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Kalra N, Verma P, Verma S. Advancements in AI based healthcare techniques with FOCUS ON diagnostic techniques. Comput Biol Med 2024; 179:108917. [PMID: 39059212 DOI: 10.1016/j.compbiomed.2024.108917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Revised: 07/15/2024] [Accepted: 07/15/2024] [Indexed: 07/28/2024]
Abstract
Since the past decade, the interest towards more precise and efficient healthcare techniques with special emphasis on diagnostic techniques has increased. Artificial Intelligence has proved to be instrumental in development of various such techniques. The various types of AI like ML, NLP, RPA etc. are being used, which have streamlined and organised the Electronic Health Records (EHR) along with aiding the healthcare provider with decision making and sample and data analysis. This article also deals with the 3 major categories of diagnostic techniques - Imaging based, Pathology based and Preventive diagnostic techniques and what all changes and modifications were brought upon them, due to use of AI. Due to such a high demand, the investment in AI based healthcare techniques has increased substantially, with predicted market size of almost 188 billon USD by 2030. In India itself, AI in healthcare is expected to raise the GDP by 25 billion USD by 2028. But there are also several challenges associated with this like unavailability of quality data, black box issue etc. One of the major challenges is the ethical considerations and issues during use of medical records as it is a very sensitive document. Due to this, there is several trust issues associated with adoption of AI by many organizations. These challenges have also been discussed in this article. Need for further development in the AI based diagnostic techniques is also done in the article. Alongside, the production of such techniques and devices which are easy to use and simple to incorporate into the daily workflows have immense scope in the upcoming times. The increasing scope of Clinical Decision Support System, Telemedicine etc. make AI a promising field in the healthcare and diagnostics arena. Concluding the article, it can be said that despite the presence of various challenges to the implementation and usage, the future prospects for AI in healthcare is immense and work needs to be done in order to ensure the availability of resources for same so that high level of accuracy can be achieved and better health outcomes can be provided to patients. Ethical concerns need to be addressed for smooth implementation and to reduce the burden of the developers, which has been discussed in this narrative review article.
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Affiliation(s)
- Nishita Kalra
- Department of Pharmaceutical Chemistry/Analysis, Delhi Pharmaceutical Sciences & Research University, Pushp Vihar, Sector 3, New Delhi, 110017, India
| | - Prachi Verma
- Department of Pharmaceutical Chemistry/Analysis, Delhi Pharmaceutical Sciences & Research University, Pushp Vihar, Sector 3, New Delhi, 110017, India
| | - Surajpal Verma
- Department of Pharmaceutical Chemistry/Analysis, Delhi Pharmaceutical Sciences & Research University, Pushp Vihar, Sector 3, New Delhi, 110017, India.
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Mi H, Sivagnanam S, Ho WJ, Zhang S, Bergman D, Deshpande A, Baras AS, Jaffee EM, Coussens LM, Fertig EJ, Popel AS. Computational methods and biomarker discovery strategies for spatial proteomics: a review in immuno-oncology. Brief Bioinform 2024; 25:bbae421. [PMID: 39179248 PMCID: PMC11343572 DOI: 10.1093/bib/bbae421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2024] [Revised: 07/11/2024] [Accepted: 08/09/2024] [Indexed: 08/26/2024] Open
Abstract
Advancements in imaging technologies have revolutionized our ability to deeply profile pathological tissue architectures, generating large volumes of imaging data with unparalleled spatial resolution. This type of data collection, namely, spatial proteomics, offers invaluable insights into various human diseases. Simultaneously, computational algorithms have evolved to manage the increasing dimensionality of spatial proteomics inherent in this progress. Numerous imaging-based computational frameworks, such as computational pathology, have been proposed for research and clinical applications. However, the development of these fields demands diverse domain expertise, creating barriers to their integration and further application. This review seeks to bridge this divide by presenting a comprehensive guideline. We consolidate prevailing computational methods and outline a roadmap from image processing to data-driven, statistics-informed biomarker discovery. Additionally, we explore future perspectives as the field moves toward interfacing with other quantitative domains, holding significant promise for precision care in immuno-oncology.
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Affiliation(s)
- Haoyang Mi
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, United States
| | - Shamilene Sivagnanam
- The Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97201, United States
- Department of Cell, Development and Cancer Biology, Oregon Health and Science University, Portland, OR 97201, United States
| | - Won Jin Ho
- Department of Oncology, Johns Hopkins University School of Medicine, MD 21205, United States
- Convergence Institute, Johns Hopkins University, Baltimore, MD 21205, United States
| | - Shuming Zhang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, United States
| | - Daniel Bergman
- Department of Oncology, Johns Hopkins University School of Medicine, MD 21205, United States
- Convergence Institute, Johns Hopkins University, Baltimore, MD 21205, United States
| | - Atul Deshpande
- Department of Oncology, Johns Hopkins University School of Medicine, MD 21205, United States
- Convergence Institute, Johns Hopkins University, Baltimore, MD 21205, United States
- Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD 21205, United States
| | - Alexander S Baras
- Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD 21205, United States
- Department of Pathology, Johns Hopkins University School of Medicine, MD 21205, United States
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21205, United States
| | - Elizabeth M Jaffee
- Department of Oncology, Johns Hopkins University School of Medicine, MD 21205, United States
- Convergence Institute, Johns Hopkins University, Baltimore, MD 21205, United States
- Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD 21205, United States
| | - Lisa M Coussens
- The Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97201, United States
- Department of Cell, Development and Cancer Biology, Oregon Health and Science University, Portland, OR 97201, United States
- Brenden-Colson Center for Pancreatic Care, Oregon Health and Science University, Portland, OR 97201, United States
| | - Elana J Fertig
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, United States
- Department of Oncology, Johns Hopkins University School of Medicine, MD 21205, United States
- Convergence Institute, Johns Hopkins University, Baltimore, MD 21205, United States
- Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD 21205, United States
- Department of Applied Mathematics and Statistics, Johns Hopkins University Whiting School of Engineering, Baltimore, MD 21218, United States
| | - Aleksander S Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, United States
- Department of Oncology, Johns Hopkins University School of Medicine, MD 21205, United States
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Tresserra F, Fabra G, Luque O, Castélla M, Gómez C, Fernández-Cid C, Rodríguez I. Validation of digital image slides for diagnosis in cervico-vaginal cytology. REVISTA ESPANOLA DE PATOLOGIA : PUBLICACION OFICIAL DE LA SOCIEDAD ESPANOLA DE ANATOMIA PATOLOGICA Y DE LA SOCIEDAD ESPANOLA DE CITOLOGIA 2024; 57:182-189. [PMID: 38971618 DOI: 10.1016/j.patol.2024.03.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 03/19/2024] [Accepted: 03/26/2024] [Indexed: 07/08/2024]
Abstract
OBJECTIVE To test the diagnostic concordance between microscopic (MI) and digital (DG) observation of cervico-vaginal (CV) cytology in a validation study of the technique. METHODS Five cytotechnologists (CT) reviewed 888 routine CV cytology cases from the Cervical Pathology Unit of our center over a 2-week period of time. The cases were first observed by MI and at the end of the day the cases were observed by DG. STATISTICAL ANALYSIS USED Agreement calculated using the Kappa index. RESULTS Most of the diagnoses corresponded to benign (64%) or inflammatory conditions (14%) and 24% corresponded to the intraepithelial lesion or malignancy (ILM) category. The overall kappa coefficient of concordance was strong (0.87). Among the different CTs it was almost perfect in two, strong in two and moderate in one. In 18 cases (10%) there were discrepancies between techniques in the category of ILM. In 10 (56%) cases there was an overdiagnosis in DG and in 8 (44%) an overdiagnosis in MI. Only in two cases, the diagnostic discrepancy exceeded one degree of difference between lesions, and they were ASCUS or AGUS for DG and CIN 2 for MI. CONCLUSIONS In this validation test in which routine cases during a two-week period have been used, observing the cases with both techniques on the same day, we have obtained a strong degree of concordance. The discordances obtained have not been considered relevant.
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Affiliation(s)
- Francisco Tresserra
- Cytology Laboratory, Gynecology Service, Dexeus Women's Health, Dexeus University Hospital, Barcelona, Spain.
| | - Gemma Fabra
- Cytology Laboratory, Gynecology Service, Dexeus Women's Health, Dexeus University Hospital, Barcelona, Spain
| | - Olga Luque
- Cytology Laboratory, Gynecology Service, Dexeus Women's Health, Dexeus University Hospital, Barcelona, Spain
| | - Miriam Castélla
- Cytology Laboratory, Gynecology Service, Dexeus Women's Health, Dexeus University Hospital, Barcelona, Spain
| | - Carla Gómez
- Cytology Laboratory, Gynecology Service, Dexeus Women's Health, Dexeus University Hospital, Barcelona, Spain
| | - Carmen Fernández-Cid
- Cytology Laboratory, Gynecology Service, Dexeus Women's Health, Dexeus University Hospital, Barcelona, Spain
| | - Ignacio Rodríguez
- Epidemiology Unit, Gynecology Service, Dexeus Women's Health, Dexeus University Hospital, Barcelona, Spain
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Browning L, Jesus C, Malacrino S, Guan Y, White K, Puddle A, Alham NK, Haghighat M, Colling R, Birks J, Rittscher J, Verrill C. Artificial Intelligence-Based Quality Assessment of Histopathology Whole-Slide Images within a Clinical Workflow: Assessment of 'PathProfiler' in a Diagnostic Pathology Setting. Diagnostics (Basel) 2024; 14:990. [PMID: 38786288 PMCID: PMC11120465 DOI: 10.3390/diagnostics14100990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 04/17/2024] [Accepted: 04/28/2024] [Indexed: 05/25/2024] Open
Abstract
Digital pathology continues to gain momentum, with the promise of artificial intelligence to aid diagnosis and for assessment of features which may impact prognosis and clinical management. Successful adoption of these technologies depends upon the quality of digitised whole-slide images (WSI); however, current quality control largely depends upon manual assessment, which is inefficient and subjective. We previously developed PathProfiler, an automated image quality assessment tool, and in this feasibility study we investigate its potential for incorporation into a diagnostic clinical pathology setting in real-time. A total of 1254 genitourinary WSI were analysed by PathProfiler. PathProfiler was developed and trained on prostate tissue and, of the prostate biopsy WSI, representing 46% of the WSI analysed, 4.5% were flagged as potentially being of suboptimal quality for diagnosis. All had concordant subjective issues, mainly focus-related, 54% severe enough to warrant remedial action which resulted in improved image quality. PathProfiler was less reliable in assessment of non-prostate surgical resection-type cases, on which it had not been trained. PathProfiler shows potential for incorporation into a digitised clinical pathology workflow, with opportunity for image quality improvement. Whilst its reliability in the current form appears greatest for assessment of prostate specimens, other specimen types, particularly biopsies, also showed benefit.
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Affiliation(s)
- Lisa Browning
- Department of Cellular Pathology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
| | - Christine Jesus
- Department of Cellular Pathology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
| | - Stefano Malacrino
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford OX3 9DU, UK
| | - Yue Guan
- Department of Cellular Pathology, Royal Berkshire Hospital, Royal Berkshire NHS Foundation Trust, Reading RG1 5AN, UK
| | - Kieron White
- Department of Cellular Pathology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
| | - Alison Puddle
- Department of Cellular Pathology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
| | | | - Maryam Haghighat
- School of Electrical Engineering and Robotics, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | - Richard Colling
- Department of Cellular Pathology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford OX3 9DU, UK
| | - Jacqueline Birks
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
| | - Jens Rittscher
- Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK
- NIHR Oxford Biomedical Research Centre, 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, University of Oxford, Oxford OX3 9DU, UK
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
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5
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Hijazi A, Bifulco C, Baldin P, Galon J. Digital Pathology for Better Clinical Practice. Cancers (Basel) 2024; 16:1686. [PMID: 38730638 PMCID: PMC11083211 DOI: 10.3390/cancers16091686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 04/24/2024] [Accepted: 04/25/2024] [Indexed: 05/13/2024] Open
Abstract
(1) Background: Digital pathology (DP) is transforming the landscape of clinical practice, offering a revolutionary approach to traditional pathology analysis and diagnosis. (2) Methods: This innovative technology involves the digitization of traditional glass slides which enables pathologists to access, analyze, and share high-resolution whole-slide images (WSI) of tissue specimens in a digital format. By integrating cutting-edge imaging technology with advanced software, DP promises to enhance clinical practice in numerous ways. DP not only improves quality assurance and standardization but also allows remote collaboration among experts for a more accurate diagnosis. Artificial intelligence (AI) in pathology significantly improves cancer diagnosis, classification, and prognosis by automating various tasks. It also enhances the spatial analysis of tumor microenvironment (TME) and enables the discovery of new biomarkers, advancing their translation for therapeutic applications. (3) Results: The AI-driven immune assays, Immunoscore (IS) and Immunoscore-Immune Checkpoint (IS-IC), have emerged as powerful tools for improving cancer diagnosis, prognosis, and treatment selection by assessing the tumor immune contexture in cancer patients. Digital IS quantitative assessment performed on hematoxylin-eosin (H&E) and CD3+/CD8+ stained slides from colon cancer patients has proven to be more reproducible, concordant, and reliable than expert pathologists' evaluation of immune response. Outperforming traditional staging systems, IS demonstrated robust potential to enhance treatment efficiency in clinical practice, ultimately advancing cancer patient care. Certainly, addressing the challenges DP has encountered is essential to ensure its successful integration into clinical guidelines and its implementation into clinical use. (4) Conclusion: The ongoing progress in DP holds the potential to revolutionize pathology practices, emphasizing the need to incorporate powerful AI technologies, including IS, into clinical settings to enhance personalized cancer therapy.
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Affiliation(s)
- Assia Hijazi
- The French National Institute of Health & Medical Research (INSERM), Laboratory of Integrative Cancer Immunology, F-75006 Paris, France;
- Equipe Labellisée Ligue Contre le Cancer, F-75006 Paris, France
- Centre de Recherche des Cordeliers, Sorbonne Université, Université Paris Cité, F-75006 Paris, France
| | - Carlo Bifulco
- Providence Genomics, Portland, OR 02912, USA;
- Earle A Chiles Research Institute, Portland, OR 97213, USA
| | - Pamela Baldin
- Department of Pathology, Cliniques Universitaires Saint Luc, UCLouvain, 1200 Brussels, Belgium;
| | - Jérôme Galon
- The French National Institute of Health & Medical Research (INSERM), Laboratory of Integrative Cancer Immunology, F-75006 Paris, France;
- Equipe Labellisée Ligue Contre le Cancer, F-75006 Paris, France
- Centre de Recherche des Cordeliers, Sorbonne Université, Université Paris Cité, F-75006 Paris, France
- Veracyte, 13009 Marseille, France
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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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Smith SI, Schulz C, Ugiagbe R, Ndip R, Dieye Y, Leja M, Onyekwere C, Ndububa D, Ajayi A, Jolaiya TF, Jaka H, Setshedi M, Gunturu R, Otegbayo JA, Lahbabi-Amrani N, Arigbabu AO, Kayamba V, Nashidengo PA. Helicobacter pylori Diagnosis and Treatment in Africa: The First Lagos Consensus Statement of the African Helicobacter and Microbiota Study Group. Dig Dis 2024; 42:240-256. [PMID: 38493766 DOI: 10.1159/000537878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 02/14/2024] [Indexed: 03/19/2024]
Abstract
BACKGROUND Helicobacter pylori (H. pylori) infection is the most prevalent type of bacterial infection. Current guidelines from different regions of the world neglect specific African conditions and requirements. The African Helicobacter and Microbiota Study Group (AHMSG), founded in 2022, aimed to create an Africa-specific consensus report reflecting Africa-specific issues. SUMMARY Eighteen experts from nine African countries and two European delegates supported by nine African collaborators from eight other countries prepared statements on the most important African issues in four working groups: (1) epidemiology, (2) diagnosis, (3) indications and prevention, and (4) treatment. Limited resources, restricted access to medical systems, and underdeveloped diagnostic facilities differ from those of other regions. The results of the individual working groups were presented for the final consensus voting, which included all board members. KEY MESSAGES There is a need for further studies on H. pylori prevalence in Africa, with diagnosis hinged on specific African situation. Treatment of H. pylori in the African setting should be based on accessibility and reimbursement, while indication and prevention should be defined in specific African countries.
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Affiliation(s)
- Stella I Smith
- Molecular Biology and Biotechnology Department, Nigerian Institute of Medical Research, Lagos, Nigeria
| | - Christian Schulz
- Medical Department II, University Hospital, Ludwig-Maximilians-Universität, Munich, Germany
- DZIF Deutsches Zentrum für Infektionsforschung, Partner Site Munich, Munich, Germany
| | - Rose Ugiagbe
- Department of Medicine, University of Benin Teaching Hospital, Benin, Nigeria
| | - Roland Ndip
- Department of Microbiology and Parasitology, University of Buea, Buea, Cameroon
| | - Yakhya Dieye
- Pole of Microbiology, Institut Pasteur de Dakar, Dakar, Senegal
| | - Marcis Leja
- Faculty of Medicine, University of Latvia, Riga, Latvia
| | - Charles Onyekwere
- Department of Medicine, Lagos State University Teaching Hospital, Ikeja, Nigeria
| | - Dennis Ndububa
- Department of Medicine, Obafemi Awolowo University Teaching Hospitals Complex, Ile-Ife, Nigeria
| | - Abraham Ajayi
- Molecular Biology and Biotechnology Department, Nigerian Institute of Medical Research, Lagos, Nigeria
| | | | - Hyasinta Jaka
- Department of Internal Medicine, Catholic University of Health and Allied Sciences, Mwanza, Tanzania
| | - Mashiko Setshedi
- Departments of Medicine, Division of Gastroenterology, University of Cape Town, Cape Town, South Africa
| | - Revathi Gunturu
- Department of Pathology, Aga Khan University Hospital, Nairobi, Kenya
| | | | - Naima Lahbabi-Amrani
- Faculty of Medicine and Pharmacy in Rabat, University Mohammed V, Rabat, Morocco
| | | | - Violet Kayamba
- Department of Internal Medicine, University of Zambia School of Medicine, Lusaka, Zambia
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8
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Schwaibold L, Mattern S, Mählmann M, Lobert L, Breunig T, Schürch CM. [Effects of upstream laboratory processes on the digitization of histological slides]. PATHOLOGIE (HEIDELBERG, GERMANY) 2024; 45:90-97. [PMID: 38386056 PMCID: PMC10901962 DOI: 10.1007/s00292-024-01303-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 01/10/2024] [Indexed: 02/23/2024]
Abstract
BACKGROUND Several factors in glass slide (GS) preparation affect the quality and data volume of a digitized histological slide. In particular, reducing contamination and selecting the appropriate coverslip have the potential to significantly reduce scan time and data volume. GOALS To objectify observations from our institute's digitization process to determine the impact of laboratory processes on the quality of digital histology slides. MATERIALS AND METHODS Experiment 1: Scanning the GS before and after installation of a central console in the microtomy area to reduce dirt and statistical analysis of the determined parameters. Experiment 2: Re-coverslipping the GS (post diagnostics) with glass and film. Scanning the GS and statistical analysis of the collected parameters. CONCLUSION The targeted restructuring in the laboratory process leads to a reduction of GS contamination. This causes a significant reduction in the amount of data generated and scanning time required for the digitized sections. Film as a coverslip material minimizes processing errors in contrast to glass. According to our estimation, all the above-mentioned points lead to considerable cost savings.
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Affiliation(s)
- Leander Schwaibold
- Institut für Pathologie, Universitätsklinikum Tübingen, Liebermeisterstr. 8, 72076, Tübingen, Deutschland
| | - Sven Mattern
- Institut für Pathologie, Universitätsklinikum Tübingen, Liebermeisterstr. 8, 72076, Tübingen, Deutschland
| | - Markus Mählmann
- Institut für Pathologie, Universitätsklinikum Tübingen, Liebermeisterstr. 8, 72076, Tübingen, Deutschland
| | - Leon Lobert
- Institut für Pathologie, Universitätsklinikum Tübingen, Liebermeisterstr. 8, 72076, Tübingen, Deutschland
| | - Thomas Breunig
- Institut für Pathologie, Universitätsklinikum Tübingen, Liebermeisterstr. 8, 72076, Tübingen, Deutschland
| | - Christian M Schürch
- Institut für Pathologie, Universitätsklinikum Tübingen, Liebermeisterstr. 8, 72076, Tübingen, Deutschland.
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9
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Chen W, Ziebell J, Arole V, Parkinson B, Yu L, Dai H, Frankel WL, Yearsley M, Esnakula A, Sun S, Gamble D, Vazzano J, Mishra M, Schoenfield L, Kneile J, Reuss S, Schumacher M, Satturwar S, Li Z, Parwani A, Lujan G. Comparing Accuracy of Helicobacter pylori Identification Using Traditional Hematoxylin and Eosin-Stained Glass Slides With Digital Whole Slide Imaging. J Transl Med 2024; 104:100262. [PMID: 37839639 DOI: 10.1016/j.labinv.2023.100262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Revised: 10/05/2023] [Accepted: 10/06/2023] [Indexed: 10/17/2023] Open
Abstract
With advancements in the field of digital pathology, there has been a growing need to compare the diagnostic abilities of pathologists using digitized whole slide images against those when using traditional hematoxylin and eosin (H&E)-stained glass slides for primary diagnosis. One of the most common specimens received in pathology practices is an endoscopic gastric biopsy with a request to rule out Helicobacter pylori (H. pylori) infection. The current standard of care is the identification of the organisms on H&E-stained slides. Immunohistochemical or histochemical stains are used selectively. However, due to their small size (2-4 μm in length by 0.5-1 μm in width), visualization of the organisms can present a diagnostic challenge. The goal of the study was to compare the ability of pathologists to identify H. pylori on H&E slides using a digital platform against the gold standard of H&E glass slides using routine light microscopy. Diagnostic accuracy rates using glass slides vs digital slides were 81% vs 72% (P = .0142) based on H&E slides alone. When H. pylori immunohistochemical slides were provided, the diagnostic accuracy was significantly improved to comparable rates (96% glass vs 99% digital, P = 0.2199). Furthermore, differences in practice settings (academic/subspecialized vs community/general) and the duration of sign-out experience did not significantly impact the accuracy of detecting H. pylori on digital slides. We concluded that digital whole slide images, although amenable in different practice settings and teaching environments, does present some shortcomings in accuracy and precision, especially in certain circumstances and thus is not yet fully capable of completely replacing glass slide review for identification of H. pylori. We specifically recommend reviewing glass slides and/or performing ancillary stains, especially when there is a discrepancy between the degree of inflammation and the presence of microorganisms on digital images.
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Affiliation(s)
- Wei Chen
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Jennifer Ziebell
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Vidya Arole
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Bryce Parkinson
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Lianbo Yu
- Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio
| | - Harrison Dai
- Eastern Virginia Medical School, Norfolk, Virginia
| | - Wendy L Frankel
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Martha Yearsley
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Ashwini Esnakula
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Shaoli Sun
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Denise Gamble
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Jennifer Vazzano
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Manisha Mishra
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Lynn Schoenfield
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Jeffrey Kneile
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Sarah Reuss
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Melinda Schumacher
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Swati Satturwar
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Zaibo Li
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Anil Parwani
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Giovanni Lujan
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio.
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10
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de Velozo G, Cordeiro J, Sousa J, Holanda AC, Pessoa G, Porfírio M, Távora F. Comparison of glass and digital slides for cervical cytopathology screening and interpretation. Diagn Cytopathol 2023; 51:735-743. [PMID: 37587842 DOI: 10.1002/dc.25209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 07/21/2023] [Accepted: 07/25/2023] [Indexed: 08/18/2023]
Abstract
Cervical cancer is the second most common form of cancer and a leading cause of premature death among women aged 15 to 44 worldwide. In Brazil, there is a high prevalence of infection by the human papillomavirus - HPV. Digital pathology optimizes time and space for reading cervicovaginal cytology slides. We evaluated the feasibility of using whole slide images (WSI) for the routine interpretation of cytology exams. A total of 99 cases of vaginal cytology were selected from a reference laboratory in Northeastern Brazil. Three cytotechnicians participated in the study. Cellular atypia was the one that most presented concordance values. Two observers almost perfectly agreed (k = 0.86 and k = 0.84, respectively) on the negative diagnoses. The performance of the evaluators for NILM (negative for intraepithelial lesion and malignancy) showed high reproducibility and sensitivity in the digital slides, mainly between evaluators A and C. In contrast, the microbiology group showed disagreement between the diagnoses by digital slides and the standard- gold. The concordance between the digital diagnoses and the gold standard for ASCUS was 89%. In the inflammatory category, Spearman's test showed similar results between raters A, B, and C (rs = 0.47, rs = 0.41, and rs = 0.47, respectively). This study reports the diagnostic validation using digital slides in view of the need to optimize the cytology visualization process. Our experience shows good diagnostic agreement between digital and optical microscopy in several analyzed categories, but mainly in relation to cellular atypia and inflammatory processes.
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Affiliation(s)
| | - Juliana Cordeiro
- Federal University of Ceará, Argos Patologia Laboratory, Fortaleza, Brazil
| | | | | | | | - Mônica Porfírio
- Federal University of Ceará, Argos Patologia Laboratory, Fortaleza, Brazil
| | - Fábio Távora
- Federal University of Ceará, Argos Patologia Laboratory, Fortaleza, Brazil
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11
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Del Amor R, Pérez-Cano J, López-Pérez M, Terradez L, Aneiros-Fernandez J, Morales S, Mateos J, Molina R, Naranjo V. Annotation protocol and crowdsourcing multiple instance learning classification of skin histological images: The CR-AI4SkIN dataset. Artif Intell Med 2023; 145:102686. [PMID: 37925214 DOI: 10.1016/j.artmed.2023.102686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 10/11/2023] [Accepted: 10/13/2023] [Indexed: 11/06/2023]
Abstract
Digital Pathology (DP) has experienced a significant growth in recent years and has become an essential tool for diagnosing and prognosis of tumors. The availability of Whole Slide Images (WSIs) and the implementation of Deep Learning (DL) algorithms have paved the way for the appearance of Artificial Intelligence (AI) systems that support the diagnosis process. These systems require extensive and varied data for their training to be successful. However, creating labeled datasets in histopathology is laborious and time-consuming. We have developed a crowdsourcing-multiple instance labeling/learning protocol that is applied to the creation and use of the CR-AI4SkIN dataset.2 CR-AI4SkIN contains 271 WSIs of 7 Cutaneous Spindle Cell (CSC) neoplasms with expert and non-expert labels at region and WSI levels. It is the first dataset of these types of neoplasms made available. The regions selected by the experts are used to learn an automatic extractor of Regions of Interest (ROIs) from WSIs. To produce the embedding of each WSI, the representations of patches within the ROIs are obtained using a contrastive learning method, and then combined. Finally, they are fed to a Gaussian process-based crowdsourcing classifier, which utilizes the noisy non-expert WSI labels. We validate our crowdsourcing-multiple instance learning method in the CR-AI4SkIN dataset, addressing a binary classification problem (malign vs. benign). The proposed method obtains an F1 score of 0.7911 on the test set, outperforming three widely used aggregation methods for crowdsourcing tasks. Furthermore, our crowdsourcing method also outperforms the supervised model with expert labels on the test set (F1-score = 0.6035). The promising results support the proposed crowdsourcing multiple instance learning annotation protocol. It also validates the automatic extraction of interest regions and the use of contrastive embedding and Gaussian process classification to perform crowdsourcing classification tasks.
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Affiliation(s)
- Rocío Del Amor
- Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano, Universitat Politècnica de València, Valencia, Spain
| | - Jose Pérez-Cano
- Department of Computer Science and Artificial Intelligence, University of Granada, 18010 Granada, Spain
| | - Miguel López-Pérez
- Department of Computer Science and Artificial Intelligence, University of Granada, 18010 Granada, Spain.
| | - Liria Terradez
- Pathology Department. Hospital Clínico Universitario de Valencia, Universidad de Valencia, Spain
| | | | - Sandra Morales
- Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano, Universitat Politècnica de València, Valencia, Spain
| | - Javier Mateos
- Department of Computer Science and Artificial Intelligence, University of Granada, 18010 Granada, Spain
| | - Rafael Molina
- Department of Computer Science and Artificial Intelligence, University of Granada, 18010 Granada, Spain
| | - Valery Naranjo
- Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano, Universitat Politècnica de València, Valencia, Spain
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12
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Hanna MG, Ardon O. Digital pathology systems enabling quality patient care. Genes Chromosomes Cancer 2023; 62:685-697. [PMID: 37458325 PMCID: PMC11265285 DOI: 10.1002/gcc.23192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 06/27/2023] [Accepted: 07/06/2023] [Indexed: 09/20/2023] Open
Abstract
Pathology laboratories are undergoing digital transformations, adopting innovative technologies to enhance patient care. Digital pathology systems impact clinical, education, and research use cases where pathologists use digital technologies to perform tasks in lieu of using glass slides and a microscope. Pathology professional societies have established clinical validation guidelines, and the US Food and Drug Administration have also authorized digital pathology systems for primary diagnosis, including image analysis and machine learning systems. Whole slide images, or digital slides, can be viewed and navigated similar to glass slides on a microscope. These modern tools not only enable pathologists to practice their routine clinical activities, but can potentially enable digital computational discovery. Assimilation of whole slide images in pathology clinical workflow can further empower machine learning systems to support computer assisted diagnostics. The potential enrichment these systems can provide is unprecedented in the field of pathology. With appropriate integration, these clinical decision support systems will allow pathologists to increase the delivery of quality patient care. This review describes the digital pathology transformation process, applicable clinical use cases, incorporation of image analysis and machine learning systems in the clinical workflow, as well as future technologies that may further disrupt pathology modalities to deliver quality patient care.
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Affiliation(s)
- Matthew G Hanna
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Orly Ardon
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, USA
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13
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Guo Y, Hu M, Min X, Wang Y, Dai M, Zhai G, Zhang XP, Yang X. Blind Image Quality Assessment for Pathological Microscopic Image Under Screen and Immersion Scenarios. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:3295-3306. [PMID: 37267133 DOI: 10.1109/tmi.2023.3282387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
The high-quality pathological microscopic images are essential for physicians or pathologists to make a correct diagnosis. Image quality assessment (IQA) can quantify the visual distortion degree of images and guide the imaging system to improve image quality, thus raising the quality of pathological microscopic images. Current IQA methods are not ideal for pathological microscopy images due to their specificity. In this paper, we present deep learning-based blind image quality assessment model with saliency block and patch block for pathological microscopic images. The saliency block and patch block can handle the local and global distortions, respectively. To better capture the area of interest of pathologists when viewing pathological images, the saliency block is fine-tuned by eye movement data of pathologists. The patch block can capture lots of global information strongly related to image quality via the interaction between different image patches from different positions. The performance of the developed model is validated by the home-made Pathological Microscopic Image Quality Database under Screen and Immersion Scenarios (PMIQD-SIS) and cross-validated by the five public datasets. The results of ablation experiments demonstrate the contribution of the added blocks. The dataset and the corresponding code are publicly available at: https://github.com/mikugyf/PMIQD-SIS.
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14
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Lin YJ, Chen CC, Lee CH, Yeh CY, Jeng YM. Two-tiered deep-learning-based model for histologic diagnosis of Helicobacter gastritis. Histopathology 2023; 83:771-781. [PMID: 37522271 DOI: 10.1111/his.15018] [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: 03/31/2023] [Revised: 07/11/2023] [Accepted: 07/15/2023] [Indexed: 08/01/2023]
Abstract
AIMS Helicobacter pylori (HP) infection is the most common cause of chronic gastritis worldwide. Due to the small size of HP and limited resolution, diagnosing HP infections is more difficult when using digital slides. METHODS AND RESULTS We developed a two-tier deep-learning-based model for diagnosing HP gastritis. A whole-slide model was trained on 885 whole-slide images (WSIs) with only slide-level labels (positive or negative slides). An auxiliary model was trained on 824 areas with HP in nine positive WSIs and 446 negative WSIs for localizing HP. The whole-slide model performed well, with an area under the receiver operating characteristic curve (AUC) of 0.9739 (95% confidence interval [CI], 0.9545-0.9932). The calculated sensitivity and specificity were 93.3% and 90.1%, respectively, whereas those of pathologists were 93.3% and 84.2%, respectively. Using the auxiliary model, the highlighted areas of the localization maps had an average precision of 0.5796. CONCLUSIONS HP gastritis can be diagnosed on haematoxylin-and-eosin-stained WSIs with human-level accuracy using a deep-learning-based model trained on slide-level labels and an auxiliary model for localizing HP and confirming the diagnosis. This two-tiered model can shorten the diagnostic process and reduce the need for special staining.
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Affiliation(s)
- Yi-Jyun Lin
- Department of Pathology, National Taiwan University Hospital, Taipei, Taiwan
| | | | - Chia-Hsiang Lee
- Department of Pathology, National Taiwan University Hospital, Taipei, Taiwan
| | | | - Yung-Ming Jeng
- Department of Pathology, National Taiwan University Hospital, Taipei, Taiwan
- Graduate Institute of Pathology, College of Medicine, National Taiwan University, Taipei, Taiwan
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15
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Evans H, Hero E, Minhas F, Wahab N, Dodd K, Sahota H, Ganguly R, Robinson A, Neerudu M, Blessing E, Borkar P, Snead D. Standardized Clinical Annotation of Digital Histopathology Slides at the Point of Diagnosis. Mod Pathol 2023; 36:100297. [PMID: 37544362 DOI: 10.1016/j.modpat.2023.100297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 06/26/2023] [Accepted: 07/25/2023] [Indexed: 08/08/2023]
Abstract
As digital pathology replaces conventional glass slide microscopy as a means of reporting cellular pathology samples, the annotation of digital pathology whole slide images is rapidly becoming part of a pathologist's regular practice. Currently, there is no recognizable organization of these annotations, and as a result, pathologists adopt an arbitrary approach to defining regions of interest, leading to irregularity and inconsistency and limiting the downstream efficient use of this valuable effort. In this study, we propose a Standardized Annotation Reporting Style for digital whole slide images. We formed a list of 167 commonly annotated entities (under 12 specialty subcategories) based on review of Royal College of Pathologists and College of American Pathologists documents, feedback from reporting pathologists in our NHS department, and experience in developing annotation dictionaries for PathLAKE research projects. Each entity was assigned a suitable annotation shape, SNOMED CT (SNOMED International) code, and unique color. Additionally, as an example of how the approach could be expanded to specific tumor types, all lung tumors in the fifth World Health Organization of thoracic tumors 2021 were included. The proposed standardization of annotations increases their utility, making them identifiable at low power and searchable across and between cases. This would aid pathologists reporting and reviewing cases and enable annotations to be used for research. This structured approach could serve as the basis for an industry standard and be easily adopted to ensure maximum functionality and efficiency in the use of annotations made during routine clinical examination of digital slides.
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Affiliation(s)
- Harriet Evans
- Histopathology Department, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, United Kingdom; Warwick Medical School, University of Warwick, Coventry, United Kingdom.
| | - Emily Hero
- Histopathology Department, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, United Kingdom; Histopathology Department, University Hospitals of Leicester NHS Trust, Leicester, United Kingdom
| | - Fayyaz Minhas
- Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, Coventry, United Kingdom
| | - Noorul Wahab
- Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, Coventry, United Kingdom
| | - Katherine Dodd
- Histopathology Department, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, United Kingdom
| | - Harvir Sahota
- Histopathology Department, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, United Kingdom; Department of Psychiatry, Coventry and Warwickshire Partnership Trust, Coventry, United Kingdom
| | - Ratnadeep Ganguly
- Histopathology Department, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, United Kingdom
| | - Andrew Robinson
- Histopathology Department, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, United Kingdom
| | - Manjuvani Neerudu
- Histopathology Department, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, United Kingdom
| | - Elaine Blessing
- Histopathology Department, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, United Kingdom
| | - Pallavi Borkar
- Histopathology Department, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, United Kingdom
| | - David Snead
- Histopathology Department, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, United Kingdom; Warwick Medical School, University of Warwick, Coventry, United Kingdom
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16
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Neary-Zajiczek L, Beresna L, Razavi B, Pawar V, Shaw M, Stoyanov D. Minimum resolution requirements of digital pathology images for accurate classification. Med Image Anal 2023; 89:102891. [PMID: 37536022 DOI: 10.1016/j.media.2023.102891] [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: 04/19/2022] [Revised: 05/22/2023] [Accepted: 07/06/2023] [Indexed: 08/05/2023]
Abstract
Digitization of pathology has been proposed as an essential mitigation strategy for the severe staffing crisis facing most pathology departments. Despite its benefits, several barriers have prevented widespread adoption of digital workflows, including cost and pathologist reluctance due to subjective image quality concerns. In this work, we quantitatively determine the minimum image quality requirements for binary classification of histopathology images of breast tissue in terms of spatial and sampling resolution. We train an ensemble of deep learning classifier models on publicly available datasets to obtain a baseline accuracy and computationally degrade these images according to our derived theoretical model to identify the minimum resolution necessary for acceptable diagnostic accuracy. Our results show that images can be degraded significantly below the resolution of most commercial whole-slide imaging systems while maintaining reasonable accuracy, demonstrating that macroscopic features are sufficient for binary classification of stained breast tissue. A rapid low-cost imaging system capable of identifying healthy tissue not requiring human assessment could serve as a triage system for reducing caseloads and alleviating the significant strain on the current workforce.
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Affiliation(s)
- Lydia Neary-Zajiczek
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, Charles Bell House, 43-45 Foley Street, Fitzrovia, London, W1W 7TS, United Kingdom; Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, United Kingdom.
| | - Linas Beresna
- Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, United Kingdom
| | - Benjamin Razavi
- University College London Medical School, 74 Huntley Street, London, WC1E 6BT, United Kingdom
| | - Vijay Pawar
- Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, United Kingdom
| | - Michael Shaw
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, Charles Bell House, 43-45 Foley Street, Fitzrovia, London, W1W 7TS, United Kingdom; Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, United Kingdom; National Physical Laboratory, Hampton Road, Teddington, TW11 0LW, United Kingdom
| | - Danail Stoyanov
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, Charles Bell House, 43-45 Foley Street, Fitzrovia, London, W1W 7TS, United Kingdom; Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, United Kingdom
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17
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Graham S, Minhas F, Bilal M, Ali M, Tsang YW, Eastwood M, Wahab N, Jahanifar M, Hero E, Dodd K, Sahota H, Wu S, Lu W, Azam A, Benes K, Nimir M, Hewitt K, Bhalerao A, Robinson A, Eldaly H, Raza SEA, Gopalakrishnan K, Snead D, Rajpoot N. Screening of normal endoscopic large bowel biopsies with interpretable graph learning: a retrospective study. Gut 2023; 72:1709-1721. [PMID: 37173125 PMCID: PMC10423541 DOI: 10.1136/gutjnl-2023-329512] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 04/15/2023] [Indexed: 05/15/2023]
Abstract
OBJECTIVE To develop an interpretable artificial intelligence algorithm to rule out normal large bowel endoscopic biopsies, saving pathologist resources and helping with early diagnosis. DESIGN A graph neural network was developed incorporating pathologist domain knowledge to classify 6591 whole-slides images (WSIs) of endoscopic large bowel biopsies from 3291 patients (approximately 54% female, 46% male) as normal or abnormal (non-neoplastic and neoplastic) using clinically driven interpretable features. One UK National Health Service (NHS) site was used for model training and internal validation. External validation was conducted on data from two other NHS sites and one Portuguese site. RESULTS Model training and internal validation were performed on 5054 WSIs of 2080 patients resulting in an area under the curve-receiver operating characteristic (AUC-ROC) of 0.98 (SD=0.004) and AUC-precision-recall (PR) of 0.98 (SD=0.003). The performance of the model, named Interpretable Gland-Graphs using a Neural Aggregator (IGUANA), was consistent in testing over 1537 WSIs of 1211 patients from three independent external datasets with mean AUC-ROC=0.97 (SD=0.007) and AUC-PR=0.97 (SD=0.005). At a high sensitivity threshold of 99%, the proposed model can reduce the number of normal slides to be reviewed by a pathologist by approximately 55%. IGUANA also provides an explainable output highlighting potential abnormalities in a WSI in the form of a heatmap as well as numerical values associating the model prediction with various histological features. CONCLUSION The model achieved consistently high accuracy showing its potential in optimising increasingly scarce pathologist resources. Explainable predictions can guide pathologists in their diagnostic decision-making and help boost their confidence in the algorithm, paving the way for its future clinical adoption.
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Affiliation(s)
- Simon Graham
- Department of Computer Science, University of Warwick, Coventry, UK
- Histofy Ltd, Birmingham, UK
| | - Fayyaz Minhas
- Department of Computer Science, University of Warwick, Coventry, UK
| | - Mohsin Bilal
- Department of Computer Science, University of Warwick, Coventry, UK
| | - Mahmoud Ali
- Department of Pathology, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
| | - Yee Wah Tsang
- Department of Pathology, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
| | - Mark Eastwood
- Department of Computer Science, University of Warwick, Coventry, UK
| | - Noorul Wahab
- Department of Computer Science, University of Warwick, Coventry, UK
| | | | - Emily Hero
- Department of Pathology, University Hospitals of Leicester NHS Trust, Leicester, UK
| | - Katherine Dodd
- Department of Pathology, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
| | - Harvir Sahota
- Department of Pathology, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
| | - Shaobin Wu
- Department of Pathology, East Suffolk and North Essex NHS Foundation Trust, Colchester, UK
| | - Wenqi Lu
- Department of Computer Science, University of Warwick, Coventry, UK
| | - Ayesha Azam
- Department of Pathology, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
| | - Ksenija Benes
- Department of Pathology, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
- Department of Pathology, Royal Wolverhampton Hospitals NHS Trust, Wolverhampton, UK
| | - Mohammed Nimir
- Department of Pathology, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
| | - Katherine Hewitt
- Department of Pathology, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
| | - Abhir Bhalerao
- Department of Computer Science, University of Warwick, Coventry, UK
| | - Andrew Robinson
- Department of Pathology, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
| | - Hesham Eldaly
- Department of Pathology, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
| | | | - Kishore Gopalakrishnan
- Department of Pathology, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
| | - David Snead
- Histofy Ltd, Birmingham, UK
- Department of Pathology, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
- Division of Biomedical Sciences, University of Warwick Warwick Medical School, Coventry, UK
| | - Nasir Rajpoot
- Department of Computer Science, University of Warwick, Coventry, UK
- Histofy Ltd, Birmingham, UK
- Department of Pathology, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
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18
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Borsekofsky S, Tsuriel S, Hagege RR, Hershkovitz D. Perineural invasion detection in pancreatic ductal adenocarcinoma using artificial intelligence. Sci Rep 2023; 13:13628. [PMID: 37604973 PMCID: PMC10442355 DOI: 10.1038/s41598-023-40833-y] [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: 05/09/2023] [Accepted: 08/17/2023] [Indexed: 08/23/2023] Open
Abstract
Perineural invasion (PNI) refers to the presence of cancer cells around or within nerves, raising the risk of residual tumor. Linked to worse prognosis in pancreatic ductal adenocarcinoma (PDAC), PNI is also being explored as a therapeutic target. The purpose of this work was to build a PNI detection algorithm to enhance accuracy and efficiency in identifying PNI in PDAC specimens. Training used 260 manually segmented nerve and tumor HD images from 6 scanned PDAC cases; Analytical performance analysis used 168 additional images; clinical analysis used 59 PDAC cases. The algorithm pinpointed key areas of tumor-nerve proximity for pathologist confirmation. Analytical performance reached sensitivity of 88% and 54%, and specificity of 78% and 85% for the detection of nerve and tumor, respectively. Incorporating tumor-nerve distance in clinical evaluation raised PNI detection from 52 to 81% of all cases. Interestingly, pathologist analysis required an average of only 24 s per case. This time-efficient tool accurately identifies PNI in PDAC, even with a small training cohort, by imitating pathologist thought processes.
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Affiliation(s)
- Sarah Borsekofsky
- Institute of Pathology, Tel-Aviv Sourasky Medical Center, 6 Weizmann Street, 6423906, Tel Aviv, Israel
| | - Shlomo Tsuriel
- Institute of Pathology, Tel-Aviv Sourasky Medical Center, 6 Weizmann Street, 6423906, Tel Aviv, Israel
| | - Rami R Hagege
- Institute of Pathology, Tel-Aviv Sourasky Medical Center, 6 Weizmann Street, 6423906, Tel Aviv, Israel
| | - Dov Hershkovitz
- Institute of Pathology, Tel-Aviv Sourasky Medical Center, 6 Weizmann Street, 6423906, Tel Aviv, Israel.
- Faculty of Medicine, Tel-Aviv University, Tel Aviv, Israel.
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19
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Hossain MS, Shahriar GM, Syeed MMM, Uddin MF, Hasan M, Shivam S, Advani S. Region of interest (ROI) selection using vision transformer for automatic analysis using whole slide images. Sci Rep 2023; 13:11314. [PMID: 37443188 PMCID: PMC10344922 DOI: 10.1038/s41598-023-38109-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 07/03/2023] [Indexed: 07/15/2023] Open
Abstract
Selecting regions of interest (ROI) is a common step in medical image analysis across all imaging modalities. An ROI is a subset of an image appropriate for the intended analysis and identified manually by experts. In modern pathology, the analysis involves processing multidimensional and high resolution whole slide image (WSI) tiles automatically with an overwhelming quantity of structural and functional information. Despite recent improvements in computing capacity, analyzing such a plethora of data is challenging but vital to accurate analysis. Automatic ROI detection can significantly reduce the number of pixels to be processed, speed the analysis, improve accuracy and reduce dependency on pathologists. In this paper, we present an ROI detection method for WSI and demonstrated it for human epidermal growth factor receptor 2 (HER2) grading for breast cancer patients. Existing HER2 grading relies on manual ROI selection, which is tedious, time-consuming and suffers from inter-observer and intra-observer variability. This study found that the HER2 grade changes with ROI selection. We proposed an ROI detection method using Vision Transformer and investigated the role of image magnification for ROI detection. This method yielded an accuracy of 99% using 20 × WSI and 97% using 10 × WSI for the ROI detection. In the demonstration, the proposed method increased the diagnostic agreement to 99.3% with the clinical scores and reduced the time to 15 seconds for automated HER2 grading.
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Affiliation(s)
- Md Shakhawat Hossain
- Department of Computer Science and Engineering, Independent University Bangladesh, Dhaka, 1229, Bangladesh.
- RIoT Research Center, Independent University Bangladesh, Dhaka, 1229, Bangladesh.
| | | | - M M Mahbubul Syeed
- Department of Computer Science and Engineering, Independent University Bangladesh, Dhaka, 1229, Bangladesh
- RIoT Research Center, Independent University Bangladesh, Dhaka, 1229, Bangladesh
| | - Mohammad Faisal Uddin
- Department of Computer Science and Engineering, Independent University Bangladesh, Dhaka, 1229, Bangladesh
- RIoT Research Center, Independent University Bangladesh, Dhaka, 1229, Bangladesh
| | - Mahady Hasan
- Department of Computer Science and Engineering, Independent University Bangladesh, Dhaka, 1229, Bangladesh
- RIoT Research Center, Independent University Bangladesh, Dhaka, 1229, Bangladesh
| | - Shingla Shivam
- Department of Pathology, SL Raheja Hospital, Mumbai, 400016, India
| | - Suresh Advani
- Department of Pathology, SL Raheja Hospital, Mumbai, 400016, India
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20
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Palazzi X, Barale-Thomas E, Bawa B, Carter J, Janardhan K, Marxfeld H, Nyska A, Saravanan C, Schaudien D, Schumacher VL, Spaet RH, Tangermann S, Turner OC, Vezzali E. Results of the European Society of Toxicologic Pathology Survey on the Use of Artificial Intelligence in Toxicologic Pathology. Toxicol Pathol 2023; 51:216-224. [PMID: 37732701 DOI: 10.1177/01926233231182115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
The European Society of Toxicologic Pathology (ESTP) initiated a survey through its Pathology 2.0 workstream in partnership with sister professional societies in Europe and North America to generate a snapshot of artificial intelligence (AI) usage in the field of toxicologic pathology. In addition to demographic information, some general questions explored AI relative to (1) the current status of adoption across organizations; (2) technical and methodological aspects; (3) perceived business value and finally; and (4) roadblocks and perspectives. AI has become increasingly established in toxicologic pathology with most pathologists being supportive of its development despite some areas of uncertainty. A salient feature consisted of the variability of AI awareness and adoption among the responders, as the spectrum extended from pathologists having developed familiarity and technical skills in AI, to colleagues who had no interest in AI as a tool in toxicologic pathology. Despite a general enthusiasm for these techniques, the overall understanding and trust in AI algorithms as well as their added value in toxicologic pathology were generally low, suggesting room for the need for increased awareness and education. This survey will serve as a basis to evaluate the evolution of AI penetration and acceptance in this domain.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Dirk Schaudien
- Fraunhofer Institute for Toxicology and Experimental Medicine, Hanover, Germany
| | | | | | | | - Oliver C Turner
- Novartis Institutes for BioMedical Research, East Hanover, New Jersey, USA
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21
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Xiao Y, Wang S, Ling R, Song Y. Application of artificial neural network algorithm in pathological diagnosis and prognosis prediction of digestive tract malignant tumors. Zhejiang Da Xue Xue Bao Yi Xue Ban 2023; 52:243-248. [PMID: 37283110 DOI: 10.3724/zdxbyxb-2022-0569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
The application of artificial neural network algorithm in pathological diagnosis of gastrointestinal malignant tumors has become a research hotspot. In the previous studies, the algorithm research mainly focused on the model development based on convolutional neural networks, while only a few studies used the combination of convolutional neural networks and recurrent neural networks. The research contents included classical histopathological diagnosis and molecular typing of malignant tumors, and the prediction of patient prognosis by utilizing artificial neural networks. This article reviews the research progress on artificial neural network algorithm in the pathological diagnosis and prognosis prediction of digestive tract malignant tumors.
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Affiliation(s)
- Ya Xiao
- Health Science Center, Ningbo University, Ningbo 315211, Zhejiang Province, China.
| | - Shuyang Wang
- Department of Pathology, School of Basic Medical Sciences, Fudan University, Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai 200032, China
| | - Ren Ling
- Shanghai Laizi Software Technology Co. Ltd., Shanghai 201499, China
| | - Yufei Song
- Department of Gastroenterology, the Affiliated Lihuili Hospital, Ningbo University, Ningbo 315046, Zhejiang Province, China.
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22
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Lennerz JK, Salgado R, Kim GE, Sirintrapun SJ, Thierauf JC, Singh A, Indave I, Bard A, Weissinger SE, Heher YK, de Baca ME, Cree IA, Bennett S, Carobene A, Ozben T, Ritterhouse LL. Diagnostic quality model (DQM): an integrated framework for the assessment of diagnostic quality when using AI/ML. Clin Chem Lab Med 2023; 61:544-557. [PMID: 36696602 DOI: 10.1515/cclm-2022-1151] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 01/13/2023] [Indexed: 01/26/2023]
Abstract
BACKGROUND Laboratory medicine has reached the era where promises of artificial intelligence and machine learning (AI/ML) seem palpable. Currently, the primary responsibility for risk-benefit assessment in clinical practice resides with the medical director. Unfortunately, there is no tool or concept that enables diagnostic quality assessment for the various potential AI/ML applications. Specifically, we noted that an operational definition of laboratory diagnostic quality - for the specific purpose of assessing AI/ML improvements - is currently missing. METHODS A session at the 3rd Strategic Conference of the European Federation of Laboratory Medicine in 2022 on "AI in the Laboratory of the Future" prompted an expert roundtable discussion. Here we present a conceptual diagnostic quality framework for the specific purpose of assessing AI/ML implementations. RESULTS The presented framework is termed diagnostic quality model (DQM) and distinguishes AI/ML improvements at the test, procedure, laboratory, or healthcare ecosystem level. The operational definition illustrates the nested relationship among these levels. The model can help to define relevant objectives for implementation and how levels come together to form coherent diagnostics. The affected levels are referred to as scope and we provide a rubric to quantify AI/ML improvements while complying with existing, mandated regulatory standards. We present 4 relevant clinical scenarios including multi-modal diagnostics and compare the model to existing quality management systems. CONCLUSIONS A diagnostic quality model is essential to navigate the complexities of clinical AI/ML implementations. The presented diagnostic quality framework can help to specify and communicate the key implications of AI/ML solutions in laboratory diagnostics.
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Affiliation(s)
- Jochen K Lennerz
- Department of Pathology, Massachusetts General Hospital/Harvard Medical, Boston, MA, USA
| | - Roberto Salgado
- Department of Pathology, GZA-ZNA Hospitals, Antwerp, Belgium
- Division of Research, Peter Mac Callum Cancer Centre, Melbourne, Australia
| | - Grace E Kim
- Department of Pathology, University of California San Francisco, San Francisco, CA, USA
| | | | - Julia C Thierauf
- Department of Pathology, Massachusetts General Hospital/Harvard Medical, Boston, MA, USA
- Department of Otorhinolaryngology, Head and Neck Surgery, German Cancer Research Center (DKFZ), Heidelberg University Hospital and Research Group Molecular Mechanisms of Head and Neck Tumors, Heidelberg, Germany
| | - Ankit Singh
- Department of Pathology, Massachusetts General Hospital/Harvard Medical, Boston, MA, USA
| | - Iciar Indave
- European Monitoring Centre for Drugs and Drug Addiction (EMCDDA), Lisbon, Portugal
| | - Adam Bard
- Department of Pathology, Massachusetts General Hospital/Harvard Medical, Boston, MA, USA
| | | | - Yael K Heher
- Department of Pathology, Massachusetts General Hospital/Harvard Medical, Boston, MA, USA
| | | | - Ian A Cree
- International Agency for Research on Cancer (IARC), World Health Organization, Lyon, France
| | - Shannon Bennett
- Department of Laboratory Medicine and Pathology (DLMP), Mayo Clinic, Rochester, MN, USA
| | - Anna Carobene
- IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Tomris Ozben
- Medical Faculty, Dept. of Clinical Biochemistry, Akdeniz University, Antalya, Türkiye
- Medical Faculty, Clinical and Experimental Medicine, Ph.D. Program, University of Modena and Reggio Emilia, Modena, Italy
| | - Lauren L Ritterhouse
- Department of Pathology, Massachusetts General Hospital/Harvard Medical, Boston, MA, USA
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23
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Alchami FS, Iqbal Z, Björkhammer CN, Saeed MO, Ramakrishnan R, Clelland C, Ahmad F, Charles A. Whole Slide Imaging Integration with Lab Information Systems, a Study of the Requirements, Processes and Procedures Enabling a Reporting-Based Workflow. PATHOLOGY AND LABORATORY MEDICINE INTERNATIONAL 2023. [DOI: 10.2147/plmi.s388981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
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24
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Kim I, Kang K, Song Y, Kim TJ. Application of Artificial Intelligence in Pathology: Trends and Challenges. Diagnostics (Basel) 2022; 12:2794. [PMID: 36428854 PMCID: PMC9688959 DOI: 10.3390/diagnostics12112794] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 11/03/2022] [Accepted: 11/11/2022] [Indexed: 11/16/2022] Open
Abstract
Given the recent success of artificial intelligence (AI) in computer vision applications, many pathologists anticipate that AI will be able to assist them in a variety of digital pathology tasks. Simultaneously, tremendous advancements in deep learning have enabled a synergy with artificial intelligence (AI), allowing for image-based diagnosis on the background of digital pathology. There are efforts for developing AI-based tools to save pathologists time and eliminate errors. Here, we describe the elements in the development of computational pathology (CPATH), its applicability to AI development, and the challenges it faces, such as algorithm validation and interpretability, computing systems, reimbursement, ethics, and regulations. Furthermore, we present an overview of novel AI-based approaches that could be integrated into pathology laboratory workflows.
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Affiliation(s)
- Inho Kim
- College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea
| | - Kyungmin Kang
- College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea
| | - Youngjae Song
- College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea
| | - Tae-Jung Kim
- Department of Hospital Pathology, Yeouido St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, 10, 63-ro, Yeongdeungpo-gu, Seoul 07345, Republic of Korea
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25
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Chong Y, Bae JM, Kang DW, Kim G, Han HS. Development of quality assurance program for digital pathology by the Korean Society of Pathologists. J Pathol Transl Med 2022; 56:370-382. [DOI: 10.4132/jptm.2022.09.30] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 09/29/2022] [Indexed: 11/19/2022] Open
Abstract
Background: Digital pathology (DP) using whole slide imaging is a recently emerging game changer technology that can fundamentally change the way of working in pathology. The Digital Pathology Study Group (DPSG) of the Korean Society of Pathologists (KSP) published a consensus report on the recommendations for pathologic practice using DP. Accordingly, the need for the development and implementation of a quality assurance program (QAP) for DP has been raised.Methods: To provide a standard baseline reference for internal and external QAP for DP, the members of the Committee of Quality Assurance of the KSP developed a checklist for the Redbook and a QAP trial for DP based on the prior DPSG consensus report. Four leading institutes participated in the QAP trial in the first year, and we gathered feedback from these institutes afterwards.Results: The newly developed checklists of QAP for DP contain 39 items (216 score): eight items for quality control of DP systems; three for DP personnel; nine for hardware and software requirements for DP systems; 15 for validation, operation, and management of DP systems; and four for data security and personal information protection. Most participants in the QAP trial replied that continuous education on unfamiliar terminology and more practical experience is demanding.Conclusions: The QAP for DP is essential for the safe implementation of DP in pathologic practice. Each laboratory should prepare an institutional QAP according to this checklist, and consecutive revision of the checklist with feedback from the QAP trial for DP needs to follow.
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26
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Imamura K, Tomita Y, Sato R, Ikeda T, Iyama S, Jodai T, Takahashi M, Takaki A, Akaike K, Hamada S, Sakata S, Saruwatari K, Saeki S, Ikeda K, Suzuki M, Sakagami T. Clinical Implications and Molecular Characterization of Drebrin-Positive, Tumor-Infiltrating Exhausted T Cells in Lung Cancer. Int J Mol Sci 2022; 23:ijms232213723. [PMID: 36430217 PMCID: PMC9694580 DOI: 10.3390/ijms232213723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 11/05/2022] [Accepted: 11/06/2022] [Indexed: 11/11/2022] Open
Abstract
T cells express an actin-binding protein, drebrin, which is recruited to the contact site between the T cells and antigen-presenting cells during the formation of immunological synapses. However, little is known about the clinical implications of drebrin-expressing, tumor-infiltrating lymphocytes (TILs). To address this issue, we evaluated 34 surgical specimens of pathological stage I-IIIA squamous cell lung cancer. The immune context of primary tumors was investigated using fluorescent multiplex immunohistochemistry. The high-speed scanning of whole-slide images was performed, and the tissue localization of TILs in the tumor cell nest and surrounding stroma was automatically profiled and quantified. Drebrin-expressing T cells were characterized using drebrin+ T cells induced in vitro and publicly available single-cell RNA sequence (scRNA-seq) database. Survival analysis using the propensity scores revealed that a high infiltration of drebrin+ TILs within the tumor cell nest was independently associated with short relapse-free survival and overall survival. Drebrin+ T cells induced in vitro co-expressed multiple exhaustion-associated molecules. The scRNA-seq analyses confirmed that the exhausted tumor-infiltrating CD8+ T cells specifically expressed drebrin. Our study suggests that drebrin-expressing T cells present an exhausted phenotype and that tumor-infiltrating drebrin+ T cells affect clinical outcomes in patients with resectable squamous cell lung cancer.
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Affiliation(s)
- Kosuke Imamura
- Department of Respiratory Medicine, Faculty of Life Sciences, Kumamoto University, Honjo 1-1-1, Chuo-ku, Kumamoto-shi 860-8556, Kumamoto, Japan
| | - Yusuke Tomita
- Department of Respiratory Medicine, Kumamoto University Hospital, Honjo 1-1-1, Chuo-ku, Kumamoto-shi 860-8556, Kumamoto, Japan
- Correspondence: ; Tel.: +81-96-373-5012; Fax: +81-96-373-5328
| | - Ryo Sato
- Laboratory of Stem Cell and Neuro-Vascular Biology, Cell and Developmental Biology Center, National Heart, Lung, and Blood Institute, National Institutes of Health, 10 Center Drive, Bethesda, MD 20892, USA
| | - Tokunori Ikeda
- Laboratory of Clinical Pharmacology and Therapeutics, Faculty of Pharmaceutical Sciences, Sojo University, 4-22-1, Ikeda Nishi-ku, Kumamoto-shi 860-0082, Kumamoto, Japan
| | - Shinji Iyama
- Department of Respiratory Medicine, Kumamoto University Hospital, Honjo 1-1-1, Chuo-ku, Kumamoto-shi 860-8556, Kumamoto, Japan
| | - Takayuki Jodai
- Department of Respiratory Medicine, Kumamoto University Hospital, Honjo 1-1-1, Chuo-ku, Kumamoto-shi 860-8556, Kumamoto, Japan
| | - Misako Takahashi
- Department of Respiratory Medicine, Faculty of Life Sciences, Kumamoto University, Honjo 1-1-1, Chuo-ku, Kumamoto-shi 860-8556, Kumamoto, Japan
| | - Akira Takaki
- Department of Respiratory Medicine, Kumamoto University Hospital, Honjo 1-1-1, Chuo-ku, Kumamoto-shi 860-8556, Kumamoto, Japan
| | - Kimitaka Akaike
- Department of Respiratory Medicine, Kumamoto University Hospital, Honjo 1-1-1, Chuo-ku, Kumamoto-shi 860-8556, Kumamoto, Japan
| | - Shohei Hamada
- Department of Respiratory Medicine, Kumamoto University Hospital, Honjo 1-1-1, Chuo-ku, Kumamoto-shi 860-8556, Kumamoto, Japan
| | - Shinya Sakata
- Department of Respiratory Medicine, Kumamoto University Hospital, Honjo 1-1-1, Chuo-ku, Kumamoto-shi 860-8556, Kumamoto, Japan
| | - Koichi Saruwatari
- Department of Respiratory Medicine, Kumamoto University Hospital, Honjo 1-1-1, Chuo-ku, Kumamoto-shi 860-8556, Kumamoto, Japan
| | - Sho Saeki
- Department of Respiratory Medicine, Kumamoto University Hospital, Honjo 1-1-1, Chuo-ku, Kumamoto-shi 860-8556, Kumamoto, Japan
| | - Koei Ikeda
- Department of Thoracic and Breast Surgery, Faculty of Life Sciences, Kumamoto University, Honjo 1-1-1, Chuo-ku, Kumamoto-shi 860-8556, Kumamoto, Japan
| | - Makoto Suzuki
- Department of Thoracic and Breast Surgery, Faculty of Life Sciences, Kumamoto University, Honjo 1-1-1, Chuo-ku, Kumamoto-shi 860-8556, Kumamoto, Japan
| | - Takuro Sakagami
- Department of Respiratory Medicine, Faculty of Life Sciences, Kumamoto University, Honjo 1-1-1, Chuo-ku, Kumamoto-shi 860-8556, Kumamoto, Japan
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27
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Bracey T. The Nomadic Digital Pathologist. Validation of a simple, dual slide scanner with remote reporting for a regional upper gastrointestinal specialist multidisciplinary meeting. J Pathol Inform 2022; 14:100161. [DOI: 10.1016/j.jpi.2022.100161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 11/16/2022] [Accepted: 11/22/2022] [Indexed: 11/27/2022] Open
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28
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Ali M, Evans H, Whitney P, Minhas F, Snead DRJ. Using Systemised Nomenclature of Medicine (SNOMED) codes to select digital pathology whole slide images for long-term archiving. J Clin Pathol 2022; 76:349-352. [PMID: 36109157 PMCID: PMC10176345 DOI: 10.1136/jcp-2022-208483] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 08/20/2022] [Indexed: 11/04/2022]
Abstract
The archiving of whole slide images represents a hurdle to digital pathology implementation largely because of the amount of data generated. The retention of glass slides is currently recommended for a minimum of 10 years, but it is for individual departments to determine how digital images are archived and for how long. In a retrospective study, we examined the combination of Systemised Nomenclature of Medicine (SNOMED) codes allocated to cases reported between July 2011 and December 2015 and recalled more than 12 months after diagnosis in comparison to non-recalled cases.Our results show that 0.2% of cases are recalled after 12 months, and SNOMED code combinations can be used to identify which cases are likely to be recalled and which are not. This approach could reduce the number of cases archived by 62% and still ensure all cases likely to be recalled remain in the archive.
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Affiliation(s)
- Mahmoud Ali
- Histopathology Department, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
- Histopathology Department, Cambridge University Hospitals NHS Foundation Trust, Cambridge, Cambridgeshire, UK
| | - Harriet Evans
- Histopathology Department, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
| | - Peter Whitney
- Histopathology Department, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
| | - Fayyaz Minhas
- Department of Computer Science, University of Warwick, Coventry, West Midlands, UK
| | - David R J Snead
- Histopathology Department, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
- Warick Medical School, University of Warwick, Coventry, UK
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29
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Liscia DS, D’Andrea M, Biletta E, Bellis D, Demo K, Ferrero F, Petti A, Butinar R, D’Andrea E, Davini G. Use of digital pathology and artificial intelligence for the diagnosis of Helicobacter pylori in gastric biopsies. Pathologica 2022; 114:295-303. [DOI: 10.32074/1591-951x-751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Accepted: 01/22/2022] [Indexed: 12/20/2022] Open
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30
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Park JH, Chung SG, Jun JY, Lee JJ, Lee K. Quantitative analysis of tendon histopathology using digital pathology in rat models with Achilles tendon injury. Connect Tissue Res 2022; 63:463-474. [PMID: 34974783 DOI: 10.1080/03008207.2021.2011251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Although digital image analysis methods that quantify histopathologic features have emerged, no validated quantitative methods are available to evaluate tendon injury. This study aimed to propose and validate a quantitative analysis method to identify the histopathologic features of tendon injuries. The histopathologic features of two Achilles tendon injury models (a partial full-thickness defect model and a collagenase injection model) using Sprague-Dawley rats were evaluated by semiquantitative grading and a quantitative analysis method using a digital pathology software at weeks 1 and 4 after tendon injury (six tendons per group at each time point). The outcome variables between tendon injury models and between time points were compared, and the correlation between semiquantitative scores and the results of the quantitative analysis was investigated. The proposed analysis method quantified the severity of the histopathological features after tendon injury. Quantitative analysis differentiated the cell morphology between tendon injury models and time points better than semiquantitative scoring. The results from quantitative measurements correlated significantly with the semiquantitative scores. The proposed quantitative method can be effective in evaluating the histopathology of tendon injuries.
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Affiliation(s)
- Jae Hyeon Park
- Department of Rehabilitation Medicine, Hanyang University Guri Hospital, Hanyang University College of Medicine, Gyeonggi-do, Republic of Korea
| | - Sun G Chung
- Department of Rehabilitation Medicine, Seoul National University Hospital, Seoul, Republic of Korea.,Institute of Aging, Seoul National University, Seoul, Republic of Korea
| | - Ji-Young Jun
- Department of Rehabilitation Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | - Jin-Ju Lee
- Department of Rehabilitation Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | - Kyoungbun Lee
- Department of Pathology, Seoul National University Hospital, Seoul, Republic of Korea
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31
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High-Resolution Histopathological Image Classification Model Based on Fused Heterogeneous Networks with Self-Supervised Feature Representation. BIOMED RESEARCH INTERNATIONAL 2022; 2022:8007713. [PMID: 36046446 PMCID: PMC9420597 DOI: 10.1155/2022/8007713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 08/02/2022] [Indexed: 11/18/2022]
Abstract
Applying machine learning technology to automatic image analysis and auxiliary diagnosis of whole slide image (WSI) may help to improve the efficiency, objectivity, and consistency of pathological diagnosis. Due to its extremely high resolution, it is still a great challenge to directly process WSI through deep neural networks. In this paper, we propose a novel model for the task of classification of WSIs. The model is composed of two parts. The first part is a self-supervised encoding network with a UNet-like architecture. Each patch from a WSI is encoded as a compressed latent representation. These features are placed according to their corresponding patch’s original location in WSI, forming a feature cube. The second part is a classification network fused by 4 famous network blocks with heterogeneous architectures, with feature cube as input. Our model effectively expresses the feature and preserves location information of each patch. The fused network integrates heterogeneous features generated by different networks which yields robust classification results. The model is evaluated on two public datasets with comparison to baseline models. The evaluation results show the effectiveness of the proposed model.
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32
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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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Mărginean CO, Meliț LE, Săsăran MO. Traditional and Modern Diagnostic Approaches in Diagnosing Pediatric Helicobacter pylori Infection. CHILDREN 2022; 9:children9070994. [PMID: 35883980 PMCID: PMC9316053 DOI: 10.3390/children9070994] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Revised: 06/24/2022] [Accepted: 06/29/2022] [Indexed: 01/10/2023]
Abstract
Helicobacter pylori (H. pylori) is the most common bacterial infection worldwide, is usually acquired during childhood and is related to gastric carcinogenesis during adulthood. Therefore, its early proper diagnosis and subsequent successful eradication represent the cornerstones of gastric cancer prevention. The aim of this narrative review was to assess traditional and modern diagnostic methods in terms of H. pylori diagnosis. Several invasive and non-invasive methods were described, each with its pros and cons. The invasive diagnostic methods comprise endoscopy with biopsy, rapid urease tests, histopathological exams, cultures and biopsy-based molecular tests. Among these, probably the most available, accurate and cost-effective test remains histology, albeit molecular tests definitely remain the most accurate despite their high costs. The non-invasive tests consist of urea breath tests, serology, stool antigens and non-invasive molecular tests. Urea breath tests and stool antigens are the most useful in clinical practice both for the diagnosis of H. pylori infection and for monitoring the eradication of this infection after therapy. The challenges related to accurate diagnosis lead to a choice that must be based on H. pylori virulence, environmental factors and host peculiarities.
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Affiliation(s)
- Cristina Oana Mărginean
- Department of Pediatrics I, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mureș, Gheorghe Marinescu Street No. 38, 540136 Targu Mures, Romania;
| | - Lorena Elena Meliț
- Department of Pediatrics I, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mureș, Gheorghe Marinescu Street No. 38, 540136 Targu Mures, Romania;
- Correspondence:
| | - Maria Oana Săsăran
- Department of Pediatrics III, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mureș, Gheorghe Marinescu Street No. 38, 540136 Targu Mures, Romania;
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Leiby JS, Hao J, Kang GH, Park JW, Kim D. Attention-based multiple instance learning with self-supervision to predict microsatellite instability in colorectal cancer from histology whole-slide images. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:3068-3071. [PMID: 36085965 DOI: 10.1109/embc48229.2022.9871553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Microsatellite instability (MSI) is a clinically important characteristic of colorectal cancer. Standard diagnosis of MSI is performed via genetic analyses, however these tests are not always included in routine care. Histopathology whole-slide images (WSIs) are the gold-standard for colorectal cancer diagnosis and are routinely collected. This study develops a model to predict MSI directly from WSIs. Making use of both weakly- and self-supervised deep learning techniques, the proposed model shows improved performance over conventional deep learning models. Additionally, the proposed framework allows for visual interpretation of model decisions. These results are validated in internal and external testing datasets.
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35
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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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Qaiser T, Lee CY, Vandenberghe M, Yeh J, Gavrielides MA, Hipp J, Scott M, Reischl J. Usability of deep learning and H&E images predict disease outcome-emerging tool to optimize clinical trials. NPJ Precis Oncol 2022; 6:37. [PMID: 35705792 PMCID: PMC9200764 DOI: 10.1038/s41698-022-00275-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 04/27/2022] [Indexed: 11/24/2022] Open
Abstract
Understanding factors that impact prognosis for cancer patients have high clinical relevance for treatment decisions and monitoring of the disease outcome. Advances in artificial intelligence (AI) and digital pathology offer an exciting opportunity to capitalize on the use of whole slide images (WSIs) of hematoxylin and eosin (H&E) stained tumor tissue for objective prognosis and prediction of response to targeted therapies. AI models often require hand-delineated annotations for effective training which may not be readily available for larger data sets. In this study, we investigated whether AI models can be trained without region-level annotations and solely on patient-level survival data. We present a weakly supervised survival convolutional neural network (WSS-CNN) approach equipped with a visual attention mechanism for predicting overall survival. The inclusion of visual attention provides insights into regions of the tumor microenvironment with the pathological interpretation which may improve our understanding of the disease pathomechanism. We performed this analysis on two independent, multi-center patient data sets of lung (which is publicly available data) and bladder urothelial carcinoma. We perform univariable and multivariable analysis and show that WSS-CNN features are prognostic of overall survival in both tumor indications. The presented results highlight the significance of computational pathology algorithms for predicting prognosis using H&E stained images alone and underpin the use of computational methods to improve the efficiency of clinical trial studies.
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Affiliation(s)
- Talha Qaiser
- Precision Medicine and Biosamples, Oncology R&D, AstraZeneca, Cambridge, UK.
| | | | | | - Joe Yeh
- AetherAI, Taipei City, Taiwan
| | | | - Jason Hipp
- Early Oncology, Oncology R&D, AstraZeneca, Cambridge, UK
| | - Marietta Scott
- Precision Medicine and Biosamples, Oncology R&D, AstraZeneca, Cambridge, UK
| | - Joachim Reischl
- Precision Medicine and Biosamples, Oncology R&D, AstraZeneca, Cambridge, UK
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Luchini C, Pantanowitz L, Adsay V, Asa SL, Antonini P, Girolami I, Veronese N, Nottegar A, Cingarlini S, Landoni L, Brosens LA, Verschuur AV, Mattiolo P, Pea A, Mafficini A, Milella M, Niazi MK, Gurcan MN, Eccher A, Cree IA, Scarpa A. Ki-67 assessment of pancreatic neuroendocrine neoplasms: Systematic review and meta-analysis of manual vs. digital pathology scoring. Mod Pathol 2022; 35:712-720. [PMID: 35249100 PMCID: PMC9174054 DOI: 10.1038/s41379-022-01055-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Revised: 02/10/2022] [Accepted: 02/14/2022] [Indexed: 12/18/2022]
Abstract
Ki-67 assessment is a key step in the diagnosis of neuroendocrine neoplasms (NENs) from all anatomic locations. Several challenges exist related to quantifying the Ki-67 proliferation index due to lack of method standardization and inter-reader variability. The application of digital pathology coupled with machine learning has been shown to be highly accurate and reproducible for the evaluation of Ki-67 in NENs. We systematically reviewed all published studies on the subject of Ki-67 assessment in pancreatic NENs (PanNENs) employing digital image analysis (DIA). The most common advantages of DIA were improvement in the standardization and reliability of Ki-67 evaluation, as well as its speed and practicality, compared to the current gold standard approach of manual counts from captured images, which is cumbersome and time consuming. The main limitations were attributed to higher costs, lack of widespread availability (as of yet), operator qualification and training issues (if it is not done by pathologists), and most importantly, the drawback of image algorithms counting contaminating non-neoplastic cells and other signals like hemosiderin. However, solutions are rapidly developing for all of these challenging issues. A comparative meta-analysis for DIA versus manual counting shows very high concordance (global coefficient of concordance: 0.94, 95% CI: 0.83-0.98) between these two modalities. These findings support the widespread adoption of validated DIA methods for Ki-67 assessment in PanNENs, provided that measures are in place to ensure counting of only tumor cells either by software modifications or education of non-pathologist operators, as well as selection of standard regions of interest for analysis. NENs, being cellular and monotonous neoplasms, are naturally more amenable to Ki-67 assessment. However, lessons of this review may be applicable to other neoplasms where proliferation activity has become an integral part of theranostic evaluation including breast, brain, and hematolymphoid neoplasms.
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Affiliation(s)
- Claudio Luchini
- Department of Diagnostics and Public Health, Section of Pathology, University of Verona, Verona, Italy.
- ARC-Net Research Center, University and Hospital Trust of Verona, Verona, Italy.
| | - Liron Pantanowitz
- Department of Pathology & Clinical Labs, University of Michigan, Ann Arbor, MI, USA
| | - Volkan Adsay
- Department of Pathology, Koç University Hospital and Koç University Research Center for Translational Medicine (KUTTAM), Istanbul, Turkey
| | - Sylvia L Asa
- University Hospitals Cleveland Medical Center, Case Western Reserve University, Cleveland, OH, USA
| | - Pietro Antonini
- Department of Diagnostics and Public Health, Section of Pathology, University of Verona, Verona, Italy
| | - Ilaria Girolami
- Division of Pathology, San Maurizio Central Hospital, Bolzano, Italy
| | - Nicola Veronese
- Department of Internal Medicine and Geriatrics, University of Palermo, Palermo, Italy
| | - Alessia Nottegar
- Pathology Unit, Azienda Ospedaliera Universitaria Integrata (AOUI), Verona, Italy
| | - Sara Cingarlini
- Department of Medicine, Section of Oncology, University and Hospital Trust of Verona, Verona, Italy
| | - Luca Landoni
- Department of Surgery, The Pancreas Institute, University and Hospital Trust of Verona, Verona, Italy
| | - Lodewijk A Brosens
- Department of Pathology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Anna V Verschuur
- Department of Pathology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Paola Mattiolo
- Department of Diagnostics and Public Health, Section of Pathology, University of Verona, Verona, Italy
| | - Antonio Pea
- Department of Surgery, The Pancreas Institute, University and Hospital Trust of Verona, Verona, Italy
| | - Andrea Mafficini
- Department of Diagnostics and Public Health, Section of Pathology, University of Verona, Verona, Italy
| | - Michele Milella
- Department of Medicine, Section of Oncology, University and Hospital Trust of Verona, Verona, Italy
| | - Muhammad K Niazi
- Center for Biomedical Informatics, Wake Forest School of Medicine, Winston Salem, NC, USA
| | - Metin N Gurcan
- Center for Biomedical Informatics, Wake Forest School of Medicine, Winston Salem, NC, USA
| | - Albino Eccher
- Pathology Unit, Azienda Ospedaliera Universitaria Integrata (AOUI), Verona, Italy
| | - Ian A Cree
- International Agency for Research on Cancer, IARC, Lyon, France
| | - Aldo Scarpa
- Department of Diagnostics and Public Health, Section of Pathology, University of Verona, Verona, Italy.
- ARC-Net Research Center, University and Hospital Trust of Verona, Verona, Italy.
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Go H. Digital Pathology and Artificial Intelligence Applications in Pathology. Brain Tumor Res Treat 2022; 10:76-82. [PMID: 35545826 PMCID: PMC9098984 DOI: 10.14791/btrt.2021.0032] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 01/17/2022] [Accepted: 03/13/2022] [Indexed: 11/20/2022] Open
Abstract
Digital pathology is revolutionizing pathology. The introduction of digital pathology made it possible to comprehensively change the pathology diagnosis workflow, apply and develop pathological artificial intelligence (AI) models, generate pathological big data, and perform telepathology. AI algorithms, including machine learning and deep learning, are used for the detection, segmentation, registration, processing, and classification of digitized pathological images. Pathological AI algorithms can be helpfully utilized for diagnostic screening, morphometric analysis of biomarkers, the discovery of new meanings of prognosis and therapeutic response in pathological images, and improvement of diagnostic efficiency. In order to develop a successful pathological AI model, it is necessary to consider the selection of a suitable type of image for a subject, utilization of big data repositories, the setting of an effective annotation strategy, image standardization, and color normalization. This review will elaborate on the advantages and perspectives of digital pathology, AI-based approaches, the applications in pathology, and considerations and challenges in the development of pathological AI models.
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Affiliation(s)
- Heounjeong Go
- Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
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Ferreira D, Vale J, Curado M, Polónia A, Eloy C. The impact of different coverslipping methods in the quality of the whole slide images used for diagnosis in pathology. J Pathol Inform 2022; 13:100098. [PMID: 36268095 PMCID: PMC9576983 DOI: 10.1016/j.jpi.2022.100098] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/03/2021] [Indexed: 11/28/2022] Open
Abstract
Digital pathology workflow aims to create whole slide images (WSIs) for diagnosis. The quality of the WSIs depends primarily on the quality of the glass slides produced by the pathology laboratory, where the coverslipping method plays an important role. In this study we compare the glass, the film, and the liquid coverslipping methods to evaluate which ones are suitable to create WSIs for diagnosis. The study included 18 formalin-fixed paraffin-embedded tissue blocks. Of each block, 3 consecutive sections were covered using 1 of the 3 methods. The slides were scanned and evaluated for quality criteria by 2 pathologists experienced in digital pathology. The coverslipping method interferes with the quality of the WSIs, as well as with the scanning time and the file size of the WSIs. All coverslipping methods were found suitable for diagnosis. The glass and liquid methods were manual and had similar results concerning the presence of air bubbles/polymer accumulation, air drying artefacts, tissue exposed, and staining alterations. The glass method was the one with more air bubbles. The liquid method was associated with more alterations on the WSIs, but with the lowest file sizes. Automation of coverslipping and calibration of the scanner for the coverslipping method chosen by the pathology laboratory are relevant for the final quality of the WSIs.
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Sorell T, Rajpoot N, Verrill C. Ethical issues in computational pathology. JOURNAL OF MEDICAL ETHICS 2022; 48:278-284. [PMID: 33658334 DOI: 10.1136/medethics-2020-107024] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2020] [Revised: 02/02/2021] [Accepted: 02/05/2021] [Indexed: 06/12/2023]
Abstract
This paper explores ethical issues raised by whole slide image-based computational pathology. After briefly giving examples drawn from some recent literature of advances in this field, we consider some ethical problems it might be thought to pose. These arise from (1) the tension between artificial intelligence (AI) research-with its hunger for more and more data-and the default preference in data ethics and data protection law for the minimisation of personal data collection and processing; (2) the fact that computational pathology lends itself to kinds of data fusion that go against data ethics norms and some norms of biobanking; (3) the fact that AI methods are esoteric and produce results that are sometimes unexplainable (the so-called 'black box'problem) and (4) the fact that computational pathology is particularly dependent on scanning technology manufacturers with interests of their own in profit-making from data collection. We shall suggest that most of these issues are resolvable.
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Affiliation(s)
- Tom Sorell
- PAIS, University of Warwick, Coventry, UK
| | - Nasir Rajpoot
- Computer Science, University of Warwick, Coventry, UK
| | - Clare Verrill
- Nuffield Department of Surgical Sciences, Oxford University, Oxford, UK
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41
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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: 67] [Impact Index Per Article: 33.5] [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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Cardos AI, Maghiar A, Zaha DC, Pop O, Fritea L, Miere (Groza) F, Cavalu S. Evolution of Diagnostic Methods for Helicobacter pylori Infections: From Traditional Tests to High Technology, Advanced Sensitivity and Discrimination Tools. Diagnostics (Basel) 2022; 12:508. [PMID: 35204598 PMCID: PMC8871415 DOI: 10.3390/diagnostics12020508] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 02/06/2022] [Accepted: 02/14/2022] [Indexed: 01/10/2023] Open
Abstract
Rapid diagnosis and treatment application in the early stages of H. pylori infection plays an important part in inhibiting the transmission of this infection as this bacterium is involved in various gastric pathologies such as gastritis, gastro-duodenal ulcer, and even gastric neoplasia. This review is devoted to a quick overview of conventional and advanced detection techniques successfully applied to the detection of H. pylori in the context of a compelling need to upgrade the standards of the diagnostic methods which are currently being used. Selecting the best diagnostic method implies evaluating different features, the use of one or another test depending on accessibility, laboratories equipment, and the clinical conditions of patients. This paper aims to expose the diagnosis methods for H. pylori that are currently available, highlighting their assets and limitations. The perspectives and the advantages of nanotechnology along with the concept of nano(bio)sensors and the development of lab-on-chip devices as advanced tools for H. pylori detection, differentiation, and discrimination is also presented, by emphasizing multiple advantages: simple, fast, cost-effective, portable, miniaturized, small volume of samples required, highly sensitive, and selective. It is generally accepted that the development of intelligent sensors will completely revolutionize the acquisition procedure and medical decision in the framework of smart healthcare monitoring systems.
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Affiliation(s)
| | - Adriana Maghiar
- Faculty of Medicine and Pharmacy, University of Oradea, P-ta 1 December 10, 410087 Oradea, Romania; (A.I.C.); (D.C.Z.); (O.P.); (L.F.); (F.M.)
| | | | | | | | | | - Simona Cavalu
- Faculty of Medicine and Pharmacy, University of Oradea, P-ta 1 December 10, 410087 Oradea, Romania; (A.I.C.); (D.C.Z.); (O.P.); (L.F.); (F.M.)
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Hanna MG, Ardon O, Reuter VE, Sirintrapun SJ, England C, Klimstra DS, Hameed MR. Integrating digital pathology into clinical practice. Mod Pathol 2022; 35:152-164. [PMID: 34599281 DOI: 10.1038/s41379-021-00929-0] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 09/03/2021] [Accepted: 09/12/2021] [Indexed: 11/09/2022]
Abstract
The field of anatomic pathology has been evolving in the last few decades and the advancements have been largely fostered by innovative technology. Immunohistochemistry enabled a paradigm shift in discovery and diagnostic evaluation, followed by booming genomic advancements which allowed for submicroscopic pathologic characterization, and now the field of digital pathology coupled with machine learning and big data acquisition is paving the way to revolutionize the pathology medical domain. Whole slide imaging (WSI) is a disruptive technology where glass slides are digitized to produce on-screen whole slide images. Specifically, in the past decade, there have been significant advances in digital pathology systems that have allowed this technology to promote integration into clinical practice. Whole slide images (WSI), or digital slides, can be viewed and navigated comparable to glass slides on a microscope, as digital files. Whole slide imaging has increased in adoption among pathologists, pathology departments, and scientists for clinical, educational, and research initiatives. Integration of digital pathology systems requires a coordinated effort with numerous stakeholders, not only within the pathology department, but across the entire enterprise. Each pathology department has distinct needs, use cases and blueprints, however the framework components and variables for successful clinical integration can be generalized across any organization seeking to undergo a digital transformation at any scale. This article will review those components and considerations for integrating digital pathology systems into clinical practice.
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Affiliation(s)
- Matthew G Hanna
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
| | - Orly Ardon
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Victor E Reuter
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Christine England
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - David S Klimstra
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Meera R Hameed
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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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 2022; 59:26-38. [PMID: 34433345 PMCID: PMC8761960 DOI: 10.1177/03009858211040476] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [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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Fast and scalable search of whole-slide images via self-supervised deep learning. Nat Biomed Eng 2022; 6:1420-1434. [PMID: 36217022 PMCID: PMC9792371 DOI: 10.1038/s41551-022-00929-8] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Accepted: 07/15/2022] [Indexed: 01/14/2023]
Abstract
The adoption of digital pathology has enabled the curation of large repositories of gigapixel whole-slide images (WSIs). Computationally identifying WSIs with similar morphologic features within large repositories without requiring supervised training can have significant applications. However, the retrieval speeds of algorithms for searching similar WSIs often scale with the repository size, which limits their clinical and research potential. Here we show that self-supervised deep learning can be leveraged to search for and retrieve WSIs at speeds that are independent of repository size. The algorithm, which we named SISH (for self-supervised image search for histology) and provide as an open-source package, requires only slide-level annotations for training, encodes WSIs into meaningful discrete latent representations and leverages a tree data structure for fast searching followed by an uncertainty-based ranking algorithm for WSI retrieval. We evaluated SISH on multiple tasks (including retrieval tasks based on tissue-patch queries) and on datasets spanning over 22,000 patient cases and 56 disease subtypes. SISH can also be used to aid the diagnosis of rare cancer types for which the number of available WSIs is often insufficient to train supervised deep-learning models.
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Romanchikova M, Thomas SA, Dexter A, Shaw M, Partarrieau I, Smith N, Venton J, Adeogun M, Brettle D, Turpin RJ. The need for measurement science in digital pathology. J Pathol Inform 2022; 13:100157. [PMID: 36405869 PMCID: PMC9646441 DOI: 10.1016/j.jpi.2022.100157] [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: 06/17/2022] [Accepted: 11/07/2022] [Indexed: 11/11/2022] Open
Abstract
Background Pathology services experienced a surge in demand during the COVID-19 pandemic. Digitalisation of pathology workflows can help to increase throughput, yet many existing digitalisation solutions use non-standardised workflows captured in proprietary data formats and processed by black-box software, yielding data of varying quality. This study presents the views of a UK-led expert group on the barriers to adoption and the required input of measurement science to improve current practices in digital pathology. Methods With an aim to support the UK's efforts in digitalisation of pathology services, this study comprised: (1) a review of existing evidence, (2) an online survey of domain experts, and (3) a workshop with 42 representatives from healthcare, regulatory bodies, pharmaceutical industry, academia, equipment, and software manufacturers. The discussion topics included sample processing, data interoperability, image analysis, equipment calibration, and use of novel imaging modalities. Findings The lack of data interoperability within the digital pathology workflows hinders data lookup and navigation, according to 80% of attendees. All participants stressed the importance of integrating imaging and non-imaging data for diagnosis, while 80% saw data integration as a priority challenge. 90% identified the benefits of artificial intelligence and machine learning, but identified the need for training and sound performance metrics.Methods for calibration and providing traceability were seen as essential to establish harmonised, reproducible sample processing, and image acquisition pipelines. Vendor-neutral data standards were seen as a "must-have" for providing meaningful data for downstream analysis. Users and vendors need good practice guidance on evaluation of uncertainty, fitness-for-purpose, and reproducibility of artificial intelligence/machine learning tools. All of the above needs to be accompanied by an upskilling of the pathology workforce. Conclusions Digital pathology requires interoperable data formats, reproducible and comparable laboratory workflows, and trustworthy computer analysis software. Despite high interest in the use of novel imaging techniques and artificial intelligence tools, their adoption is slowed down by the lack of guidance and evaluation tools to assess the suitability of these techniques for specific clinical question. Measurement science expertise in uncertainty estimation, standardisation, reference materials, and calibration can help establishing reproducibility and comparability between laboratory procedures, yielding high quality data and providing higher confidence in diagnosis.
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Affiliation(s)
- Marina Romanchikova
- National Physical Laboratory, Hampton Road, Teddington, Middlesex TW11 0LW, United Kingdom,Corresponding author
| | - Spencer Angus Thomas
- National Physical Laboratory, Hampton Road, Teddington, Middlesex TW11 0LW, United Kingdom
| | - Alex Dexter
- National Physical Laboratory, Hampton Road, Teddington, Middlesex TW11 0LW, United Kingdom
| | - Mike Shaw
- National Physical Laboratory, Hampton Road, Teddington, Middlesex TW11 0LW, United Kingdom
| | - Ignacio Partarrieau
- National Physical Laboratory, Hampton Road, Teddington, Middlesex TW11 0LW, United Kingdom
| | - Nadia Smith
- National Physical Laboratory, Hampton Road, Teddington, Middlesex TW11 0LW, United Kingdom
| | - Jenny Venton
- National Physical Laboratory, Hampton Road, Teddington, Middlesex TW11 0LW, United Kingdom
| | - Michael Adeogun
- National Physical Laboratory, Hampton Road, Teddington, Middlesex TW11 0LW, United Kingdom
| | - David Brettle
- Leeds Teaching Hospitals NHS Trust, St. James's University Hospital, Beckett Street, Leeds, West Yorkshire LS9 7TF, United Kingdom
| | - Robert James Turpin
- British Standards Institution, 389 Chiswick High Road, London W4 4AL, United Kingdom
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Mayall F, Smethurst HB, Semkin L, Mandalia T, Sohail M, Hadden R, Biddlestone L. A feasibility study of multisite networked digital pathology reporting in England. J Pathol Inform 2022; 13:4. [PMID: 35136671 PMCID: PMC8794026 DOI: 10.4103/jpi.jpi_61_21] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 12/16/2021] [Accepted: 11/18/2021] [Indexed: 11/04/2022] Open
Abstract
Background: Materials and Methods: Results: Conclusions:
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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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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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Girolami I, Neri S, Eccher A, Brunelli M, Hanna M, Pantanowitz L, Hanspeter E, Mazzoleni G. Frozen section telepathology service: Efficiency and benefits of an e-health policy in South Tyrol. Digit Health 2022; 8:20552076221116776. [PMID: 35923756 PMCID: PMC9340333 DOI: 10.1177/20552076221116776] [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: 05/30/2022] [Accepted: 07/13/2022] [Indexed: 12/03/2022] Open
Abstract
Objective/Background Telepathology has been widely adopted to allow intraoperative pathology
examinations to be performed remotely and for obtaining second opinion
teleconsultation. In the Italian northern region of South Tyrol, the
widespread geographical distances and consequent cost for the health system
of having a travelling pathologist cover intraoperative consultations in
peripheral hospitals was a key driver for the implementation of a
telepathology system. Methods In 2010, four Menarini D-Sight whole slide scanners to digitize entire
pathology slides were placed in the peripheral hospitals of Merano,
Bressanone, Brunico, and in the hub hospital of Bolzano. Digital
workstations were also installed to allow pathologists to remotely perform
intraoperative consultations with digital slides. This study reviews the
outcome after 12 years of telepathology for this intended clinical use. Results After an initial validation phase with 100 cases which yielded a sensitivity
of 65% (CI 43–84%) and specificity of 100% (CI 95–100%), there were 2058
intraoperative consultations handled by telepathology. The cases evaluated
were mainly breast sentinel lymph nodes, followed by urological,
gynecological and general surgical pathology frozen section specimens. There
were no false-positive cases and 165 (8%) false-negative cases, yielding an
overall sensitivity and specificity of 65% (CI 61–69%) and 100% (CI
99–100%), respectively. Conclusion Telepathology is reliable for remote intraoperative diagnosis and, despite
technical issues and initial acquaintance issues, proved beneficial for
patient care in satellite hospitals, improved standardization, promoted
innovation, and resulted in cost savings for the health system.
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Affiliation(s)
- Ilaria Girolami
- Department of Pathology, Provincial Hospital of Bolzano (SABES-ASDAA), Bolzano-Bozen, Italy
| | - Stefania Neri
- Department of Pathology, Provincial Hospital of Bolzano (SABES-ASDAA), Bolzano-Bozen, Italy
| | - Albino Eccher
- Department of Pathology and Diagnostics, University and Hospital Trust of Verona, Verona, Italy
| | - Matteo Brunelli
- Department of Diagnostics and Public Health, University and Hospital Trust of Verona, Verona, Italy
| | - Mattew Hanna
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Liron Pantanowitz
- Department of Pathology & Clinical Labs, University of Michigan, Ann Arbor, MI, USA
| | - Esther Hanspeter
- Department of Pathology, Provincial Hospital of Bolzano (SABES-ASDAA), Bolzano-Bozen, Italy
| | - Guido Mazzoleni
- Department of Pathology, Provincial Hospital of Bolzano (SABES-ASDAA), Bolzano-Bozen, Italy
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