy
| | | | | | - Alfredo Santinelli
- Anatomic Pathology, Azienda Sanitaria Territoriale di Pesaro-Urbino, Pesaro, Italy
| | - Cristian Scatena
- Division of Pathology, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Giuseppe Curigliano
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy; Division of New Drugs and Early Drug Development for Innovative Therapies, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Elena Guerini-Rocco
- Division of Pathology, IEO, European Institute of Oncology IRCCS, Milan, Italy; Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Maurizio Martini
- Department of Human and Developmental Pathology, University of Messina, Messina, Italy
| | - Paolo Graziano
- Pathology Unit, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo (FG), Italy
| | | | - Giulia d'Amati
- Department of Medical-Surgical Sciences and Biotechnologies Sapienza University of Rome, Rome, Italy
| |
Collapse
|
32
|
Bychkov A, Yoshikawa A, Munkhdelger J, Hori T, Fukuoka J. Integrating cytology into routine digital pathology workflow: a 5-year journey. Virchows Arch 2023; 483:555-559. [PMID: 37119336 DOI: 10.1007/s00428-023-03547-0] [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: 01/22/2023] [Revised: 03/13/2023] [Accepted: 04/20/2023] [Indexed: 05/01/2023]
Abstract
Despite recent advances in digital imaging, the adoption of digital cytology is challenging due to technical limitations. This study describes our 5-year institutional experience with the implementation of digital cytology. The routine cytology workflow included conventional two-step screening by cytotechnologists, followed by sign out by pathologists. We introduced sign out of cytologic cases using a microscopic digital imaging platform operated by cytotechnologists, which allowed for remote review of slides by cytopathologists via video streaming. We also provided cytologic correlation to support the virtual slide-based sign out of histopathological specimens and for a weekly pathology-radiology conference. In addition, positive cytology cases were archived for integration into the laboratory information system and for prospective computational pathology studies. We also summarized lessons learned over the years and outlined our vision for future developments. This unique experience may serve as a role model for other institutions.
Collapse
Affiliation(s)
- Andrey Bychkov
- Department of Pathology, Kameda Medical Center, Kamogawa, 929 Higashi-Cho, Kamogawa, Chiba, Japan.
| | - Akira Yoshikawa
- Department of Pathology, Kameda Medical Center, Kamogawa, 929 Higashi-Cho, Kamogawa, Chiba, Japan
| | - Jijgee Munkhdelger
- Department of Pathology, Kameda Medical Center, Kamogawa, 929 Higashi-Cho, Kamogawa, Chiba, Japan
| | - Takashi Hori
- Department of Pathology, Kameda Medical Center, Kamogawa, 929 Higashi-Cho, Kamogawa, Chiba, Japan
| | - Junya Fukuoka
- Department of Pathology, Kameda Medical Center, Kamogawa, 929 Higashi-Cho, Kamogawa, Chiba, Japan
- Department of Pathology Informatics, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan
| |
Collapse
|
33
|
Chaudhari P, Gupta S, Srivastav S, Sanker V, Medarametla GD, Pandey A, Agarwal Y. Digital Versus Conventional Teaching of Surgical Pathology: A Comparative Study. Cureus 2023; 15:e45747. [PMID: 37872909 PMCID: PMC10590475 DOI: 10.7759/cureus.45747] [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] [Accepted: 09/22/2023] [Indexed: 10/25/2023] Open
Abstract
OBJECTIVE To compare the digital method and the conventional method of teaching surgical pathology to medical students. METHODS A prospective case-control study was conducted on second-year students during the period of August 20, 2022, through January 15, 2023. Students, divided into two groups of 45 each, were taught surgical pathology via both conventional and digital methods. Four specimens and four slides were taught in total to the same set of students. A pre-test and a post-test were used to evaluate students' performance and the impact of the teaching method. The answers were analyzed using a paired t-test. In the end, students' responses were obtained regarding their views on a better method of teaching on a Likert scale. RESULTS To study gross pathology, 50.7% of students were in favor of the digital method, and 21% were not in favor. For the microscopic examination of tissues, 56.92% of students were in favor of the digital method, and 15% were not in favor. There was a significant increase in post-test scores (12.54-9.79 = 2.75, p=0.007) when digital methods for teaching surgical pathology were applied. CONCLUSION The Likert scale demonstrated that the digital method of teaching surgical pathology not only improved student performance but also resulted in a better understanding of the subject.
Collapse
Affiliation(s)
| | | | | | - Vivek Sanker
- General Surgery, Noorul Islam Institute of Medical Science (NIMS), Trivandrum, IND
| | | | - Akash Pandey
- Internal Medicine, Dr. Rajendra Prasad Government Medical College, Tanda, IND
| | - Yash Agarwal
- Medicine, West Bengal University of Health Sciences, Kolkata, IND
| |
Collapse
|
34
|
Chu ML, Ge XYM, Eastham J, Nguyen T, Fuji RN, Sullivan R, Ruderman D. Assessment of Color Reproducibility and Mitigation of Color Variation in Whole Slide Image Scanners for Toxicologic Pathology. Toxicol Pathol 2023; 51:313-328. [PMID: 38288712 DOI: 10.1177/01926233231224468] [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: 02/17/2024]
Abstract
Digital pathology workflows in toxicologic pathology rely on whole slide images (WSIs) from histopathology slides. Inconsistent color reproduction by WSI scanners of different models and from different manufacturers can result in different color representations and inter-scanner color variation in the WSIs. Although pathologists can accommodate a range of color variation during their evaluation of WSIs, color variability can degrade the performance of computational applications in digital pathology. In particular, color variability can compromise the generalization of artificial intelligence applications to large volumes of data from diverse sources. To address these challenges, we developed a process that includes two modules: (1) assessing the color reproducibility of our scanners and the color variation among them and (2) applying color correction to WSIs to minimize the color deviation and variation. Our process ensures consistent color reproduction across WSI scanners and enhances color homogeneity in WSIs, and its flexibility enables easy integration as a post-processing step following scanning by WSI scanners of different models and from different manufacturers.
Collapse
Affiliation(s)
- Mei-Lan Chu
- Genentech Inc., South San Francisco, California, USA
| | - Xing-Yue M Ge
- Genentech Inc., South San Francisco, California, USA
| | | | - Trung Nguyen
- Genentech Inc., South San Francisco, California, USA
| | - Reina N Fuji
- Genentech Inc., South San Francisco, California, USA
| | - Ruth Sullivan
- Genentech Inc., South San Francisco, California, USA
| | | |
Collapse
|
35
|
Pereira-Prado V, Martins-Silveira F, Sicco E, Hochmann J, Isiordia-Espinoza MA, González RG, Pandiar D, Bologna-Molina R. Artificial Intelligence for Image Analysis in Oral Squamous Cell Carcinoma: A Review. Diagnostics (Basel) 2023; 13:2416. [PMID: 37510160 PMCID: PMC10378350 DOI: 10.3390/diagnostics13142416] [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: 06/26/2023] [Revised: 07/12/2023] [Accepted: 07/17/2023] [Indexed: 07/30/2023] Open
Abstract
Head and neck tumor differential diagnosis and prognosis have always been a challenge for oral pathologists due to their similarities and complexity. Artificial intelligence novel applications can function as an auxiliary tool for the objective interpretation of histomorphological digital slides. In this review, we present digital histopathological image analysis applications in oral squamous cell carcinoma. A literature search was performed in PubMed MEDLINE with the following keywords: "artificial intelligence" OR "deep learning" OR "machine learning" AND "oral squamous cell carcinoma". Artificial intelligence has proven to be a helpful tool in histopathological image analysis of tumors and other lesions, even though it is necessary to continue researching in this area, mainly for clinical validation.
Collapse
Affiliation(s)
- Vanesa Pereira-Prado
- Molecular Pathology Area, School of Dentistry, Universidad de la República, Montevideo 11400, Uruguay
| | - Felipe Martins-Silveira
- Molecular Pathology Area, School of Dentistry, Universidad de la República, Montevideo 11400, Uruguay
| | - Estafanía Sicco
- Molecular Pathology Area, School of Dentistry, Universidad de la República, Montevideo 11400, Uruguay
| | - Jimena Hochmann
- Molecular Pathology Area, School of Dentistry, Universidad de la República, Montevideo 11400, Uruguay
| | - Mario Alberto Isiordia-Espinoza
- Department of Clinics, Los Altos University Center, Institute of Research in Medical Sciences, University of Guadalajara, Guadalajara 44100, Mexico
| | - Rogelio González González
- Research Department, School of Dentistry, Universidad Juárez del Estado de Durango, Durango 34000, Mexico
| | - Deepak Pandiar
- Department of Oral Pathology and Microbiology, Saveetha Dental College and Hospitals, Chennai 600077, India
| | - Ronell Bologna-Molina
- Molecular Pathology Area, School of Dentistry, Universidad de la República, Montevideo 11400, Uruguay
- Research Department, School of Dentistry, Universidad Juárez del Estado de Durango, Durango 34000, Mexico
| |
Collapse
|
36
|
Hossain MS, Shahriar GM, Syeed MMM, Uddin MF, Hasan M, Shivam S, Advani S. Region of interest (ROI) selection using vision transformer for automatic analysis using whole slide images. Sci Rep 2023; 13:11314. [PMID: 37443188 PMCID: PMC10344922 DOI: 10.1038/s41598-023-38109-6] [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: 05/09/2023] [Accepted: 07/03/2023] [Indexed: 07/15/2023] Open
Abstract
Selecting regions of interest (ROI) is a common step in medical image analysis across all imaging modalities. An ROI is a subset of an image appropriate for the intended analysis and identified manually by experts. In modern pathology, the analysis involves processing multidimensional and high resolution whole slide image (WSI) tiles automatically with an overwhelming quantity of structural and functional information. Despite recent improvements in computing capacity, analyzing such a plethora of data is challenging but vital to accurate analysis. Automatic ROI detection can significantly reduce the number of pixels to be processed, speed the analysis, improve accuracy and reduce dependency on pathologists. In this paper, we present an ROI detection method for WSI and demonstrated it for human epidermal growth factor receptor 2 (HER2) grading for breast cancer patients. Existing HER2 grading relies on manual ROI selection, which is tedious, time-consuming and suffers from inter-observer and intra-observer variability. This study found that the HER2 grade changes with ROI selection. We proposed an ROI detection method using Vision Transformer and investigated the role of image magnification for ROI detection. This method yielded an accuracy of 99% using 20 × WSI and 97% using 10 × WSI for the ROI detection. In the demonstration, the proposed method increased the diagnostic agreement to 99.3% with the clinical scores and reduced the time to 15 seconds for automated HER2 grading.
Collapse
Affiliation(s)
- Md Shakhawat Hossain
- Department of Computer Science and Engineering, Independent University Bangladesh, Dhaka, 1229, Bangladesh.
- RIoT Research Center, Independent University Bangladesh, Dhaka, 1229, Bangladesh.
| | | | - M M Mahbubul Syeed
- Department of Computer Science and Engineering, Independent University Bangladesh, Dhaka, 1229, Bangladesh
- RIoT Research Center, Independent University Bangladesh, Dhaka, 1229, Bangladesh
| | - Mohammad Faisal Uddin
- Department of Computer Science and Engineering, Independent University Bangladesh, Dhaka, 1229, Bangladesh
- RIoT Research Center, Independent University Bangladesh, Dhaka, 1229, Bangladesh
| | - Mahady Hasan
- Department of Computer Science and Engineering, Independent University Bangladesh, Dhaka, 1229, Bangladesh
- RIoT Research Center, Independent University Bangladesh, Dhaka, 1229, Bangladesh
| | - Shingla Shivam
- Department of Pathology, SL Raheja Hospital, Mumbai, 400016, India
| | - Suresh Advani
- Department of Pathology, SL Raheja Hospital, Mumbai, 400016, India
| |
Collapse
|
37
|
Miguel R, Gregorio B, Santos C, Andriotti C, Valle L, Saieg M. Validation of cytopathology specimens for digital pathology. Cytopathology 2023; 34:302-307. [PMID: 36974500 DOI: 10.1111/cyt.13234] [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: 10/09/2022] [Revised: 03/02/2023] [Accepted: 03/05/2023] [Indexed: 03/29/2023]
Abstract
INTRODUCTION Digital cytopathology is being progressively implemented in centres worldwide, but impediments such as the three-dimensionality of specimens and the size of scanned images have prevented its use from becoming widespread. This study aimed to validate the use of digital whole slide image scanning of cytopathology samples for routine sign-out. METHODS Specimens were scanned using the Leica Aperio GT 450 System. The following sample types were used: liquid-based cytology, direct conventional smears from fine needle aspirates and cytospins. Cases were validated by the same pathologist who originally rendered the conventional diagnosis, with a washout of at least 3 months. Final digital diagnoses were compared to the original analogical diagnoses, and cases were considered concordant up to a one-degree difference between the original and digital diagnoses. Reasons for the unsuccessful scanning of slides were also noted. The technical procedures followed the College of American Pathologists' guidelines for digital pathology validation. RESULTS A total of 730 slides from 383 cases (337 female, 51 male; median age 42) were successfully scanned. These cases consisted of the following sample types: 81 (21.1%) conventional smears, 240 (62.7%) liquid-based cytology samples and 62 (16.2%) cytospins. There were only five discordant cases, with a 98.7% agreement between original and digital diagnoses using the difference rate of up to one degree. Seventy-seven slides (10.5%) had to be rescanned due to technical problems. The main reasons for unsuccessful scanning were paucicellular samples (44; 57.1%), the thickness of the smears (18; 23.4%) and issues with the coverslip (15; 19.5%). CONCLUSION Cytological specimens can be successfully scanned and used for digital pathology, with excellent agreement with the original diagnoses.
Collapse
Affiliation(s)
| | | | | | | | | | - Mauro Saieg
- Fleury Group, São Paulo, Brazil
- Santa Casa Medical School, São Paulo, Brazil
| |
Collapse
|
38
|
Hwang JH, Lim M, Han G, Park H, Kim YB, Park J, Jun SY, Lee J, Cho JW. A comparative study on the implementation of deep learning algorithms for detection of hepatic necrosis in toxicity studies. Toxicol Res 2023; 39:399-408. [PMID: 37398569 PMCID: PMC10313597 DOI: 10.1007/s43188-023-00173-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Revised: 02/16/2023] [Accepted: 02/20/2023] [Indexed: 07/04/2023] Open
Abstract
Deep learning has recently become one of the most popular methods of image analysis. In non-clinical studies, several tissue slides are generated to investigate the toxicity of a test compound. These are converted into digital image data using a slide scanner, which is then studied by researchers to investigate abnormalities, and the deep learning method has been started to adopt in this study. However, comparative studies evaluating different deep learning algorithms for analyzing abnormal lesions are scarce. In this study, we applied three algorithms, SSD, Mask R-CNN, and DeepLabV3+, to detect hepatic necrosis in slide images and determine the best deep learning algorithm for analyzing abnormal lesions. We trained each algorithm on 5750 images and 5835 annotations of hepatic necrosis including validation and test, augmented with 500 image tiles of 448 × 448 pixels. Precision, recall, and accuracy were calculated for each algorithm based on the prediction results of 60 test images of 2688 × 2688 pixels. The two segmentation algorithms, DeepLabV3+ and Mask R-CNN, showed over 90% of accuracy (0.94 and 0.92, respectively), whereas SSD, an object detection algorithm, showed lower accuracy. The trained DeepLabV3+ outperformed all others in recall while also successfully separating hepatic necrosis from other features in the test images. It is important to localize and separate the abnormal lesion of interest from other features to investigate it on a slide level. Therefore, we suggest that segmentation algorithms are more appropriate than object detection algorithms for use in the pathological analysis of images in non-clinical studies. Supplementary Information The online version contains supplementary material available at 10.1007/s43188-023-00173-5.
Collapse
Affiliation(s)
- Ji-Hee Hwang
- Toxicologic Pathology Research Group, Department of Advanced Toxicology Research, Korea Institute of Toxicology, Daejeon, 34114 Republic of Korea
| | - Minyoung Lim
- Toxicologic Pathology Research Group, Department of Advanced Toxicology Research, Korea Institute of Toxicology, Daejeon, 34114 Republic of Korea
| | - Gyeongjin Han
- Toxicologic Pathology Research Group, Department of Advanced Toxicology Research, Korea Institute of Toxicology, Daejeon, 34114 Republic of Korea
| | - Heejin Park
- Toxicologic Pathology Research Group, Department of Advanced Toxicology Research, Korea Institute of Toxicology, Daejeon, 34114 Republic of Korea
| | - Yong-Bum Kim
- Department of Advanced Toxicology Research, Korea Institute of Toxicology, Daejeon, 34114 Republic of Korea
| | - Jinseok Park
- Research & Development Team, LAC Inc, Seoul, 07807 Republic of Korea
| | - Sang-Yeop Jun
- Research & Development Team, LAC Inc, Seoul, 07807 Republic of Korea
| | - Jaeku Lee
- Research & Development Team, LAC Inc, Seoul, 07807 Republic of Korea
| | - Jae-Woo Cho
- Toxicologic Pathology Research Group, Department of Advanced Toxicology Research, Korea Institute of Toxicology, Daejeon, 34114 Republic of Korea
| |
Collapse
|
39
|
Kelleher M, Colling R, Browning L, Roskell D, Roberts-Gant S, Shah KA, Hemsworth H, White K, Rees G, Dolton M, Soares MF, Verrill C. Department Wide Validation in Digital Pathology-Experience from an Academic Teaching Hospital Using the UK Royal College of Pathologists' Guidance. Diagnostics (Basel) 2023; 13:2144. [PMID: 37443538 DOI: 10.3390/diagnostics13132144] [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: 04/18/2023] [Revised: 06/08/2023] [Accepted: 06/14/2023] [Indexed: 07/15/2023] Open
Abstract
AIM we describe our experience of validating departmental pathologists for digital pathology reporting, based on the UK Royal College of Pathologists (RCPath) "Best Practice Recommendations for Implementing Digital Pathology (DP)," at a large academic teaching hospital that scans 100% of its surgical workload. We focus on Stage 2 of validation (prospective experience) prior to full validation sign-off. METHODS AND RESULTS twenty histopathologists completed Stage 1 of the validation process and subsequently completed Stage 2 validation, prospectively reporting a total of 3777 cases covering eight specialities. All cases were initially viewed on digital whole slide images (WSI) with relevant parameters checked on glass slides, and discordances were reconciled before the case was signed out. Pathologists kept an electronic log of the cases, the preferred reporting modality used, and their experiences. At the end of each validation, a summary was compiled and reviewed with a mentor. This was submitted to the DP Steering Group who assessed the scope of cases and experience before sign-off for full validation. A total of 1.3% (49/3777) of the cases had a discordance between WSI and glass slides. A total of 61% (30/49) of the discordances were categorised as a minor error in a supplementary parameter without clinical impact. The most common reasons for diagnostic discordances across specialities included identification and grading of dysplasia, assessment of tumour invasion, identification of small prognostic or diagnostic objects, interpretation of immunohistochemistry/special stains, and mitotic count assessment. Pathologists showed similar mean diagnostic confidences (on Likert scale from 0 to 7) with a mean of 6.8 on digital and 6.9 on glass slide reporting. CONCLUSION we describe one of the first real-world experiences of a department-wide effort to implement, validate, and roll out digital pathology reporting by applying the RCPath Recommendations for Implementing DP. We have shown a very low rate of discordance between WSI and glass slides.
Collapse
Affiliation(s)
- Mai Kelleher
- Department of Cellular Pathology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
| | - Richard Colling
- Department of Cellular Pathology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
- Nuffield Department of Surgical Sciences, Oxford University, Oxford OX3 9DU, UK
| | - Lisa Browning
- Department of Cellular Pathology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
| | - Derek Roskell
- Department of Cellular Pathology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
| | - Sharon Roberts-Gant
- Department of Cellular Pathology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
| | - Ketan A Shah
- Department of Cellular Pathology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
| | - Helen Hemsworth
- Department of Cellular Pathology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
| | - Kieron White
- Department of Cellular Pathology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
| | - Gabrielle Rees
- Department of Cellular Pathology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
| | - Monica Dolton
- Nuffield Department of Surgical Sciences, Oxford University, Oxford OX3 9DU, UK
| | - Maria Fernanda Soares
- Department of Cellular Pathology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
| | - Clare Verrill
- Department of Cellular Pathology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
- Nuffield Department of Surgical Sciences, Oxford University, Oxford OX3 9DU, UK
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
| |
Collapse
|
40
|
Nuutinen M, Leskelä RL. Systematic review of the performance evaluation of clinicians with or without the aid of machine learning clinical decision support system. HEALTH AND TECHNOLOGY 2023; 13:1-14. [PMID: 37363342 PMCID: PMC10262137 DOI: 10.1007/s12553-023-00763-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 06/01/2023] [Indexed: 06/28/2023]
Abstract
Background For the adoption of machine learning clinical decision support systems (ML-CDSS) it is critical to understand the performance aid of the ML-CDSS. However, it is not trivial, how the performance aid should be evaluated. To design reliable performance evaluation study, both the knowledge from the practical framework of experimental study design and the understanding of domain specific design factors are required. Objective The aim of this review study was to form a practical framework and identify key design factors for experimental design in evaluating the performance of clinicians with or without the aid of ML-CDSS. Methods The study was based on published ML-CDSS performance evaluation studies. We systematically searched articles published between January 2016 and December 2022. From the articles we collected a set of design factors. Only the articles comparing the performance of clinicians with or without the aid of ML-CDSS using experimental study methods were considered. Results The identified key design factors for the practical framework of ML-CDSS experimental study design were performance measures, user interface, ground truth data and the selection of samples and participants. In addition, we identified the importance of randomization, crossover design and training and practice rounds. Previous studies had shortcomings in the rationale and documentation of choices regarding the number of participants and the duration of the experiment. Conclusion The design factors of ML-CDSS experimental study are interdependent and all factors must be considered in individual choices. Supplementary Information The online version contains supplementary material available at 10.1007/s12553-023-00763-1.
Collapse
Affiliation(s)
- Mikko Nuutinen
- Nordic Healthcare Group, Helsinki, Finland
- Haartman Institute, University of Helsinki, Helsinki, Finland
| | | |
Collapse
|
41
|
Petersen JM, Jhala N, Jhala DN. The Critical Value of Telepathology in the COVID-19 Era. Fed Pract 2023; 40:186-193. [PMID: 37860072 PMCID: PMC10584409 DOI: 10.12788/fp.0381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2023]
Abstract
Background Telepathology, which includes the use of telecommunication links, helps enable transmission of digital pathology images for primary diagnosis, quality assurance, education, research, or second opinion diagnoses. Observations This review covers all aspects of telepathology implementation, including the selection of platforms, budgets and regulations, validation, implementation, education, quality monitoring, and the potential to improve practice. Considering the long-term trends, the lessons of the COVID-19 pandemic, and the potential for future pandemics or other disasters, the validation and implementation of telepathology remains a reasonable choice for laboratories looking to improve their practice. Conclusions Though barriers to implementation exist, there are potential benefits, such as the wide spectrum of uses like frozen section, telecytology, primary diagnosis, and second opinions. Telepathology represents an innovation that may transform the future of pathology practice.
Collapse
Affiliation(s)
- Jeffrey M Petersen
- Corporal Michael J Crescenz Veteran Affairs Medical Center, Philadelphia, Pennsylvania
- University of Pennsylvania, Philadelphia
| | | | - Darshana N Jhala
- Corporal Michael J Crescenz Veteran Affairs Medical Center, Philadelphia, Pennsylvania
- University of Pennsylvania, Philadelphia
| |
Collapse
|
42
|
Ghiringhelli F, Bibeau F, Greillier L, Fumet JD, Ilie A, Monville F, Laugé C, Catteau A, Boquet I, Majdi A, Morgand E, Oulkhouir Y, Brandone N, Adam J, Sbarrato T, Kassambara A, Fieschi J, Garcia S, Lepage AL, Tomasini P, Galon J. Immunoscore immune checkpoint using spatial quantitative analysis of CD8 and PD-L1 markers is predictive of the efficacy of anti- PD1/PD-L1 immunotherapy in non-small cell lung cancer. EBioMedicine 2023; 92:104633. [PMID: 37244159 DOI: 10.1016/j.ebiom.2023.104633] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 04/14/2023] [Accepted: 05/11/2023] [Indexed: 05/29/2023] Open
Abstract
BACKGROUND Anti-PD-1 and PD-L1 antibodies (mAbs) are approved immunotherapy agents to treat metastatic non-small cell lung cancer (NSCLC) patients. Only a minority of patients responds to these treatments and biomarkers predicting response are currently lacking. METHODS Immunoscore-Immune-Checkpoint (Immunoscore-IC), an in vitro diagnostic test, was used on 471 routine single FFPE-slides, and the duplex-immunohistochemistry CD8 and PD-L1 staining was quantified using digital-pathology. Analytical validation was performed on two independent cohorts of 206 NSCLC patients. Quantitative parameters related to cell location, number, proximity and clustering were analysed. The Immunoscore-IC was applied on a first cohort of metastatic NSCLC patients (n = 133), treated with anti-PD1 or anti-PD-L1 mAbs. Another independent cohort (n = 132) served as validation. FINDINGS Anti-PDL1 clone (HDX3) has similar characteristics as anti-PD-L1 clones (22C3, SP263). Densities of PD-L1+ cells, CD8+ cells and distances between CD8+ and PD-L1+ cells were quantified and the Immunoscore-IC classification was computed. Using univariate Cox model, 5 histological dichotomised variables (CD8 free of PD-L1+ cells, CD8 clusters, CD8 cells in proximity of PD-L1 cells, CD8 density and PD-L1 cells in proximity of CD8 cells) were significantly associated with Progression-Free Survival (PFS) (all P < 0.0001). Immunoscore-IC classification improved the discriminating power of prognostic model, which included clinical variables and pathologist PD-L1 assessment. In two categories, the Immunoscore-IC risk-score was significantly associated with patients' PFS (HR = 0.39, 95% CI (0.26-0.59), P < 0.0001) and Overall Survival (OS) (HR = 0.42, 95% CI (0.27-0.65), P < 0.0001) in the training-set. Further increased hazard ratios (HR) were found when stratifying patients into three-category Immunoscore-IC (IS-IC). All patients with Low-IS-IC progressed in less than 18 months, whereas PFS at 36 months were 34% and 33% of High-IS-IC patients in the training and validation sets, respectively. INTERPRETATION Immunoscore-IC is a powerful tool to predict the efficacy of immune-checkpoint inhibitors (ICIs) in patients with NSCLC. FUNDING Veracyte, INSERM, Labex Immuno-Oncology, Transcan ERAnet European project, ARC, SIRIC, CARPEM, Ligue Contre le Cancer, ANR, QNRF, INCa France, Louis Jeantet Prize Foundation.
Collapse
Affiliation(s)
- François Ghiringhelli
- Platform of Transfer in Biological Oncology, Georges François Leclerc Cancer Center - UNICANCER, Dijon, France; Genomic and Immunotherapy Medical Institute, Dijon University Hospital, Dijon, France; University of Burgundy-Franche Comté, Maison de l'Université Esplanade Erasme, Dijon, France; UMR INSERM 1231, Dijon, France; Department of Medical Oncology, Georges François Leclerc Cancer Center - UNICANCER, Dijon, France
| | - Frederic Bibeau
- Department of Pathology, Besançon University Hospital, Franche-Comté University, Besançon, France; Department of Pathology, Caen University Hospital, Normandy University, Caen, France
| | - Laurent Greillier
- Multidisciplinary Oncology and Therapeutic Innovations Department, APHM, INSERM, CNRS, CRCM, Hôpital Nord, Aix Marseille University, Marseille, France
| | - Jean-David Fumet
- Platform of Transfer in Biological Oncology, Georges François Leclerc Cancer Center - UNICANCER, Dijon, France; Genomic and Immunotherapy Medical Institute, Dijon University Hospital, Dijon, France; University of Burgundy-Franche Comté, Maison de l'Université Esplanade Erasme, Dijon, France; UMR INSERM 1231, Dijon, France; Department of Medical Oncology, Georges François Leclerc Cancer Center - UNICANCER, Dijon, France
| | - Alis Ilie
- Platform of Transfer in Biological Oncology, Georges François Leclerc Cancer Center - UNICANCER, Dijon, France
| | | | | | | | | | - Amine Majdi
- INSERM, Laboratory of Integrative Cancer Immunology, Paris, France; Equipe Labellisée Ligue Contre le Cancer, Paris, France; Centre de Recherche des Cordeliers, Sorbonne Université, Université de Paris, Paris, France
| | - Erwan Morgand
- INSERM, Laboratory of Integrative Cancer Immunology, Paris, France; Equipe Labellisée Ligue Contre le Cancer, Paris, France; Centre de Recherche des Cordeliers, Sorbonne Université, Université de Paris, Paris, France
| | - Youssef Oulkhouir
- Department of Thoracic Oncology, Caen University Hospital, Normandy University, Caen, France
| | - Nicolas Brandone
- Eurofins Pathologie, Bd Charles Moretti, Marseille 13014, France
| | - Julien Adam
- Anatomie et Cytologie Pathologiques, Hôpital Paris Saint-Joseph, INSERM, Gustave Roussy, Université Paris Saclay, Paris, France
| | | | | | | | - Stéphane Garcia
- Multidisciplinary Oncology and Therapeutic Innovations Department, APHM, INSERM, CNRS, CRCM, Hôpital Nord, Aix Marseille University, Marseille, France
| | - Anne Laure Lepage
- Platform of Transfer in Biological Oncology, Georges François Leclerc Cancer Center - UNICANCER, Dijon, France; Department of Pathology, Besançon University Hospital, Franche-Comté University, Besançon, France
| | - Pascale Tomasini
- Multidisciplinary Oncology and Therapeutic Innovations Department, APHM, INSERM, CNRS, CRCM, Hôpital Nord, Aix Marseille University, Marseille, France
| | - Jérôme Galon
- Veracyte, Marseille, France; INSERM, Laboratory of Integrative Cancer Immunology, Paris, France; Equipe Labellisée Ligue Contre le Cancer, Paris, France; Centre de Recherche des Cordeliers, Sorbonne Université, Université de Paris, Paris, France.
| |
Collapse
|
43
|
Ardon O, Klein E, Manzo A, Corsale L, England C, Mazzella A, Geneslaw L, Philip J, Ntiamoah P, Wright J, Sirintrapun SJ, Lin O, Elenitoba-Johnson K, Reuter VE, Hameed MR, Hanna MG. Digital pathology operations at a tertiary cancer center: Infrastructure requirements and operational cost. J Pathol Inform 2023; 14:100318. [PMID: 37811334 PMCID: PMC10550754 DOI: 10.1016/j.jpi.2023.100318] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 05/12/2023] [Accepted: 05/12/2023] [Indexed: 10/10/2023] Open
Abstract
Whole slide imaging is revolutionizing the field of pathology and is currently being used for clinical, educational, and research initiatives by an increasing number of institutions. Pathology departments have distinct needs for digital pathology systems, yet the cost of digital workflows is cited as a major barrier for widespread adoption by many organizations. Memorial Sloan Kettering Cancer Center (MSK) is an early adopter of whole slide imaging with incremental investments in resources that started more than 15 years ago. This experience and the large-scale scan operations led to the identification of required framework components of digital pathology operations. The cost of these components for the 2021 digital pathology operations at MSK were studied and calculated to enable an understanding of the operation and benchmark the accompanying costs. This paper describes the unique infrastructure cost and the costs associated with the digital pathology clinical operation use cases in a large, tertiary cancer center. These calculations can serve as a blueprint for other institutions to provide the necessary concepts and offer insights towards the financial requirements for digital pathology adoption by other institutions.
Collapse
Affiliation(s)
- Orly Ardon
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Eric Klein
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Allyne Manzo
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Lorraine Corsale
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Christine England
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Allix Mazzella
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Luke Geneslaw
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - John Philip
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Peter Ntiamoah
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jeninne Wright
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Oscar Lin
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Kojo Elenitoba-Johnson
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Victor E. Reuter
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Meera R. Hameed
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Matthew G. Hanna
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| |
Collapse
|
44
|
Xiao Y, Wang S, Ling R, Song Y. Application of artificial neural network algorithm in pathological diagnosis and prognosis prediction of digestive tract malignant tumors. Zhejiang Da Xue Xue Bao Yi Xue Ban 2023; 52:243-248. [PMID: 37283110 DOI: 10.3724/zdxbyxb-2022-0569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
The application of artificial neural network algorithm in pathological diagnosis of gastrointestinal malignant tumors has become a research hotspot. In the previous studies, the algorithm research mainly focused on the model development based on convolutional neural networks, while only a few studies used the combination of convolutional neural networks and recurrent neural networks. The research contents included classical histopathological diagnosis and molecular typing of malignant tumors, and the prediction of patient prognosis by utilizing artificial neural networks. This article reviews the research progress on artificial neural network algorithm in the pathological diagnosis and prognosis prediction of digestive tract malignant tumors.
Collapse
Affiliation(s)
- Ya Xiao
- Health Science Center, Ningbo University, Ningbo 315211, Zhejiang Province, China.
| | - Shuyang Wang
- Department of Pathology, School of Basic Medical Sciences, Fudan University, Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai 200032, China
| | - Ren Ling
- Shanghai Laizi Software Technology Co. Ltd., Shanghai 201499, China
| | - Yufei Song
- Department of Gastroenterology, the Affiliated Lihuili Hospital, Ningbo University, Ningbo 315046, Zhejiang Province, China.
| |
Collapse
|
45
|
Eccher A, Scarpa A, Dei Tos AP. Impact of a centralized archive for pathology laboratories on the health system. Pathol Res Pract 2023; 245:154488. [PMID: 37116365 DOI: 10.1016/j.prp.2023.154488] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Revised: 04/22/2023] [Accepted: 04/24/2023] [Indexed: 04/30/2023]
Abstract
The pathology archive of any hospital is likely to contain tens of thousands of slides and formalin-fixed and paraffin-embedded (FFPE) blocks, with their number constantly increasing. As a result, serious space and management issues are created. There has always been a favorable location for the pathology laboratory to rapidly and efficiently collect specimens and to meet the different service requirements of clinicians and patients. However, archiving may be one of the most neglected issues in the planning of spaces and activities, so much so that many laboratories are currently in trouble and looking for space inside and outside their hospitals. Another crucial issue is related to the environmental conditions of the identified preservation place, which, based on their characteristics, probably provide suboptimal habitats in most cases. For FFPE blocks, controlled temperature (<27 °C) and humidity (>30% and <70%) are recommended, with control systems for parasite infestation. For glass slides, systems suitable for guaranteeing their safety, traceability and conservation suitable for possible revision are recommended. The aim of this position paper is to outline the issues that currently exist in archives and to suggest a rational health policy solution to overcome the problems raised.
Collapse
Affiliation(s)
- Albino Eccher
- Department of Pathology and Diagnostics, University and Hospital Trust of Verona, Verona, Italy.
| | - Aldo Scarpa
- Department of Pathology and Diagnostics, University and Hospital Trust of Verona, Verona, Italy
| | - Angelo Paolo Dei Tos
- Surgical Pathology & Cytopathology Unit, Department of Medicine - DIMED, University of Padua, Padua, Italy
| |
Collapse
|
46
|
Kaushal RK, Yadav S, Sahay A, Karnik N, Agrawal T, Dave V, Singh N, Shah A, Desai SB. Validation of Remote Digital Pathology based diagnostic reporting of Frozen Sections from home. J Pathol Inform 2023; 14:100312. [PMID: 37214151 PMCID: PMC10192998 DOI: 10.1016/j.jpi.2023.100312] [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: 03/03/2023] [Revised: 04/07/2023] [Accepted: 04/12/2023] [Indexed: 05/24/2023] Open
Abstract
Background Despite the promising applications of whole-slide imaging (WSI) for frozen section (FS) diagnosis, its adoption for remote reporting is limited. Objective To assess the feasibility and performance of home-based remote digital consultation for FS diagnosis. Material & Method Cases accessioned beyond regular working hours (5 pm-10 pm) were reported simultaneously using optical microscopy (OM) and WSI. Validation of WSI for FS diagnosis from a remote site, i.e. home, was performed by 5 pathologists. Cases were scanned using a portable scanner (Grundium Ocus®40) and previewed on consumer-grade computer devices through a web-based browser (http://grundium.net). Clinical data and diagnostic reports were shared through a google spreadsheet. The diagnostic concordance, inter- and intra-observer agreement for FS diagnosis by WSI versus OM, and turnaround time (TAT), were recorded. Results The overall diagnostic accuracy for OM and WSI (from home) was 98.2% (range 97%-100%) and 97.6% (range 95%-99%), respectively, when compared with the reference standard. Almost perfect inter-observer (k = 0.993) and intra-observer (k = 0.987) agreement for WSI was observed by 4 pathologists. Pathologists used consumer-grade laptops/desktops with an average screen size of 14.58 inches (range = 12.3-17.7 inches) and a network speed of 64 megabits per second (range: 10-90 Mbps). The mean diagnostic assessment time per case for OM and WSI was 1:48 min and 5:54 min, respectively. Mean TAT of 27.27 min per case was observed using WSI from home. Seamless connectivity was observed in approximately 75% of cases. Conclusion This study validates the role of WSI for remote FS diagnosis for its safe and efficient adoption in clinical use.
Collapse
Affiliation(s)
- Rajiv Kumar Kaushal
- Corresponding author at: Department of Pathology, Tata Memorial Hospital, Homi Bhabha National Institute, Dr Ernest Borges Marg, Parel, Mumbai 400 012, India.
| | | | | | | | | | | | | | | | | |
Collapse
|
47
|
Rojansky R, Jhun I, Dussaq AM, Chirieleison SM, Nirschl JJ, Born D, Fralick J, Hetherington W, Kerr AM, Lavezo J, Lawrence DB, Lummus S, Macasaet R, Montine TJ, Ryan E, Shen J, Shoemaker J, Tan B, Vogel H, Waraich PS, Yang E, Young A, Folkins A. Rapid Deployment of Whole Slide Imaging for Primary Diagnosis in Surgical Pathology at Stanford Medicine: Responding to Challenges of the COVID-19 Pandemic. Arch Pathol Lab Med 2023; 147:359-367. [PMID: 35802938 PMCID: PMC9904534 DOI: 10.5858/arpa.2021-0438-oa] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/22/2022] [Indexed: 11/06/2022]
Abstract
CONTEXT.— Stanford Pathology began stepwise subspecialty implementation of whole slide imaging (WSI) in 2018 soon after the first US Food and Drug Administration approval. In 2020, during the COVID-19 pandemic, the Centers for Medicare & Medicaid Services waived the requirement for pathologists to perform diagnostic tests in Clinical Laboratory Improvement Amendments (CLIA)-licensed facilities. This encouraged rapid implementation of WSI across all surgical pathology subspecialties. OBJECTIVE.— To present our experience with validation and implementation of WSI at a large academic medical center encompassing a caseload of more than 50 000 cases per year. DESIGN.— Validation was performed independently for 3 subspecialty services with a diagnostic concordance threshold above 95%. Analysis of user experience, staffing, infrastructure, and information technology was performed after department-wide expansion. RESULTS.— Diagnostic concordance was achieved in 96% of neuropathology cases, 100% of gynecologic pathology cases, and 98% of immunohistochemistry cases. After full implementation, 8 high-capacity scanners were operational, with whole slide images generated on greater than 2000 slides per weekday, accounting for approximately 80% of histologic slides at Stanford Medicine. Multiple modifications in workflow and information technology were needed to improve performance. Within months of full implementation, most attending pathologists and trainees had adopted WSI for primary diagnosis. CONCLUSIONS.— WSI across all surgical subspecialities is achievable at scale at an academic medical center; however, adoption required flexibility to adjust workflows and develop tailored solutions. WSI at scale supported the health and safety of medical staff while facilitating high-quality patient care and education during COVID-19 restrictions.
Collapse
Affiliation(s)
- Rebecca Rojansky
- From the Department of Pathology, School of Medicine, Stanford University, Stanford, California (Rojansky, Jhun, Dussaq, Chirieleison, Nirschl, Born, Montine, Ryan, Shen, Tan, Vogel, Yang, Folkins)
| | - Iny Jhun
- From the Department of Pathology, School of Medicine, Stanford University, Stanford, California (Rojansky, Jhun, Dussaq, Chirieleison, Nirschl, Born, Montine, Ryan, Shen, Tan, Vogel, Yang, Folkins)
| | - Alex M Dussaq
- From the Department of Pathology, School of Medicine, Stanford University, Stanford, California (Rojansky, Jhun, Dussaq, Chirieleison, Nirschl, Born, Montine, Ryan, Shen, Tan, Vogel, Yang, Folkins)
| | - Steven M Chirieleison
- From the Department of Pathology, School of Medicine, Stanford University, Stanford, California (Rojansky, Jhun, Dussaq, Chirieleison, Nirschl, Born, Montine, Ryan, Shen, Tan, Vogel, Yang, Folkins)
| | - Jeffrey J Nirschl
- From the Department of Pathology, School of Medicine, Stanford University, Stanford, California (Rojansky, Jhun, Dussaq, Chirieleison, Nirschl, Born, Montine, Ryan, Shen, Tan, Vogel, Yang, Folkins)
| | - Don Born
- From the Department of Pathology, School of Medicine, Stanford University, Stanford, California (Rojansky, Jhun, Dussaq, Chirieleison, Nirschl, Born, Montine, Ryan, Shen, Tan, Vogel, Yang, Folkins)
| | - Jennifer Fralick
- Anatomic Pathology and Clinical Laboratories (Fralick, Hetherington, Macasaet, Young), Stanford Health Care, Stanford, California
| | - William Hetherington
- Anatomic Pathology and Clinical Laboratories (Fralick, Hetherington, Macasaet, Young), Stanford Health Care, Stanford, California
| | - Alison M Kerr
- Clinical Operations (Kerr), Stanford Health Care, Stanford, California
| | - Jonathan Lavezo
- The Department of Pathology, Health Sciences Center, Texas Tech University, El Paso (Lavezo)
| | - Daniel B Lawrence
- Information Technology (Lawrence, Shoemaker, Waraich), Stanford Health Care, Stanford, California
| | - Seth Lummus
- The Department of Human Physiology and Nutrition, University of Colorado, Colorado Springs (Lummus)
| | - Ronald Macasaet
- Anatomic Pathology and Clinical Laboratories (Fralick, Hetherington, Macasaet, Young), Stanford Health Care, Stanford, California
| | - Thomas J Montine
- From the Department of Pathology, School of Medicine, Stanford University, Stanford, California (Rojansky, Jhun, Dussaq, Chirieleison, Nirschl, Born, Montine, Ryan, Shen, Tan, Vogel, Yang, Folkins)
| | - Emily Ryan
- From the Department of Pathology, School of Medicine, Stanford University, Stanford, California (Rojansky, Jhun, Dussaq, Chirieleison, Nirschl, Born, Montine, Ryan, Shen, Tan, Vogel, Yang, Folkins)
| | - Jeanne Shen
- From the Department of Pathology, School of Medicine, Stanford University, Stanford, California (Rojansky, Jhun, Dussaq, Chirieleison, Nirschl, Born, Montine, Ryan, Shen, Tan, Vogel, Yang, Folkins)
| | - Jonathan Shoemaker
- Information Technology (Lawrence, Shoemaker, Waraich), Stanford Health Care, Stanford, California
| | - Brent Tan
- From the Department of Pathology, School of Medicine, Stanford University, Stanford, California (Rojansky, Jhun, Dussaq, Chirieleison, Nirschl, Born, Montine, Ryan, Shen, Tan, Vogel, Yang, Folkins)
| | - Hannes Vogel
- From the Department of Pathology, School of Medicine, Stanford University, Stanford, California (Rojansky, Jhun, Dussaq, Chirieleison, Nirschl, Born, Montine, Ryan, Shen, Tan, Vogel, Yang, Folkins)
| | - Puneet Singh Waraich
- Information Technology (Lawrence, Shoemaker, Waraich), Stanford Health Care, Stanford, California
| | - Eric Yang
- From the Department of Pathology, School of Medicine, Stanford University, Stanford, California (Rojansky, Jhun, Dussaq, Chirieleison, Nirschl, Born, Montine, Ryan, Shen, Tan, Vogel, Yang, Folkins)
| | - April Young
- Anatomic Pathology and Clinical Laboratories (Fralick, Hetherington, Macasaet, Young), Stanford Health Care, Stanford, California
| | - Ann Folkins
- From the Department of Pathology, School of Medicine, Stanford University, Stanford, California (Rojansky, Jhun, Dussaq, Chirieleison, Nirschl, Born, Montine, Ryan, Shen, Tan, Vogel, Yang, Folkins)
| |
Collapse
|
48
|
Eloy C, Marques A, Pinto J, Pinheiro J, Campelos S, Curado M, Vale J, Polónia A. Artificial intelligence-assisted cancer diagnosis improves the efficiency of pathologists in prostatic biopsies. Virchows Arch 2023; 482:595-604. [PMID: 36809483 PMCID: PMC10033575 DOI: 10.1007/s00428-023-03518-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 02/07/2023] [Accepted: 02/09/2023] [Indexed: 02/23/2023]
Abstract
Paige Prostate is a clinical-grade artificial intelligence tool designed to assist the pathologist in detecting, grading, and quantifying prostate cancer. In this work, a cohort of 105 prostate core needle biopsies (CNBs) was evaluated through digital pathology. Then, we compared the diagnostic performance of four pathologists diagnosing prostatic CNB unaided and, in a second phase, assisted by Paige Prostate. In phase 1, pathologists had a diagnostic accuracy for prostate cancer of 95.00%, maintaining their performance in phase 2 (93.81%), with an intraobserver concordance rate between phases of 98.81%. In phase 2, pathologists reported atypical small acinar proliferation (ASAP) less often (about 30% less). Additionally, they requested significantly fewer immunohistochemistry (IHC) studies (about 20% less) and second opinions (about 40% less). The median time required for reading and reporting each slide was about 20% lower in phase 2, in both negative and cancer cases. Lastly, the average total agreement with the software performance was observed in about 70% of the cases, being significantly higher in negative cases (about 90%) than in cancer cases (about 30%). Most of the diagnostic discordances occurred in distinguishing negative cases with ASAP from small foci of well-differentiated (less than 1.5 mm) acinar adenocarcinoma. In conclusion, the synergic usage of Paige Prostate contributes to a significant decrease in IHC studies, second opinion requests, and time for reporting while maintaining highly accurate diagnostic standards.
Collapse
Affiliation(s)
- Catarina Eloy
- Pathology Laboratory, Institute of Molecular Pathology and Immunology of the University of Porto (Ipatimup), Porto, Portugal
- i3S - Instituto de Investigação E Inovação Em Saúde, Porto, Portugal
- Faculty of Medicine, University of Porto, Porto, Portugal
| | - Ana Marques
- Pathology Laboratory, Institute of Molecular Pathology and Immunology of the University of Porto (Ipatimup), Porto, Portugal
- Serviço de Anatomia Patológica, Centro Hospitalar Universitário de São João, Porto, Portugal
| | - João Pinto
- Pathology Laboratory, Institute of Molecular Pathology and Immunology of the University of Porto (Ipatimup), Porto, Portugal
- Serviço de Anatomia Patológica, Hospital Pedro Hispano - Unidade Local de Saúde de Matosinhos, Matosinhos, Portugal
| | - Jorge Pinheiro
- Pathology Laboratory, Institute of Molecular Pathology and Immunology of the University of Porto (Ipatimup), Porto, Portugal
- Serviço de Anatomia Patológica, Centro Hospitalar Universitário de São João, Porto, Portugal
| | - Sofia Campelos
- Pathology Laboratory, Institute of Molecular Pathology and Immunology of the University of Porto (Ipatimup), Porto, Portugal
| | - Mónica Curado
- Pathology Laboratory, Institute of Molecular Pathology and Immunology of the University of Porto (Ipatimup), Porto, Portugal
| | - João Vale
- Pathology Laboratory, Institute of Molecular Pathology and Immunology of the University of Porto (Ipatimup), Porto, Portugal
| | - António Polónia
- Pathology Laboratory, Institute of Molecular Pathology and Immunology of the University of Porto (Ipatimup), Porto, Portugal.
- i3S - Instituto de Investigação E Inovação Em Saúde, Porto, Portugal.
| |
Collapse
|
49
|
Abele N, Tiemann K, Krech T, Wellmann A, Schaaf C, Länger F, Peters A, Donner A, Keil F, Daifalla K, Mackens M, Mamilos A, Minin E, Krümmelbein M, Krause L, Stark M, Zapf A, Päpper M, Hartmann A, Lang T. Noninferiority of Artificial Intelligence-Assisted Analysis of Ki-67 and Estrogen/Progesterone Receptor in Breast Cancer Routine Diagnostics. Mod Pathol 2023; 36:100033. [PMID: 36931740 DOI: 10.1016/j.modpat.2022.100033] [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: 01/12/2022] [Revised: 09/19/2022] [Accepted: 09/22/2022] [Indexed: 03/17/2023]
Abstract
Image analysis assistance with artificial intelligence (AI) has become one of the great promises over recent years in pathology, with many scientific studies being published each year. Nonetheless, and perhaps surprisingly, only few image AI systems are already in routine clinical use. A major reason for this is the missing validation of the robustness of many AI systems: beyond a narrow context, the large variability in digital images due to differences in preanalytical laboratory procedures, staining procedures, and scanners can be challenging for the subsequent image analysis. Resulting faulty AI analysis may bias the pathologist and contribute to incorrect diagnoses and, therefore, may lead to inappropriate therapy or prognosis. In this study, a pretrained AI assistance tool for the quantification of Ki-67, estrogen receptor (ER), and progesterone receptor (PR) in breast cancer was evaluated within a realistic study set representative of clinical routine on a total of 204 slides (72 Ki-67, 66 ER, and 66 PR slides). This represents the cohort with the largest image variance for AI tool evaluation to date, including 3 staining systems, 5 whole-slide scanners, and 1 microscope camera. These routine cases were collected without manual preselection and analyzed by 10 participant pathologists from 8 sites. Agreement rates for individual pathologists were found to be 87.6% for Ki-67 and 89.4% for ER/PR, respectively, between scoring with and without the assistance of the AI tool regarding clinical categories. Individual AI analysis results were confirmed by the majority of pathologists in 95.8% of Ki-67 cases and 93.2% of ER/PR cases. The statistical analysis provides evidence for high interobserver variance between pathologists (Krippendorff's α, 0.69) in conventional immunohistochemical quantification. Pathologist agreement increased slightly when using AI support (Krippendorff α, 0.72). Agreement rates of pathologist scores with and without AI assistance provide evidence for the reliability of immunohistochemical scoring with the support of the investigated AI tool under a large number of environmental variables that influence the quality of the diagnosed tissue images.
Collapse
Affiliation(s)
- Niklas Abele
- Friedrich-Alexander-Universität Erlangen-Nürnberg, Institut für Pathologie, Erlangen, Germany.
| | | | - Till Krech
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Institute of Pathology, Clinical Center Osnabrueck, Osnabrueck, Germany
| | | | - Christian Schaaf
- Department of Internal Medicine II, Klinikum rechts der Isar of the TU Munich, Munich, Germany
| | - Florian Länger
- Institut für Pathologie, Medizinische Hochschule Hannover, Hannover, Germany
| | - Anja Peters
- Institut für Pathologie, Städtisches Klinikum Lüneburg gGmbH, Lüneburg, Germany
| | - Andreas Donner
- Zentrum für Pathologie, Zytologie und Molekularpathologie Neuss, Neuss, Germany
| | - Felix Keil
- Institute of Pathology, University of Regensburg, Regensburg, Germany
| | | | | | - Andreas Mamilos
- Institute of Pathology, University of Regensburg, Regensburg, Germany
| | - Evgeny Minin
- Institute of Pathology, Clinical Center Osnabrueck, Osnabrueck, Germany
| | | | - Linda Krause
- Institute of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Maria Stark
- Institute of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Antonia Zapf
- Institute of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | | | - Arndt Hartmann
- Friedrich-Alexander-Universität Erlangen-Nürnberg, Institut für Pathologie, Erlangen, Germany
| | | |
Collapse
|
50
|
Nuutinen M, Hiltunen AM, Korhonen S, Haavisto I, Poikonen-Saksela P, Mattson J, Manikis G, Kondylakis H, Simos P, Mazzocco K, Pat-Horenczyk R, Sousa B, Cardoso F, Manica I, Kudel I, Leskelä RL. Aid of a machine learning algorithm can improve clinician predictions of patient quality of life during breast cancer treatments. HEALTH AND TECHNOLOGY 2023. [DOI: 10.1007/s12553-023-00733-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
|