J Pathol Transl Med.  2022 Nov;56(6):370-382. 10.4132/jptm.2022.09.30.

Development of quality assurance program for digital pathology by the Korean Society of Pathologists

• Chong Y
• Bae JM
• Kang DW
• Kim G
• Han HS

• KMID: 2535857
• DOI: http://doi.org/10.4132/jptm.2022.09.30

Abstract

Background
Digital pathology (DP) using whole slide imaging is a recently emerging game changer technology that can fundamentally change the way of working in pathology. The Digital Pathology Study Group (DPSG) of the Korean Society of Pathologists (KSP) published a consensus report on the recommendations for pathologic practice using DP. Accordingly, the need for the development and implementation of a quality assurance program (QAP) for DP has been raised.
Methods
To provide a standard baseline reference for internal and external QAP for DP, the members of the Committee of Quality Assurance of the KSP developed a checklist for the Redbook and a QAP trial for DP based on the prior DPSG consensus report. Four leading institutes participated in the QAP trial in the first year, and we gathered feedback from these institutes afterwards.
Results
The newly developed checklists of QAP for DP contain 39 items (216 score): eight items for quality control of DP systems; three for DP personnel; nine for hardware and software requirements for DP systems; 15 for validation, operation, and management of DP systems; and four for data security and personal information protection. Most participants in the QAP trial replied that continuous education on unfamiliar terminology and more practical experience is demanding.
Conclusions
The QAP for DP is essential for the safe implementation of DP in pathologic practice. Each laboratory should prepare an institutional QAP according to this checklist, and consecutive revision of the checklist with feedback from the QAP trial for DP needs to follow.

Keyword

Digital pathology; Quality assurance program; Recommendations; Whole slide image; Quality control

Reference

References

1. Nam S, Chong Y, Jung CK, et al. Introduction to digital pathology and computer-aided pathology. J Pathol Transl Med. 2020; 54:125–34.

2. Chong Y, Kim DC, Jung CK, et al. Recommendations for pathologic practice using digital pathology: consensus report of the Korean Society of Pathologists. J Pathol Transl Med. 2020; 54:437–52.

3. Canadian Association of Pathologists Telepathology Guidelines Committee; Bernard C, Chandrakanth SA, et al. Guidelines from the Canadian Association of Pathologists for establishing a telepathology service for anatomic pathology using whole-slide imaging. J Pathol Inform. 2014; 5:15.

4. College of American Pathologists. Validating whole slide imaging for diagnostic purposes in pathology. Northfield: College of American Pathologists;2013.

5. College of American Pathologists. Anatomic pathology checklist: CAP accreditation program. Northfield: College of American Pathologists;2011.

6. Cross S, Furness P, Igali L, Snead D, Treanor D. Best practice recommendations for implementing digital pathology. London: The Royal College of Pathologists;2018. p. 1–43.

7. Federal Association of German Pathologists Bundesverband deutscher Pathologen (FAGP-BDP). Guidelines digital pathology for diagnosis on (and reports of) digital images, 2018. Berlin: Federal Association of German Pathologists Bundesverband deutscher Pathologen (FAGP-BDP);2018.

8. Garcia-Rojo M, Conde A, Ordi J, et al. Guia practica para la implantacion de la patologia digital [Practical guide for the implementation of digital pathology]. In: Guerra Merino I, ed. Libro Blanco de la Anatomia Patologica en Espana 2015 [White book of pathological anatomy in Spain 2015]. Madrid: Sociedad Espanola de Anatomia Patologica;2015. p. 247–78.

9. Japanese Society of Pathology. Digital Pathology Assessment Committee: technical standards for digital pathology system for pathologic diagnosis. Tokyo: Japanese Society of Pathology;2018.

10. Japanese Society of Pathology. Digital Pathology Assessment Committee: guidelines for pathologic diagnosis using digital pathology images (clinical questions and answers). Tokyo: Japanese Society of Pathology;2019.

11. Department of In-Vitro Diagnostic Device, Ministry of Food and Drug Safety. Assessment and approval guideline on the artificial intelligence (AI)-based histopathologic in-vitro diagnostic devices (software). Cheongju: Ministry of Food and Drug Safety;2021.

12. Health Insurance Review and Assessment Service. Reimbursement assessment guideline of innovative medical technology (pathology AI-based technology). Wonju: Health Insurance Review and Assessment Service;2021.

13. Mukhopadhyay S, Feldman MD, Abels E, et al. Whole slide imaging versus microscopy for primary diagnosis in surgical pathology: a multicenter blinded randomized noninferiority study of 1992 cases (pivotal study). Am J Surg Pathol. 2018; 42:39–52.

14. Pantanowitz L, Sinard JH, Henricks WH, 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; 137:1710–22.

15. Snead DR, Tsang YW, Meskiri A, et al. Validation of digital pathology imaging for primary histopathological diagnosis. Histopathology. 2016; 68:1063–72.

16. Fraggetta F, Yagi Y, Garcia-Rojo M, et al. The importance of eSlide macro images for primary diagnosis with whole slide imaging. J Pathol Inform. 2018; 9:46.

17. Chae SW. Present and future of digital pathology: experience of Kangbuk Samsung Hospital. In : In: Proceedings of the 73rd Annual Fall Meeting of The Korean Society of Pathologists; 2021 Oct 27-29; Seoul, Korea. Seoul: The Korean Society of Pathologists;2021. 21.

18. Han N. Present and future of digital pathology: experience of National Cancer Center. In : In: Proceedings of the 73rd Annual Fall Meeting of The Korean Society of Pathologist; 2021 Oct 27-29; Seoul, Korea. Seoul: The Korean Society of Pathologists;2021. 21.

19. Hong SW. Present and future of digital pathology: experience of Yongin Severance Hospital. In : In: Proceedings of the 73rd Annual Fall Meeting of The Korean Society of Pathologists; 2021 Oct 27-29; Seoul, Korea. Seoul: The Korean Society of Pathologists;2021. 21.

20. Jang KT. Present and future of digital pathology: experience of Samsung Medical Center. In : In: Proceedings of the 46th Annual Spring Meeting of The Korean Society of Pathologists; 2021 May 13-14; Virtual. Seoul: The Korean Society of Pathologists;2021. 17.

21. Jang S. Present and future of digital pathology: experience of Seoul Asan Medical Center. In : In: Proceedings of The 46th Annual Spring Meeting of The Korean Society of Pathologists; 2021 May 13-14; Virtual. Seoul: The Korean Society of Pathologists;2021. 21.

22. Jung CK. Present and future of digital pathology: experience of the Catholic University of Korea St. Mary’s Hospital. In : In: Proceedings of The 46th Annual Spring Meeting of The Korean Society of Pathologists; 2021 May 13-14; Virtual. Seoul: The Korean Society of Pathologists;2021. 19.

23. Kim S. Present and future of digital pathology: experience of Yonsei University Severance Hospital. In : In: Proceedings of The 46th Annual Spring Meeting of The Korean Society of Pathologists; 2021 May 13-14; Virtual. Seoul: The Korean Society of Pathologists;2021. 20.

24. Lee KB. Present and future of digital pathology: laboratory information system leads whole slide image diagnosis: integration digital pathology with paperless pathology workflow in SNUH. In : In: Proceedings of The 46th Annual Spring Meeting of The Korean Society of Pathologists; 2021 May 13-14; Virtual. Seoul: The Korean Society of Pathologists;2021. 18.

25. Lee KB. Preparation of Reimbursement Assessment Guideline of Innovative Medical Technology (pathology AI-based technology). Seoul: Health Insurance Review and Assessment Service;2019.

26. DICOM Standards Committee Working Groups 26. Digital imaging and communications in medicine (DICOM). Supplement 145: Whole slide microscopic image IOD and SOP classes. Rosslyn: DICOM;2010.

27. Singh R, Chubb L, Pantanowitz L, Parwani A. Standardization in digital pathology: Supplement 145 of the DICOM standards. J Pathol Inform. 2011; 2:23.

28. Zwonitzer R, Kalinski T, Hofmann H, Roessner A, Bernarding J. Digital pathology: DICOM-conform draft, testbed, and first results. Comput Methods Programs Biomed. 2007; 87:181–8.

29. Ailia MJ, Thakur N, Abdul-Ghafar J, Jung CK, Yim K, Chong Y. Current trend of artificial intelligence patents in digital pathology: a systematic evaluation of the patent landscape. Cancers (Basel). 2022; 14:2400.

30. Alam MR, Abdul-Ghafar J, Yim K, et al. Recent applications of artificial intelligence from histopathologic image-based prediction of microsatellite instability in solid cancers: a systematic review. Cancers (Basel). 2022; 14:2590.

31. Kim H, Yoon H, Thakur N, et al. Deep learning-based histopathological segmentation for whole slide images of colorectal cancer in a compressed domain. Sci Rep. 2021; 11:22520.