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Bontoux C, Hofman V, Chamorey E, Schiappa R, Lassalle S, Long-Mira E, Zahaf K, Lalvée S, Fayada J, Bonnetaud C, Goffinet S, Ilié M, Hofman P. Reproducibility of c-Met Immunohistochemical Scoring (Clone SP44) for Non-Small Cell Lung Cancer Using Conventional Light Microscopy and Whole Slide Imaging. Am J Surg Pathol 2024:00000478-990000000-00386. [PMID: 38980727 DOI: 10.1097/pas.0000000000002274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/11/2024]
Abstract
Emerging therapies for non-small cell lung cancer targeting c-Met overexpression have recently demonstrated promising results. However, the evaluation of c-Met expression can be challenging. We aimed to study the inter and intraobserver reproducibility of c-Met expression evaluation. One hundred ten cases with non-small cell lung cancer (40 biopsies and 70 surgical specimens) were retrospectively selected in a single laboratory (LPCE) and evaluated for c-Met expression. Six pathologists (4 seniors and 2 juniors) evaluated the H-score and made a 3-tier classification of c-Met expression for all cases, using conventional light microscopy (CLM) and whole slide imaging (WSI). The interobserver reproducibility with CLM gave global Cohen Kappa coefficients (ƙ) ranging from 0.581 (95% CI: 0.364-0.771) to 0.763 (95% CI: 0.58-0.92) using the c-Met 3-tier classification and H-score, respectively. ƙ was higher for senior pathologists and biopsy samples. The interobserver reproducibility with WSI gave a global ƙ ranging from 0.543 (95% CI: 0.33-0.724) to 0.905 (95% CI: 0.618-1) using the c-Met H-score and 2-tier classification (≥25% 3+), respectively. ƙ for intraobserver reproducibility between CLM and WSI ranged from 0.713 to 0.898 for the c-Met H-score and from 0.600 to 0.779 for the c-Met 3-tier classification. We demonstrated a moderate to excellent interobserver agreement for c-Met expression with a substantial to excellent intraobserver agreement between CLM and WSI, thereby supporting the development of digital pathology. However, some factors (scoring method, type of tissue samples, and expertise level) affect reproducibility. Our findings highlight the importance of establishing a consensus definition and providing further training, particularly for inexperienced pathologists, for c-Met immunohistochemistry assessment in clinical practice.
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Affiliation(s)
- Christophe Bontoux
- Laboratory of Clinical and Experimental Pathology
- Hospital-Integrated Biobank
- Team 4, Institute of Research on Cancer and Aging of Nice Inserm U1081, CNRS UMR7284, Côte d'Azur University
- FHU OncoAge, Côte d'Azur University
- University Hospital Institute RespirERA, Côte d'Azur University, Pasteur Hospital, CHU of Nice
| | - Véronique Hofman
- Laboratory of Clinical and Experimental Pathology
- Hospital-Integrated Biobank
- Team 4, Institute of Research on Cancer and Aging of Nice Inserm U1081, CNRS UMR7284, Côte d'Azur University
- FHU OncoAge, Côte d'Azur University
- University Hospital Institute RespirERA, Côte d'Azur University, Pasteur Hospital, CHU of Nice
| | - Emmanuel Chamorey
- Department of Statistics, Antoine Lacassagne Cancer Center, Nice, France
| | - Renaud Schiappa
- Department of Statistics, Antoine Lacassagne Cancer Center, Nice, France
| | - Sandra Lassalle
- Laboratory of Clinical and Experimental Pathology
- Hospital-Integrated Biobank
- Team 4, Institute of Research on Cancer and Aging of Nice Inserm U1081, CNRS UMR7284, Côte d'Azur University
- FHU OncoAge, Côte d'Azur University
- University Hospital Institute RespirERA, Côte d'Azur University, Pasteur Hospital, CHU of Nice
| | - Elodie Long-Mira
- Laboratory of Clinical and Experimental Pathology
- Hospital-Integrated Biobank
- Team 4, Institute of Research on Cancer and Aging of Nice Inserm U1081, CNRS UMR7284, Côte d'Azur University
- FHU OncoAge, Côte d'Azur University
- University Hospital Institute RespirERA, Côte d'Azur University, Pasteur Hospital, CHU of Nice
| | - Katia Zahaf
- Laboratory of Clinical and Experimental Pathology
- Hospital-Integrated Biobank
- FHU OncoAge, Côte d'Azur University
- University Hospital Institute RespirERA, Côte d'Azur University, Pasteur Hospital, CHU of Nice
| | - Salomé Lalvée
- Laboratory of Clinical and Experimental Pathology
- Hospital-Integrated Biobank
- FHU OncoAge, Côte d'Azur University
- University Hospital Institute RespirERA, Côte d'Azur University, Pasteur Hospital, CHU of Nice
| | - Julien Fayada
- Laboratory of Clinical and Experimental Pathology
- Hospital-Integrated Biobank
- FHU OncoAge, Côte d'Azur University
- University Hospital Institute RespirERA, Côte d'Azur University, Pasteur Hospital, CHU of Nice
| | - Christelle Bonnetaud
- Laboratory of Clinical and Experimental Pathology
- Hospital-Integrated Biobank
- FHU OncoAge, Côte d'Azur University
- University Hospital Institute RespirERA, Côte d'Azur University, Pasteur Hospital, CHU of Nice
| | | | - Marius Ilié
- Laboratory of Clinical and Experimental Pathology
- Hospital-Integrated Biobank
- Team 4, Institute of Research on Cancer and Aging of Nice Inserm U1081, CNRS UMR7284, Côte d'Azur University
- FHU OncoAge, Côte d'Azur University
- University Hospital Institute RespirERA, Côte d'Azur University, Pasteur Hospital, CHU of Nice
| | - Paul Hofman
- Laboratory of Clinical and Experimental Pathology
- Hospital-Integrated Biobank
- Team 4, Institute of Research on Cancer and Aging of Nice Inserm U1081, CNRS UMR7284, Côte d'Azur University
- FHU OncoAge, Côte d'Azur University
- University Hospital Institute RespirERA, Côte d'Azur University, Pasteur Hospital, CHU of Nice
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2
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Shehabeldin A, Rohra P, Sellen LD, Zhao J, Alqaidy D, Aramin H, Hameed N, Perez YE, Lai Z, Tong YT, Milton DR, Edgerton ME, Fuller G, Hansel D, Prieto VG, Ballester LY, Aung PP. Utility of Whole Slide Imaging for Intraoperative Consultation: Experience of a Large Academic Center. Arch Pathol Lab Med 2024; 148:715-721. [PMID: 37756559 DOI: 10.5858/arpa.2023-0105-oa] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/13/2023] [Indexed: 09/29/2023]
Abstract
CONTEXT.— In the United States, review of digital whole slide images (WSIs) using specific systems is approved for primary diagnosis but has not been implemented for intraoperative consultation. OBJECTIVE.— To evaluate the safety of review of WSIs and compare the efficiency of review of WSIs and glass slides (GSs) for intraoperative consultation. DESIGN.— Ninety-one cases previously submitted for frozen section evaluation were randomly selected from 8 different anatomic pathology subspecialties. GSs from these cases were scanned on a Leica Aperio AT2 scanner at ×20 magnification (0.25 μm/pixel). The slides were deidentified, and a short relevant clinical history was provided for each slide. Nine board-certified general pathologists who do not routinely establish primary diagnoses using WSIs reviewed the WSIs using Leica Aperio ImageScope viewing software. After a washout period of 2-3 weeks, the pathologists reviewed the corresponding GSs using a light microscope (Olympus BX43). The pathologists recorded the diagnosis and time to reach the diagnosis. Intraobserver concordance, time to diagnosis, and specificity and sensitivity compared to the original diagnosis were evaluated. RESULTS.— The rate of intraobserver concordance between GS results and WSI results was 93.7%. Mean time to diagnosis was 1.25 minutes for GSs and 1.76 minutes for WSIs (P < .001). Specificity was 91% for GSs and 90% for WSIs; sensitivity was 92% for GSs and 92% for WSIs. CONCLUSIONS.— Time to diagnosis was longer with WSIs than with GSs, and scanning GSs and uploading the data to whole slide imaging systems takes time. However, review of WSIs appears to be a safe alternative to review of GSs. Use of WSIs allows reporting from a remote site during a public health emergency such as the COVID-19 pandemic and facilitates subspecialty histopathology services.
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Affiliation(s)
- Ahmed Shehabeldin
- From the Departments of Pathology (Shehabeldin, Rohra, Sellen, Zhao, Alqaidy, Aramin, Hameed, Perez, Lai, Tong, Edgerton, Fuller, Hansel, Prieto, Ballester, Aung) and Biostatistics (Milton), The University of Texas MD Anderson Cancer Center, Houston
| | - Prih Rohra
- From the Departments of Pathology (Shehabeldin, Rohra, Sellen, Zhao, Alqaidy, Aramin, Hameed, Perez, Lai, Tong, Edgerton, Fuller, Hansel, Prieto, Ballester, Aung) and Biostatistics (Milton), The University of Texas MD Anderson Cancer Center, Houston
| | - Linton D Sellen
- From the Departments of Pathology (Shehabeldin, Rohra, Sellen, Zhao, Alqaidy, Aramin, Hameed, Perez, Lai, Tong, Edgerton, Fuller, Hansel, Prieto, Ballester, Aung) and Biostatistics (Milton), The University of Texas MD Anderson Cancer Center, Houston
| | - Jianping Zhao
- From the Departments of Pathology (Shehabeldin, Rohra, Sellen, Zhao, Alqaidy, Aramin, Hameed, Perez, Lai, Tong, Edgerton, Fuller, Hansel, Prieto, Ballester, Aung) and Biostatistics (Milton), The University of Texas MD Anderson Cancer Center, Houston
| | - Doaa Alqaidy
- From the Departments of Pathology (Shehabeldin, Rohra, Sellen, Zhao, Alqaidy, Aramin, Hameed, Perez, Lai, Tong, Edgerton, Fuller, Hansel, Prieto, Ballester, Aung) and Biostatistics (Milton), The University of Texas MD Anderson Cancer Center, Houston
| | - Hermineh Aramin
- From the Departments of Pathology (Shehabeldin, Rohra, Sellen, Zhao, Alqaidy, Aramin, Hameed, Perez, Lai, Tong, Edgerton, Fuller, Hansel, Prieto, Ballester, Aung) and Biostatistics (Milton), The University of Texas MD Anderson Cancer Center, Houston
| | - Nadia Hameed
- From the Departments of Pathology (Shehabeldin, Rohra, Sellen, Zhao, Alqaidy, Aramin, Hameed, Perez, Lai, Tong, Edgerton, Fuller, Hansel, Prieto, Ballester, Aung) and Biostatistics (Milton), The University of Texas MD Anderson Cancer Center, Houston
| | - Ydamis Estrella Perez
- From the Departments of Pathology (Shehabeldin, Rohra, Sellen, Zhao, Alqaidy, Aramin, Hameed, Perez, Lai, Tong, Edgerton, Fuller, Hansel, Prieto, Ballester, Aung) and Biostatistics (Milton), The University of Texas MD Anderson Cancer Center, Houston
| | - Zongshan Lai
- From the Departments of Pathology (Shehabeldin, Rohra, Sellen, Zhao, Alqaidy, Aramin, Hameed, Perez, Lai, Tong, Edgerton, Fuller, Hansel, Prieto, Ballester, Aung) and Biostatistics (Milton), The University of Texas MD Anderson Cancer Center, Houston
| | - Yi Tat Tong
- From the Departments of Pathology (Shehabeldin, Rohra, Sellen, Zhao, Alqaidy, Aramin, Hameed, Perez, Lai, Tong, Edgerton, Fuller, Hansel, Prieto, Ballester, Aung) and Biostatistics (Milton), The University of Texas MD Anderson Cancer Center, Houston
| | - Denái R Milton
- From the Departments of Pathology (Shehabeldin, Rohra, Sellen, Zhao, Alqaidy, Aramin, Hameed, Perez, Lai, Tong, Edgerton, Fuller, Hansel, Prieto, Ballester, Aung) and Biostatistics (Milton), The University of Texas MD Anderson Cancer Center, Houston
| | - Mary E Edgerton
- From the Departments of Pathology (Shehabeldin, Rohra, Sellen, Zhao, Alqaidy, Aramin, Hameed, Perez, Lai, Tong, Edgerton, Fuller, Hansel, Prieto, Ballester, Aung) and Biostatistics (Milton), The University of Texas MD Anderson Cancer Center, Houston
| | - Gregory Fuller
- From the Departments of Pathology (Shehabeldin, Rohra, Sellen, Zhao, Alqaidy, Aramin, Hameed, Perez, Lai, Tong, Edgerton, Fuller, Hansel, Prieto, Ballester, Aung) and Biostatistics (Milton), The University of Texas MD Anderson Cancer Center, Houston
| | - Donna Hansel
- From the Departments of Pathology (Shehabeldin, Rohra, Sellen, Zhao, Alqaidy, Aramin, Hameed, Perez, Lai, Tong, Edgerton, Fuller, Hansel, Prieto, Ballester, Aung) and Biostatistics (Milton), The University of Texas MD Anderson Cancer Center, Houston
| | - Victor G Prieto
- From the Departments of Pathology (Shehabeldin, Rohra, Sellen, Zhao, Alqaidy, Aramin, Hameed, Perez, Lai, Tong, Edgerton, Fuller, Hansel, Prieto, Ballester, Aung) and Biostatistics (Milton), The University of Texas MD Anderson Cancer Center, Houston
| | - Leomar Y Ballester
- From the Departments of Pathology (Shehabeldin, Rohra, Sellen, Zhao, Alqaidy, Aramin, Hameed, Perez, Lai, Tong, Edgerton, Fuller, Hansel, Prieto, Ballester, Aung) and Biostatistics (Milton), The University of Texas MD Anderson Cancer Center, Houston
| | - Phyu P Aung
- From the Departments of Pathology (Shehabeldin, Rohra, Sellen, Zhao, Alqaidy, Aramin, Hameed, Perez, Lai, Tong, Edgerton, Fuller, Hansel, Prieto, Ballester, Aung) and Biostatistics (Milton), The University of Texas MD Anderson Cancer Center, Houston
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Williams DKA, Graifman G, Hussain N, Amiel M, Tran P, Reddy A, Haider A, Kavitesh BK, Li A, Alishahian L, Perera N, Efros C, Babu M, Tharakan M, Etienne M, Babu BA. Digital pathology, deep learning, and cancer: a narrative review. Transl Cancer Res 2024; 13:2544-2560. [PMID: 38881914 PMCID: PMC11170525 DOI: 10.21037/tcr-23-964] [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: 06/05/2023] [Accepted: 03/24/2024] [Indexed: 06/18/2024]
Abstract
Background and Objective Cancer is a leading cause of morbidity and mortality worldwide. The emergence of digital pathology and deep learning technologies signifies a transformative era in healthcare. These technologies can enhance cancer detection, streamline operations, and bolster patient care. A substantial gap exists between the development phase of deep learning models in controlled laboratory environments and their translations into clinical practice. This narrative review evaluates the current landscape of deep learning and digital pathology, analyzing the factors influencing model development and implementation into clinical practice. Methods We searched multiple databases, including Web of Science, Arxiv, MedRxiv, BioRxiv, Embase, PubMed, DBLP, Google Scholar, IEEE Xplore, Semantic Scholar, and Cochrane, targeting articles on whole slide imaging and deep learning published from 2014 and 2023. Out of 776 articles identified based on inclusion criteria, we selected 36 papers for the analysis. Key Content and Findings Most articles in this review focus on the in-laboratory phase of deep learning model development, a critical stage in the deep learning lifecycle. Challenges arise during model development and their integration into clinical practice. Notably, lab performance metrics may not always match real-world clinical outcomes. As technology advances and regulations evolve, we expect more clinical trials to bridge this performance gap and validate deep learning models' effectiveness in clinical care. High clinical accuracy is vital for informed decision-making throughout a patient's cancer care. Conclusions Deep learning technology can enhance cancer detection, clinical workflows, and patient care. Challenges may arise during model development. The deep learning lifecycle involves data preprocessing, model development, and clinical implementation. Achieving health equity requires including diverse patient groups and eliminating bias during implementation. While model development is integral, most articles focus on the pre-deployment phase. Future longitudinal studies are crucial for validating models in real-world settings post-deployment. A collaborative approach among computational pathologists, technologists, industry, and healthcare providers is essential for driving adoption in clinical settings.
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Affiliation(s)
| | | | - Nowair Hussain
- Department of Internal Medicine, Overlook Medical Center, Summit, NJ, USA
| | | | | | - Arjun Reddy
- Applied Mathematics & Statistics Stony Brook University, Stony Brook, NY, USA
| | - Ali Haider
- Department of Artificial Intelligence, Yeshiva University, New York, NY, USA
| | - Bali Kumar Kavitesh
- Centre for Frontier AI Research (CFAR), Agency for Science, Technology, and Research (A*STAR), Singapore, Singapore
| | - Austin Li
- New York Medical College, Valhalla, NY, USA
| | | | | | | | - Myoungmee Babu
- Artificial Intelligence and Mathematics, New York City Department of Education, New York, NY, USA
| | | | - Mill Etienne
- Department of Neurology, New York Medical College, Valhalla, NY, USA
| | - Benson A Babu
- New York Medical College, Valhalla, NY, USA
- Department of Hospital Medicine, Wyckoff, Medical Center, New York, NY, USA
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4
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Liu BL, Haghighi M, Westra WH. Digital Pathology is a Fast and Effective Platform for Providing Head and Neck Pathology Consultations. Am J Surg Pathol 2024:00000478-990000000-00343. [PMID: 38712588 DOI: 10.1097/pas.0000000000002239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Surgical pathology of the head and neck is one of the more challenging areas in all of diagnostic pathology. Its unparalleled diversity and complexity renders it highly vulnerable to diagnostic error compelling unconstrained access to specialized diagnostic expertise. Digital pathology (DP) is a state-of-the-art tool that could facilitate access to specialized expertise, but it is relatively untested in the context of pathology consultations. In a collaboration between Labcorp Dianon and a large academic hospital with subspecialized surgical pathology, DP was implemented to provide the pathology community access to head and neck pathology expertise. From this collaborative experience, glass slides from consecutive consult cases that had been previously diagnosed using DP were reviewed by an expert consultant in a blinded manner following an extended wash-out period. The intraobserver discrepancy rate was recorded. Major discrepancies were defined as those resulting in significant impact on clinical management and/or prognosis, whereas minor discrepancies were those with no impact on care or prognosis. Slides from 57 cases were available for review. The average wash-out period was 19 months. Five discrepancies were recorded (intraobserver concordance rate of 91%). All discrepancies were minor (major discrepancy rate, 0%; minor discrepancy rate, 9%). On appraisal of the discrepant cases, discordant diagnoses were attributed to subjective differences in interpretation rather than objective differences related to the inferiority of DP. DP decreased the median turnaround time by 97% (from 70 h 26 min to 2 h 25 min). DP provides efficient and fast access to expert consultants. The speed of case delivery does not compromise diagnostic precision. Discrepancies are uncommon, minor, and reflect subjective interpretative differences inherent to difficult and ambiguous head and neck cases, and not the inferiority of DP as a diagnostic platform. High concordance can be achieved even for those difficult and complex cases that are concentrated in the consultation practice. This observation carries profound implications regarding universal health care access to specialized diagnostic expertise.
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Affiliation(s)
- Bella L Liu
- Department of Pathology, Molecular and Cell Based Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
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5
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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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Kusta O, Bearman M, Gorur R, Risør T, Brodersen JB, Hoeyer K. Speed, accuracy, and efficiency: The promises and practices of digitization in pathology. Soc Sci Med 2024; 345:116650. [PMID: 38364720 DOI: 10.1016/j.socscimed.2024.116650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 12/17/2023] [Accepted: 02/02/2024] [Indexed: 02/18/2024]
Abstract
Digitization is often presented in policy discourse as a panacea to a multitude of contemporary problems, not least in healthcare. How can policy promises relating to digitization be assessed and potentially countered in particular local contexts? Based on a study in Denmark, we suggest scrutinizing the politics of digitization by comparing policy promises about the future with practitioners' experience in the present. While Denmark is one of the most digitalized countries in the world, digitization of pathology has only recently been given full policy attention. As pathology departments are faced with an increased demand for pathology analysis and a shortage of pathologists, Danish policymakers have put forward digitization as a way to address these challenges. Who is it that wants to digitize pathology, why, and how does digitization unfold in routine work practices? Using online search and document analysis, we identify actors and analyze the policy promises describing expectations associated with digitization. We then use interviews and observations to juxtapose these expectations with observations of everyday pathology practices as experienced by pathologists. We show that policymakers expect digitization to improve speed, patient safety, and diagnostic accuracy, as well as efficiency. In everyday practice, however, digitization does not deliver on these expectations. Fulfillment of policy expectations instead hinges on the types of artificial intelligence (AI) applications that are still to be developed and implemented. Some pathologists remark that AI might work in the easy cases, but this would leave them with only the difficult cases, which they consider too burdensome. Our particular mode of juxtaposing policy and practice throws new light on the political work done by policy promises and helps to explain why the discipline of pathology does not seem to easily lend itself to the digital embrace.
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Affiliation(s)
- Olsi Kusta
- Department of Public Health, University of Copenhagen, Denmark; Centre for Research in Assessment and Digital Learning (CRADLE), Deakin University, Melbourne, Australia; Øster Farimagsgade 5 opg. B, Building: 15-0-11, 1014, Copenhagen, Denmark.
| | - Margaret Bearman
- Centre for Research in Assessment and Digital Learning (CRADLE), Deakin University, Melbourne, Australia; Centre for Research in Assessment and Digital Learning (CRADLE), Deakin University, Level 12, Tower 2, 727 Collins St, Docklands, Melbourne, VIC, 3008, Australia.
| | - Radhika Gorur
- School of Education, Deakin University, Melbourne, Australia; Deakin University (Deakin), 221 Burwood Hwy, Burwood, VIC, 3125, Australia.
| | - Torsten Risør
- Centre for General Practice, Department of Public Health, University of Copenhagen, Denmark; Norwegian Centre for E-health Research, UiT The Arctic University of Norway, Tromsø, Norway; Øster Farimagsgade 5 opg. Q, Building: 24-1, 1014, Copenhagen, Denmark.
| | - John Brandt Brodersen
- Centre for General Practice, Department of Public Health, University of Copenhagen, Denmark; Primary Health Care Research Unit, Region Zealand, Denmark; Øster Farimagsgade 5 opg. Q, Building: 24-1-21, 1014, Copenhagen, Denmark.
| | - Klaus Hoeyer
- Section for Health Services Research, Department of Public Health, University of Copenhagen, Denmark; Øster Farimagsgade 5 opg. B, 1353, København K, Copenhagen, Denmark.
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7
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Koudounas P, Koniaris E, Manolis I, Asvestas P, Kostopoulos S, Cavouras D, Glotsos D. An Experimental Platform for Tomographic Reconstruction of Tissue Images in Brightfield Microscopy. SENSORS (BASEL, SWITZERLAND) 2023; 23:9344. [PMID: 38067718 PMCID: PMC10708601 DOI: 10.3390/s23239344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 11/13/2023] [Accepted: 11/22/2023] [Indexed: 12/18/2023]
Abstract
(1) Background: Reviewing biological material under the microscope is a demanding and time-consuming process, prone to diagnostic pitfalls. In this study, a methodology for tomographic imaging of tissue sections is presented, relying on the idea that each tissue sample has a finite thickness and, therefore, it is possible to create images at different levels within the sample, revealing details that would probably not be seen otherwise. (2) Methods: Optical slicing was possible by developing a custom-made microscopy stage controlled by an ARDUINO. The custom-made stage, besides the normal sample movements that it should provide along the x-, y-, and z- axes, may additionally rotate the sample around the horizontal axis of the microscope slide. This rotation allows the conversion of the optical microscope into a CT geometry, enabling optical slicing of the sample using projection-based tomographic reconstruction algorithms. (3) Results: The resulting images were of satisfactory quality, but they exhibited some artifacts, which are particularly evident in the axial plane images. (4) Conclusions: Using classical tomographic reconstruction algorithms at limited angles, it is possible to investigate the sample at any desired optical plane, revealing information that would be difficult to identify when focusing only on the conventional 2D images.
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Affiliation(s)
- Panteleimon Koudounas
- Medical Image and Signal Processing Laboratory, Department of Biomedical Engineering, University of West Attica, Egaleo, 12243 Athens, Greece; (P.K.); (P.A.); (S.K.)
| | - Efthymios Koniaris
- Department of Pathology, Hippocration General Hospital, 11527 Athens, Greece; (E.K.); (I.M.)
| | - Ioannis Manolis
- Department of Pathology, Hippocration General Hospital, 11527 Athens, Greece; (E.K.); (I.M.)
| | - Panteleimon Asvestas
- Medical Image and Signal Processing Laboratory, Department of Biomedical Engineering, University of West Attica, Egaleo, 12243 Athens, Greece; (P.K.); (P.A.); (S.K.)
| | - Spiros Kostopoulos
- Medical Image and Signal Processing Laboratory, Department of Biomedical Engineering, University of West Attica, Egaleo, 12243 Athens, Greece; (P.K.); (P.A.); (S.K.)
| | - Dionisis Cavouras
- Medical Image and Signal Processing Laboratory, Department of Biomedical Engineering, University of West Attica, Egaleo, 12243 Athens, Greece; (P.K.); (P.A.); (S.K.)
| | - Dimitris Glotsos
- Medical Image and Signal Processing Laboratory, Department of Biomedical Engineering, University of West Attica, Egaleo, 12243 Athens, Greece; (P.K.); (P.A.); (S.K.)
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8
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Schwen LO, Kiehl TR, Carvalho R, Zerbe N, Homeyer A. Digitization of Pathology Labs: A Review of Lessons Learned. J Transl Med 2023; 103:100244. [PMID: 37657651 DOI: 10.1016/j.labinv.2023.100244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 07/18/2023] [Accepted: 08/23/2023] [Indexed: 09/03/2023] Open
Abstract
Pathology laboratories are increasingly using digital workflows. This has the potential of increasing laboratory efficiency, but the digitization process also involves major challenges. Several reports have been published describing the individual experiences of specific laboratories with the digitization process. However, a comprehensive overview of the lessons learned is still lacking. We provide an overview of the lessons learned for different aspects of the digitization process, including digital case management, digital slide reading, and computer-aided slide reading. We also cover metrics used for monitoring performance and pitfalls and corresponding values observed in practice. The overview is intended to help pathologists, information technology decision makers, and administrators to benefit from the experiences of others and to implement the digitization process in an optimal way to make their own laboratory future-proof.
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Affiliation(s)
- Lars Ole Schwen
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany.
| | - Tim-Rasmus Kiehl
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Institute of Pathology, Berlin, Germany
| | - Rita Carvalho
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Institute of Pathology, Berlin, Germany
| | - Norman Zerbe
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Institute of Pathology, Berlin, Germany
| | - André Homeyer
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
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9
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Browning L, Winter L, Cooper RA, Ghosh A, Dytor T, Colling R, Fryer E, Rittscher J, Verrill C. Impact of the transition to digital pathology in a clinical setting on histopathologists in training: experiences and perceived challenges within a UK training region. J Clin Pathol 2023; 76:712-718. [PMID: 35906044 PMCID: PMC10511979 DOI: 10.1136/jcp-2022-208416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 07/08/2022] [Indexed: 11/03/2022]
Abstract
AIMS With increasing utility of digital pathology (DP), it is important to consider the experiences of histopathologists in training, particularly in view of the varied access to DP across a training region and the consequent need to remain competent in reporting on glass slides (GS), which is also relevant for the Fellowship of the Royal College of Pathologists part 2 examination. Understanding the impact of DP on training is limited but could aid development of guidance to support the transition. We sought to investigate the perceptions of histopathologists in training around the introduction of DP for clinical diagnosis within a training region, and the potential training benefits and challenges. METHODS An anonymous online survey was circulated to 24 histopathologists in training within a UK training region, including a hospital which has been fully digitised since summer 2020. RESULTS 19 of 24 histopathologists in training responded (79%). The results indicate that DP offers many benefits to training, including ease of access to cases to enhance individual learning and teaching in general. Utilisation of DP for diagnosis appears variable; almost half of the (10 of 19) respondents with DP experience using it only for ancillary purposes such as measurements, reporting varying levels of confidence in using DP clinically. For those yet to undergo the transition, there was a perceived anxiety regarding digital reporting despite experience with DP in other contexts. CONCLUSIONS The survey evidences the need for provision of training and support for histopathologists in training during the transition to DP, and for consideration of their need to maintain competence and confidence with GS reporting.
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Affiliation(s)
- Lisa Browning
- NIHR Oxford Biomedical Research Centre, Oxford, UK
- Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Lucinda Winter
- Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | | | - Abhisek Ghosh
- Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- Nuffield Department of Clinical Laboratory Sciences, University of Oxford, Oxford, UK
| | - Thomas Dytor
- Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Richard Colling
- Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Eve Fryer
- Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Jens Rittscher
- NIHR Oxford Biomedical Research Centre, Oxford, UK
- Department of Engineering Science, University of Oxford, Oxford, UK
- Big Data Institute, University of Oxford, Oxford, UK
| | - Clare Verrill
- NIHR Oxford Biomedical Research Centre, Oxford, UK
- Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
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10
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Bychkov A, Yoshikawa A, Munkhdelger J, Hori T, Fukuoka J. Integrating cytology into routine digital pathology workflow: a 5-year journey. Virchows Arch 2023; 483:555-559. [PMID: 37119336 DOI: 10.1007/s00428-023-03547-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2023] [Revised: 03/13/2023] [Accepted: 04/20/2023] [Indexed: 05/01/2023]
Abstract
Despite recent advances in digital imaging, the adoption of digital cytology is challenging due to technical limitations. This study describes our 5-year institutional experience with the implementation of digital cytology. The routine cytology workflow included conventional two-step screening by cytotechnologists, followed by sign out by pathologists. We introduced sign out of cytologic cases using a microscopic digital imaging platform operated by cytotechnologists, which allowed for remote review of slides by cytopathologists via video streaming. We also provided cytologic correlation to support the virtual slide-based sign out of histopathological specimens and for a weekly pathology-radiology conference. In addition, positive cytology cases were archived for integration into the laboratory information system and for prospective computational pathology studies. We also summarized lessons learned over the years and outlined our vision for future developments. This unique experience may serve as a role model for other institutions.
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Affiliation(s)
- Andrey Bychkov
- Department of Pathology, Kameda Medical Center, Kamogawa, 929 Higashi-Cho, Kamogawa, Chiba, Japan.
| | - Akira Yoshikawa
- Department of Pathology, Kameda Medical Center, Kamogawa, 929 Higashi-Cho, Kamogawa, Chiba, Japan
| | - Jijgee Munkhdelger
- Department of Pathology, Kameda Medical Center, Kamogawa, 929 Higashi-Cho, Kamogawa, Chiba, Japan
| | - Takashi Hori
- Department of Pathology, Kameda Medical Center, Kamogawa, 929 Higashi-Cho, Kamogawa, Chiba, Japan
| | - Junya Fukuoka
- Department of Pathology, Kameda Medical Center, Kamogawa, 929 Higashi-Cho, Kamogawa, Chiba, Japan
- Department of Pathology Informatics, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan
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11
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Moore JL, Patterson NH, Norris JL, Caprioli RM. Prospective on Imaging Mass Spectrometry in Clinical Diagnostics. Mol Cell Proteomics 2023; 22:100576. [PMID: 37209813 PMCID: PMC10545939 DOI: 10.1016/j.mcpro.2023.100576] [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: 01/19/2023] [Revised: 05/10/2023] [Accepted: 05/12/2023] [Indexed: 05/22/2023] Open
Abstract
Imaging mass spectrometry (IMS) is a molecular technology utilized for spatially driven research, providing molecular maps from tissue sections. This article reviews matrix-assisted laser desorption ionization (MALDI) IMS and its progress as a primary tool in the clinical laboratory. MALDI mass spectrometry has been used to classify bacteria and perform other bulk analyses for plate-based assays for many years. However, the clinical application of spatial data within a tissue biopsy for diagnoses and prognoses is still an emerging opportunity in molecular diagnostics. This work considers spatially driven mass spectrometry approaches for clinical diagnostics and addresses aspects of new imaging-based assays that include analyte selection, quality control/assurance metrics, data reproducibility, data classification, and data scoring. It is necessary to implement these tasks for the rigorous translation of IMS to the clinical laboratory; however, this requires detailed standardized protocols for introducing IMS into the clinical laboratory to deliver reliable and reproducible results that inform and guide patient care.
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Affiliation(s)
| | - Nathan Heath Patterson
- Frontier Diagnostics, Nashville, Tennessee, USA; Vanderbilt University Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee, USA
| | - Jeremy L Norris
- Frontier Diagnostics, Nashville, Tennessee, USA; Vanderbilt University Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee, USA
| | - Richard M Caprioli
- Frontier Diagnostics, Nashville, Tennessee, USA; Vanderbilt University Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee, USA; Departments of Biochemistry, Pharmacology, Chemistry, and Medicine, Vanderbilt University, Nashville, Tennessee, USA.
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12
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Kelleher M, Colling R, Browning L, Roskell D, Roberts-Gant S, Shah KA, Hemsworth H, White K, Rees G, Dolton M, Soares MF, Verrill C. Department Wide Validation in Digital Pathology-Experience from an Academic Teaching Hospital Using the UK Royal College of Pathologists' Guidance. Diagnostics (Basel) 2023; 13:2144. [PMID: 37443538 DOI: 10.3390/diagnostics13132144] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 06/08/2023] [Accepted: 06/14/2023] [Indexed: 07/15/2023] Open
Abstract
AIM we describe our experience of validating departmental pathologists for digital pathology reporting, based on the UK Royal College of Pathologists (RCPath) "Best Practice Recommendations for Implementing Digital Pathology (DP)," at a large academic teaching hospital that scans 100% of its surgical workload. We focus on Stage 2 of validation (prospective experience) prior to full validation sign-off. METHODS AND RESULTS twenty histopathologists completed Stage 1 of the validation process and subsequently completed Stage 2 validation, prospectively reporting a total of 3777 cases covering eight specialities. All cases were initially viewed on digital whole slide images (WSI) with relevant parameters checked on glass slides, and discordances were reconciled before the case was signed out. Pathologists kept an electronic log of the cases, the preferred reporting modality used, and their experiences. At the end of each validation, a summary was compiled and reviewed with a mentor. This was submitted to the DP Steering Group who assessed the scope of cases and experience before sign-off for full validation. A total of 1.3% (49/3777) of the cases had a discordance between WSI and glass slides. A total of 61% (30/49) of the discordances were categorised as a minor error in a supplementary parameter without clinical impact. The most common reasons for diagnostic discordances across specialities included identification and grading of dysplasia, assessment of tumour invasion, identification of small prognostic or diagnostic objects, interpretation of immunohistochemistry/special stains, and mitotic count assessment. Pathologists showed similar mean diagnostic confidences (on Likert scale from 0 to 7) with a mean of 6.8 on digital and 6.9 on glass slide reporting. CONCLUSION we describe one of the first real-world experiences of a department-wide effort to implement, validate, and roll out digital pathology reporting by applying the RCPath Recommendations for Implementing DP. We have shown a very low rate of discordance between WSI and glass slides.
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Affiliation(s)
- Mai Kelleher
- Department of Cellular Pathology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
| | - Richard Colling
- Department of Cellular Pathology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
- Nuffield Department of Surgical Sciences, Oxford University, Oxford OX3 9DU, UK
| | - Lisa Browning
- Department of Cellular Pathology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
| | - Derek Roskell
- Department of Cellular Pathology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
| | - Sharon Roberts-Gant
- Department of Cellular Pathology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
| | - Ketan A Shah
- Department of Cellular Pathology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
| | - Helen Hemsworth
- Department of Cellular Pathology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
| | - Kieron White
- Department of Cellular Pathology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
| | - Gabrielle Rees
- Department of Cellular Pathology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
| | - Monica Dolton
- Nuffield Department of Surgical Sciences, Oxford University, Oxford OX3 9DU, UK
| | - Maria Fernanda Soares
- Department of Cellular Pathology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
| | - Clare Verrill
- Department of Cellular Pathology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
- Nuffield Department of Surgical Sciences, Oxford University, Oxford OX3 9DU, UK
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
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Vu QD, Rajpoot K, Raza SEA, Rajpoot N. Handcrafted Histological Transformer (H2T): Unsupervised representation of whole slide images. Med Image Anal 2023; 85:102743. [PMID: 36702037 DOI: 10.1016/j.media.2023.102743] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 11/30/2022] [Accepted: 01/05/2023] [Indexed: 01/20/2023]
Abstract
Diagnostic, prognostic and therapeutic decision-making of cancer in pathology clinics can now be carried out based on analysis of multi-gigapixel tissue images, also known as whole-slide images (WSIs). Recently, deep convolutional neural networks (CNNs) have been proposed to derive unsupervised WSI representations; these are attractive as they rely less on expert annotation which is cumbersome. However, a major trade-off is that higher predictive power generally comes at the cost of interpretability, posing a challenge to their clinical use where transparency in decision-making is generally expected. To address this challenge, we present a handcrafted framework based on deep CNN for constructing holistic WSI-level representations. Building on recent findings about the internal working of the Transformer in the domain of natural language processing, we break down its processes and handcraft them into a more transparent framework that we term as the Handcrafted Histological Transformer or H2T. Based on our experiments involving various datasets consisting of a total of 10,042 WSIs, the results demonstrate that H2T based holistic WSI-level representations offer competitive performance compared to recent state-of-the-art methods and can be readily utilized for various downstream analysis tasks. Finally, our results demonstrate that the H2T framework can be up to 14 times faster than the Transformer models.
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Affiliation(s)
- Quoc Dang Vu
- Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, UK
| | - Kashif Rajpoot
- School of Computer Science, University of Birmingham, UK
| | - Shan E Ahmed Raza
- Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, UK
| | - Nasir Rajpoot
- Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, UK; The Alan Turing Institute, London, UK; Department of Pathology, University Hospitals Coventry & Warwickshire, UK.
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14
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[Impact of digital pathology implementation in Reunion Island]. Bull Cancer 2023; 110:433-439. [PMID: 36803978 DOI: 10.1016/j.bulcan.2023.01.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 01/10/2023] [Accepted: 01/13/2023] [Indexed: 02/17/2023]
Abstract
In recent decades, the major scientific advances in oncology have complexified anatomic pathology practice. Collaboration with local and national pathologists is essential for ensuring a high-quality diagnosis. Anatomic pathology is undergoing a digital revolution that implements whole slide imaging in routine pathologic diagnosis. Digital pathology improves diagnostic efficiency, allows remote peer review and consultations (telepathology), and enables the use of artificial intelligence. The implementation of digital pathology is of particular interest in isolated territories, facilitating access to expertise and therefore to specialized diagnosis. This review discusses the impact of digital pathology implementation in French overseas territories, particularly in Reunion Island.
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15
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Vassilakos P, Clarke H, Murtas M, Stegmüller T, Wisniak A, Akhoundova F, Sando Z, Orock GE, Sormani J, Thiran JP, Petignat P. Telecytologic diagnosis of cervical smears for triage of self-sampled human papillomavirus-positive women in a resource-limited setting: concept development before implementation. J Am Soc Cytopathol 2023; 12:170-180. [PMID: 36922319 DOI: 10.1016/j.jasc.2023.02.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 01/12/2023] [Accepted: 02/08/2023] [Indexed: 02/17/2023]
Abstract
INTRODUCTION Cytology is an option for triaging human papillomavirus (HPV)-positive women. The interpretation of cytologic slides requires expertise and financial resources that are not always available in resource-limited settings. A solution could be offered by manual preparation and digitization of slides on site for real-time remote cytologic diagnosis by specialists. In the present study, we evaluated the operational feasibility and cost of manual preparation and digitization of thin-layer slides and the diagnostic accuracy of screening with virtual microscopy. MATERIALS AND METHODS Operational feasibility was evaluated on 30 cervical samples obtained during colposcopy. The simplicity of the process and cellularity and quality of digitized thin-layer slides were evaluated. The diagnostic accuracy of digital versus glass slides to detect cervical intraepithelial neoplasia grade 2 or worse was assessed using a cohort of 264 HPV-positive Cameroonian women aged 30 to 49 years. The histologic results served as the reference standard. RESULTS Manual preparation was found to be feasible and economically viable. The quality characteristics of the digital slides were satisfactory, and the mean cellularity was 6078 squamous cells per slide. When using the atypical squamous cells of undetermined significance or worse threshold for positivity, the diagnostic performance of screening digital slides was not significantly different statistically compared with the same set of slides screened using a light microscope (P = 0.26). CONCLUSIONS We have developed an innovative triage concept for HPV-positive women. A quality-ensured telecytologic diagnosis could be an effective solution in areas with a shortage of specialists, applying a same day "test-triage-treat" approach. Our results warrant further on-site clinical validation in a large prospective screening trial.
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Affiliation(s)
- Pierre Vassilakos
- Gynecology Division, Geneva University Hospital, Geneva, Switzerland; Geneva Foundation for Medical Education and Research, Geneva, Switzerland
| | - Holly Clarke
- Gynecology Division, Geneva University Hospital, Geneva, Switzerland.
| | - Micol Murtas
- Gynecology Division, Geneva University Hospital, Geneva, Switzerland
| | - Thomas Stegmüller
- Swiss Federal Institute of Technology Lausanne, Lausanne, Switzerland
| | - Ania Wisniak
- Gynecology Division, Geneva University Hospital, Geneva, Switzerland
| | - Farida Akhoundova
- Gynecology Division, Geneva University Hospital, Geneva, Switzerland
| | - Zacharie Sando
- Gyneco-Obstetrics and Paediatric Hospital, Yaoundé, Cameroon
| | | | - Jessica Sormani
- Gynecology Division, Geneva University Hospital, Geneva, Switzerland
| | | | - Patrick Petignat
- Gynecology Division, Geneva University Hospital, Geneva, Switzerland
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16
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Fischer K, Dubben B, Debrah LB, Kuehlwein JM, Ricchiuto A, Debrah AY, Hoerauf A, Weil GJ, Fischer PU, Klarmann-Schulz U. Histopathological evaluation of Onchocerca volvulus nodules by microscopy and by digital image analysis for the study of macrofilaricidal drug efficacy. Front Med (Lausanne) 2023; 10:1099926. [PMID: 36817770 PMCID: PMC9932808 DOI: 10.3389/fmed.2023.1099926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 01/17/2023] [Indexed: 02/05/2023] Open
Abstract
Background Novel drugs or drug combinations that kill or permanently sterilize adult Onchocerca volvulus worms would be very helpful for treatment and elimination of onchocerciasis. In absence of a reliable biomarker for viable adult worms, histopathological assessment of worms within onchocercal nodules is a standard method to determine macrofilaricidal activity. The goal of the present study was to determine the agreement between two independent experts in the analysis of nodule sections and to assess the value of digital imaging as a means of standardizing the analysis. Material and methods Two expert microscopists independently assessed 605 nodules by direct microscopy. At least two sections with two different stains hematoxylin & eosin (H&E, APR immunostain) of paraffin-embedded, ethanol-fixed whole-nodule cross-sections were analyzed. After variables were identified prone to observer discrepancies, we performed a second study to compare consolidated results for 100 nodules obtained by the two readers by microscopy and by analysis of scanned, high resolution digital images (20x magnification). The last data set analyzed was a quality panel of 100 nodules that has been previously examined by microscopy, and included additional immunostains for Wolbachia endobacteria. These slides were digitalized, read by the two assessors and results were compared with original microscopy results. Results The degree of agreement between assessors varied for different parameters. Agreement for female worm counts in nodules was approximately 80%, while agreement regarding female worm viability was 98%. There were no major differences observed between results obtained by microscopy or digital images. Good agreement for important parameters was also observed for the nodules of the quality panel. Conclusion Nodule analysis by experienced microscopists was reproducible with regard to important parameters such as identification of living female worms or detection of normal embryogenesis. Assessments varied more for other parameters, and we recommend continued use of two independent readers for detailed analyzes. Analysis of scanned images provided similar results to direct microscopy. This facilitates training and comparison of nodule findings by readers in different locations. Analysis of high quality digital images that can be viewed remotely should improve the quality and availability of nodule assessments that are primary endpoints for onchocerciasis clinical trials.
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Affiliation(s)
- Kerstin Fischer
- Division of Infectious Diseases, Department of Medicine, School of Medicine, Washington University, St. Louis, MO, United States
| | - Bettina Dubben
- Institute for Medical Microbiology, Immunology and Parasitology (IMMIP), University Hospital Bonn, Bonn, Germany
| | - Linda B. Debrah
- Kumasi Center for Collaborative Research (KCCR), Kumasi, Ghana,Department of Clinical Microbiology, School of Medicine and Dentistry, Kwame Nkrumah University of Science and Technology (KNUST), Kumasi, Ghana
| | - Janina M. Kuehlwein
- Institute for Medical Microbiology, Immunology and Parasitology (IMMIP), University Hospital Bonn, Bonn, Germany,German Center for Infection Research (DZIF), Bonn-Cologne site, Bonn, Germany
| | - Arcangelo Ricchiuto
- Institute for Medical Microbiology, Immunology and Parasitology (IMMIP), University Hospital Bonn, Bonn, Germany,Institute of Medical Biometry, Informatics and Epidemiology (IMBIE), University Hospital Bonn, Bonn, Germany
| | - Alexander Y. Debrah
- Kumasi Center for Collaborative Research (KCCR), Kumasi, Ghana,Faculty of Allied Health Sciences, Kwame Nkrumah University of Science and Technology (KNUST), Kumasi, Ghana
| | - Achim Hoerauf
- Institute for Medical Microbiology, Immunology and Parasitology (IMMIP), University Hospital Bonn, Bonn, Germany,German Center for Infection Research (DZIF), Bonn-Cologne site, Bonn, Germany
| | - Gary J. Weil
- Division of Infectious Diseases, Department of Medicine, School of Medicine, Washington University, St. Louis, MO, United States
| | - Peter U. Fischer
- Division of Infectious Diseases, Department of Medicine, School of Medicine, Washington University, St. Louis, MO, United States
| | - Ute Klarmann-Schulz
- Institute for Medical Microbiology, Immunology and Parasitology (IMMIP), University Hospital Bonn, Bonn, Germany,German Center for Infection Research (DZIF), Bonn-Cologne site, Bonn, Germany,Institute of Medical Biometry, Informatics and Epidemiology (IMBIE), University Hospital Bonn, Bonn, Germany,*Correspondence: Ute Klarmann-Schulz,
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Zhao J, Han Z, Ma Y, Liu H, Yang T. Research progress in digital pathology: A bibliometric and visual analysis based on Web of Science. Pathol Res Pract 2022; 240:154171. [DOI: 10.1016/j.prp.2022.154171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 10/12/2022] [Accepted: 10/13/2022] [Indexed: 11/05/2022]
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Naoumov NV, Brees D, Loeffler J, Chng E, Ren Y, Lopez P, Tai D, Lamle S, Sanyal AJ. Digital pathology with artificial intelligence analyses provides greater insights into treatment-induced fibrosis regression in NASH. J Hepatol 2022; 77:1399-1409. [PMID: 35779659 DOI: 10.1016/j.jhep.2022.06.018] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 05/21/2022] [Accepted: 06/10/2022] [Indexed: 12/12/2022]
Abstract
BACKGROUND & AIMS Liver fibrosis is a key prognostic determinant for clinical outcomes in non-alcoholic steatohepatitis (NASH). Current scoring systems have limitations, especially in assessing fibrosis regression. Second harmonic generation/two-photon excitation fluorescence (SHG/TPEF) microscopy with artificial intelligence analyses provides standardized evaluation of NASH features, especially liver fibrosis and collagen fiber quantitation on a continuous scale. This approach was applied to gain in-depth understanding of fibrosis dynamics after treatment with tropifexor (TXR), a non-bile acid farnesoid X receptor agonist in patients participating in the FLIGHT-FXR study (NCT02855164). METHOD Unstained sections from 198 liver biopsies (paired: baseline and end-of-treatment) from 99 patients with NASH (fibrosis stage F2 or F3) who received placebo (n = 34), TXR 140 μg (n = 37), or TXR 200 μg (n = 28) for 48 weeks were examined. Liver fibrosis (qFibrosis®), hepatic fat (qSteatosis®), and ballooned hepatocytes (qBallooning®) were quantitated using SHG/TPEF microscopy. Changes in septa morphology, collagen fiber parameters, and zonal distribution within liver lobules were also quantitatively assessed. RESULTS Digital analyses revealed treatment-associated reductions in overall liver fibrosis (qFibrosis®), unlike conventional microscopy, as well as marked regression in perisinusoidal fibrosis in patients who had either F2 or F3 fibrosis at baseline. Concomitant zonal quantitation of fibrosis and steatosis revealed that patients with greater qSteatosis reduction also have the greatest reduction in perisinusoidal fibrosis. Regressive changes in septa morphology and reduction in septa parameters were observed almost exclusively in F3 patients, who were adjudged as 'unchanged' with conventional scoring. CONCLUSION Fibrosis regression following hepatic fat reduction occurs initially in the perisinusoidal regions, around areas of steatosis reduction. Digital pathology provides new insights into treatment-induced fibrosis regression in NASH, which are not captured by current staging systems. LAY SUMMARY The degree of liver fibrosis (tissue scarring) in non-alcoholic steatohepatitis (NASH) is the main predictor of negative clinical outcomes. Accurate assessment of the quantity and architecture of liver fibrosis is fundamental for patient enrolment in NASH clinical trials and for determining treatment efficacy. Using digital microscopy with artificial intelligence analyses, the present study demonstrates that this novel approach has greater sensitivity in demonstrating treatment-induced reversal of fibrosis in the liver than current systems. Furthermore, additional details are obtained regarding the pathogenesis of NASH disease and the effects of therapy.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Arun J Sanyal
- Virginia Commonwealth University School of Medicine, Richmond, United States
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19
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Lesko P, Chovanec M, Mego M. Biomarkers of disease recurrence in stage I testicular germ cell tumours. Nat Rev Urol 2022; 19:637-658. [PMID: 36028719 DOI: 10.1038/s41585-022-00624-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/29/2022] [Indexed: 11/09/2022]
Abstract
Stage I testicular cancer is a disease restricted to the testicle. After orchiectomy, patients are considered to be without disease; however, the tumour is prone to relapse in ~4-50% of patients. Current predictive markers of relapse, which are tumour size and invasion to rete testis (in seminoma) or lymphovascular invasion (in non-seminoma), have limited clinical utility and are unable to correctly predict relapse in a substantial proportion of patients. Adjuvant therapeutic strategies based on available biomarkers can lead to overtreatment of 50-85% of patients. Discovery and implementation of novel biomarkers into treatment decision making will help to reduce the burden of adjuvant treatments and improve patient selection for adjuvant therapy.
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Affiliation(s)
- Peter Lesko
- 2nd Department of Oncology, Faculty of Medicine, Comenius University and National Cancer Institute, Bratislava, Slovakia
| | - Michal Chovanec
- 2nd Department of Oncology, Faculty of Medicine, Comenius University and National Cancer Institute, Bratislava, Slovakia
| | - Michal Mego
- 2nd Department of Oncology, Faculty of Medicine, Comenius University and National Cancer Institute, Bratislava, Slovakia.
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20
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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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21
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Browning L, White K, Siiankoski D, Colling R, Roskell D, Fryer E, Hemsworth H, Roberts-Gant S, Roelofsen R, Rittscher J, Verrill C. RFID analysis of the complexity of cellular pathology workflow—An opportunity for digital pathology. Front Med (Lausanne) 2022; 9:933933. [PMID: 35979219 PMCID: PMC9377528 DOI: 10.3389/fmed.2022.933933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Accepted: 07/07/2022] [Indexed: 12/02/2022] Open
Abstract
Digital pathology (DP) offers potential for time efficiency gains over an analog workflow however, to date, evidence supporting this claim is relatively lacking. Studies available concentrate on specific workflow points such as diagnostic reporting time, rather than overall efficiencies in slide logistics that might be expected. This is in part a result of the complexity and variation in analog working, and the challenge therefore in capturing this. We have utilized RFID technology to conduct a novel study capturing the movement of diagnostic cases within the analog pathway in a large teaching hospital setting, thus providing benchmark data for potential efficiency gains with DP. This technology overcomes the need to manually record data items and has facilitated the capture of both the physical journey of a case and the time associated with relevant components of the analog pathway predicted to be redundant in the digital setting. RFID tracking of 1,173 surgical pathology cases and over 30 staff in an analog cellular pathology workflow illustrates the complexity of the physical movement of slides within the department, which impacts on case traceability within the system. Detailed analysis of over 400 case journeys highlights redundant periods created by batching of slides at workflow points, including potentially 2–3 h for a case to become available for reporting after release from the lab, and variable lag-times prior to collection for reporting, and provides an illustration of patterns of lab and pathologist working within the analog setting. This study supports the challenge in evidencing efficiency gains to be anticipated with DP in the context of the variation and complexity of the analog pathway, but also evidences the efficiency gains that may be expected through a greater understanding of patterns of working and movement of cases. Such data may benefit other departments building a business case for DP.
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Affiliation(s)
- Lisa Browning
- Department of Cellular Pathology, Oxford University Hospitals NHS Trust, John Radcliffe Hospital, Oxford, United Kingdom
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
- *Correspondence: Lisa Browning
| | - Kieron White
- Department of Cellular Pathology, Oxford University Hospitals NHS Trust, John Radcliffe Hospital, Oxford, United Kingdom
| | - Darrin Siiankoski
- Department of Cellular Pathology, Oxford University Hospitals NHS Trust, John Radcliffe Hospital, Oxford, United Kingdom
| | - Richard Colling
- Department of Cellular Pathology, Oxford University Hospitals NHS Trust, John Radcliffe Hospital, Oxford, United Kingdom
- Nuffield Department of Surgical Sciences, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
| | - Derek Roskell
- Department of Cellular Pathology, Oxford University Hospitals NHS Trust, John Radcliffe Hospital, Oxford, United Kingdom
| | - Eve Fryer
- Department of Cellular Pathology, Oxford University Hospitals NHS Trust, John Radcliffe Hospital, Oxford, United Kingdom
| | - Helen Hemsworth
- Department of Cellular Pathology, Oxford University Hospitals NHS Trust, John Radcliffe Hospital, Oxford, United Kingdom
| | - Sharon Roberts-Gant
- Department of Cellular Pathology, Oxford University Hospitals NHS Trust, John Radcliffe Hospital, Oxford, United Kingdom
| | - Ruud Roelofsen
- Philips Digital and Computational Pathology, Precision Diagnosis Solutions, Best, Netherlands
| | - Jens Rittscher
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
- Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, United Kingdom
| | - Clare Verrill
- Department of Cellular Pathology, Oxford University Hospitals NHS Trust, John Radcliffe Hospital, Oxford, United Kingdom
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
- Nuffield Department of Surgical Sciences, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
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22
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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: 0] [Impact Index Per Article: 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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23
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Teranikar T, Lim J, Ijaseun T, Lee J. Development of Planar Illumination Strategies for Solving Mysteries in the Sub-Cellular Realm. Int J Mol Sci 2022; 23:ijms23031643. [PMID: 35163562 PMCID: PMC8835835 DOI: 10.3390/ijms23031643] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 12/22/2021] [Accepted: 01/25/2022] [Indexed: 02/04/2023] Open
Abstract
Optical microscopy has vastly expanded the frontiers of structural and functional biology, due to the non-invasive probing of dynamic volumes in vivo. However, traditional widefield microscopy illuminating the entire field of view (FOV) is adversely affected by out-of-focus light scatter. Consequently, standard upright or inverted microscopes are inept in sampling diffraction-limited volumes smaller than the optical system's point spread function (PSF). Over the last few decades, several planar and structured (sinusoidal) illumination modalities have offered unprecedented access to sub-cellular organelles and 4D (3D + time) image acquisition. Furthermore, these optical sectioning systems remain unaffected by the size of biological samples, providing high signal-to-noise (SNR) ratios for objective lenses (OLs) with long working distances (WDs). This review aims to guide biologists regarding planar illumination strategies, capable of harnessing sub-micron spatial resolution with a millimeter depth of penetration.
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Affiliation(s)
| | | | | | - Juhyun Lee
- Correspondence: ; Tel.: +1-817-272-6534; Fax: +1-817-272-2251
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24
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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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25
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Mehrvar S, Himmel LE, Babburi P, Goldberg AL, Guffroy M, Janardhan K, Krempley AL, Bawa B. Deep Learning Approaches and Applications in Toxicologic Histopathology: Current Status and Future Perspectives. J Pathol Inform 2021; 12:42. [PMID: 34881097 PMCID: PMC8609289 DOI: 10.4103/jpi.jpi_36_21] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 07/18/2021] [Indexed: 12/13/2022] Open
Abstract
Whole slide imaging enables the use of a wide array of digital image analysis tools that are revolutionizing pathology. Recent advances in digital pathology and deep convolutional neural networks have created an enormous opportunity to improve workflow efficiency, provide more quantitative, objective, and consistent assessments of pathology datasets, and develop decision support systems. Such innovations are already making their way into clinical practice. However, the progress of machine learning - in particular, deep learning (DL) - has been rather slower in nonclinical toxicology studies. Histopathology data from toxicology studies are critical during the drug development process that is required by regulatory bodies to assess drug-related toxicity in laboratory animals and its impact on human safety in clinical trials. Due to the high volume of slides routinely evaluated, low-throughput, or narrowly performing DL methods that may work well in small-scale diagnostic studies or for the identification of a single abnormality are tedious and impractical for toxicologic pathology. Furthermore, regulatory requirements around good laboratory practice are a major hurdle for the adoption of DL in toxicologic pathology. This paper reviews the major DL concepts, emerging applications, and examples of DL in toxicologic pathology image analysis. We end with a discussion of specific challenges and directions for future research.
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Affiliation(s)
- Shima Mehrvar
- Preclinical Safety, AbbVie Inc., North Chicago, IL, USA
| | | | - Pradeep Babburi
- Business Technology Solutions, AbbVie Inc., North Chicago, IL, USA
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Digital Pathology Transformation in a Supraregional Germ Cell Tumour Network. Diagnostics (Basel) 2021; 11:diagnostics11122191. [PMID: 34943429 PMCID: PMC8700654 DOI: 10.3390/diagnostics11122191] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 11/16/2021] [Accepted: 11/23/2021] [Indexed: 01/21/2023] Open
Abstract
Background: In this article we share our experience of creating a digital pathology (DP) supraregional germ cell tumour service, including full digitisation of the central laboratory. Methods: DP infrastructure (Philips) was deployed across our hospital network to allow full central digitisation with partial digitisation of two peripheral sites in the supraregional testis germ cell tumour network. We used a survey-based approach to capture the quantitative and qualitative experiences of the multidisciplinary teams involved. Results: The deployment enabled case sharing for the purposes of diagnostic reporting, second opinion, and supraregional review. DP was seen as a positive step forward for the departments involved, and for the wider germ cell tumour network, and was completed without significant issues. Whilst there were challenges, the transition to DP was regarded as worthwhile, and examples of benefits to patients are already recognised. Conclusion: Pathology networks, including highly specialised services, such as in this study, are ideally suited to be digitised. We highlight many of the benefits but also the challenges that must be overcome for such clinical transformation. Overall, from the survey, the change was seen as universally positive for our service and highlights the importance of engagement of the whole team to achieve success.
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Fraggetta F, L’Imperio V, Ameisen D, Carvalho R, Leh S, Kiehl TR, Serbanescu M, Racoceanu D, Della Mea V, Polonia A, Zerbe N, Eloy C. Best Practice Recommendations for the Implementation of a Digital Pathology Workflow in the Anatomic Pathology Laboratory by the European Society of Digital and Integrative Pathology (ESDIP). Diagnostics (Basel) 2021; 11:2167. [PMID: 34829514 PMCID: PMC8623219 DOI: 10.3390/diagnostics11112167] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 11/15/2021] [Accepted: 11/19/2021] [Indexed: 12/12/2022] Open
Abstract
The interest in implementing digital pathology (DP) workflows to obtain whole slide image (WSI) files for diagnostic purposes has increased in the last few years. The increasing performance of technical components and the Food and Drug Administration (FDA) approval of systems for primary diagnosis led to increased interest in applying DP workflows. However, despite this revolutionary transition, real world data suggest that a fully digital approach to the histological workflow has been implemented in only a minority of pathology laboratories. The objective of this study is to facilitate the implementation of DP workflows in pathology laboratories, helping those involved in this process of transformation to identify: (a) the scope and the boundaries of the DP transformation; (b) how to introduce automation to reduce errors; (c) how to introduce appropriate quality control to guarantee the safety of the process and (d) the hardware and software needed to implement DP systems inside the pathology laboratory. The European Society of Digital and Integrative Pathology (ESDIP) provided consensus-based recommendations developed through discussion among members of the Scientific Committee. The recommendations are thus based on the expertise of the panel members and on the agreement obtained after virtual meetings. Prior to publication, the recommendations were reviewed by members of the ESDIP Board. The recommendations comprehensively cover every step of the implementation of the digital workflow in the anatomic pathology department, emphasizing the importance of interoperability, automation and tracking of the entire process before the introduction of a scanning facility. Compared to the available national and international guidelines, the present document represents a practical, handy reference for the correct implementation of the digital workflow in Europe.
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Affiliation(s)
- Filippo Fraggetta
- European Society of Digital and Integrative Pathology (ESDIP), Rua da Constituição n°668, 1° Esq/Traseiras, 4200-194 Porto, Portugal; (F.F.); (V.L.); (D.A.); (R.C.); (S.L.); (T.-R.K.); (M.S.); (D.R.); (V.D.M.); (A.P.); (N.Z.)
- Pathology Unit, “Gravina” Hospital, Caltagirone, ASP Catania, Via Portosalvo 1, 95041 Caltagirone, Italy
| | - Vincenzo L’Imperio
- European Society of Digital and Integrative Pathology (ESDIP), Rua da Constituição n°668, 1° Esq/Traseiras, 4200-194 Porto, Portugal; (F.F.); (V.L.); (D.A.); (R.C.); (S.L.); (T.-R.K.); (M.S.); (D.R.); (V.D.M.); (A.P.); (N.Z.)
- Department of Medicine and Surgery, Pathology, ASST Monza, San Gerardo Hospital, University of Milano-Bicocca, 20900 Monza, Italy
| | - David Ameisen
- European Society of Digital and Integrative Pathology (ESDIP), Rua da Constituição n°668, 1° Esq/Traseiras, 4200-194 Porto, Portugal; (F.F.); (V.L.); (D.A.); (R.C.); (S.L.); (T.-R.K.); (M.S.); (D.R.); (V.D.M.); (A.P.); (N.Z.)
- Imginit SAS, 152 Boulevard du Montparnasse, 75014 Paris, France
| | - Rita Carvalho
- European Society of Digital and Integrative Pathology (ESDIP), Rua da Constituição n°668, 1° Esq/Traseiras, 4200-194 Porto, Portugal; (F.F.); (V.L.); (D.A.); (R.C.); (S.L.); (T.-R.K.); (M.S.); (D.R.); (V.D.M.); (A.P.); (N.Z.)
- Charité–Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Pathology, Charitéplatz 1, 10117 Berlin, Germany
| | - Sabine Leh
- European Society of Digital and Integrative Pathology (ESDIP), Rua da Constituição n°668, 1° Esq/Traseiras, 4200-194 Porto, Portugal; (F.F.); (V.L.); (D.A.); (R.C.); (S.L.); (T.-R.K.); (M.S.); (D.R.); (V.D.M.); (A.P.); (N.Z.)
- Department of Pathology, Haukeland University Hospital, Jonas Lies Vei 65, 5021 Bergen, Norway
- Department of Clinical Medicine, University of Bergen, Jonas Lies Vei 87, 5021 Bergen, Norway
| | - Tim-Rasmus Kiehl
- European Society of Digital and Integrative Pathology (ESDIP), Rua da Constituição n°668, 1° Esq/Traseiras, 4200-194 Porto, Portugal; (F.F.); (V.L.); (D.A.); (R.C.); (S.L.); (T.-R.K.); (M.S.); (D.R.); (V.D.M.); (A.P.); (N.Z.)
- Charité–Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Pathology, Charitéplatz 1, 10117 Berlin, Germany
| | - Mircea Serbanescu
- European Society of Digital and Integrative Pathology (ESDIP), Rua da Constituição n°668, 1° Esq/Traseiras, 4200-194 Porto, Portugal; (F.F.); (V.L.); (D.A.); (R.C.); (S.L.); (T.-R.K.); (M.S.); (D.R.); (V.D.M.); (A.P.); (N.Z.)
- Department of Medical Informatics and Biostatistics, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania
| | - Daniel Racoceanu
- European Society of Digital and Integrative Pathology (ESDIP), Rua da Constituição n°668, 1° Esq/Traseiras, 4200-194 Porto, Portugal; (F.F.); (V.L.); (D.A.); (R.C.); (S.L.); (T.-R.K.); (M.S.); (D.R.); (V.D.M.); (A.P.); (N.Z.)
- Sorbonne Université, Institut du Cerveau—Paris Brain Institute—ICM, Inserm, CNRS, APHP, Inria Team “Aramis”, Hôpital de la Pitié Salpêtrière, 75013 Paris, France
| | - Vincenzo Della Mea
- European Society of Digital and Integrative Pathology (ESDIP), Rua da Constituição n°668, 1° Esq/Traseiras, 4200-194 Porto, Portugal; (F.F.); (V.L.); (D.A.); (R.C.); (S.L.); (T.-R.K.); (M.S.); (D.R.); (V.D.M.); (A.P.); (N.Z.)
- Department of Mathematics, Computer Science and Physics, University of Udine, 33100 Udine, Italy
| | - Antonio Polonia
- European Society of Digital and Integrative Pathology (ESDIP), Rua da Constituição n°668, 1° Esq/Traseiras, 4200-194 Porto, Portugal; (F.F.); (V.L.); (D.A.); (R.C.); (S.L.); (T.-R.K.); (M.S.); (D.R.); (V.D.M.); (A.P.); (N.Z.)
- Ipatimup Diagnostics, Institute of Molecular Pathology and Immunology of Porto University (Ipatimup), 4200-804 Porto, Portugal
- Medical Faculty, University of Porto, 4200-319 Porto, Portugal
| | - Norman Zerbe
- European Society of Digital and Integrative Pathology (ESDIP), Rua da Constituição n°668, 1° Esq/Traseiras, 4200-194 Porto, Portugal; (F.F.); (V.L.); (D.A.); (R.C.); (S.L.); (T.-R.K.); (M.S.); (D.R.); (V.D.M.); (A.P.); (N.Z.)
- Charité–Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Pathology, Charitéplatz 1, 10117 Berlin, Germany
| | - Catarina Eloy
- European Society of Digital and Integrative Pathology (ESDIP), Rua da Constituição n°668, 1° Esq/Traseiras, 4200-194 Porto, Portugal; (F.F.); (V.L.); (D.A.); (R.C.); (S.L.); (T.-R.K.); (M.S.); (D.R.); (V.D.M.); (A.P.); (N.Z.)
- Ipatimup Diagnostics, Institute of Molecular Pathology and Immunology of Porto University (Ipatimup), 4200-804 Porto, Portugal
- Medical Faculty, University of Porto, 4200-319 Porto, Portugal
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28
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Shi W, Georgiou P, Akram A, Proute MC, Serhiyenia T, Kerolos ME, Pradeep R, Kothur NR, Khan S. Diagnostic Pitfalls of Digital Microscopy Versus Light Microscopy in Gastrointestinal Pathology: A Systematic Review. Cureus 2021; 13:e17116. [PMID: 34548958 PMCID: PMC8437006 DOI: 10.7759/cureus.17116] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 08/11/2021] [Indexed: 12/15/2022] Open
Abstract
Digital microscopy (DM) is one of the cutting-edge advances in pathology, which entails improved efficiency, diagnostic advantages, and potential application in virtual diagnosis, particularly in the current era of the coronavirus disease (COVID-19) pandemic. However, the diagnostic challenges are the remaining concerns for its wider adoption by pathologists, and these concerns should be addressed in a specific subspecialty. We aim to identify the common diagnostic pitfalls of whole slide imaging (WSI), one modality of DM, in gastrointestinal (GI) pathology. From validating studies of primary diagnosis performance, we included 16 records with features on GI cases involved, at least two weeks wash-out periods, and more than 60 case study designs. A tailored quality appraisal assessment was utilized to evaluate the risks of bias for these diagnostic accuracy studies. Furthermore, due to the highly heterogeneous studies and unstandardized definition of discordance, we extract the discordant cases in GI pathology and calculate the discrepant rate, resulting from 0.5% to 64.28%. Targeting discrepancy cases between digital microscopy and light microscopy, we demonstrate five main diagnostic pitfalls regarding WSI as follows: additional time to review slides in WSI, hard to identify dysplasia nucleus, missed organisms like Helicobacter pylori (H. pylori), specific cell recognitions, and technical issues. After detailed reviews and analysis, we generate two essential suggestions for further GI cases signing out by DM. One is to use systematized 20x scans for diagnostic workouts and requesting 40x or even 60x scans for challenging cases; another is that a high-volume slides training should be set before the real clinical application of WSI for primary diagnosis, particularly in GI pathology.
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Affiliation(s)
- Wangpan Shi
- Pathology, California Institute of Behavioral Neurosciences & Psychology, Fairfield, USA
| | - Petros Georgiou
- Internal Medicine, California Institute of Behavioral Neurosciences & Psychology, Fairfield, USA.,Department of Oncology, Oxford University, Oxford, GBR
| | - Aqsa Akram
- Internal Medicine, California Institute of Behavioral Neurosciences & Psychology, Fairfield, USA
| | - Matthew C Proute
- Internal Medicine, California Institute of Behavioral Neurosciences & Psychology, Fairfield, USA
| | - Tatsiana Serhiyenia
- Internal Medicine, California Institute of Behavioral Neurosciences & Psychology, Fairfield, USA
| | - Mina E Kerolos
- General Medicine, California Institute of Behavioral Neurosciences & Psychology, Fairfield, USA
| | - Roshini Pradeep
- Internal Medicine, California Institute of Behavioral Neurosciences & Psychology, Fairfield, USA
| | - Nageshwar R Kothur
- Internal Medicine, California Institute of Behavioral Neurosciences & Psychology, Fairfield, USA
| | - Safeera Khan
- Internal Medicine, California Institute of Behavioral Neurosciences & Psychology, Fairfield, USA
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29
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Bertram CA, Stathonikos N, Donovan TA, Bartel A, Fuchs-Baumgartinger A, Lipnik K, van Diest PJ, Bonsembiante F, Klopfleisch R. Validation of digital microscopy: Review of validation methods and sources of bias. Vet Pathol 2021; 59:26-38. [PMID: 34433345 PMCID: PMC8761960 DOI: 10.1177/03009858211040476] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Digital microscopy (DM) is increasingly replacing traditional light microscopy (LM) for performing routine diagnostic and research work in human and veterinary pathology. The DM workflow encompasses specimen preparation, whole-slide image acquisition, slide retrieval, and the workstation, each of which has the potential (depending on the technical parameters) to introduce limitations and artifacts into microscopic examination by pathologists. Performing validation studies according to guidelines established in human pathology ensures that the best-practice approaches for patient care are not deteriorated by implementing DM. Whereas current publications on validation studies suggest an overall high reliability of DM, each laboratory is encouraged to perform an individual validation study to ensure that the DM workflow performs as expected in the respective clinical or research environment. With the exception of validation guidelines developed by the College of American Pathologists in 2013 and its update in 2021, there is no current review of the application of methods fundamental to validation. We highlight that there is high methodological variation between published validation studies, each having advantages and limitations. The diagnostic concordance rate between DM and LM is the most relevant outcome measure, which is influenced (regardless of the viewing modality used) by different sources of bias including complexity of the cases examined, diagnostic experience of the study pathologists, and case recall. Here, we review 3 general study designs used for previous publications on DM validation as well as different approaches for avoiding bias.
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Affiliation(s)
- Christof A Bertram
- University of Veterinary Medicine, Vienna, Austria.,Freie Universität Berlin, Berlin, Germany
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30
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Browning L, Colling R, Verrill C. WHO/ISUP grading of clear cell renal cell carcinoma and papillary renal cell carcinoma; validation of grading on the digital pathology platform and perspectives on reproducibility of grade. Diagn Pathol 2021; 16:75. [PMID: 34419085 PMCID: PMC8380382 DOI: 10.1186/s13000-021-01130-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 07/12/2021] [Indexed: 11/10/2022] Open
Abstract
Background There are recognised potential pitfalls in digital diagnosis in urological pathology, including the grading of dysplasia. The World Health Organisation/International Society of Urological Pathology (WHO/ISUP) grading system for renal cell carcinoma (RCC) is prognostically important in clear cell RCC (CCRCC) and papillary RCC (PRCC), and is included in risk stratification scores for CCRCC, thus impacting on patient management. To date there are no systematic studies examining the concordance of WHO/ISUP grading between digital pathology (DP) and glass slide (GS) images. We present a validation study examining intraobserver agreement in WHO/ISUP grade of CCRCC and PRCC. Methods Fifty CCRCCs and 10 PRCCs were graded (WHO/ISUP system) by three specialist uropathologists on three separate occasions (DP once then two GS assessments; GS1 and GS2) separated by wash-out periods of at least two-weeks. The grade was recorded for each assessment, and compared using Cohen’s and Fleiss’s kappa. Results There was 65 to 78% concordance of WHO/ISUP grading on DP and GS1. Furthermore, for the individual pathologists, the comparative kappa scores for DP versus GS1, and GS1 versus GS2, were 0.70 and 0.70, 0.57 and 0.73, and 0.71 and 0.74, and with no apparent tendency to upgrade or downgrade on DP versus GS. The interobserver kappa agreement was less, at 0.58 on DP and 0.45 on GS. Conclusion Our results demonstrate that the assessment of WHO/ISUP grade on DP is noninferior to that on GS. There is an apparent slight improvement in agreement between pathologists on RCC grade when assessed on DP, which may warrant further study.
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Affiliation(s)
- Lisa Browning
- Department of Cellular Pathology, Oxford University Hospitals NHS Trust, John Radcliffe Hospital, Headley Way, OX3 9DU, Oxford, UK. .,NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, Oxfordshire, UK.
| | - Richard Colling
- Department of Cellular Pathology, Oxford University Hospitals NHS Trust, John Radcliffe Hospital, Headley Way, OX3 9DU, Oxford, UK.,Nuffield Department of Surgical Sciences, University of Oxford, John Radcliffe Hospital, OX3 9DU, Oxford, UK
| | - Clare Verrill
- Department of Cellular Pathology, Oxford University Hospitals NHS Trust, John Radcliffe Hospital, Headley Way, OX3 9DU, Oxford, UK.,NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, Oxfordshire, UK.,Nuffield Department of Surgical Sciences, University of Oxford, John Radcliffe Hospital, OX3 9DU, Oxford, UK
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31
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Kuklyte J, Fitzgerald J, Nelissen S, Wei H, Whelan A, Power A, Ahmad A, Miarka M, Gregson M, Maxwell M, Raji R, Lenihan J, Finn-Moloney E, Rafferty M, Cary M, Barale-Thomas E, O’Shea D. Evaluation of the Use of Single- and Multi-Magnification Convolutional Neural Networks for the Determination and Quantitation of Lesions in Nonclinical Pathology Studies. Toxicol Pathol 2021; 49:815-842. [PMID: 33618634 PMCID: PMC8091423 DOI: 10.1177/0192623320986423] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Digital pathology platforms with integrated artificial intelligence have the potential to increase the efficiency of the nonclinical pathologist's workflow through screening and prioritizing slides with lesions and highlighting areas with specific lesions for review. Herein, we describe the comparison of various single- and multi-magnification convolutional neural network (CNN) architectures to accelerate the detection of lesions in tissues. Different models were evaluated for defining performance characteristics and efficiency in accurately identifying lesions in 5 key rat organs (liver, kidney, heart, lung, and brain). Cohorts for liver and kidney were collected from TG-GATEs open-source repository, and heart, lung, and brain from internally selected R&D studies. Annotations were performed, and models were trained on each of the available lesion classes in the available organs. Various class-consolidation approaches were evaluated from generalized lesion detection to individual lesion detections. The relationship between the amount of annotated lesions and the precision/accuracy of model performance is elucidated. The utility of multi-magnification CNN implementations in specific tissue subtypes is also demonstrated. The use of these CNN-based models offers users the ability to apply generalized lesion detection to whole-slide images, with the potential to generate novel quantitative data that would not be possible with conventional image analysis techniques.
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Affiliation(s)
| | | | | | - Haolin Wei
- Deciphex, Dublin City University, Dublin, Ireland
| | - Aoife Whelan
- Deciphex, Dublin City University, Dublin, Ireland
| | - Adam Power
- Deciphex, Dublin City University, Dublin, Ireland
| | - Ajaz Ahmad
- Deciphex, Dublin City University, Dublin, Ireland
| | | | - Mark Gregson
- Deciphex, Dublin City University, Dublin, Ireland
| | | | - Ruka Raji
- Deciphex, Dublin City University, Dublin, Ireland
| | | | | | | | - Maurice Cary
- Pathology Experts GmbH, Technologie Zentrum Witterswil, Witters, Switzerland
| | | | - Donal O’Shea
- Deciphex, Dublin City University, Dublin, Ireland
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32
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Breast Digital Pathology: Way of the Future. CURRENT BREAST CANCER REPORTS 2021. [DOI: 10.1007/s12609-021-00413-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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33
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Levy JJ, Azizgolshani N, Andersen MJ, Suriawinata A, Liu X, Lisovsky M, Ren B, Bobak CA, Christensen BC, Vaickus LJ. A large-scale internal validation study of unsupervised virtual trichrome staining technologies on nonalcoholic steatohepatitis liver biopsies. Mod Pathol 2021; 34:808-822. [PMID: 33299110 PMCID: PMC7985027 DOI: 10.1038/s41379-020-00718-1] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 11/06/2020] [Accepted: 11/08/2020] [Indexed: 02/07/2023]
Abstract
Non-alcoholic steatohepatitis (NASH) is a fatty liver disease characterized by accumulation of fat in hepatocytes with concurrent inflammation and is associated with morbidity, cirrhosis and liver failure. After extraction of a liver core biopsy, tissue sections are stained with hematoxylin and eosin (H&E) to grade NASH activity, and stained with trichrome to stage fibrosis. Methods to computationally transform one stain into another on digital whole slide images (WSI) can lessen the need for additional physical staining besides H&E, reducing personnel, equipment, and time costs. Generative adversarial networks (GAN) have shown promise for virtual staining of tissue. We conducted a large-scale validation study of the viability of GANs for H&E to trichrome conversion on WSI (n = 574). Pathologists were largely unable to distinguish real images from virtual/synthetic images given a set of twelve Turing Tests. We report high correlation between staging of real and virtual stains ([Formula: see text]; 95% CI: 0.84-0.88). Stages assigned to both virtual and real stains correlated similarly with a number of clinical biomarkers and progression to End Stage Liver Disease (Hazard Ratio HR = 2.06, 95% CI: 1.36-3.12, p < 0.001 for real stains; HR = 2.02, 95% CI: 1.40-2.92, p < 0.001 for virtual stains). Our results demonstrate that virtual trichrome technologies may offer a software solution that can be employed in the clinical setting as a diagnostic decision aid.
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Affiliation(s)
- Joshua J Levy
- Emerging Diagnostic and Investigative Technologies, Clinical Genomics and Advanced Technologies, Department of Pathology and Laboratory Medicine, Dartmouth Hitchcock Medical Center, Lebanon, NH, 03756, USA.
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH, 03756, USA.
- Program in Quantitative Biomedical Sciences, Geisel School of Medicine at Dartmouth, Lebanon, NH, 03756, USA.
| | - Nasim Azizgolshani
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH, 03756, USA
| | - Michael J Andersen
- Emerging Diagnostic and Investigative Technologies, Clinical Genomics and Advanced Technologies, Department of Pathology and Laboratory Medicine, Dartmouth Hitchcock Medical Center, Lebanon, NH, 03756, USA
| | - Arief Suriawinata
- Emerging Diagnostic and Investigative Technologies, Clinical Genomics and Advanced Technologies, Department of Pathology and Laboratory Medicine, Dartmouth Hitchcock Medical Center, Lebanon, NH, 03756, USA
| | - Xiaoying Liu
- Emerging Diagnostic and Investigative Technologies, Clinical Genomics and Advanced Technologies, Department of Pathology and Laboratory Medicine, Dartmouth Hitchcock Medical Center, Lebanon, NH, 03756, USA
| | - Mikhail Lisovsky
- Emerging Diagnostic and Investigative Technologies, Clinical Genomics and Advanced Technologies, Department of Pathology and Laboratory Medicine, Dartmouth Hitchcock Medical Center, Lebanon, NH, 03756, USA
| | - Bing Ren
- Emerging Diagnostic and Investigative Technologies, Clinical Genomics and Advanced Technologies, Department of Pathology and Laboratory Medicine, Dartmouth Hitchcock Medical Center, Lebanon, NH, 03756, USA
| | - Carly A Bobak
- Program in Quantitative Biomedical Sciences, Geisel School of Medicine at Dartmouth, Lebanon, NH, 03756, USA
- Thayer School of Engineering, Dartmouth College, Hanover, NH, 03755, USA
- Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, NH, 03756, USA
- The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth, Lebanon, NH, 03756, USA
| | - Brock C Christensen
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH, 03756, USA
- Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Lebanon, NH, 03756, USA
- Department of Community and Family Medicine, Geisel School of Medicine at Dartmouth, Lebanon, NH, 03756, USA
| | - Louis J Vaickus
- Emerging Diagnostic and Investigative Technologies, Clinical Genomics and Advanced Technologies, Department of Pathology and Laboratory Medicine, Dartmouth Hitchcock Medical Center, Lebanon, NH, 03756, USA
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34
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Schumacher VL, Aeffner F, Barale-Thomas E, Botteron C, Carter J, Elies L, Engelhardt JA, Fant P, Forest T, Hall P, Hildebrand D, Klopfleisch R, Lucotte T, Marxfeld H, Mckinney L, Moulin P, Neyens E, Palazzi X, Piton A, Riccardi E, Roth DR, Rousselle S, Vidal JD, Williams B. The Application, Challenges, and Advancement Toward Regulatory Acceptance of Digital Toxicologic Pathology: Results of the 7th ESTP International Expert Workshop (September 20-21, 2019). Toxicol Pathol 2020; 49:720-737. [PMID: 33297858 DOI: 10.1177/0192623320975841] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
With advancements in whole slide imaging technology and improved understanding of the features of pathologist workstations required for digital slide evaluation, many institutions are investigating broad digital pathology adoption. The benefits of digital pathology evaluation include remote access to study or diagnostic case materials and integration of analysis and reporting tools. Diagnosis based on whole slide images is established in human medical pathology, and the use of digital pathology in toxicologic pathology is increasing. However, there has not been broad adoption in toxicologic pathology, particularly in the context of regulatory studies, due to lack of precedence. To address this topic, as well as practical aspects, the European Society of Toxicologic Pathology coordinated an expert international workshop to assess current applications and challenges and outline a set of minimal requirements needed to gain future regulatory acceptance for the use of digital toxicologic pathology workflows in research and development, so that toxicologic pathologists can benefit from digital slide technology.
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Affiliation(s)
- Vanessa L Schumacher
- 1529Roche Innovation Center Basel, Pharma Research and Early Development, F. Hoffmann-La Roche, Ltd, Basel, Switzerland
| | - Famke Aeffner
- Amgen Inc, Amgen Research, Translational Safety and Bioanalytical Sciences, South San Francisco, CA, USA
| | | | | | | | - Laëtitia Elies
- 72810Bayer Crop Science Division, Sophia Antipolis, France.,25913Charles River Laboratories, Lyon, France
| | | | | | | | | | | | - Robert Klopfleisch
- 9166Freie Universitaet Berlin, Institute of Veterinary Pathology, Berlin, Germany
| | - Thomas Lucotte
- 56511Agence nationale de sécurité du médicament et des produits de santé (ANSM), Saint-Denis, France
| | | | - LuAnn Mckinney
- 4137US Food and Drug Administration, Silver Spring, MD, USA
| | | | - Elizabeth Neyens
- Elizabethtoxpath Consulting Inc, Vancouver, British Columbia, Canada
| | | | - Alain Piton
- ALP Quality Systems, Sophia Antipolis, France
| | | | | | | | | | - Bethany Williams
- 572272Department of Histopathology, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom.,Faculty of Medicine and Health, University of Leeds, Leeds, United Kingdom
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35
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Jahn SW, Plass M, Moinfar F. Digital Pathology: Advantages, Limitations and Emerging Perspectives. J Clin Med 2020; 9:E3697. [PMID: 33217963 PMCID: PMC7698715 DOI: 10.3390/jcm9113697] [Citation(s) in RCA: 90] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 10/27/2020] [Accepted: 11/13/2020] [Indexed: 12/11/2022] Open
Abstract
Digital pathology is on the verge of becoming a mainstream option for routine diagnostics. Faster whole slide image scanning has paved the way for this development, but implementation on a large scale is challenging on technical, logistical, and financial levels. Comparative studies have published reassuring data on safety and feasibility, but implementation experiences highlight the need for training and the knowledge of pitfalls. Up to half of the pathologists are reluctant to sign out reports on only digital slides and are concerned about reporting without the tool that has represented their profession since its beginning. Guidelines by international pathology organizations aim to safeguard histology in the digital realm, from image acquisition over the setup of work-stations to long-term image archiving, but must be considered a starting point only. Cost-efficiency analyses and occupational health issues need to be addressed comprehensively. Image analysis is blended into the traditional work-flow, and the approval of artificial intelligence for routine diagnostics starts to challenge human evaluation as the gold standard. Here we discuss experiences from past digital pathology implementations, future possibilities through the addition of artificial intelligence, technical and occupational health challenges, and possible changes to the pathologist's profession.
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Affiliation(s)
- Stephan W. Jahn
- Diagnostic and Research Institute of Pathology, Medical University of Graz, Neue Stiftingtalstraße 6, 8010 Graz, Austria; (M.P.); (F.M.)
| | - Markus Plass
- Diagnostic and Research Institute of Pathology, Medical University of Graz, Neue Stiftingtalstraße 6, 8010 Graz, Austria; (M.P.); (F.M.)
| | - Farid Moinfar
- Diagnostic and Research Institute of Pathology, Medical University of Graz, Neue Stiftingtalstraße 6, 8010 Graz, Austria; (M.P.); (F.M.)
- Department of Pathology, Ordensklinikum/Hospital of the Sisters of Charity, Seilerstätte 4, 4010 Linz, Austria
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