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)
Abstract
:1. Introduction
2. Involvement of the Team in the Digital Pathology Transformation of the Laboratory
3. Optimization of Resources in the DP Workflow
4. The Role and Potentialities of Laboratory Information (Management) System (LIS/LIMS) and Informatics Resources
5. Automation of Workflow and Tracking System
6. Quality Control Program and Definition of Checkpoints
6.1. Accessioning Checkpoints
- Patient ID
- C ID
- Specimen container ID (entry lab)
- Sample IDs
- Block IDs
- Slide IDs
- Sample/slides arrive in the pathology laboratory with a label containing a code (entry lab, preferentially 2D type) associated with patient and case data.
- By scanning the code on the label of the case, the administrative is able to open the digital request on pathology LIS automatically, allowing the automatic synchronization of the information from the hospital system or creating the specific page for cases/patients coming from outside.
- A case ID for the sample is generated.
- The case ID is then used in all sorts of assets generated for that case (cassettes, new slides, special stains, digital slides).
- The administrator can take pictures of both the container and the specimen, and these photos will be attached to the case file.
- All of the documents received together with the specimen are scanned and attached to the case file or directly transmitted to the LIS digitally (Optical Character Recognition, OCR).
6.2. Grossing Checkpoints
- The grossing operator (e.g., pathologist/resident/pathologist’s assistant) can access the case/patient file by directly scanning the code on the sample container.
- Pictures are taken of the sample before it is described and grossed, as well as during grossing, and finally of all tissue cassettes with slices; those images are directly linked to the case using the software integration paths between the LIS and the image capture instrument.
- The grossing operator performs a macroscopic description of the sample through automated speech recognition systems that report the text in the appropriate section of the case/patient file using the software integrations paths between the LIS and the dictation system instrument.
- The operator can produce cassettes by using a specific printer (preferably laser printer) to assign an identification code corresponding to the particular case, as established during the accessioning and using the software integration paths between the LIS and the printer. The cassettes and marker media should be appropriately tested to demonstrate the indelibility or impossibility of washing away or removing the identification code. The suggested code is 2D (e.g., QR code), which can include a greater character count (higher data density), require a smaller footprint, and have fewer scan and printer failures than 1D codes.
- An image of the cassette with the grossed specimen should be obtained at the bench, allowing retrieval of this at the following steps.
6.3. Grossing-to-Processing and Processing Checkpoints
6.4. Embedding Checkpoints
6.5. Sectioning Checkpoints
6.6. Staining and Mounting Checkpoints
6.7. Correct Assigning of the WSI to the Case Checkpoints
6.8. Archiving Checkpoints
7. Scanner for Slide Digitization
- The scanning process.
- Virtual slide quality control.
8. Validation of WSI for Clinical Use
8.1. The Visualization Chain: The Most Appropriate Monitor and Display. The Pathologist Workstation
8.2. Scan Quality Assessment
8.3. Tissue Coverage
- The overview (rendering the macro/slide label files) that is a low-resolution snapshot of the entire glass slide.
- The “digital image” of the glass slide generated by a microscope camera (rendering baseline tiled image, thumbnail and multiple intermediate tiled images stacked in a pyramid) often acquired at the chosen magnification.
8.4. Assignment of Images to the Correct Case/Patient File
9. Open Topics (Not Fully Addressed in This Document)
9.1. Data Retention Policies and Image Storage Solutions
9.2. Evaluation of the Results Obtained with the Digital Transition
9.3. Preparing for the Subsequent Steps after Implementing the Digital Workflow
10. Closing Remarks
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Evans, A.J.; Bauer, T.W.; Bui, M.M.; Cornish, T.C.; Duncan, H.; Glassy, E.F.; Hipp, J.; McGee, R.S.; Murphy, D.; Myers, C.; et al. US Food and Drug Administration Approval of Whole Slide Imaging for Primary Diagnosis: A Key Milestone Is Reached and New Questions Are Raised. Arch. Pathol. Lab. Med. 2018, 142, 1383–1387. [Google Scholar] [CrossRef] [Green Version]
- Snead, D.R.J.; Tsang, Y.-W.; Meskiri, A.; Kimani, P.K.; Crossman, R.; Rajpoot, N.M.; Blessing, E.; Chen, K.; Gopalakrishnan, K.; Matthews, P.; et al. Validation of Digital Pathology Imaging for Primary Histopathological Diagnosis. Histopathology 2016, 68, 1063–1072. [Google Scholar] [CrossRef] [Green Version]
- Goacher, E.; Randell, R.; Williams, B.; Treanor, D. The Diagnostic Concordance of Whole Slide Imaging and Light Microscopy: A Systematic Review. Arch. Pathol. Lab. Med. 2017, 141, 151–161. [Google Scholar] [CrossRef] [Green Version]
- Mukhopadhyay, S.; Feldman, M.D.; Abels, E.; Ashfaq, R.; Beltaifa, S.; Cacciabeve, N.G.; Cathro, H.P.; Cheng, L.; Cooper, K.; Dickey, G.E.; et al. Whole Slide Imaging Versus Microscopy for Primary Diagnosis in Surgical Pathology. Am. J. Surg. Pathol. 2018, 42, 39–52. [Google Scholar] [CrossRef]
- Evans, A.J.; Salama, M.E.; Henricks, W.H.; Pantanowitz, L. Implementation of Whole Slide Imaging for Clinical Purposes: Issues to Consider from the Perspective of Early Adopters. Arch. Pathol. Lab. Med. 2017, 141, 944–959. [Google Scholar] [CrossRef] [Green Version]
- Available online: https://www.virtualpathology.leeds.ac.uk/research/clinical/docs/2018/pdfs/18778_Leeds%20Guide%20to%20Digital%20Pathology_Brochure_A4_final_hi.pdf (accessed on 10 November 2021).
- Available online: https://www.usa.philips.com/c-dam/b2bhc/us/landing-pages/pdxus/how-to-go-digital-in-pathology.pdf (accessed on 10 November 2021).
- Retamero, J.A.; Aneiros-Fernandez, J.; del Moral, R.G. Complete Digital Pathology for Routine Histopathology Diagnosis in a Multicenter Hospital Network. Arch. Pathol. Lab. Med. 2020, 144, 221–228. [Google Scholar] [CrossRef] [Green Version]
- Fraggetta, F.; Garozzo, S.; Zannoni, G.F.; Pantanowitz, L.; Rossi, E.D. Routine Digital Pathology Workflow: The Catania Experience. J. Pathol. Inform. 2017, 8, 51. [Google Scholar]
- Fraggetta, F.; Caputo, A.; Guglielmino, R.; Pellegrino, M.G.; Runza, G.; L’Imperio, V. A Survival Guide for the Rapid Transition to a Fully Digital Workflow: The “Caltagirone Example”. Diagnostics 2021, 11, 1916. [Google Scholar] [CrossRef]
- Eloy, C.; Vale, J.; Curado, M.; Polónia, A.; Campelos, S.; Caramelo, A.; Sousa, R.; Sobrinho-Simões, M. Digital Pathology Workflow Implementation at IPATIMUP. Diagnostics 2021, 11, 2111. [Google Scholar] [CrossRef]
- Sinard, J.H.; Castellani, W.J.; Wilkerson, M.L.; Henricks, W.H. Stand-Alone Laboratory Information Systems versus Laboratory Modules Incorporated in the Electronic Health Record. Arch. Pathol. Lab. Med. 2015, 139, 311–318. [Google Scholar] [CrossRef]
- Sepulveda, J.L.; Young, D.S. The Ideal Laboratory Information System. Arch. Pathol. Lab. Med. 2013, 137, 1129–1140. [Google Scholar] [CrossRef]
- Pantanowitz, L.; Asa, S.; Bodén, A.; Treanor, D.; Jarkman, S.; Lundström, C. 2020 Vision of Digital Pathology in Action. J. Pathol. Inform. 2019, 10, 27. [Google Scholar] [CrossRef]
- Quigley, J.; Lujan, G.; Hartman, D.; Parwani, A.; Roehmholdt, B.; Meter, B.; Ardon, O.; Hanna, M.; Kelly, D.; Sowards, C.; et al. Dissecting the Business Case for Adoption and Implementation of Digital Pathology: A White Paper from the Digital Pathology Association. J. Pathol. Inform. 2021, 12, 17. [Google Scholar] [CrossRef]
- Hanna, M.G.; Reuter, V.E.; Ardon, O.; Kim, D.; Sirintrapun, S.J.; Schüffler, P.J.; Busam, K.J.; Sauter, J.L.; Brogi, E.; Tan, L.K.; et al. Validation of a Digital Pathology System Including Remote Review during the COVID-19 Pandemic. Mod. Pathol. 2020, 33, 2115–2127. [Google Scholar] [CrossRef]
- Zarbo, R.J. Creating and Sustaining a Lean Culture of Continuous Process Improvement. Am. J. Clin. Pathol. 2012, 138, 321–326. [Google Scholar] [CrossRef] [Green Version]
- Tuthill, J.; Friedman, B.; Splitz, A.; Balis, U. The Laboratory Information System Functionality Assessment Tool: Ensuring Optimal Software Support for Your Laboratory. J. Pathol. Inform. 2014, 5, 7. [Google Scholar] [CrossRef]
- Petrides, A.K.; Bixho, I.; Goonan, E.M.; Bates, D.W.; Shaykevich, S.; Lipsitz, S.R.; Landman, A.B.; Tanasijevic, M.J.; Melanson, S.E.F. The Benefits and Challenges of an Interfaced Electronic Health Record and Laboratory Information System: Effects on Laboratory Processes. Arch. Pathol. Lab. Med. 2017, 141, 410–417. [Google Scholar] [CrossRef]
- Krupinski, E.A. Optimizing the Pathology Workstation “Cockpit”: Challenges and Solutions. J. Pathol. Inform. 2010, 1, 19. [Google Scholar] [CrossRef]
- Dash, R.C.; Jones, N.; Merrick, R.; Haroske, G.; Harrison, J.; Sayers, C.; Haarselhorst, N.; Wintell, M.; Herrmann, M.D.; Macary, F. Integrating the Health-Care Enterprise Pathology and Laboratory Medicine Guideline for Digital Pathology Interoperability. J. Pathol. Inform. 2021, 12, 16. [Google Scholar] [CrossRef]
- Roy, S.; Pfeifer, J.D.; LaFramboise, W.A.; Pantanowitz, L. Molecular Digital Pathology: Progress and Potential of Exchanging Molecular Data. Expert Rev. Mol. Diagn. 2016, 16, 941–947. [Google Scholar] [CrossRef]
- Phelan, S.M. Impact of the Introduction of a Novel Automated Embedding System on Quality in a University Hospital Histopathology Department. J. Histol. Histopathol. 2014, 1, 3. [Google Scholar] [CrossRef] [Green Version]
- Hanna, M.G.; Pantanowitz, L. Bar Coding and Tracking in Pathology. Clin. Lab. Med. 2016, 36, 13–30. [Google Scholar] [CrossRef] [PubMed]
- Bostwick, D.G. Radiofrequency Identification Specimen Tracking in Anatomical Pathology: Pilot Study of 1067 Consecutive Prostate Biopsies. Ann. Diagn. Pathol. 2013, 17, 391–402. [Google Scholar] [CrossRef] [PubMed]
- Lou, J.J.; Andrechak, G.; Riben, M.; Yong, W.H. A Review of Radio Frequency Identification Technology for the Anatomic Pathology or Biorepository Laboratory: Much Promise, Some Progress, and More Work Needed. J. Pathol. Inform. 2011, 2, 34. [Google Scholar] [PubMed]
- Snyder, S.R.; Favoretto, A.M.; Derzon, J.H.; Christenson, R.H.; Kahn, S.E.; Shaw, C.S.; Baetz, R.A.; Mass, D.; Fantz, C.R.; Raab, S.S.; et al. Effectiveness of Barcoding for Reducing Patient Specimen and Laboratory Testing Identification Errors: A Laboratory Medicine Best Practices Systematic Review and Meta-Analysis. Clin. Biochem. 2012, 45, 988–998. [Google Scholar] [CrossRef] [Green Version]
- L’Imperio, V.; Gibilisco, F.; Fraggetta, F. What Is Essential Is (No More) Invisible to the Eyes: The Introduction of Blocdoc in the Digital Pathology Workflow. J. Pathol. Inform. 2021, 12, 32. [Google Scholar]
- Pantanowitz, L.; Farahani, N.; Parwani, A. Whole Slide Imaging in Pathology: Advantages, Limitations, and Emerging Perspectives. Pathol. Lab. Med. Int. 2015, 23, 23–33. [Google Scholar] [CrossRef] [Green Version]
- Janowczyk, A.; Zuo, R.; Gilmore, H.; Feldman, M.; Madabhushi, A. HistoQC: An Open-Source Quality Control Tool for Digital Pathology Slides. JCO Clin. Cancer Inform. 2019, 3, 1–7. [Google Scholar] [CrossRef]
- Ferrini, F.; Sannino, G.; Chiola, C.; Capparé, P.; Gastaldi, G.; Gherlone, E. Influence of Intra-Oral Scanner (I.O.S.) on The Marginal Accuracy of CAD/CAM Single Crowns. Int. J. Environ. Res. Public Health 2019, 16, 544. [Google Scholar] [CrossRef] [Green Version]
- Hufnagl, P.; Zwönitzer, R.; Haroske, G. Guidelines Digital Pathology for Diagnosis on (and Reports Of) Digital Images Version 1.0 Bundesverband Deutscher Pathologen e.V. (Federal Association of German Pathologist). Diagn. Pathol. 2018, 4, 266. [Google Scholar] [CrossRef]
- Available online: https://www.rcpath.org/uploads/assets/f465d1b3-797b-4297-b7fedc00b4d77e51/Best-practice-recommendations-for-implementing-digital-pathology.pdf (accessed on 10 November 2021).
- Hanna, M.G.; Reuter, V.E.; Hameed, M.R.; Tan, L.K.; Chiang, S.; Sigel, C.; Hollmann, T.; Giri, D.; Samboy, J.; Moradel, C.; et al. Whole Slide Imaging Equivalency and Efficiency Study: Experience at a Large Academic Center. Mod. Pathol. 2019, 32, 916–928. [Google Scholar] [CrossRef]
- L’Imperio, V.; Brambilla, V.; Cazzaniga, G.; Ferrario, F.; Nebuloni, M.; Pagni, F. Digital Pathology for the Routine Diagnosis of Renal Diseases: A Standard Model. J. Nephrol. 2021, 34, 681–688. [Google Scholar] [CrossRef]
- Azam, A.S.; Miligy, I.M.; Kimani, P.K.-U.; Maqbool, H.; Hewitt, K.; Rajpoot, N.M.; Snead, D.R.J. Diagnostic Concordance and Discordance in Digital Pathology: A Systematic Review and Meta-Analysis. J. Clin. Pathol. 2021, 74, 448–455. [Google Scholar] [CrossRef]
- Thorstenson, S.; Molin, J.; Lundström, C. Implementation of Large-Scale Routine Diagnostics Using Whole Slide Imaging in Sweden: Digital Pathology Experiences 2006–2013. J. Pathol. Inform. 2014, 5, 14. [Google Scholar]
- Williams, B.J.; Treanor, D. Practical Guide to Training and Validation for Primary Diagnosis with Digital Pathology. J. Clin. Pathol. 2020, 73, 418–422. [Google Scholar] [CrossRef]
- Pantanowitz, L.; Sinard, J.H.; Henricks, W.H.; Fatheree, L.A.; Carter, A.B.; Contis, L.; Beckwith, B.A.; Evans, A.J.; Lal, A.; Parwani, A.V.; et al. Validating Whole Slide Imaging for Diagnostic Purposes in Pathology: Guideline from the College of American Pathologists Pathology and Laboratory Quality Center. Arch. Pathol. Lab. Med. 2013, 137, 1710–1722. [Google Scholar] [CrossRef] [Green Version]
- Evans, A.J.; Brown, R.W.; Bui, M.M.; Chlipala, E.A.; Lacchetti, C.; Milner, D.A.; Pantanowitz, L.; Parwani, A.V.; Reid, K.; Riben, M.W.; et al. Validating Whole Slide Imaging Systems for Diagnostic Purposes in Pathology: Guideline Update from the College of American Pathologists in Collaboration with the American Society for Clinical Pathology and the Association for Pathology Informatics. Arch. Pathol. Lab. Med. 2021. online ahead of print. [Google Scholar] [CrossRef]
- McClintock, D.; Abel, J.; Ouillette, P.; Williams, C.; Blau, J.; Cheng, J.; Yao, K.; Lee, W.; Cornish, T.; Balis, U.J. Display Characteristics and Their Impact on Digital Pathology: A Current Review of Pathologists’ Future “microscope”. J. Pathol. Inform. 2020, 11, 23. [Google Scholar] [CrossRef] [PubMed]
- Point of Use QA Pathology. Available online: https://www.virtualpathology.leeds.ac.uk/research/systems/pouqa/pathology/ (accessed on 10 November 2021).
- Kohlberger, T.; Liu, Y.; Moran, M.; Chen, P.-H.C.; Brown, T.; Hipp, J.D.; Mermel, C.H.; Stumpe, M.C. Whole-Slide Image Focus Quality: Automatic Assessment and Impact on AI Cancer Detection. J. Pathol. Inform. 2019, 10, 39. [Google Scholar] [CrossRef] [PubMed]
- Senaras, C.; Niazi, M.K.K.; Lozanski, G.; Gurcan, M.N. DeepFocus: Detection of out-of-Focus Regions in Whole Slide Digital Images Using Deep Learning. PLoS ONE 2018, 13, e0205387. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Hosseini, M.S.; Brawley-Hayes, J.A.Z.; Zhang, Y.; Chan, L.; Plataniotis, K.; Damaskinos, S. Focus Quality Assessment of High-Throughput Whole Slide Imaging in Digital Pathology. IEEE Trans. Med. Imaging 2020, 39, 62–74. [Google Scholar] [CrossRef] [Green Version]
- Fraggetta, F.; Yagi, Y.; Garcia-Rojo, M.; Evans, A.; Tuthill, J.; Baidoshvili, A.; Hartman, D.; Fukuoka, J.; Pantanowitz, L. The Importance of eSlide Macro Images for Primary Diagnosis with Whole Slide Imaging. J. Pathol. Inform. 2018, 9, 46. [Google Scholar] [CrossRef] [PubMed]
- Available online: https://elss.cap.org/elss/ShowProperty?nodePath=/UCMCON/Contribution%20Folders/WebApplications/pdf/retention-laboratory-records-and-materials.pdf (accessed on 10 November 2021).
- Available online: https://digitalpathologyassociation.org/_data/cms_files/files/Archival_and_Retrieval_in_Digital_Pathology_Systems.pdf (accessed on 13 November 2021).
- Stathonikos, N.; Nguyen, T.Q.; van Diest, P.J. Rocky Road to Digital Diagnostics: Implementation Issues and Exhilarating Experiences. J. Clin. Pathol. 2021, 74, 415–420. [Google Scholar] [CrossRef] [PubMed]
- Lehne, M.; Sass, J.; Essenwanger, A.; Schepers, J.; Thun, S. Why Digital Medicine Depends on Interoperability. NPJ Digit. Med. 2019, 2, 79. [Google Scholar] [CrossRef] [PubMed]
- Pantanowitz, L.; Sharma, A.; Carter, A.B.; Kurc, T.; Sussman, A.; Saltz, J. Twenty Years of Digital Pathology: An Overview of the Road Travelled, What Is on the Horizon, and the Emergence of Vendor-Neutral Archives. J. Pathol. Inform. 2018, 9, 40. [Google Scholar] [CrossRef]
- Janowczyk, A.; Madabhushi, A. Deep Learning for Digital Pathology Image Analysis: A Comprehensive Tutorial with Selected Use Cases. J. Pathol. Inform. 2016, 7, 29. [Google Scholar] [CrossRef] [PubMed]
- Aeffner, F.; Zarella, M.; Buchbinder, N.; Bui, M.; Goodman, M.; Hartman, D.; Lujan, G.; Molani, M.; Parwani, A.; Lillard, K.; et al. Introduction to Digital Image Analysis in Whole-Slide Imaging: A White Paper from the Digital Pathology Association. J. Pathol. Inform. 2019, 10, 9. [Google Scholar] [CrossRef] [PubMed]
- Racoceanu, D.; Capron, F. Towards Semantic-Driven High-Content Image Analysis: An Operational Instantiation for Mitosis Detection in Digital Histopathology. Comput. Med. Imaging Graph. 2015, 42, 2–15. [Google Scholar] [CrossRef] [Green Version]
- Cui, M.; Zhang, D.Y. Artificial Intelligence and Computational Pathology. Lab. Investig. 2021, 101, 412–422. [Google Scholar] [CrossRef]
- Racoceanu, D.; Capron, F. Semantic Integrative Digital Pathology: Insights into Microsemiological Semantics and Image Analysis Scalability. Pathobiology 2016, 83, 148–155. [Google Scholar] [CrossRef] [Green Version]
Accessioning Checkpoints |
---|
|
|
|
|
Grossing Checkpoints |
---|
|
|
|
|
|
Sectioning Checkpoints |
---|
|
|
|
|
|
Scanning Checkpoints |
---|
|
|
|
|
|
Principles | Type of Action |
---|---|
| Recommendation |
| Recommendation |
| Suggestion |
| Recommendation |
| Recommendation |
| Recommendation |
| Suggestion |
| Recommendation |
| Recommendation |
| Recommendation |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Fraggetta, F.; L’Imperio, V.; Ameisen, D.; Carvalho, R.; Leh, S.; Kiehl, T.-R.; Serbanescu, M.; Racoceanu, D.; Della Mea, V.; Polonia, A.; et al. 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 2021, 11, 2167. https://doi.org/10.3390/diagnostics11112167
Fraggetta F, L’Imperio V, Ameisen D, Carvalho R, Leh S, Kiehl T-R, Serbanescu M, Racoceanu D, Della Mea V, Polonia A, et al. 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. 2021; 11(11):2167. https://doi.org/10.3390/diagnostics11112167
Chicago/Turabian StyleFraggetta, Filippo, Vincenzo L’Imperio, David Ameisen, Rita Carvalho, Sabine Leh, Tim-Rasmus Kiehl, Mircea Serbanescu, Daniel Racoceanu, Vincenzo Della Mea, Antonio Polonia, and et al. 2021. "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 11, no. 11: 2167. https://doi.org/10.3390/diagnostics11112167
APA StyleFraggetta, F., L’Imperio, V., Ameisen, D., Carvalho, R., Leh, S., Kiehl, T.-R., Serbanescu, M., Racoceanu, D., Della Mea, V., Polonia, A., Zerbe, N., & Eloy, C. (2021). 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, 11(11), 2167. https://doi.org/10.3390/diagnostics11112167