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Hosseini MS, Bejnordi BE, Trinh VQH, Chan L, Hasan D, Li X, Yang S, Kim T, Zhang H, Wu T, Chinniah K, Maghsoudlou S, Zhang R, Zhu J, Khaki S, Buin A, Chaji F, Salehi A, Nguyen BN, Samaras D, Plataniotis KN. Computational pathology: A survey review and the way forward. J Pathol Inform 2024; 15:100357. [PMID: 38420608 PMCID: PMC10900832 DOI: 10.1016/j.jpi.2023.100357] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 12/21/2023] [Accepted: 12/23/2023] [Indexed: 03/02/2024] Open
Abstract
Computational Pathology (CPath) is an interdisciplinary science that augments developments of computational approaches to analyze and model medical histopathology images. The main objective for CPath is to develop infrastructure and workflows of digital diagnostics as an assistive CAD system for clinical pathology, facilitating transformational changes in the diagnosis and treatment of cancer that are mainly address by CPath tools. With evergrowing developments in deep learning and computer vision algorithms, and the ease of the data flow from digital pathology, currently CPath is witnessing a paradigm shift. Despite the sheer volume of engineering and scientific works being introduced for cancer image analysis, there is still a considerable gap of adopting and integrating these algorithms in clinical practice. This raises a significant question regarding the direction and trends that are undertaken in CPath. In this article we provide a comprehensive review of more than 800 papers to address the challenges faced in problem design all-the-way to the application and implementation viewpoints. We have catalogued each paper into a model-card by examining the key works and challenges faced to layout the current landscape in CPath. We hope this helps the community to locate relevant works and facilitate understanding of the field's future directions. In a nutshell, we oversee the CPath developments in cycle of stages which are required to be cohesively linked together to address the challenges associated with such multidisciplinary science. We overview this cycle from different perspectives of data-centric, model-centric, and application-centric problems. We finally sketch remaining challenges and provide directions for future technical developments and clinical integration of CPath. For updated information on this survey review paper and accessing to the original model cards repository, please refer to GitHub. Updated version of this draft can also be found from arXiv.
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Affiliation(s)
- Mahdi S Hosseini
- Department of Computer Science and Software Engineering (CSSE), Concordia Univeristy, Montreal, QC H3H 2R9, Canada
| | | | - Vincent Quoc-Huy Trinh
- Institute for Research in Immunology and Cancer of the University of Montreal, Montreal, QC H3T 1J4, Canada
| | - Lyndon Chan
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Danial Hasan
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Xingwen Li
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Stephen Yang
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Taehyo Kim
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Haochen Zhang
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Theodore Wu
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Kajanan Chinniah
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Sina Maghsoudlou
- Department of Computer Science and Software Engineering (CSSE), Concordia Univeristy, Montreal, QC H3H 2R9, Canada
| | - Ryan Zhang
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Jiadai Zhu
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Samir Khaki
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Andrei Buin
- Huron Digitial Pathology, St. Jacobs, ON N0B 2N0, Canada
| | - Fatemeh Chaji
- Department of Computer Science and Software Engineering (CSSE), Concordia Univeristy, Montreal, QC H3H 2R9, Canada
| | - Ala Salehi
- Department of Electrical and Computer Engineering, University of New Brunswick, Fredericton, NB E3B 5A3, Canada
| | - Bich Ngoc Nguyen
- University of Montreal Hospital Center, Montreal, QC H2X 0C2, Canada
| | - Dimitris Samaras
- Department of Computer Science, Stony Brook University, Stony Brook, NY 11794, United States
| | - Konstantinos N Plataniotis
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
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Lan H, Chen P, Wang C, Chen C, Yao C, Jin F, Wan T, Lv X, Wang J. CUNet3+: A Multiscale Connected UNet for the Segmentation of Lung Cancer Cells in Pathology Sections Stained Using Rapid On-Site Cytopathological Evaluation. THE AMERICAN JOURNAL OF PATHOLOGY 2024:S0002-9440(24)00210-4. [PMID: 38897537 DOI: 10.1016/j.ajpath.2024.05.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Revised: 04/30/2024] [Accepted: 05/23/2024] [Indexed: 06/21/2024]
Abstract
Lung cancer is an increasingly serious health problem worldwide, and early detection and diagnosis are crucial for successful treatment. With the development of artificial intelligence and the growth of data volume, machine learning techniques can play a significant role in improving the accuracy of early detection in lung cancer. This study proposes a deep learning-based segmentation algorithm for rapid on-site cytopathological evaluation (ROSE) to enhance the diagnostic efficiency of endobronchial ultrasound-guided transbronchial needle aspiration biopsy (EBUS-TBNA) during surgery. By utilizing the CUNet3+ network model, cell clusters, including cancer cell clusters, can be accurately segmented in ROSE-stained pathological sections. The model demonstrated high accuracy, with an F1-score of 0.9604, recall of 0.9609, precision of 0.9654, and accuracy of 0.9834 on the internal testing data set. It also achieved an area under the receiver-operating characteristic curve of 0.9972 for cancer identification. The proposed algorithm provides time savings for on-site diagnosis, improves EBUS-TBNA efficiency, and outperforms classical segmentation algorithms in accurately identifying lung cancer cell clusters in ROSE-stained images. It effectively reduces over-segmentation, decreases network parameters, and enhances computational efficiency, making it suitable for real-time patient evaluation during surgical procedures.
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Affiliation(s)
- Hongyi Lan
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Pei Chen
- Department of Pulmonary and Critical Care Medicine, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - ChenXi Wang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Chen Chen
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Cuiping Yao
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Fang Jin
- Department of Respiratory and Critical Care Medicine, Tianjin Medical University General Hospital, Tianjin, China
| | - Tao Wan
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xing Lv
- Department of Pulmonary and Critical Care Medicine, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
| | - Jing Wang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China.
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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.
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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
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Chicaud M, Montero-Macias R, Taconet S. [Ecology: The blind spot in pathology research]. Ann Pathol 2024; 44:47-56. [PMID: 38097471 DOI: 10.1016/j.annpat.2023.09.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 08/25/2023] [Accepted: 09/12/2023] [Indexed: 02/07/2024]
Abstract
INTRODUCTION The 2015 Paris Agreement has been the first restrictive agreement in the fight against climate change. The newer generations of pathologists, who feel more anxiety due to environmental problems than their predecessors, are asked to publish research works while they are harder and harder to and in a context of demographical tensions. We wanted to measure the rise of ecology research in pathology since the Paris Agreement. MATERIAL & METHODS Over a ten years study period (2013-2022), we have identified via PubMed the number of articles in which forty-three terms taken from the sustainable development vocabulary appeared in ten renowned international pathology journals, selected for their SJR index from ScimagoJr and their impact factor, plus the Annales de pathologie, and compared their means of incidence between the 2013-2015 (m1) and 2016-2022 (m2) periods. The same process has been applied for "artificial intelligence", "deep learning" and "digital pathology". RESULTS A total of 1336 articles have been identified. Only "digital pathology" (fromm1=8,33 to m2=23,29; p=0,010) and "deep learning" (fromm1=0 to m2=10,14; p=0,034) saw their incidence rise significantly. A significant decrease has been observed with "biological" (fromm1=70,00 to m2=56,86; p=0,020). DISCUSSION-CONCLUSIONS Pathology reacts to trends but research in ecology has remained in the blind spot since 2015. However there seems to be an awakening as editorials, articles and communications in congress have blossomed the last two years.
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Affiliation(s)
- Matthieu Chicaud
- Service d'anatomie & cytologie pathologiques, hôpital Simone-Veil, 14, rue de Saint-Prix, 95600 Eaubonne, France.
| | - Rosa Montero-Macias
- Service de gynécologie-obstétrique, hôpital Simone Veil, 14, rue de Saint-Prix, 95600 Eaubonne, France
| | - Sarah Taconet
- Service d'anatomie & cytologie pathologiques, hôpital Simone-Veil, 14, rue de Saint-Prix, 95600 Eaubonne, France
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Singh A, Paruthy SB, Belsariya V, Chandra J N, Singh SK, Manivasagam SS, Choudhary S, Kumar MA, Khera D, Kuraria V. Revolutionizing Breast Healthcare: Harnessing the Role of Artificial Intelligence. Cureus 2023; 15:e50203. [PMID: 38192969 PMCID: PMC10772314 DOI: 10.7759/cureus.50203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/09/2023] [Indexed: 01/10/2024] Open
Abstract
Breast cancer has the highest incidence and second-highest mortality rate among all cancers. The management of breast cancer is being revolutionized by artificial intelligence (AI), which is improving early detection, pathological diagnosis, risk assessment, individualized treatment recommendations, and treatment response prediction. Nuclear medicine has used artificial intelligence (AI) for over 50 years, but more recent advances in machine learning (ML) and deep learning (DL) have given AI in nuclear medicine additional capabilities. AI accurately analyzes breast imaging scans for early detection, minimizing false negatives while offering radiologists reliable, swift image processing assistance. It smoothly fits into radiology workflows, which may result in early treatments and reduced expenditures. In pathological diagnosis, artificial intelligence improves the quality of diagnostic data by ensuring accurate diagnoses, lowering inter-observer variability, speeding up the review process, and identifying errors or poor slides. By taking into consideration nutritional, genetic, and environmental factors, providing individualized risk assessments, and recommending more regular tests for higher-risk patients, AI aids with the risk assessment of breast cancer. The integration of clinical and genetic data into individualized treatment recommendations by AI facilitates collaborative decision-making and resource allocation optimization while also enabling patient progress monitoring, drug interaction consideration, and alignment with clinical guidelines. AI is used to analyze patient data, imaging, genomic data, and pathology reports in order to forecast how a treatment would respond. These models anticipate treatment outcomes, make sure that clinical recommendations are followed, and learn from historical data. The implementation of AI in medicine is hampered by issues with data quality, integration with healthcare IT systems, data protection, bias reduction, and ethical considerations, necessitating transparency and constant surveillance. Protecting patient privacy, resolving biases, maintaining transparency, identifying fault for mistakes, and ensuring fair access are just a few examples of ethical considerations. To preserve patient trust and address the effect on the healthcare workforce, ethical frameworks must be developed. The amazing potential of AI in the treatment of breast cancer calls for careful examination of its ethical and practical implications. We aim to review the comprehensive role of artificial intelligence in breast cancer management.
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Affiliation(s)
- Arun Singh
- General Surgery, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, IND
| | - Shivani B Paruthy
- General Surgery, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, IND
| | - Vivek Belsariya
- General Surgery, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, IND
| | - Nemi Chandra J
- General Surgery, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, IND
| | - Sunil Kumar Singh
- Surgical Oncology, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, IND
| | | | - Sushila Choudhary
- General Surgery, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, IND
| | - M Anil Kumar
- General Surgery, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, IND
| | - Dhananjay Khera
- General Surgery, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, IND
| | - Vaibhav Kuraria
- General Surgery, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, IND
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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.
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Affiliation(s)
- Matthew G Hanna
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY
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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.
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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
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8
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Wong ANN, He Z, Leung KL, To CCK, Wong CY, Wong SCC, Yoo JS, Chan CKR, Chan AZ, Lacambra MD, Yeung MHY. Current Developments of Artificial Intelligence in Digital Pathology and Its Future Clinical Applications in Gastrointestinal Cancers. Cancers (Basel) 2022; 14:3780. [PMID: 35954443 PMCID: PMC9367360 DOI: 10.3390/cancers14153780] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 07/27/2022] [Accepted: 08/01/2022] [Indexed: 02/05/2023] Open
Abstract
The implementation of DP will revolutionize current practice by providing pathologists with additional tools and algorithms to improve workflow. Furthermore, DP will open up opportunities for development of AI-based tools for more precise and reproducible diagnosis through computational pathology. One of the key features of AI is its capability to generate perceptions and recognize patterns beyond the human senses. Thus, the incorporation of AI into DP can reveal additional morphological features and information. At the current rate of AI development and adoption of DP, the interest in computational pathology is expected to rise in tandem. There have already been promising developments related to AI-based solutions in prostate cancer detection; however, in the GI tract, development of more sophisticated algorithms is required to facilitate histological assessment of GI specimens for early and accurate diagnosis. In this review, we aim to provide an overview of the current histological practices in AP laboratories with respect to challenges faced in image preprocessing, present the existing AI-based algorithms, discuss their limitations and present clinical insight with respect to the application of AI in early detection and diagnosis of GI cancer.
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Affiliation(s)
- Alex Ngai Nick Wong
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China; (A.N.N.W.); (Z.H.); (K.L.L.); (C.Y.W.); (S.C.C.W.); (J.S.Y.)
| | - Zebang He
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China; (A.N.N.W.); (Z.H.); (K.L.L.); (C.Y.W.); (S.C.C.W.); (J.S.Y.)
| | - Ka Long Leung
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China; (A.N.N.W.); (Z.H.); (K.L.L.); (C.Y.W.); (S.C.C.W.); (J.S.Y.)
| | - Curtis Chun Kit To
- Department of Anatomical and Cellular Pathology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, Hong Kong SAR, China; (C.C.K.T.); (C.K.R.C.); (M.D.L.)
| | - Chun Yin Wong
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China; (A.N.N.W.); (Z.H.); (K.L.L.); (C.Y.W.); (S.C.C.W.); (J.S.Y.)
| | - Sze Chuen Cesar Wong
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China; (A.N.N.W.); (Z.H.); (K.L.L.); (C.Y.W.); (S.C.C.W.); (J.S.Y.)
| | - Jung Sun Yoo
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China; (A.N.N.W.); (Z.H.); (K.L.L.); (C.Y.W.); (S.C.C.W.); (J.S.Y.)
| | - Cheong Kin Ronald Chan
- Department of Anatomical and Cellular Pathology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, Hong Kong SAR, China; (C.C.K.T.); (C.K.R.C.); (M.D.L.)
| | - Angela Zaneta Chan
- Department of Anatomical and Cellular Pathology, Prince of Wales Hospital, Shatin, Hong Kong SAR, China;
| | - Maribel D. Lacambra
- Department of Anatomical and Cellular Pathology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, Hong Kong SAR, China; (C.C.K.T.); (C.K.R.C.); (M.D.L.)
| | - Martin Ho Yin Yeung
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China; (A.N.N.W.); (Z.H.); (K.L.L.); (C.Y.W.); (S.C.C.W.); (J.S.Y.)
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9
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Rizzo PC, Girolami I, Marletta S, Pantanowitz L, Antonini P, Brunelli M, Santonicco N, Vacca P, Tumino N, Moretta L, Parwani A, Satturwar S, Eccher A, Munari E. Technical and Diagnostic Issues in Whole Slide Imaging Published Validation Studies. Front Oncol 2022; 12:918580. [PMID: 35785212 PMCID: PMC9246412 DOI: 10.3389/fonc.2022.918580] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 05/24/2022] [Indexed: 01/07/2023] Open
Abstract
ObjectiveDigital pathology with whole-slide imaging (WSI) has many potential clinical and non-clinical applications. In the past two decades, despite significant advances in WSI technology adoption remains slow for primary diagnosis. The aim of this study was to identify common pitfalls of WSI reported in validation studies and offer measures to overcome these challenges.MethodsA systematic search was conducted in the electronic databases Pubmed-MEDLINE and Embase. Inclusion criteria were all validation studies designed to evaluate the feasibility of WSI for diagnostic clinical use in pathology. Technical and diagnostic problems encountered with WSI in these studies were recorded.ResultsA total of 45 studies were identified in which technical issues were reported in 15 (33%), diagnostic issues in 8 (18%), and 22 (49%) reported both. Key technical problems encompassed slide scan failure, prolonged time for pathologists to review cases, and a need for higher image resolution. Diagnostic challenges encountered were concerned with grading dysplasia, reliable assessment of mitoses, identification of microorganisms, and clearly defining the invasive front of tumors.ConclusionDespite technical advances with WSI technology, some critical concerns remain that need to be addressed to ensure trustworthy clinical diagnostic use. More focus on the quality of the pre-scanning phase and training of pathologists could help reduce the negative impact of WSI technical difficulties. WSI also seems to exacerbate specific diagnostic tasks that are already challenging among pathologists even when examining glass slides with conventional light microscopy.
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Affiliation(s)
- Paola Chiara Rizzo
- Department of Pathology and Diagnostics and Public Health, Section of Pathology, University Hospital of Verona, Verona, Italy
| | | | - Stefano Marletta
- Department of Pathology and Diagnostics and Public Health, Section of Pathology, University Hospital of Verona, Verona, Italy
- Department of Pathology, Pederzoli Hospital, Peschiera del Garda, Italy
| | - Liron Pantanowitz
- Department of Pathology & Clinical Labs, University of Michigan, Ann Arbor, MI, United States
| | - Pietro Antonini
- Department of Pathology and Diagnostics and Public Health, Section of Pathology, University Hospital of Verona, Verona, Italy
| | - Matteo Brunelli
- Department of Pathology and Diagnostics and Public Health, Section of Pathology, University Hospital of Verona, Verona, Italy
| | - Nicola Santonicco
- Department of Pathology and Diagnostics and Public Health, Section of Pathology, University Hospital of Verona, Verona, Italy
| | - Paola Vacca
- Bambino Gesù Children’s Hospital, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Nicola Tumino
- Bambino Gesù Children’s Hospital, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Lorenzo Moretta
- Bambino Gesù Children’s Hospital, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Anil Parwani
- Department of Pathology, Ohio State University Medical Center, Columbus, OH, United States
| | - Swati Satturwar
- Department of Pathology, Ohio State University Medical Center, Columbus, OH, United States
| | - Albino Eccher
- Department of Pathology and Diagnostics, University and Hospital Trust of Verona, Verona, Italy
- *Correspondence: Albino Eccher,
| | - Enrico Munari
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
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10
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Evans AJ, Brown RW, Bui MM, Chlipala EA, Lacchetti C, Milner DA, Pantanowitz L, Parwani AV, Reid K, Riben MW, Reuter VE, Stephens L, Stewart RL, Thomas NE. Validating Whole Slide Imaging Systems for Diagnostic Purposes in Pathology. Arch Pathol Lab Med 2022; 146:440-450. [PMID: 34003251 DOI: 10.5858/arpa.2020-0723-cp] [Citation(s) in RCA: 64] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/26/2021] [Indexed: 11/06/2022]
Abstract
CONTEXT.— The original guideline, "Validating Whole Slide Imaging for Diagnostic Purposes in Pathology," was published in 2013 and included 12 guideline statements. The College of American Pathologists convened an expert panel to update the guideline following standards established by the National Academies of Medicine for developing trustworthy clinical practice guidelines. OBJECTIVE.— To assess evidence published since the release of the original guideline and provide updated recommendations for validating whole slide imaging (WSI) systems used for diagnostic purposes. DESIGN.— An expert panel performed a systematic review of the literature. Frozen sections, anatomic pathology specimens (biopsies, curettings, and resections), and hematopathology cases were included. Cytology cases were excluded. Using the Grading of Recommendations Assessment, Development, and Evaluation approach, the panel reassessed and updated the original guideline recommendations. RESULTS.— Three strong recommendations and 9 good practice statements are offered to assist laboratories with validating WSI digital pathology systems. CONCLUSIONS.— Systematic review of literature following release of the 2013 guideline reaffirms the use of a validation set of at least 60 cases, establishing intraobserver diagnostic concordance between WSI and glass slides and the use of a 2-week washout period between modalities. Although all discordances between WSI and glass slide diagnoses discovered during validation need to be reconciled, laboratories should be particularly concerned if their overall WSI-glass slide concordance is less than 95%.
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Affiliation(s)
- Andrew J Evans
- From the Department of Pathology, Mackenzie Health, Richmond Hill, Ontario, Canada (Evans)
| | - Richard W Brown
- The Department of Pathology, Memorial Hermann Southwest Hospital, Houston, Texas (Brown)
| | - Marilyn M Bui
- The Department of Pathology, Moffitt Cancer Center, Tampa, Florida (Bui)
| | | | - Christina Lacchetti
- Policy and Advocacy, American Society of Clinical Oncology, Alexandria, Virginia (Lacchetti)
| | - Danny A Milner
- American Society for Clinical Pathology, Chicago, Illinois (Milner)
| | - Liron Pantanowitz
- The Department of Pathology, University of Michigan, Ann Arbor (Pantanowitz)
| | - Anil V Parwani
- The Department of Pathology, Ohio State University Medical Center, Columbus (Parwani)
| | | | - Michael W Riben
- The Department of Pathology, University of Texas MD Anderson Cancer Center, Houston (Riben)
| | - Victor E Reuter
- The Department of Pathology, Memorial Sloan-Kettering Cancer Center, New York, New York (Reuter)
| | - Lisa Stephens
- The Department of Anatomic Pathology, Cleveland Clinic, Cleveland, Ohio (Stephens)
| | - Rachel L Stewart
- Janssen Research & Development, Spring House, Pennsylvania (Stewart)
| | - Nicole E Thomas
- Surveys (Thomas), College of American Pathologists, Northfield, Illinois
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11
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Clarke E, Doherty D, Randell R, Grek J, Thomas R, Ruddle RA, Treanor D. Faster than light (microscopy): superiority of digital pathology over microscopy for assessment of immunohistochemistry. J Clin Pathol 2022; 76:333-338. [PMID: 35039452 PMCID: PMC10176378 DOI: 10.1136/jclinpath-2021-207961] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 11/23/2021] [Indexed: 11/03/2022]
Abstract
AIMS Digital pathology offers the potential for significant benefits in diagnostic pathology, but currently the efficiency of slide viewing is a barrier to adoption. We hypothesised that presenting digital slides for simultaneous viewing of multiple sections of tissue for comparison, as in those with immunohistochemical panels, would allow pathologists to review cases more quickly. METHODS Novel software was developed to view synchronised parallel tissue sections on a digital pathology workstation. Sixteen histopathologists reviewed three liver biopsy cases including an immunohistochemical panel using the digital microscope, and three different liver biopsy cases including an immunohistochemical panel using the light microscope. The order of cases and interface was fully counterbalanced. Time to diagnosis was recorded and mean times are presented as data approximated to a normalised distribution. RESULTS Mean time to diagnosis was 4 min 3 s using the digital microscope and 5 min 24 s using the light microscope, saving 1 min 21 s (95% CI 16 s to 2 min 26 s; p=0.02), using the digital microscope. Overall normalised mean time to diagnosis was 85% on the digital pathology workstation compared with 115% on the microscope, a relative reduction of 26%. CONCLUSIONS With appropriate interface design, it is quicker to review immunohistochemical slides using a digital microscope than the conventional light microscope, without incurring any major diagnostic errors. As digital pathology becomes more integrated with routine clinical workflow and pathologists increase their experience of the technology, it is anticipated that other tasks will also become more time-efficient.
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Affiliation(s)
- Emily Clarke
- Division of Pathology and Data Analytics, University of Leeds, Leeds, UK .,Department of Histopathology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Daniel Doherty
- Division of Pathology and Data Analytics, University of Leeds, Leeds, UK.,Department of Histopathology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Rebecca Randell
- Faculty of Health Studies, University of Bradford, Bradford, UK.,Wolfson Centre for Applied Health Research, Bradford, UK
| | - Jonathan Grek
- Northern Ontario School of Medicine, Thunder Bay, Ontario, Canada
| | - Rhys Thomas
- Division of Pathology and Data Analytics, University of Leeds, Leeds, UK
| | - Roy A Ruddle
- School of Computing and Leeds Institute of Data Analytics, University of Leeds, Leeds, UK
| | - Darren Treanor
- Division of Pathology and Data Analytics, University of Leeds, Leeds, UK.,Department of Histopathology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
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12
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Atallah NM, Toss MS, Verrill C, Salto-Tellez M, Snead D, Rakha EA. Potential quality pitfalls of digitalized whole slide image of breast pathology in routine practice. Mod Pathol 2022; 35:903-910. [PMID: 34961765 PMCID: PMC8711290 DOI: 10.1038/s41379-021-01000-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 12/11/2021] [Accepted: 12/12/2021] [Indexed: 12/26/2022]
Abstract
Using digitalized whole slide images (WSI) in routine histopathology practice is a revolutionary technology. This study aims to assess the clinical impacts of WSI quality and representation of the corresponding glass slides. 40,160 breast WSIs were examined and compared with their corresponding glass slides. The presence, frequency, location, tissue type, and the clinical impacts of missing tissue were assessed. Scanning time, type of the specimens, time to WSIs implementation, and quality control (QC) measures were also considered. The frequency of missing tissue ranged from 2% to 19%. The area size of the missed tissue ranged from 1-70%. In most cases (>75%), the missing tissue area size was <10% and peripherally located. In all cases the missed tissue was fat with or without small entrapped normal breast parenchyma. No missing tissue was identified in WSIs of the core biopsy specimens. QC measures improved images quality and reduced WSI failure rates by seven-fold. A negative linear correlation between the frequency of missing tissue and both the scanning time and the image file size was observed (p < 0.05). None of the WSI with missing tissues resulted in a change in the final diagnosis. Missing tissue on breast WSI is observed but with variable frequency and little diagnostic consequence. Balancing between WSI quality and scanning time/image file size should be considered and pathology laboratories should undertake their own assessments of risk and provide the relevant mitigations with the appropriate level of caution.
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Affiliation(s)
- Nehal M. Atallah
- grid.4563.40000 0004 1936 8868Department of Histopathology, School of Medicine, the University of Nottingham and Nottingham University Hospitals NHS Trust, Nottingham, UK ,grid.411775.10000 0004 0621 4712Department of Pathology, Faculty of Medicine, Menoufia University, Shebin Elkom, Al-Menoufia, Egypt
| | - Michael S. Toss
- grid.4563.40000 0004 1936 8868Department of Histopathology, School of Medicine, the University of Nottingham and Nottingham University Hospitals NHS Trust, Nottingham, UK
| | - Clare Verrill
- grid.4991.50000 0004 1936 8948Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK ,grid.4991.50000 0004 1936 8948NIHR Oxford Biomedical Research Centre, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Manuel Salto-Tellez
- grid.4777.30000 0004 0374 7521Precision Medicine Centre of Excellence, The Patrick G Johnston Centre for Cancer Research, Queen’s University, Belfast, UK
| | - David Snead
- grid.15628.380000 0004 0393 1193Cellular Pathology, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, Coventry, UK
| | - Emad A. Rakha
- grid.4563.40000 0004 1936 8868Department of Histopathology, School of Medicine, the University of Nottingham and Nottingham University Hospitals NHS Trust, Nottingham, UK ,grid.411775.10000 0004 0621 4712Department of Pathology, Faculty of Medicine, Menoufia University, Shebin Elkom, Al-Menoufia, Egypt
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13
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Mutter G, Milstone D, Hwang D, Siegmund S, Bruce A. Measuring digital pathology throughput and tissue dropouts. J Pathol Inform 2022; 13:8. [PMID: 35136675 PMCID: PMC8794031 DOI: 10.4103/jpi.jpi_5_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 06/05/2021] [Accepted: 06/20/2021] [Indexed: 11/04/2022] Open
Abstract
Background: Digital pathology operations that precede viewing by a pathologist have a substantial impact on costs and fidelity of the digital image. Scan time and file size determine throughput and storage costs, whereas tissue omission during digital capture (“dropouts”) compromises downstream interpretation. We compared how these variables differ across scanners. Methods: A 212 slide set randomly selected from a gynecologic-gestational pathology practice was used to benchmark scan time, file size, and image completeness. Workflows included the Hamamatsu S210 scanner (operated under default and optimized profiles) and the Leica GT450. Digital tissue dropouts were detected by the aligned overlay of macroscopic glass slide camera images (reference) with images created by the slide scanners whole slide images. Results: File size and scan time were highly correlated within each platform. Differences in GT450, default S210, and optimized S210 performance were seen in average file size (1.4 vs. 2.5 vs. 3.4 GB) and scan time (93 vs. 376 vs. 721 s). Dropouts were seen in 29.5% (186/631) of successful scans overall: from a low of 13.7% (29/212) for the optimized S210 profile, followed by 34.6% (73/211) for the GT450 and 40.4% (84/208) for the default profile S210 profile. Small dislodged fragments, “shards,” were dropped in 22.2% (140/631) of slides, followed by tissue marginalized at the glass slide edges, 6.2% (39/631). “Unique dropouts,” those for which no equivalent appeared elsewhere in the scan, occurred in only three slides. Of these, 67% (2/3) were “floaters” or contaminants from other cases. Conclusions: Scanning speed and resultant file size vary greatly by scanner type, scanner operation settings, and clinical specimen mix (tissue type, tissue area). Digital image fidelity as measured by tissue dropout frequency and dropout type also varies according to the tissue type and scanner. Dropped tissues very rarely (1/631) represent actual specimen tissues that are not represented elsewhere in the scan, so in most cases cannot alter the diagnosis. Digital pathology platforms vary in their output efficiency and image fidelity to the glass original and should be matched to the intended application.
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14
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Salama AM, Hanna MG, Giri D, Kezlarian B, Jean MH, Lin O, Vallejo C, Brogi E, Edelweiss M. Digital validation of breast biomarkers (ER, PR, AR, and HER2) in cytology specimens using three different scanners. Mod Pathol 2022; 35:52-59. [PMID: 34518629 PMCID: PMC8702445 DOI: 10.1038/s41379-021-00908-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 08/08/2021] [Accepted: 08/09/2021] [Indexed: 11/30/2022]
Abstract
Progression in digital pathology has yielded new opportunities for a remote work environment. We evaluated the utility of digital review of breast cancer immunohistochemical prognostic markers (IHC) using whole slide images (WSI) from formalin fixed paraffin embedded (FFPE) cytology cell block specimens (CB) using three different scanners.CB from 20 patients with breast cancer diagnosis and available IHC were included. Glass slides including 20 Hematoxylin and eosin (H&E), 20 Estrogen Receptor (ER), 20 Progesterone Receptor (PR), 16 Androgen Receptor (AR), and 20 Human Epidermal Growth Factor Receptor 2 (HER2) were scanned on 3 different scanners. Four breast pathologists reviewed the WSI and recorded their semi-quantitative scoring for each marker. Kappa concordance was defined as complete agreement between glass/digital pairs. Discordances between microscopic and digital reads were classified as a major when a clinically relevant change was seen. Minor discordances were defined as differences in scoring percentages/staining pattern that would not have resulted in a clinical implication. Scanner precision was tabulated according to the success rate of each scan on all three scanners.In total, we had 228 paired glass/digital IHC reads on all 3 scanners. There was strong concordance kappa ≥0.85 for all pathologists when comparing paired microscopic/digital reads. Strong concordance (kappa ≥0.86) was also seen when comparing reads between scanners.Twenty-three percent of the WSI required rescanning due to barcode detection failures, 14% due to tissue detection failures, and 2% due to focus issues. Scanner 1 had the best average precision of 92%. HER2 IHC had the lowest intra-scanner precision (64%) among all stains.This study is the first to address the utility of WSI in breast cancer IHC in CB and to validate its reporting using 3 different scanners. Digital images are reliable for breast IHC assessment in CB and offer similar reproducibility to microscope reads.
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Affiliation(s)
- Abeer M Salama
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Matthew G Hanna
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Dilip Giri
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Brie Kezlarian
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Marc-Henri Jean
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Oscar Lin
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Christina Vallejo
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Edi Brogi
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Marcia Edelweiss
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.
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15
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Atypia in breast pathology: what pathologists need to know. Pathology 2021; 54:20-31. [PMID: 34872753 DOI: 10.1016/j.pathol.2021.09.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 09/07/2021] [Accepted: 09/14/2021] [Indexed: 10/19/2022]
Abstract
Despite the importance of atypia in diagnosing and classifying breast lesions, the definition of atypia varies depending on the context, with a lack of consistent and objective criteria for assessment. Atypia in breast pathology may be cytonuclear and/or architectural with different applications and implications. Cytonuclear atypia is used to assist the distinction of various intraductal epithelial proliferative lesions including usual ductal hyperplasia (UDH) versus atypical ductal hyperplasia (ADH) or ductal carcinoma in situ (DCIS), and to grade DCIS. In invasive carcinoma, nuclear atypia (i.e., nuclear pleomorphism) is a component of the histological grading system. Stromal cell cytonuclear atypia is one of the key features used to distinguish fibroadenoma from phyllodes tumour (PT) and to classify PT as benign, borderline or malignant. Similarly, cytonuclear atypia is used in the evaluation of myoepithelial cell alterations in the breast. Architectural atypia is used to differentiate flat epithelial atypia (FEA) from ADH or DCIS. In addition to the inherent subjectivity in the interpretation of atypia, which presents as a morphological continuum reflecting a biological spectrum, the lack of standardisation in defining atypia augments diagnostic discordance in breast pathology, with potential implications for patient management. Evidence to date suggests that the traditional criteria used to assess atypia may require modification in the era of digital pathology primary diagnosis. This review aims to provide a comprehensive review of atypia in breast pathology with reference to inconsistencies, challenges and limitations.
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16
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Brockmoeller S, Toh EW, Kouvidi K, Hepworth S, Morris E, Quirke P. Improving the management of early colorectal cancers (eCRC) by using quantitative markers to predict lymph node involvement and thus the need for major resection of pT1 cancers. J Clin Pathol 2021; 75:545-550. [PMID: 34645701 DOI: 10.1136/jclinpath-2021-207482] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 03/12/2021] [Accepted: 03/18/2021] [Indexed: 12/24/2022]
Abstract
BACKGROUND Since implementing the NHS bowel cancer screening programme, the rate of early colorectal cancer (eCRC; pT1) has increased threefold to 17%, but how these lesions should be managed is currently unclear. AIM To improve risk stratification of eCRC by developing reproducible quantitative markers to build a multivariate model to predict lymph node metastasis (LNM). METHODS Our retrospective cohort of 207 symptomatic pT1 eCRC was assessed for quantitative markers. Associations between categorical data and LNM were performed using χ2 test and Fisher's exact test. Multivariable modelling was performed using logistic regression. Youden's rule gave the cut-point for LNM. RESULTS All significant parameters in the univariate analysis were included in a multivariate model; tumour stroma (95% CI 2.3 to 41.0; p=0.002), area of submucosal invasion (95% CI 2.1 to 284.6; p=0.011), poor tumour differentiation (95% CI 2.0 to 358.3; p=0.003) and lymphatic invasion (95% CI 1.3 to 192.6; p=0.028) were predictive of LNM. Youden's rule gave a cut-off of p>5%, capturing 18/19 LNM (94.7%) cases and leading to a resection recommendation for 34% of cases. The model that only included quantitative factors were also significant, capturing 17/19 LNM cases (90%) and leading to resection rate of 35% of cases (72/206). CONCLUSIONS In this study, we were able to reduce the potential resection rate of pT1 with the multivariate qualitative and/or quantitative model to 34% or 35% while detecting 95% or 90% of all LNM cases, respectively. While these findings need to be validated, this model could lead to a reduction of the major resection rate in eCRC.
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Affiliation(s)
- Scarlet Brockmoeller
- Pathology and Data Analytics, Leeds Institute of Medical Research at St. Jame's, School of Medicine, Leeds, UK
| | - Eu-Wing Toh
- Department of Histopathology, Sheffield, Sheffield, UK
| | - Katerina Kouvidi
- Pathology and Data Analytics, Leeds Institute of Medical Research at St. Jame's, School of Medicine, Leeds, UK
| | | | - Eva Morris
- Nuffield Department of Popular Health, Big Data Institute, Oxford, Oxford, UK
| | - Philip Quirke
- Pathology and Data Analytics, Leeds Institute of Medical Research at St. Jame's, School of Medicine, Leeds, UK
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17
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Katayama A, Toss MS, Parkin M, Sano T, Oyama T, Quinn CM, Ellis IO, Rakha EA. Nuclear morphology in breast lesions: refining its assessment to improve diagnostic concordance. Histopathology 2021; 80:515-528. [PMID: 34605058 DOI: 10.1111/his.14577] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 09/20/2021] [Accepted: 09/30/2021] [Indexed: 11/29/2022]
Abstract
BACKGROUND Although evaluation of nuclear morphology plays a crucial role in the diagnosis and categorisation of breast lesions, the criteria used to assess nuclear atypia rely on the subjective evaluation of several features that may result in inter- and intra-observer variation. This study aims to refine the definitions of cytonuclear features in various breast lesions. METHODS ImageJ was used to assess the nuclear morphological features including nuclear diameter, axis length, perimeter, area, circularity, and roundness in 160 breast lesions comprising ductal carcinoma in situ (DCIS), invasive breast carcinoma of no special type (IBC-NST), tubular carcinoma, usual ductal hyperplasia (UDH), columnar cell change (CCC) and flat epithelial atypia (FEA). Reference cells included normal epithelial cells, red blood cells (RBCs) and lymphocytes. RESULTS Reference cells showed size differences not only between normal epithelial cells and RBCs but also between RBCs in varied-sized blood vessels. Nottingham grade nuclear pleomorphism scores 1 and 3 cut-offs in IBC, compared to normal epithelial cells, were <1.2x and >1.4x that of mean maximum Feret's diameter and <1.6x and >2.4x that of mean nuclear area, respectively. Nuclear morphometrics were significantly different in low-grade IBC-NST vs. tubular carcinoma, low-grade DCIS vs. UDH, and in CCC vs. FEA. No differences in the nuclear features between grade matched DCIS and IBC were identified. CONCLUSION This study provides a guide for the assessment of nuclear atypia in breast lesions, refines the comparison with reference cells and highlights the potential diagnostic value of image analysis tools in the era of digital pathology.
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Affiliation(s)
- Ayaka Katayama
- Translational Medical Sciences Unit, School of Medicine, University of Nottingham, City Hospital, Nottingham, UK.,Diagnostic Pathology, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Michael S Toss
- Translational Medical Sciences Unit, School of Medicine, University of Nottingham, City Hospital, Nottingham, UK
| | - Matthew Parkin
- Translational Medical Sciences Unit, School of Medicine, University of Nottingham, City Hospital, Nottingham, UK
| | - Takaaki Sano
- Diagnostic Pathology, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Tetsunari Oyama
- Diagnostic Pathology, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Cecily M Quinn
- Department of Histopathology, St Vincent's University Hospital, University College, Dublin, Ireland
| | - Ian O Ellis
- Translational Medical Sciences Unit, School of Medicine, University of Nottingham, City Hospital, Nottingham, UK.,Department of Histopathology, Nottingham University Hospitals NHS Trust, City Hospital, Nottingham, UK
| | - Emad A Rakha
- Translational Medical Sciences Unit, School of Medicine, University of Nottingham, City Hospital, Nottingham, UK.,Department of Histopathology, Nottingham University Hospitals NHS Trust, City Hospital, Nottingham, UK
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18
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Ibrahim A, Lashen A, Toss M, Mihai R, Rakha E. Assessment of mitotic activity in breast cancer: revisited in the digital pathology era. J Clin Pathol 2021; 75:365-372. [PMID: 34556501 DOI: 10.1136/jclinpath-2021-207742] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 09/06/2021] [Indexed: 11/04/2022]
Abstract
The assessment of cell proliferation is a key morphological feature for diagnosing various pathological lesions and predicting their clinical behaviour. Visual assessment of mitotic figures in routine histological sections remains the gold-standard method to evaluate the proliferative activity and grading of cancer. Despite the apparent simplicity of such a well-established method, visual assessment of mitotic figures in breast cancer (BC) remains a challenging task with low concordance among pathologists which can lead to under or overestimation of tumour grade and hence affects management. Guideline recommendations for counting mitoses in BC have been published to standardise methodology and improve concordance; however, the results remain less satisfactory. Alternative approaches such as the use of the proliferation marker Ki67 have been recommended but these did not show better performance in terms of concordance or prognostic stratification. The advent of whole slide image technology has brought the issue of mitotic counting in BC into the light again with more challenges to develop objective criteria for identifying and scoring mitotic figures in digitalised images. Using reliable and reproducible morphological criteria can provide the highest degree of concordance among pathologists and could even benefit the further application of artificial intelligence (AI) in breast pathology, and this relies mainly on the explicit description of these figures. In this review, we highlight the morphology of mitotic figures and their mimickers, address the current caveats in counting mitoses in breast pathology and describe how to strictly apply the morphological criteria for accurate and reliable histological grade and AI models.
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Affiliation(s)
- Asmaa Ibrahim
- Division of Cancer and Stem Cell, University of Nottingham, Nottingham, UK.,Department of Pathology, Suez Canal University, Ismailia, Egypt
| | - Ayat Lashen
- Division of Cancer and Stem Cell, University of Nottingham, Nottingham, UK.,Department of Pathology, Menoufia University, Shebin El-Kom, Egypt
| | - Michael Toss
- Division of Cancer and Stem Cell, University of Nottingham, Nottingham, UK
| | - Raluca Mihai
- Department of Pathology, Queen Elizabeth University Hospital, Glasgow, UK
| | - Emad Rakha
- Division of Cancer and Stem Cell, University of Nottingham, Nottingham, UK .,Department of Pathology, Menoufia University, Shebin El-Kom, Egypt
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Lashen A, Ibrahim A, Katayama A, Ball G, Mihai R, Toss M, Rakha E. Visual assessment of mitotic figures in breast cancer: a comparative study between light microscopy and whole slide images. Histopathology 2021; 79:913-925. [PMID: 34455620 DOI: 10.1111/his.14543] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2021] [Revised: 07/24/2021] [Accepted: 08/15/2021] [Indexed: 11/29/2022]
Abstract
BACKGROUND AND AIMS Visual assessment of mitotic figures in breast cancer (BC) remains a challenge. This is expected to be more pronounced in the digital pathology era. This study aims to refine the criteria of mitotic figure recognition, particularly in whole slide images (WSI). METHOD AND RESULTS Haematoxylin and eosin (H&E)-stained BC sections (n = 506) were examined using light microscopy (LM) and WSI. A set of features for identifying mitosis in WSI and to distinguish true figures from mimickers was developed. Changes in the mitotic count between the two platforms was explored. Morphological features of mitoses were recorded separately, including absence of nuclear membrane, chromatin hairy-like projections, shape, cytoplasmic features, mitotic cell size and relationship to surrounding cells. Each mitotic phase has its own mimickers. Fifty-eight per cent of mitoses showed absent hairy-like projection in WSI; however, 89% retained their ragged nuclear border, which distinguished them from mimickers including apoptotic cells, lymphocytes and dark elongated hyperchromatic structures. Mitosis in WSI showed loss of fine details, and there was a 20% average reduction rate of mitotic counts when compared to the same area on LM. Using refined mitosis recognition criteria in WSI resulted in a twofold improvement of interobserver concordance. However, when compared to LM, 19% of cases were underscored in WSIs. CONCLUSIONS All morphological features of mitosis should be considered to enable recognition and differentiation from their mimickers, particularly in WSI, to ensure reliable BC grading. Refining mitotic cut-offs per specific area when using WSI, based on the degree of reduction and association with outcome, is warranted.
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Affiliation(s)
- Ayat Lashen
- Division of Cancer and Stem Cells, School of Medicine, University of Nottingham, Nottingham, UK.,Department of Pathology, Faculty of Medicine, Menoufia University, Shebin El Kom, Egypt
| | - Asmaa Ibrahim
- Division of Cancer and Stem Cells, School of Medicine, University of Nottingham, Nottingham, UK.,Department of Pathology, Faculty of Medicine, Suez Canal University, Ismailia, Egypt
| | - Ayaka Katayama
- Division of Cancer and Stem Cells, School of Medicine, University of Nottingham, Nottingham, UK.,Diagnostic Pathology, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Graham Ball
- John van Geest Cancer Research Centre, School of Science and Technology, Nottingham Trent University, Nottingham, UK
| | - Raluca Mihai
- Department of Pathology, Queen Elizabeth University Hospital, Glasgow, UK
| | - Michael Toss
- Division of Cancer and Stem Cells, School of Medicine, University of Nottingham, Nottingham, UK
| | - Emad Rakha
- Division of Cancer and Stem Cells, School of Medicine, University of Nottingham, Nottingham, UK.,Department of Pathology, Faculty of Medicine, Menoufia University, Shebin El Kom, Egypt
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Bertram CA, Stathonikos N, Donovan TA, Bartel A, Fuchs-Baumgartinger A, Lipnik K, van Diest PJ, Bonsembiante F, Klopfleisch R. Validation of digital microscopy: Review of validation methods and sources of bias. Vet Pathol 2021; 59:26-38. [PMID: 34433345 PMCID: PMC8761960 DOI: 10.1177/03009858211040476] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Digital microscopy (DM) is increasingly replacing traditional light microscopy (LM) for performing routine diagnostic and research work in human and veterinary pathology. The DM workflow encompasses specimen preparation, whole-slide image acquisition, slide retrieval, and the workstation, each of which has the potential (depending on the technical parameters) to introduce limitations and artifacts into microscopic examination by pathologists. Performing validation studies according to guidelines established in human pathology ensures that the best-practice approaches for patient care are not deteriorated by implementing DM. Whereas current publications on validation studies suggest an overall high reliability of DM, each laboratory is encouraged to perform an individual validation study to ensure that the DM workflow performs as expected in the respective clinical or research environment. With the exception of validation guidelines developed by the College of American Pathologists in 2013 and its update in 2021, there is no current review of the application of methods fundamental to validation. We highlight that there is high methodological variation between published validation studies, each having advantages and limitations. The diagnostic concordance rate between DM and LM is the most relevant outcome measure, which is influenced (regardless of the viewing modality used) by different sources of bias including complexity of the cases examined, diagnostic experience of the study pathologists, and case recall. Here, we review 3 general study designs used for previous publications on DM validation as well as different approaches for avoiding bias.
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Affiliation(s)
- Christof A Bertram
- University of Veterinary Medicine, Vienna, Austria.,Freie Universität Berlin, Berlin, Germany
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21
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Katare P, Gorthi SS. Recent technical advances in whole slide imaging instrumentation. J Microsc 2021; 284:103-117. [PMID: 34254690 DOI: 10.1111/jmi.13049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Revised: 07/05/2021] [Accepted: 07/06/2021] [Indexed: 11/28/2022]
Abstract
Microscopic observation of biological specimen smears is the mainstay of diagnostic pathology, as defined by the Digital Pathology Association. Though automated systems for this are commercially available, their bulky size and high cost renders them unusable for remote areas. The research community is investing much effort towards building equivalent but portable, low-cost systems. An overview of such research is presented here, including a comparative analysis of recent reports. This paper also reviews recently reported systems for automated staining and smear formation, including microfluidic devices; and optical and computational automated microscopy systems including smartphone-based devices. Image pre-processing and analysis methods for automated diagnosis are also briefly discussed. It concludes with a set of foreseeable research directions that could lead to affordable, integrated and accurate whole slide imaging systems.
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Affiliation(s)
- Prateek Katare
- Department of Instrumentation and Applied Physics, Indian Institute of Science, Bangalore, India
| | - Sai Siva Gorthi
- Department of Instrumentation and Applied Physics, Indian Institute of Science, Bangalore, India
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22
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Azam AS, Miligy IM, Kimani PKU, Maqbool H, Hewitt K, Rajpoot NM, Snead DRJ. Diagnostic concordance and discordance in digital pathology: a systematic review and meta-analysis. J Clin Pathol 2021; 74:448-455. [PMID: 32934103 PMCID: PMC8223673 DOI: 10.1136/jclinpath-2020-206764] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Accepted: 07/10/2020] [Indexed: 01/10/2023]
Abstract
BACKGROUND Digital pathology (DP) has the potential to fundamentally change the way that histopathology is practised, by streamlining the workflow, increasing efficiency, improving diagnostic accuracy and facilitating the platform for implementation of artificial intelligence-based computer-assisted diagnostics. Although the barriers to wider adoption of DP have been multifactorial, limited evidence of reliability has been a significant contributor. A meta-analysis to demonstrate the combined accuracy and reliability of DP is still lacking in the literature. OBJECTIVES We aimed to review the published literature on the diagnostic use of DP and to synthesise a statistically pooled evidence on safety and reliability of DP for routine diagnosis (primary and secondary) in the context of validation process. METHODS A comprehensive literature search was conducted through PubMed, Medline, EMBASE, Cochrane Library and Google Scholar for studies published between 2013 and August 2019. The search protocol identified all studies comparing DP with light microscopy (LM) reporting for diagnostic purposes, predominantly including H&E-stained slides. Random-effects meta-analysis was used to pool evidence from the studies. RESULTS Twenty-five studies were deemed eligible to be included in the review which examined a total of 10 410 histology samples (average sample size 176). For overall concordance (clinical concordance), the agreement percentage was 98.3% (95% CI 97.4 to 98.9) across 24 studies. A total of 546 major discordances were reported across 25 studies. Over half (57%) of these were related to assessment of nuclear atypia, grading of dysplasia and malignancy. These were followed by challenging diagnoses (26%) and identification of small objects (16%). CONCLUSION The results of this meta-analysis indicate equivalent performance of DP in comparison with LM for routine diagnosis. Furthermore, the results provide valuable information concerning the areas of diagnostic discrepancy which may warrant particular attention in the transition to DP.
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Affiliation(s)
- Ayesha S Azam
- Cellular Pathology, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, Coventry, UK
- Tissue Image Analytics Laboratory, Department of Computer Science, University of Warwick, Coventry, West Midlands, UK
| | - Islam M Miligy
- Nottingham Breast Cancer Research Centre (NBCRC), School of Medicine, University of Nottingham, Nottingham, Nottinghamshire, UK
| | - Peter K-U Kimani
- Warwick Medical School, University of Warwick, Coventry, West Midlands, UK
| | - Heeba Maqbool
- Cellular Pathology, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, Coventry, UK
| | - Katherine Hewitt
- Cellular Pathology, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, Coventry, UK
| | - Nasir M Rajpoot
- Tissue Image Analytics Laboratory, Department of Computer Science, University of Warwick, Coventry, West Midlands, UK
| | - David R J Snead
- Cellular Pathology, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, Coventry, UK
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23
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Samuelson MI, Chen SJ, Boukhar SA, Schnieders EM, Walhof ML, Bellizzi AM, Robinson RA, Rajan K D A. Rapid Validation of Whole-Slide Imaging for Primary Histopathology Diagnosis. Am J Clin Pathol 2021; 155:638-648. [PMID: 33511392 PMCID: PMC7929400 DOI: 10.1093/ajcp/aqaa280] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
OBJECTIVES The ongoing global severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic necessitates adaptations in the practice of surgical pathology at scale. Primary diagnosis by whole-slide imaging (WSI) is a key component that would aid departments in providing uninterrupted histopathology diagnosis and maintaining revenue streams from disruption. We sought to perform rapid validation of the use of WSI in primary diagnosis meeting recommendations of the College of American Pathologists guidelines. METHODS Glass slides from clinically reported cases from 5 participating pathologists with a preset washout period were digitally scanned and reviewed in settings identical to typical reporting. Cases were classified as concordant or with minor or major disagreement with the original diagnosis. Randomized subsampling was performed, and mean concordance rates were calculated. RESULTS In total, 171 cases were included and distributed equally among participants. For the group as a whole, the mean concordance rate in sampled cases (n = 90) was 83.6% counting all discrepancies and 94.6% counting only major disagreements. The mean pathologist concordance rate in sampled cases (n = 18) ranged from 90.49% to 97%. CONCLUSIONS We describe a novel double-blinded method for rapid validation of WSI for primary diagnosis. Our findings highlight the occurrence of a range of diagnostic reproducibility when deploying digital methods.
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Affiliation(s)
- Megan I Samuelson
- Department of Pathology, University of Iowa Hospitals and Clinics, University of Iowa, Iowa City, IA, USA
| | - Stephanie J Chen
- Department of Pathology, University of Iowa Hospitals and Clinics, University of Iowa, Iowa City, IA, USA
| | - Sarag A Boukhar
- Department of Pathology, University of Iowa Hospitals and Clinics, University of Iowa, Iowa City, IA, USA
| | - Eric M Schnieders
- Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, IA, USA
| | - Mackenzie L Walhof
- Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, IA, USA
| | - Andrew M Bellizzi
- Department of Pathology, University of Iowa Hospitals and Clinics, University of Iowa, Iowa City, IA, USA
| | - Robert A Robinson
- Department of Pathology, University of Iowa Hospitals and Clinics, University of Iowa, Iowa City, IA, USA
| | - Anand Rajan K D
- Department of Pathology, University of Iowa Hospitals and Clinics, University of Iowa, Iowa City, IA, USA
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24
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Breast Digital Pathology: Way of the Future. CURRENT BREAST CANCER REPORTS 2021. [DOI: 10.1007/s12609-021-00413-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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25
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Ramaswamy V, Tejaswini BN, Uthaiah SB. Remote Reporting During a Pandemic Using Digital Pathology Solution: Experience from a Tertiary Care Cancer Center. J Pathol Inform 2021; 12:20. [PMID: 34267985 PMCID: PMC8274304 DOI: 10.4103/jpi.jpi_109_20] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 12/28/2020] [Accepted: 03/01/2021] [Indexed: 12/23/2022] Open
Abstract
Background: Remote reporting in anatomic pathology is an important advantage of digital pathology that has not been much explored. The COVID-19 pandemic has provided an opportunity to explore this important application of digital pathology system in a tertiary care cancer center to ensure patient care and staff safety. Regulatory guidelines have been described for remote reporting following the pandemic. Herein, we describe our experience of validation of digital pathology workflow for remote reporting to encourage pathologists to utilize this facility which opens door for multiple, multidisciplinary collaborations. Objective: To demonstrate the validation and the operational feasibility of remote reporting using a digital pathology system. Materials and Methods: Our retrospective validation included whole-slide images (WSIs) of 60 cases of histopathology and 20 cases each of frozen sections and a digital image-based breast algorithm after a washout period of 3 months. Three pathologists with different models of consumer-grade laptops reviewed the cases remotely to assess the diagnostic concordance and operational feasibility of the modified workflow. The slides were digitized on a USFDA-approved Philips UFS 300 scanner at ×40 resolution (0.25 μm/pixel) and viewed on the Image Management System through a web browser. All the essential parameters were reported for each case. After successful validation, 886 cases were reported remotely from March 29, 2020, to June 30, 2020, prospectively. Light microscopy formed the gold standard reference in remote reporting. Results: 100% major diagnostic concordance was observed in the validation of remote reporting in the retrospective and prospective studies using consumer-grade laptops. The deferral rate was 0.34%. 97.6% of histopathology and 100% of frozen sections were signed out within the turnaround time. Network speed and a lack of virtual private network did not significantly affect the study. Conclusion: This study of validation and reporting of complete pathology cases remotely, including their operational feasibility during a public health emergency, proves that remote sign-out using a digital pathology system is not inferior to WSIs on medical-grade monitors and light microscopy. Such studies on remote reporting open the door for the use of digital pathology for interinstitutional consultation and collaboration: Its main intended use.
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Affiliation(s)
- Veena Ramaswamy
- Department of Histopathology, Strand Life Sciences - Health Care Global Cancer Hospital, Bengaluru, Karnataka, India
| | - B N Tejaswini
- Department of Histopathology, Strand Life Sciences - Health Care Global Cancer Hospital, Bengaluru, Karnataka, India
| | - Sowmya B Uthaiah
- Department of Histopathology, Strand Life Sciences - Health Care Global Cancer Hospital, Bengaluru, Karnataka, India
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Lujan G, Quigley JC, Hartman D, Parwani A, Roehmholdt B, Meter BV, Ardon O, Hanna MG, Kelly D, Sowards C, Montalto M, Bui M, Zarella MD, LaRosa V, Slootweg G, Retamero JA, Lloyd MC, Madory J, Bowman D. Dissecting the Business Case for Adoption and Implementation of Digital Pathology: A White Paper from the Digital Pathology Association. J Pathol Inform 2021; 12:17. [PMID: 34221633 PMCID: PMC8240548 DOI: 10.4103/jpi.jpi_67_20] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Revised: 09/20/2020] [Accepted: 11/02/2020] [Indexed: 12/13/2022] Open
Abstract
We believe the switch to a digital pathology (DP) workflow is imminent and it is essential to understand the economic implications of conversion. Many aspects of the adoption of DP will be disruptive and have a direct financial impact, both in short term costs, such as investment in equipment and personnel, and long term revenue potential, such as improved productivity and novel tests. The focus of this whitepaper is to educate pathologists, laboratorians and other stakeholders about the business and monetary considerations of converting to a digital pathology workflow. The components of a DP business plan will be thoroughly summarized, and guidance will be provided on how to build a case for adoption and implementation as well as a roadmap for transitioning from an analog to a digital pathology workflow in various laboratory settings. It is important to clarify that this publication is not intended to list prices although some financials will be mentioned as examples. The authors encourage readers who are evaluating conversion to a DP workflow to use this paper as a foundational guide for conducting a thorough and complete assessment while incorporating in current market pricing. Contributors to this paper analyzed peer-reviewed literature and data collected from various institutions, some of which are mentioned. Digital pathology will change the way we practice through facilitating patient access to expert pathology services and enabling image analysis tools and assays to aid in diagnosis, prognosis, risk stratification and therapeutic selection. Together, they will result in the delivery of valuable information from which to make better decisions and improve the health of patients.
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Affiliation(s)
- Giovanni Lujan
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, OH, USA
| | | | - Douglas Hartman
- Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Anil Parwani
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Brian Roehmholdt
- Department of Pathology, Southern California Permanente Medical Group, La Canada Flintridge, CA, USA
| | | | - Orly Ardon
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Matthew G. Hanna
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | | | | | - Marilyn Bui
- Department of Anatomic Pathology, H. Lee Moffitt Cancer Center, Tampa, FL, USA
| | - Mark D. Zarella
- Johns Hopkins Medicine Pathology Informatics, Baltimore, MD 21287, USA
| | - Victoria LaRosa
- Education Services Department, Oracle Corp, Austin, Texas, USA
| | | | | | | | - James Madory
- Department of Pathology, Medical University of South Carolina, Charleston, SC, USA
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Guiter GE, Sapia S, Wright AI, Hutchins GGA, Arayssi T. Development of a Remote Online Collaborative Medical School Pathology Curriculum with Clinical Correlations, across Several International Sites, through the Covid-19 Pandemic. MEDICAL SCIENCE EDUCATOR 2021; 31:549-556. [PMID: 33495717 PMCID: PMC7815444 DOI: 10.1007/s40670-021-01212-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 01/08/2021] [Indexed: 05/30/2023]
Abstract
INTRODUCTION Due to the Covid-19 social distancing restrictions, in March 2020, Weill Cornell Medicine-Qatar decided to replace students' clinical instruction with novel online electives. Hence, we implemented an innovative online and remote pathology curriculum, anchored on virtual microscopy and Zoom videoconferencing: ideal tools to support online teaching. OBJECTIVE To assess a new curriculum implementation at Weill Cornell Medicine-Qatar. MATERIALS AND METHODS This for-credit, 2-week elective included 6 synchronous Zoom sessions where complex clinicopathological cases were discussed in small groups. We used open access digital microscopy slides from the University of Leeds' Virtual Pathology Library (http://www.virtualpathology.leeds.ac.uk/slides/library/). Students independently prepared for these sessions by reviewing cases, slides, readings, and questions in advance (asynchronous self-directed learning anchored on a flipped classroom model), and wrote a final review of a case. An assessment and feedback were given to each student. RESULTS Four elective iterations were offered to a total of 29 students, with learners and faculty spread over 4 countries. During the Zoom sessions, students controlled the digital slides and offered their own diagnoses, followed by group discussions to strengthen autonomy and confidence. We surveyed learners about the elective's performance (program evaluation). Students conveyed high levels of satisfaction about the elective's overall quality, their pathology learning and online interactions, with minimal challenges related to the remote nature of the course. DISCUSSION AND CONCLUSIONS Technological innovations mitigate sudden disruptions in medical education. A remote curriculum allows instruction at any distance, at any time, from anywhere, enhancing educational exchanges, flexibility and globalization in medical education.
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Affiliation(s)
- Gerardo E. Guiter
- Division of Medical Education, Weill Cornell Medicine-Qatar, 445 East 69 Street, RM 432, New York, NY 10021 USA
| | - Sandra Sapia
- Division of Medical Education, Weill Cornell Medicine- Qatar, Qatar Foundation - Education City, P.O. Box 24144, Doha, Qatar
| | - Alexander I. Wright
- Section of Pathology, Leeds Institute of Medical Research, University of Leeds, 4.11 Wellcome Trust Brenner Building, St James’s University Hospital, Beckett Street, Leeds, LS9 7TF UK
| | - Gordon G. A. Hutchins
- Leeds Teaching Hospitals NHS Trust/University of Leeds. Histopathology and Molecular Pathology, St James’ University Hospital, Beckett Street, Leeds, LS9 7TF UK
| | - Thurayya Arayssi
- Division of Medical Education, Weill Cornell Medicine-Qatar, 445 East 69 Street, RM 432, New York, NY 10021 USA
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Gavrielides MA, Ronnett BM, Vang R, Sheikhzadeh F, Seidman JD. Selection of Representative Histologic Slides in Interobserver Reproducibility Studies: Insights from Expert Review for Ovarian Carcinoma Subtype Classification. J Pathol Inform 2021; 12:15. [PMID: 34012719 PMCID: PMC8112350 DOI: 10.4103/jpi.jpi_56_20] [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: 06/25/2020] [Revised: 09/02/2020] [Accepted: 10/28/2020] [Indexed: 11/04/2022] Open
Abstract
BACKGROUND Observer studies in pathology often utilize a limited number of representative slides per case, selected and reported in a nonstandardized manner. Reference diagnoses are commonly assumed to be generalizable to all slides of a case. We examined these issues in the context of pathologist concordance for histologic subtype classification of ovarian carcinomas (OCs). MATERIALS AND METHODS A cohort of 114 OCs consisting of 72 cases with a single representative slide (Group 1) and 42 cases with multiple representative slides (148 slides, 2-6 sections per case, Group 2) was independently reviewed by three experts in gynecologic pathology (case-based review). In a follow-up study, each individual slide was independently reviewed in a randomized order by the same pathologists (section-based review). RESULTS Average interobserver concordance varied from 100% for Group 1 to 64.3% for Group 2 (86.8% across all cases). Across Group 2, 19 cases (45.2%) had at least one slide classified as a different subtype than the subtype assigned from case-based review, demonstrating the impact of intratumoral heterogeneity. Section-based concordance across individual sections from Group 2 was comparable to case-based concordance for those cases indicating diagnostic challenges at the individual section level. Findings demonstrate the increased diagnostic complexity of heterogeneous tumors that require multiple section sampling and its impact on pathologist performance. CONCLUSIONS The proportion of cases with multiple representative slides in cohorts used in validation studies, such as those conducted to evaluate artificial intelligence/machine learning tools, can influence diagnostic performance, and if not accounted for, can cause disparities between research and real-world observations and between research studies. Case selection in validation studies should account for tumor heterogeneity to create balanced datasets in terms of diagnostic complexity.
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Affiliation(s)
- Marios A. Gavrielides
- Division of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA, (Currently at AstraZeneca, Precision Medicine and Biosamples, Gaithersburg, Maryland, USA)
| | - Brigitte M. Ronnett
- Department of Pathology and Gynecology and Obstetrics, The Johns Hopkins Hospital, Baltimore, Maryland, USA
| | - Russell Vang
- Department of Pathology and Gynecology and Obstetrics, The Johns Hopkins Hospital, Baltimore, Maryland, USA
| | - Fahime Sheikhzadeh
- Electrical and Computer Engineering Department, University of British Columbia, Vancouver, Canada, (Currently at Roche Diagnostics, San Francisco, California, USA)
| | - Jeffrey D Seidman
- Division of Molecular Genetics and Pathology, Office of In Vitro Diagnostics and Radiological Health, Office of Product Evaluation and Quality, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
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29
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Fully digital pathology laboratory routine and remote reporting of oral and maxillofacial diagnosis during the COVID-19 pandemic: a validation study. Virchows Arch 2021; 479:585-595. [PMID: 33713188 PMCID: PMC7955219 DOI: 10.1007/s00428-021-03075-9] [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: 01/13/2021] [Revised: 02/25/2021] [Accepted: 03/02/2021] [Indexed: 11/25/2022]
Abstract
The role of digital pathology in remote reporting has seen an increase during the COVID-19 pandemic. Recently, recommendations had been made regarding the urgent need of reorganizing head and neck cancer diagnostic services to provide a safe work environment for the staff. A total of 162 glass slides from 109 patients over a period of 5 weeks were included in this validation and were assessed by all pathologists in both analyses (digital and conventional) to allow intraobserver comparison. The intraobserver agreement between the digital method (DM) and conventional method (CM) was considered almost perfect (κ ranged from 0.85 to 0.98, with 95% CI, ranging from 0.81 to 1). The most significant and frequent disagreements within trainees encompassed epithelial dysplasia grading and differentiation among severe dysplasia (carcinoma in situ) and oral squamous cell carcinoma. The most frequent pitfall from DM was lag in screen mirroring. The lack of details of inflammatory cells and the need for a higher magnification to assess dysplasia were pointed in one case each. The COVID-19 crisis has accelerated and consolidated the use of online meeting tools, which would be a valuable resource even in the post-pandemic scenario. Adaptation in laboratory workflow, the advent of digital pathology and remote reporting can mitigate the impact of similar future disruptions to the oral and maxillofacial pathology laboratory workflow avoiding delays in diagnosis and report, to facilitate timely management of head and neck cancer patients. Graphical abstract ![]()
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30
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Gavrielides MA, Ronnett BM, Vang R, Barak S, Lee E, Staats PN, Jenson E, Skaria P, Sheikhzadeh F, Miller M, Hagemann IS, Petrick N, Seidman JD. Pathologist Concordance for Ovarian Carcinoma Subtype Classification and Identification of Relevant Histologic Features Using Microscope and Whole Slide Imaging: A Multisite Observer Study. Arch Pathol Lab Med 2021; 145:1516-1525. [PMID: 33635941 DOI: 10.5858/arpa.2020-0579-oa] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/09/2020] [Indexed: 11/06/2022]
Abstract
CONTEXT.— Despite several studies focusing on the validation of whole slide imaging (WSI) across organ systems or subspecialties, the use of WSI for specific primary diagnosis tasks has been underexamined. OBJECTIVE.— To assess pathologist performance for the histologic subtyping of individual sections of ovarian carcinomas using the light microscope and WSI. DESIGN.— A panel of 3 experienced gynecologic pathologists provided reference subtype diagnoses for 212 histologic sections from 109 ovarian carcinomas based on optical microscopy review. Two additional attending pathologists provided diagnoses and also identified the presence of a set of 8 histologic features important for ovarian tumor subtyping. Two experienced gynecologic pathologists and 2 fellows reviewed the corresponding WSI images for subtype classification and feature identification. RESULTS.— Across pathologists specialized in gynecologic pathology, concordance with the reference diagnosis for the 5 major ovarian carcinoma subtypes was significantly higher for a pathologist reading on microscope than each of 2 pathologists reading on WSI. Differences were primarily due to more frequent classification of mucinous carcinomas as endometrioid with WSI. Pathologists had generally low agreement in identifying histologic features important to ovarian tumor subtype classification, with either optical microscopy or WSI. This result suggests the need for refined histologic criteria for identifying such features. Interobserver agreement was particularly low for identifying intracytoplasmic mucin with WSI. Inconsistencies in evaluating nuclear atypia and mitoses with WSI were also observed. CONCLUSIONS.— Further research is needed to specify the reasons for these diagnostic challenges and to inform users and manufacturers of WSI technology.
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Affiliation(s)
- Marios A Gavrielides
- From the Division of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories (Gavrielides and Petrick)
| | - Brigitte M Ronnett
- the Departments of Pathology and Gynecology & Obstetrics, The Johns Hopkins Hospital, Baltimore, Maryland (Ronnett, Vang, Jenson)
| | - Russell Vang
- the Departments of Pathology and Gynecology & Obstetrics, The Johns Hopkins Hospital, Baltimore, Maryland (Ronnett, Vang, Jenson)
| | - Stephanie Barak
- the Department of Pathology, The George Washington University, Washington, District of Columbia (Barak, Lee)
| | - Elsie Lee
- Gavrielides is currently at AstraZeneca, Gaithersburg, Maryland.,the Department of Pathology, The George Washington University, Washington, District of Columbia (Barak, Lee)
| | - Paul N Staats
- the Department of Pathology, University of Maryland School of Medicine, Baltimore (Staats)
| | - Erik Jenson
- Lee is currently at HNL Lab Medicine, Allentown, Pennsylvania.,the Departments of Pathology and Gynecology & Obstetrics, The Johns Hopkins Hospital, Baltimore, Maryland (Ronnett, Vang, Jenson)
| | - Priya Skaria
- the Departments of Pathology and Immunology (Skaria and Hagemann), Washington University School of Medicine, St Louis, Missouri
| | - Fahime Sheikhzadeh
- Jenson is now with Hospital Pathology Associates, Minneapolis/St Paul, Minnesota.,the Electrical and Computer Engineering Department, University of British Columbia, Vancouver, Canada (Sheikhzadeh)
| | - Meghan Miller
- and the Department of Bioengineering, University of Maryland, College Park (Miller)
| | - Ian S Hagemann
- the Departments of Pathology and Immunology (Skaria and Hagemann), Washington University School of Medicine, St Louis, Missouri.,and Obstetrics and Gynecology (Hagemann), Washington University School of Medicine, St Louis, Missouri
| | - Nicholas Petrick
- From the Division of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories (Gavrielides and Petrick)
| | - Jeffrey D Seidman
- and the Division of Molecular Genetics and Pathology, Office of In Vitro Diagnostics and Radiological Health (Seidman), Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland
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31
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Rao V, Kumar R, Rajaganesan S, Rane S, Deshpande G, Yadav S, Patil A, Pai T, Menon S, Shah A, Rabade K, Ramadwar M, Panjwani P, Mittal N, Sahay A, Rekhi B, Bal M, Sakhadeo U, Gujral S, Desai S. Remote Reporting from Home for Primary Diagnosis in Surgical Pathology: A Tertiary Oncology Center Experience during the COVID-19 Pandemic. J Pathol Inform 2021; 12:3. [PMID: 34012707 PMCID: PMC8112339 DOI: 10.4103/jpi.jpi_72_20] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2020] [Revised: 10/13/2020] [Accepted: 10/28/2020] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND The COVID-19 pandemic accelerated the widespread adoption of digital pathology (DP) for primary diagnosis in surgical pathology. This paradigm shift is likely to influence how we function routinely in the postpandemic era. We present learnings from early adoption of DP for a live digital sign-out from home in a risk-mitigated environment. MATERIALS AND METHODS We aimed to validate DP for remote reporting from home in a real-time environment and evaluate the parameters influencing the efficiency of a digital workflow. Eighteen pathologists prospectively validated DP for remote use on 567 biopsy cases including 616 individual parts from 7 subspecialties over a duration from March 21, 2020, to June 30, 2020. The slides were digitized using Roche Ventana DP200 whole-slide scanner and reported from respective homes in a risk-mitigated environment. RESULTS Following re-review of glass slides, there was no major discordance and 1.2% (n = 7/567) minor discordance. The deferral rate was 4.5%. All pathologists reported from their respective homes from laptops with an average network speed of 20 megabits per second. CONCLUSION We successfully validated and adopted a digital workflow for remote reporting with available resources and were able to provide our patients, an undisrupted access to subspecialty expertise during these unprecedented times.
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Affiliation(s)
- Vidya Rao
- Department of Pathology, Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, Maharashtra, India
| | - Rajiv Kumar
- Department of Pathology, Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, Maharashtra, India
| | | | - Swapnil Rane
- Department of Pathology, Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, Maharashtra, India
| | - Gauri Deshpande
- Department of Pathology, Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, Maharashtra, India
| | - Subhash Yadav
- Department of Pathology, Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, Maharashtra, India
| | - Asawari Patil
- Department of Pathology, Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, Maharashtra, India
| | - Trupti Pai
- Department of Pathology, Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, Maharashtra, India
| | - Santosh Menon
- Department of Pathology, Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, Maharashtra, India
| | - Aekta Shah
- Department of Pathology, Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, Maharashtra, India
| | - Katha Rabade
- Department of Pathology, Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, Maharashtra, India
| | - Mukta Ramadwar
- Department of Pathology, Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, Maharashtra, India
| | - Poonam Panjwani
- Department of Pathology, Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, Maharashtra, India
| | - Neha Mittal
- Department of Pathology, Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, Maharashtra, India
| | - Ayushi Sahay
- Department of Pathology, Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, Maharashtra, India
| | - Bharat Rekhi
- Department of Pathology, Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, Maharashtra, India
| | - Munita Bal
- Department of Pathology, Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, Maharashtra, India
| | - Uma Sakhadeo
- Department of Pathology, Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, Maharashtra, India
| | - Sumeet Gujral
- Department of Pathology, Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, Maharashtra, India
| | - Sangeeta Desai
- Department of Pathology, Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, Maharashtra, India
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32
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Sabdyusheva Litschauer I, Becker K, Saghafi S, Ballke S, Bollwein C, Foroughipour M, Gaugeler J, Foroughipour M, Schavelová V, László V, Döme B, Brostjan C, Weichert W, Dodt HU. 3D histopathology of human tumours by fast clearing and ultramicroscopy. Sci Rep 2020; 10:17619. [PMID: 33077794 PMCID: PMC7572501 DOI: 10.1038/s41598-020-71737-w] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Accepted: 07/02/2020] [Indexed: 12/31/2022] Open
Abstract
Here, we describe a novel approach that allows pathologists to three-dimensionally analyse malignant tissues, including the tumour-host tissue interface. Our visualization technique utilizes a combination of ultrafast chemical tissue clearing and light-sheet microscopy to obtain virtual slices and 3D reconstructions of up to multiple centimetre sized tumour resectates. For the clearing of tumours we propose a preparation technique comprising three steps: (a) Fixation and enhancement of tissue autofluorescence with formalin/5-sulfosalicylic acid. (b) Ultrafast active chemical dehydration with 2,2-dimethoxypropane and (c) refractive index matching with dibenzyl ether at up to 56 °C. After clearing, the tumour resectates are imaged. The images are computationally post-processed for contrast enhancement and artefact removal and then 3D reconstructed. Importantly, the sequence a–c is fully reversible, allowing the morphological correlation of one and the same histological structures, once visualized with our novel technique and once visualized by standard H&E- and IHC-staining. After reverting the clearing procedure followed by standard H&E processing, the hallmarks of ductal carcinoma in situ (DCIS) found in the cleared samples could be successfully correlated with the corresponding structures present in H&E and IHC staining. Since the imaging of several thousands of optical sections is a fast process, it is possible to analyse a larger part of the tumour than by mechanical slicing. As this also adds further information about the 3D structure of malignancies, we expect that our technology will become a valuable addition for histological diagnosis in clinical pathology.
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Affiliation(s)
- Inna Sabdyusheva Litschauer
- Department of Bioelectronics, TU Wien, Vienna, Austria. .,Center for Brain Research, Medical University of Vienna, Vienna, Austria.
| | - Klaus Becker
- Department of Bioelectronics, TU Wien, Vienna, Austria.,Center for Brain Research, Medical University of Vienna, Vienna, Austria
| | - Saiedeh Saghafi
- Department of Bioelectronics, TU Wien, Vienna, Austria.,Center for Brain Research, Medical University of Vienna, Vienna, Austria
| | - Simone Ballke
- Institute of Pathology, TUM School of Medicine, Technical University of Munich, Munich, Germany
| | - Christine Bollwein
- Institute of Pathology, TUM School of Medicine, Technical University of Munich, Munich, Germany
| | - Meraaj Foroughipour
- Department of Bioelectronics, TU Wien, Vienna, Austria.,Center for Brain Research, Medical University of Vienna, Vienna, Austria
| | - Julia Gaugeler
- Department of Bioelectronics, TU Wien, Vienna, Austria.,Center for Brain Research, Medical University of Vienna, Vienna, Austria
| | - Massih Foroughipour
- Department of Bioelectronics, TU Wien, Vienna, Austria.,Center for Brain Research, Medical University of Vienna, Vienna, Austria
| | - Viktória Schavelová
- Department of Bioelectronics, TU Wien, Vienna, Austria.,Center for Brain Research, Medical University of Vienna, Vienna, Austria
| | - Viktória László
- Department of Surgery, Anna Spiegel Center of Translational Research, Medical University of Vienna, Vienna, Austria
| | - Balazs Döme
- Department of Surgery, Anna Spiegel Center of Translational Research, Medical University of Vienna, Vienna, Austria
| | - Christine Brostjan
- Department of Surgery, Anna Spiegel Center of Translational Research, Medical University of Vienna, Vienna, Austria
| | - Wilko Weichert
- Institute of Pathology, TUM School of Medicine, Technical University of Munich, Munich, Germany
| | - Hans-Ulrich Dodt
- Department of Bioelectronics, TU Wien, Vienna, Austria. .,Center for Brain Research, Medical University of Vienna, Vienna, Austria.
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33
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Rakha EA, Toss M, Shiino S, Gamble P, Jaroensri R, Mermel CH, Chen PHC. Current and future applications of artificial intelligence in pathology: a clinical perspective. J Clin Pathol 2020; 74:409-414. [PMID: 32763920 DOI: 10.1136/jclinpath-2020-206908] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 07/07/2020] [Indexed: 12/17/2022]
Abstract
During the last decade, a dramatic rise in the development and application of artificial intelligence (AI) tools for use in pathology services has occurred. This trend is often expected to continue and reshape the field of pathology in the coming years. The deployment of computational pathology and applications of AI tools can be considered as a paradigm shift that will change pathology services, making them more efficient and capable of meeting the needs of this era of precision medicine. Despite the success of AI models, the translational process from discovery to clinical applications has been slow. The gap between self-contained research and clinical environment may be too wide and has been largely neglected. In this review, we cover the current and prospective applications of AI in pathology. We examine its applications in diagnosis and prognosis, and we offer insights for considerations that could improve clinical applicability of these tools. Then, we discuss its potential to improve workflow efficiency, and its benefits in pathologist education. Finally, we review the factors that could influence adoption in clinical practices and the associated regulatory processes.
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Affiliation(s)
- Emad A Rakha
- Histopathology, University of Nottingham School of Medicine, Nottingham, UK
| | - Michael Toss
- Histopathology, University of Nottingham School of Medicine, Nottingham, UK
| | - Sho Shiino
- Histopathology, University of Nottingham School of Medicine, Nottingham, UK
| | - Paul Gamble
- Google Health, Google, Palo Alto, California, USA
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34
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Williams B, Hanby A, Millican-Slater R, Verghese E, Nijhawan A, Wilson I, Besusparis J, Clark D, Snead D, Rakha E, Treanor D. Digital pathology for primary diagnosis of screen-detected breast lesions - experimental data, validation and experience from four centres. Histopathology 2020; 76:968-975. [PMID: 31994224 DOI: 10.1111/his.14079] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Revised: 01/14/2020] [Accepted: 01/24/2020] [Indexed: 12/28/2022]
Abstract
AIM The rate of deployment of digital pathology (DP) systems for primary diagnosis in the UK is accelerating. The flexibility and resilience of digital versus standard glass slides could be of great benefit in the NHS breast screening programme (NHSBSP). This study aims to document the safety and benefits of DP for preoperative tissue diagnosis of screen-detected breast lesions. METHODS AND RESULTS Concordance data for glass and digital slides of the same cases from four sites were subjected to detailed concordance-discordance analysis. A literature review of DP in the primary diagnosis of breast lesions is presented, making this the most comprehensive synthesis of digital breast cancer histopathological diagnostic data to date. Detailed concordance analysis of experimental data from two histopathology departments reveals clinical concordance rates for breast biopsies of 96% (216 of 225) and 99.6% (249 of 250). Data from direct comparison validation studies in two histopathology departments, utilising the protocol recommended by the Royal College of Pathologists, found concordance rates for breast histology cases of 99.4% (180 of 181) and 99.0% (887 of 896). An intraobserver variation study for glass versus digital slides for difficult cases from the NHSBSP yielded a kappa statistic of 0.80, indicating excellent agreement. Discordances encountered in the studies most frequently concerned discrepancies in grading attributable to mitotic count-scoring and identification of weddelite. CONCLUSIONS The experience of four histopathology laboratories and our review of pre-existing literature suggests that DP is safe for the primary diagnosis of NHSBSP breast histology specimens, and does not increase the risk of misclassification.
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Affiliation(s)
- Bethany Williams
- Leeds Teaching Hospitals NHS Trust, Leeds, UK.,University of Leeds, Leeds, UK
| | - Andrew Hanby
- Leeds Teaching Hospitals NHS Trust, Leeds, UK.,University of Leeds, Leeds, UK
| | | | - Eldo Verghese
- Leeds Teaching Hospitals NHS Trust, Leeds, UK.,University of Leeds, Leeds, UK
| | | | | | | | - David Clark
- United Lincolnshire Hospitals NHS Trust, Grantham, UK
| | - David Snead
- University Hospitals Coventry and Warwickshire, Coventry, UK.,University of Warwick, Warwick, UK
| | - Emad Rakha
- Nottingham University Hospitals NHS Trust, Nottingham, UK.,University of Nottingham, Nottingham, UK
| | - Darren Treanor
- Leeds Teaching Hospitals NHS Trust, Leeds, UK.,University of Leeds, Leeds, UK
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35
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Chang MC, Mrkonjic M. Review of the current state of digital image analysis in breast pathology. Breast J 2020; 26:1208-1212. [PMID: 32342590 DOI: 10.1111/tbj.13858] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Accepted: 11/05/2019] [Indexed: 01/10/2023]
Abstract
Advances in digital image analysis have the potential to transform the practice of breast pathology. In the near future, a move to a digital workflow offers improvements in efficiency. Coupled with artificial intelligence (AI), digital pathology can assist pathologist interpretation, automate time-consuming tasks, and discover novel morphologic patterns. Opportunities for digital enhancements abound in breast pathology, from increasing reproducibility in grading and biomarker interpretation, to discovering features that correlate with patient outcome and treatment. Our objective is to review the most recent developments in digital pathology with clear impact to breast pathology practice. Although breast pathologists currently undertake limited adoption of digital methods, the field is rapidly evolving. Care is needed to validate emerging technologies for effective patient care.
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Affiliation(s)
- Martin C Chang
- University of Vermont Cancer Center, Burlington, VT, USA.,Department of Pathology and Laboratory Medicine, Larner College of Medicine at the University of Vermont, Burlington, VT, USA
| | - Miralem Mrkonjic
- Sinai Health System, Toronto, ON, Canada.,Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
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36
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Williams BJ, Brettle D, Aslam M, Barrett P, Bryson G, Cross S, Snead D, Verrill C, Clarke E, Wright A, Treanor D. Guidance for Remote Reporting of Digital Pathology Slides During Periods of Exceptional Service Pressure: An Emergency Response from the UK Royal College of Pathologists. J Pathol Inform 2020; 11:12. [PMID: 32477618 PMCID: PMC7245343 DOI: 10.4103/jpi.jpi_23_20] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Revised: 03/27/2020] [Accepted: 03/31/2020] [Indexed: 12/12/2022] Open
Abstract
Pathology departments must rise to new staffing challenges caused by the coronavirus disease-19 pandemic and may need to work more flexibly for the foreseeable future. In light of this, many pathologists and departments are considering the merits of remote or home reporting of digital cases. While some individuals have experience of this, little work has been done to determine optimum conditions for home reporting, including technical and training considerations. In this publication produced in response to the pandemic, we provide information regarding risk assessment of home reporting of digital slides, summarize available information on specifications for home reporting computing equipment, and share access to a novel point-of-use quality assurance tool for assessing the suitability of home reporting screens for digital slide diagnosis. We hope this study provides a useful starting point and some practical guidance in a difficult time. This study forms the basis of the guidance issued by the Royal College of Pathologists, available at: https://www.rcpath.org/uploads/assets/626ead77-d7dd-42e1-949988e43dc84c97/RCPath-guidance-for-remote-digital-pathology.pdf.
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Affiliation(s)
| | - David Brettle
- Leeds Teaching Hospitals NHS Trust, Leeds, UK
- University of Leeds, Leeds, UK
| | | | - Paul Barrett
- County Durham and Darlington NHS Foundation Trust, Darlington, UK
| | | | | | - David Snead
- University Hospitals Coventry and Warwickshire, Coventry, UK
- University of Warwick, Warwick, UK
| | - Clare Verrill
- Nuffield Department of Surgical Sciences and Oxford NIHR Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Emily Clarke
- Leeds Teaching Hospitals NHS Trust, Leeds, UK
- University of Leeds, Leeds, UK
| | | | - Darren Treanor
- Leeds Teaching Hospitals NHS Trust, Leeds, UK
- University of Leeds, Leeds, UK
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37
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Williams BJ, Ismail A, Chakrabarty A, Treanor D. Clinical digital neuropathology: experience and observations from a departmental digital pathology training programme, validation and deployment. J Clin Pathol 2020; 74:456-461. [PMID: 32139375 DOI: 10.1136/jclinpath-2019-206343] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Revised: 02/03/2020] [Accepted: 02/04/2020] [Indexed: 11/04/2022]
Abstract
AIM To train and individually validate the neuropathologists in digital primary diagnosis and frozen section reporting using a novel protocol endorsed by the Royal College of Pathologists. The protocol allows early exposure to live digital reporting in a risk mitigated environment. METHODS Two specialist neuropathologists completed training in the use of a digital microscopy system for primary neuropathological diagnosis and frozen section assessment. Participants were exposed to training sets of 20 histology cases and 10 frozen sections designed to help them identify their personal digital diagnostic pitfalls. Following this, the pathologists viewed 340 live, complete neuropathology cases. All primary diagnoses were made on digital slides with immediate glass slide reconciliation before final case sign-out. RESULTS There was 100% clinical concordance between the digital slide and glass slide assessment of frozen section cases for each pathologist, and these assessments corresponded with the ground truth diagnoses obtained from examination of definitive histology. For primary diagnosis, there was complete clinical concordance between digital slide and glass slide diagnosis in 98.1% of cases. The majority of discordances were related to grading differences attributable to mitotic count differences. CONCLUSION Neuropathologists can develop the ability to make primary digital diagnosis competently and confidently following a course of individual training and validation.
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Affiliation(s)
- Bethany Jill Williams
- Histopathology, Leeds Teaching Hospitals NHS Trust, Leeds, UK .,Faculty of Medicine and Health, University of Leeds, Leeds, UK
| | - Azzam Ismail
- Histopathology, Leeds Teaching Hospitals NHS Trust, Leeds, UK.,Faculty of Medicine and Health, University of Leeds, Leeds, UK
| | - Arundhati Chakrabarty
- Histopathology, Leeds Teaching Hospitals NHS Trust, Leeds, UK.,Faculty of Medicine and Health, University of Leeds, Leeds, UK
| | - Darren Treanor
- Histopathology, Leeds Teaching Hospitals NHS Trust, Leeds, UK.,Faculty of Medicine and Health, University of Leeds, Leeds, UK
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38
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Ibrahim A, Gamble P, Jaroensri R, Abdelsamea MM, Mermel CH, Chen PHC, Rakha EA. Artificial intelligence in digital breast pathology: Techniques and applications. Breast 2019; 49:267-273. [PMID: 31935669 PMCID: PMC7375550 DOI: 10.1016/j.breast.2019.12.007] [Citation(s) in RCA: 78] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Accepted: 12/12/2019] [Indexed: 12/16/2022] Open
Abstract
Breast cancer is the most common cancer and second leading cause of cancer-related death worldwide. The mainstay of breast cancer workup is histopathological diagnosis - which guides therapy and prognosis. However, emerging knowledge about the complex nature of cancer and the availability of tailored therapies have exposed opportunities for improvements in diagnostic precision. In parallel, advances in artificial intelligence (AI) along with the growing digitization of pathology slides for the primary diagnosis are a promising approach to meet the demand for more accurate detection, classification and prediction of behaviour of breast tumours. In this article, we cover the current and prospective uses of AI in digital pathology for breast cancer, review the basics of digital pathology and AI, and outline outstanding challenges in the field.
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Affiliation(s)
- Asmaa Ibrahim
- Department of Histopathology, Division of Cancer and Stem Cells, School of Medicine, The University of Nottingham and Nottingham University Hospitals NHS Trust, Nottingham City Hospital, Nottingham, NG5 1PB, UK
| | | | | | - Mohammed M Abdelsamea
- School of Computing and Digital Technology, Birmingham City University, Birmingham, UK
| | | | | | - Emad A Rakha
- Department of Histopathology, Division of Cancer and Stem Cells, School of Medicine, The University of Nottingham and Nottingham University Hospitals NHS Trust, Nottingham City Hospital, Nottingham, NG5 1PB, UK.
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39
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Griffin J, Kitsanta P, Perunovic B, Suvarna SK, Bury J. Digital pathology for intraoperative frozen section diagnosis of thoracic specimens: an evaluation of a system using remote sampling and whole slide imaging diagnosis. J Clin Pathol 2019; 73:503-506. [PMID: 31806732 DOI: 10.1136/jclinpath-2019-206236] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2019] [Revised: 11/12/2019] [Accepted: 11/19/2019] [Indexed: 11/03/2022]
Abstract
BACKGROUND Digital pathology is now used for primary diagnostic work as well as teaching, research and consultation. In our multisite institution service reorganisation led to histopathology being located in a separate hospital from some surgical specialities. We implemented remotely supervised specimen sampling and frozen section diagnosis using digital pathology. In this study we assessed the concordance of glass and digital slide diagnosis using this system. METHODS We reviewed cases from the first 2 years of digital frozen section reporting at our institution. Cases with potential digital to glass slide discordance were reviewed by three experienced thoracic histopathologists. The reasons for discordance were determined and common themes identified. We also reviewed critical incidents relating to digital pathology during the study period. RESULTS The study population comprised 211 cases. Frozen section to final diagnosis concordance between digital and glass slide diagnosis was found in 196 (92.6%) cases. The 15 potentially discordant cases were reviewed. Intraobserver concordance between glass and digital slide review ranged from 9/15 to 12/15 cases across the three pathologists. Glass slide review diagnosis showed better concordance with ground truth in two cases; digital slide review was more accurate in two cases. One relevant critical incident was identified during the study period. DISCUSSION This is the largest study to examine digital pathology for thoracic frozen section diagnosis and shows that this is a safe and feasible alternative to glass slide diagnosis. Discordance between digital and glass slide diagnoses were unrelated to the processes of whole slide imaging and digital microscopy.
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Affiliation(s)
- Jon Griffin
- Department of Histopathology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Panagiota Kitsanta
- Department of Histopathology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Branko Perunovic
- Department of Histopathology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - S Kim Suvarna
- Department of Histopathology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Jonathan Bury
- Department of Histopathology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
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40
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Turnquist C, Roberts-Gant S, Hemsworth H, White K, Browning L, Rees G, Roskell D, Verrill C. On the Edge of a Digital Pathology Transformation: Views from a Cellular Pathology Laboratory Focus Group. J Pathol Inform 2019; 10:37. [PMID: 31897354 PMCID: PMC6909548 DOI: 10.4103/jpi.jpi_38_19] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Accepted: 07/08/2019] [Indexed: 01/18/2023] Open
Abstract
Introduction: Digital pathology has the potential to revolutionize the way clinical diagnoses are made while improving safety and quality. With a few notable exceptions in the UK, few National Health Service (NHS) departments have deployed digital pathology platforms. Thus, in the next few years, many departments are anticipated to undergo the transition to digital pathology. In this period of transition, capturing attitudes and experiences can elucidate issues to be addressed and foster collaboration between NHS Trusts. This study aims to qualitatively ascertain the benefits and challenges of transitioning to digital pathology from the perspectives of pathologists and biomedical scientists in a department about to undergo the transition from diagnostic reporting via traditional microscopy to digital pathology. Methods: A focus group discussion was held in the setting of a large NHS teaching hospital's cellular pathology department which was on the brink of transitioning to digital pathology. A set of open questions were developed and posed to a group of pathologists and biomedical scientists in a focus group setting. Notes of the discussion were made along with an audio recording with permission. The discussion was subsequently turned into a series of topic headings and analyzed using content analysis. Results: Identified benefits of digital pathology included enhanced collaboration, teaching, cost savings, research, growth of specialty, multidisciplinary teams, and patient-centered care. Barriers to transitioning to digital pathology included standardization, validation, national implementation, storage and backups, training, logistical implementation, cost-effectiveness, privacy, and legality. Conclusion: Many benefits of digital pathology were identified, but key barriers need to be addressed in order to fully implement digital pathology on a trust and national level.
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Affiliation(s)
- Casmir Turnquist
- University of Oxford Medical School, John Radcliffe Hospital, Oxford, UK
| | - Sharon Roberts-Gant
- Department of Cellular Pathology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Helen Hemsworth
- Department of Cellular Pathology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Kieron White
- Department of Cellular Pathology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Lisa Browning
- Department of Cellular Pathology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Gabrielle Rees
- Department of Cellular Pathology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Derek Roskell
- Department of Cellular Pathology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Clare Verrill
- Department of Cellular Pathology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.,Nuffield Department of Surgical Sciences, Oxford NIHR Biomedical Research Centre, University of Oxford, Oxford, UK
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Wang S, Wang T, Yang L, Yang DM, Fujimoto J, Yi F, Luo X, Yang Y, Yao B, Lin S, Moran C, Kalhor N, Weissferdt A, Minna J, Xie Y, Wistuba II, Mao Y, Xiao G. ConvPath: A software tool for lung adenocarcinoma digital pathological image analysis aided by a convolutional neural network. EBioMedicine 2019; 50:103-110. [PMID: 31767541 PMCID: PMC6921240 DOI: 10.1016/j.ebiom.2019.10.033] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2019] [Revised: 10/16/2019] [Accepted: 10/16/2019] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND The spatial distributions of different types of cells could reveal a cancer cell's growth pattern, its relationships with the tumor microenvironment and the immune response of the body, all of which represent key "hallmarks of cancer". However, the process by which pathologists manually recognize and localize all the cells in pathology slides is extremely labor intensive and error prone. METHODS In this study, we developed an automated cell type classification pipeline, ConvPath, which includes nuclei segmentation, convolutional neural network-based tumor cell, stromal cell, and lymphocyte classification, and extraction of tumor microenvironment-related features for lung cancer pathology images. To facilitate users in leveraging this pipeline for their research, all source scripts for ConvPath software are available at https://qbrc.swmed.edu/projects/cnn/. FINDINGS The overall classification accuracy was 92.9% and 90.1% in training and independent testing datasets, respectively. By identifying cells and classifying cell types, this pipeline can convert a pathology image into a "spatial map" of tumor, stromal and lymphocyte cells. From this spatial map, we can extract features that characterize the tumor micro-environment. Based on these features, we developed an image feature-based prognostic model and validated the model in two independent cohorts. The predicted risk group serves as an independent prognostic factor, after adjusting for clinical variables that include age, gender, smoking status, and stage. INTERPRETATION The analysis pipeline developed in this study could convert the pathology image into a "spatial map" of tumor cells, stromal cells and lymphocytes. This could greatly facilitate and empower comprehensive analysis of the spatial organization of cells, as well as their roles in tumor progression and metastasis.
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Affiliation(s)
- Shidan Wang
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX
| | - Tao Wang
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX; Center for the Genetics of Host Defense, University of Texas Southwestern Medical Center, Dallas, TX
| | - Lin Yang
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX; Department of Pathology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences (CHCAMS), China
| | - Donghan M Yang
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX
| | - Junya Fujimoto
- Department of Translational Molecular Pathology, University of Texas MD Anderson Cancer Center, Houston, TX
| | - Faliu Yi
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX
| | - Xin Luo
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX
| | - Yikun Yang
- Department of Thoracic Surgery, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences (CHCAMS), China
| | - Bo Yao
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX
| | - ShinYi Lin
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX
| | - Cesar Moran
- Department of Pathology, Division of Pathology/Lab Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Neda Kalhor
- Department of Pathology, Division of Pathology/Lab Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Annikka Weissferdt
- Department of Pathology, Division of Pathology/Lab Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - John Minna
- Hamon Center for Therapeutic Oncology Research, Department of Internal Medicine and Department of Pharmacology, University of Texas Southwestern Medical Center, Dallas, TX; Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX
| | - Yang Xie
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX; Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX; Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX
| | - Ignacio I Wistuba
- Department of Translational Molecular Pathology, University of Texas MD Anderson Cancer Center, Houston, TX
| | - Yousheng Mao
- Department of Thoracic Surgery, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences (CHCAMS), China
| | - Guanghua Xiao
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX; Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX; Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX.
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42
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Williams BJ, Treanor D. Practical guide to training and validation for primary diagnosis with digital pathology. J Clin Pathol 2019; 73:418-422. [PMID: 31784420 DOI: 10.1136/jclinpath-2019-206319] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Revised: 11/12/2019] [Accepted: 11/13/2019] [Indexed: 12/11/2022]
Abstract
Numerous clinical pathology departments are deploying or planning to deploy digital pathology systems for all or part of their diagnostic output. Digital pathology is an evolving technology, and it is important that departments uphold or improve on current standards. Leeds Teaching Hospitals NHS Trust has been scanning 100% of histology slides since September 2018. In this practical paper, we will share our approach to training and validation, which has been incorporated into the Royal College of Pathologists' guidance for digital pathology implementation. We will offer an overview of the Royal College endorsed training and validation protocol and the evidence base on which it is based. We will provide practical advice on implementation of the protocol and highlight areas of digital reporting that can prove difficult for the novice digital pathologist. In addition, we will share a detailed topographical list of types of diagnostic tasks and features which should form the basis of digital slide training sets.
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Affiliation(s)
- Bethany Jill Williams
- Department of Histopathology, Leeds Teaching Hospitals NHS Trust, Leeds, UK .,Faculty of Medicine and Health, University of Leeds, Leeds, UK
| | - Darren Treanor
- Department of Histopathology, Leeds Teaching Hospitals NHS Trust, Leeds, UK.,Faculty of Medicine and Health, University of Leeds, Leeds, UK
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Wang S, Yang DM, Rong R, Zhan X, Fujimoto J, Liu H, Minna J, Wistuba II, Xie Y, Xiao G. Artificial Intelligence in Lung Cancer Pathology Image Analysis. Cancers (Basel) 2019; 11:E1673. [PMID: 31661863 PMCID: PMC6895901 DOI: 10.3390/cancers11111673] [Citation(s) in RCA: 97] [Impact Index Per Article: 19.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 10/17/2019] [Accepted: 10/21/2019] [Indexed: 12/12/2022] Open
Abstract
OBJECTIVE Accurate diagnosis and prognosis are essential in lung cancer treatment selection and planning. With the rapid advance of medical imaging technology, whole slide imaging (WSI) in pathology is becoming a routine clinical procedure. An interplay of needs and challenges exists for computer-aided diagnosis based on accurate and efficient analysis of pathology images. Recently, artificial intelligence, especially deep learning, has shown great potential in pathology image analysis tasks such as tumor region identification, prognosis prediction, tumor microenvironment characterization, and metastasis detection. MATERIALS AND METHODS In this review, we aim to provide an overview of current and potential applications for AI methods in pathology image analysis, with an emphasis on lung cancer. RESULTS We outlined the current challenges and opportunities in lung cancer pathology image analysis, discussed the recent deep learning developments that could potentially impact digital pathology in lung cancer, and summarized the existing applications of deep learning algorithms in lung cancer diagnosis and prognosis. DISCUSSION AND CONCLUSION With the advance of technology, digital pathology could have great potential impacts in lung cancer patient care. We point out some promising future directions for lung cancer pathology image analysis, including multi-task learning, transfer learning, and model interpretation.
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Affiliation(s)
- Shidan Wang
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
| | - Donghan M Yang
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
| | - Ruichen Rong
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
| | - Xiaowei Zhan
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
| | - Junya Fujimoto
- Department of Translational Molecular Pathology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
| | - Hongyu Liu
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
| | - John Minna
- Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
- Hamon Center for Therapeutic Oncology Research, UT Southwestern Medical Center, Dallas, TX 75390, USA.
- Departments of Internal Medicine and Pharmacology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
| | - Ignacio Ivan Wistuba
- Department of Translational Molecular Pathology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
| | - Yang Xie
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
- Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
- Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
| | - Guanghua Xiao
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
- Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
- Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
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44
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Wei BR, Halsey CH, Hoover SB, Puri M, Yang HH, Gallas BD, Lee MP, Chen W, Durham AC, Dwyer JE, Sánchez MD, Traslavina RP, Frank C, Bradley C, McGill LD, Esplin DG, Schaffer PA, Cramer SD, Lyle LT, Beck J, Buza E, Gong Q, Hewitt SM, Simpson RM. Agreement in Histological Assessment of Mitotic Activity Between Microscopy and Digital Whole Slide Images Informs Conversion for Clinical Diagnosis. Acad Pathol 2019; 6:2374289519859841. [PMID: 31321298 PMCID: PMC6628521 DOI: 10.1177/2374289519859841] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Revised: 05/15/2019] [Accepted: 05/19/2019] [Indexed: 01/27/2023] Open
Abstract
Validating digital pathology as substitute for conventional microscopy in diagnosis
remains a priority to assure effectiveness. Intermodality concordance studies typically
focus on achieving the same diagnosis by digital display of whole slide images and
conventional microscopy. Assessment of discrete histological features in whole slide
images, such as mitotic figures, has not been thoroughly evaluated in diagnostic practice.
To further gauge the interchangeability of conventional microscopy with digital display
for primary diagnosis, 12 pathologists examined 113 canine naturally occurring mucosal
melanomas exhibiting a wide range of mitotic activity. Design reflected diverse diagnostic
settings and investigated independent location, interpretation, and enumeration of mitotic
figures. Intermodality agreement was assessed employing conventional microscopy (CM40×),
and whole slide image specimens scanned at 20× (WSI20×) and at 40× (WSI40×) objective
magnifications. An aggregate 1647 mitotic figure count observations were available from
conventional microscopy and whole slide images for comparison. The intraobserver
concordance rate of paired observations was 0.785 to 0.801; interobserver rate was 0.784
to 0.794. Correlation coefficients between the 2 digital modes, and as compared to
conventional microscopy, were similar and suggest noninferiority among modalities,
including whole slide image acquired at lower 20× resolution. As mitotic figure counts
serve for prognostic grading of several tumor types, including melanoma, 6 of 8
pathologists retrospectively predicted survival prognosis using whole slide images,
compared to 9 of 10 by conventional microscopy, a first evaluation of whole slide image
for mitotic figure prognostic grading. This study demonstrated agreement of replicate
reads obtained across conventional microscopy and whole slide images. Hence, quantifying
mitotic figures served as surrogate histological feature with which to further credential
the interchangeability of whole slide images for primary diagnosis.
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Affiliation(s)
- Bih-Rong Wei
- Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.,Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., Frederick, MD, USA
| | - Charles H Halsey
- Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Shelley B Hoover
- Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Munish Puri
- Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Howard H Yang
- Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Brandon D Gallas
- Division of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD, USA
| | - Maxwell P Lee
- Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Weijie Chen
- Division of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD, USA
| | - Amy C Durham
- Department of Pathobiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Jennifer E Dwyer
- Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Melissa D Sánchez
- Department of Pathobiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Ryan P Traslavina
- Section of Infections of the Nervous System, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Chad Frank
- Department of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins, CO, USA
| | - Charles Bradley
- Department of Pathobiology, University of Pennsylvania, Philadelphia, PA, USA
| | | | | | - Paula A Schaffer
- Department of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins, CO, USA
| | - Sarah D Cramer
- Cancer and Inflammation Program, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - L Tiffany Lyle
- Women's Malignancies Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Jessica Beck
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Elizabeth Buza
- Department of Pathobiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Qi Gong
- Division of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD, USA
| | - Stephen M Hewitt
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - R Mark Simpson
- Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
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Brockmoeller SF, West NP. Predicting systemic spread in early colorectal cancer: Can we do better? World J Gastroenterol 2019; 25:2887-2897. [PMID: 31249447 PMCID: PMC6589731 DOI: 10.3748/wjg.v25.i23.2887] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Revised: 05/07/2019] [Accepted: 05/08/2019] [Indexed: 02/06/2023] Open
Abstract
Through the implementation of national bowel cancer screening programmes we have seen a three-fold increase in early pT1 colorectal cancers, but how these lesions should be managed is currently unclear. Local excision can be an attractive option, especially for fragile patients with multiple comorbidities, but it is only safe from an oncological point of view in the absence of lymph node metastasis. Patient risk stratification through careful analysis of histopathological features in local excision or polypectomy specimens should be performed according to national guidelines to avoid under- or over-treatment. Currently national guidelines vary in their recommendations as to which factors should be routinely reported and there is no established multivariate risk stratification model to determine which patients should be offered major resectional surgery. Conventional histopathological parameters such as tumour grading or lymphovascular invasion have been shown to be predictive of lymph node metastasis in a number of studies but the inter- and intra-observer variation in reporting is high. Newer parameters including tumour budding and poorly differentiated clusters have been shown to have great potential, but again some improvement in the inter-observer variation is required. With the implementation of digital pathology into clinical practice, quantitative parameters like depth/area of submucosal invasion and proportion of stroma can be routinely assessed. In this review we present the various histopathological risk factors for predicting systemic spread in pT1 colorectal cancer and introduce potential novel quantitative variables and multivariable risk models that could be used to better define the optimal treatment of this increasingly common disease.
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Affiliation(s)
- Scarlet Fiona Brockmoeller
- Pathology and Data Analytics, Leeds Institute of Medical Research at St. James’s, University of Leeds, School of Medicine, Leeds LS9 7TF, United Kingdom
| | - Nicholas Paul West
- Pathology and Data Analytics, Leeds Institute of Medical Research at St. James’s, University of Leeds, School of Medicine, Leeds LS9 7TF, United Kingdom
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46
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Williams BJ, Knowles C, Treanor D. Maintaining quality diagnosis with digital pathology: a practical guide to ISO 15189 accreditation. J Clin Pathol 2019; 72:663-668. [PMID: 31177084 DOI: 10.1136/jclinpath-2019-205944] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Revised: 05/22/2019] [Accepted: 05/23/2019] [Indexed: 11/04/2022]
Abstract
An ever-increasing number of clinical pathology departments are deploying, or planning to deploy digital pathology systems for all, or part of their diagnostic output. Digital pathology is an evolving technology, and it is important that departments uphold or improve on current standards. Leeds Teaching Hospitals NHS Trust has been scanning 100% of histology slides since September 2018, and has developed validation and validation protocols to train 38 histopathology consultants in primary digital diagnosis. In this practical paper, we will share our approach to ISO inspection of our digital pathology service, which resulted in successful ISO accreditation for primary digital diagnosis. We will offer practical advice on what types of procedure and documentation are necessary, both from the point of view of the laboratory and your reporting pathologists. We will explore topics including risk assessment, standard operating procedures, validation and training, calibration and quality assurance, and provide a checklist of the key digital pathology components you need to consider in your inspection preparations. The continuous quest for quality and safety improvements in our practice should underpin everything we do in pathology, including our digital pathology operations. We hope this publication will make it easier for subsequent departments to successfully achieve ISO 15189 accreditation and feel confident in their digital pathology services.
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Affiliation(s)
- Bethany Jill Williams
- Leeds Teaching Hospitals NHS Trust, Leeds, UK .,Department of Pathology, University of Leeds, Leeds, UK
| | | | - Darren Treanor
- Leeds Teaching Hospitals NHS Trust, Leeds, UK.,Department of Pathology, University of Leeds, Leeds, UK
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Advancing diagnostic hematopathology: pigeons or pixels? J Hematop 2019. [DOI: 10.1007/s12308-019-00358-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022] Open
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48
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Oliver CR, Altemus MA, Westerhof TM, Cheriyan H, Cheng X, Dziubinski M, Wu Z, Yates J, Morikawa A, Heth J, Castro MG, Leung BM, Takayama S, Merajver SD. A platform for artificial intelligence based identification of the extravasation potential of cancer cells into the brain metastatic niche. LAB ON A CHIP 2019; 19:1162-1173. [PMID: 30810557 PMCID: PMC6510031 DOI: 10.1039/c8lc01387j] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
Brain metastases are the most lethal complication of advanced cancer; therefore, it is critical to identify when a tumor has the potential to metastasize to the brain. There are currently no interventions that shed light on the potential of primary tumors to metastasize to the brain. We constructed and tested a platform to quantitatively profile the dynamic phenotypes of cancer cells from aggressive triple negative breast cancer cell lines and patient derived xenografts (PDXs), generated from a primary tumor and brain metastases from tumors of diverse organs of origin. Combining an advanced live cell imaging algorithm and artificial intelligence, we profile cancer cell extravasation within a microfluidic blood-brain niche (μBBN) chip, to detect the minute differences between cells with brain metastatic potential and those without with a PPV of 0.91 in the context of this study. The results show remarkably sharp and reproducible distinction between cells that do and those which do not metastasize inside of the device.
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Affiliation(s)
- C Ryan Oliver
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA.
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Mukundan R. Analysis of Image Feature Characteristics for Automated Scoring of HER2 in Histology Slides. J Imaging 2019; 5:jimaging5030035. [PMID: 34460463 PMCID: PMC8320919 DOI: 10.3390/jimaging5030035] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2018] [Revised: 03/01/2019] [Accepted: 03/06/2019] [Indexed: 12/12/2022] Open
Abstract
The evaluation of breast cancer grades in immunohistochemistry (IHC) slides takes into account various types of visual markers and morphological features of stained membrane regions. Digital pathology algorithms using whole slide images (WSIs) of histology slides have recently been finding several applications in such computer-assisted evaluations. Features that are directly related to biomarkers used by pathologists are generally preferred over the pixel values of entire images, even though the latter has more information content. This paper explores in detail various types of feature measurements that are suitable for the automated scoring of human epidermal growth factor receptor 2 (HER2) in histology slides. These are intensity features known as characteristic curves, texture features in the form of uniform local binary patterns (ULBPs), morphological features specifying connectivity of regions, and first-order statistical features of the overall intensity distribution. This paper considers important properties of the above features and outlines methods for reducing information redundancy, maximizing inter-class separability, and improving classification accuracy in the combined feature set. This paper also presents a detailed experimental analysis performed using the aforementioned features on a WSI dataset of IHC stained slides.
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Affiliation(s)
- Ramakrishnan Mukundan
- Department of Computer Science and Software Engineering, University of Canterbury, Christchurch 8140, New Zealand
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50
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The performance of digital microscopy for primary diagnosis in human pathology: a systematic review. Virchows Arch 2019; 474:269-287. [DOI: 10.1007/s00428-018-02519-z] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2018] [Revised: 12/25/2018] [Accepted: 12/28/2018] [Indexed: 02/06/2023]
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