J Pathol Transl Med.  2023 Sep;57(5):251-264. 10.4132/jptm.2023.07.17.

Diagnostic proficiency test using digital cytopathology and comparative assessment of whole slide images of cytologic samples for quality assurance program in Korea

• Chong Y
• Hong SA
• Oh HK
• Jung SJ
• Kim BS
• Jeong JY
• Lee HC
• Gong G
• The Committee of Quality Improvement of Korean Society for Cytopathology

• KMID: 2545968
• DOI: http://doi.org/10.4132/jptm.2023.07.17

Abstract

Background
The Korean Society for Cytopathology introduced a digital proficiency test (PT) in 2021. However, many doubtful opinions remain on whether digitally scanned images can satisfactorily present subtle differences in the nuclear features and chromatin patterns of cytological samples.
Methods
We prepared 30 whole-slide images (WSIs) from the conventional PT archive by a selection process for digital PT. Digital and conventional PT were performed in parallel for volunteer institutes, and the results were compared using feedback. To assess the quality of cytological assessment WSIs, 12 slides were collected and scanned using five different scanners, with four cytopathologists evaluating image quality through a questionnaire.
Results
Among the 215 institutes, 108 and 107 participated in glass and digital PT, respectively. No significant difference was noted in category C (major discordance), although the number of discordant cases was slightly higher in the digital PT group. Leica, 3DHistech Pannoramic 250 Flash, and Hamamatsu NanoZoomer 360 systems showed comparable results in terms of image quality, feature presentation, and error rates for most cytological samples. Overall satisfaction was observed with the general convenience and image quality of digital PT.
Conclusions
As three-dimensional clusters are common and nuclear/chromatin features are critical for cytological interpretation, careful selection of scanners and optimal conditions are mandatory for the successful establishment of digital quality assurance programs in cytology.

Keyword

Cytology; Quality assurance; Digital pathology; Whole-slide image; Whole-slide scanner

Figure

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  Fig. 1. Concordance rate of digital pathology (DP) and glass proficiency tests in 2020 (A), 2021 (B), and 2022 (C). O, concordancy; A, minimal discordancy; B, minor discordancy; C, major discordancy. GYN, gynecologic samples; FNA, fine-needle aspiration.

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  Fig. 2. Post-test feedback survey after digital pathology (DP) proficiency test (PT) in 2020 (A-D), 2021 (E-H), and 2022 (I-L). GYN, gynecologic samples; FNA, fine-needle aspiration.

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  Fig. 3. Difference in scanning area coverage between whole-slide scanners in representative cases.

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  Fig. 4. Difference in scanned images of a few representative gynecologic samples (Pap smear). (A) High squamous intraepithelial lesion on a conventional Pap smear scanned with five layers of z-stacking. (B) Low squamous intraepithelial lesion on a liquid-based preparation scanned without z-stacking.

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  Fig. 5. Difference in the scanned images of a few representative body fluid samples. (A) Squamous cell carcinoma of the lungs on a conventional bronchial washing smear scanned with three layers of z-stacking. (B) Metastatic adenocarcinoma on a conventional pleural fluid smear scanned with three layers of z-stacking. (C) Serous carcinoma on a conventional ascitic fluid smear scanned without z-stacking. (D) Non-invasive papillary urothelial carcinoma, high grade, on a urine cytology sample scanned with three layers of z-stacking.

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  Fig. 6. Difference in scanned images of a few representative fine-needle aspiration cytology samples. (A) Pleomorphic adenoma of salivary gland on conventional smear scanned with three layers of z-stacking and (B) metastatic ductal carcinoma of lymph node on a conventional smear scanned with three layers of z-stacking.

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  Fig. 7. Result of image quality assessment using a questionnaire by four experienced cytopathologists.

Reference

References

1. Lee HK, Kim SN, Khang SK, Kang CS, Yoon HK. Quality control program and its results of Korean Society for Cytopathologists. Korean J Cytopathol. 2008; 19:65–71.

2. The Korean Society for Cytopathology [Internet]. Seoul: The Korean Society for Cytopathology, 2023 [cited 2023 Apr 25]. Availabe from: http://eng.cytopathol.or.kr/index.asp.

3. Oh EJ, Jung CK, Kim DH, et al. Current cytology practices in Korea: a nationwide survey by the Korean Society for Cytopathology. J Pathol Transl Med. 2017; 51:579–87.

4. Chong Y, Bae JM, Kang DW, Kim G, Han HS. Development of quality assurance program for digital pathology by the Korean Society of Pathologists. J Pathol Transl Med. 2022; 56:370–82.

5. Hong SA, Jung H, Kim SS, et al. Current status of cytopathology practice in Korea: impact of the coronavirus pandemic on cytopathology practice. J Pathol Transl Med. 2022; 56:361–9.

6. Chong Y, Jung H, Pyo JS, Hong SW, Oh HK. Current status of cytopathology practices in Korea: annual report on the Continuous Quality Improvement program of the Korean Society for Cytopathology for 2018. J Pathol Transl Med. 2020; 54:318–31.

7. Choi YD, Oh HK, Kim SJ, et al. Continuous quality improvement program and its results of Korean Society for Cytopathology. J Pathol Transl Med. 2020; 54:246–52.

8. 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.

9. The Royal College of Pathologist of Australasia Quality Assurance Programs (RCPAQAP) [Internet]. St. Leonards: The Royal College of Pathologist of Australasia, 2023 [cited 2023 Apr 15]. Available from: https://rcpaqap.com.au/about-us/.

10. UK National External Quality Assessment Service (NEQAS) [Internet]. Sheffield: UK NEQAS, 2023 [cited 2023 Apr 25]. Available from: https://ukneqas.org.uk/about-us/.

11. Pannoramic 250 Flash Series Digital Scnners [Internet]. Budapest: 3DHISTECH, 2023 [cited 2023 Apr 26]. Avaliable from: https://www.3dhistech.com/research/pannoramic-digital-slide-scanners/pannoramic-250-flash-iii/.

12. Nano Zoomer S360 Digital slide scanner [Internet]. Hanamatsu: HAMAMATSU Photonics K.K., 2023 [cited 2023 Apr 26]. Available from: https://www.hamamatsu.com/us/en/product/life-science-and-medical-systems/digital-slide-scanner/C13220-01.html/.

13. VENTANA DP 200 slide scanner [Internet]. Rotkreuz: F. HoffmannLa Roche Ltd., 2023 [cited 2023 Apr 26]. Available from: https://diagnostics.roche.com/global/en/products/instruments/ventana-dp200-ins-6320.html.

14. Ultra Fast Scanner [Internet]. Amsterdam: Philips Intellisite, 2023 [cited 2023 Apr 26]. Available from: https://www.usa.philips.com/healthcare/product/HCNOCTN442/ultra-fast-scanner-digital-pathology-slide-scanner/.

15. Akbar S, Sincair D, Shen R, Parwani AV, Li Z. Comparative assessment of digital sccnners for scanning cytology slides. In: USCAP Annual Meeting; 2023 Mar 11-16; New Orleans, LA.

16. Genius Digital Diagnostics System [Internet]. Marlborough: HOLOGIC, 2023 [cited 2023 Apr 25]. Available from: https://www.hologic.com/hologic-products/cytology/genius-digital-diagnosticssystem/.

17. Yao K, Sadimin E, Chang S, Schmolze D, Li Z. Current applications and challenges of digital pathology in cytopathology. Hum Pathol Rep. 2022; 28:300634.

18. Capitanio A, Dina RE, Treanor D. Digital cytology: a short review of technical and methodological approaches and applications. Cytopathology. 2018; 29:317–25.

19. Niazi MK, Parwani AV, Gurcan MN. Digital pathology and artificial intelligence. Lancet Oncol. 2019; 20:e253–61.

20. Hanna MG, Monaco SE, Cuda J, Xing J, Ahmed I, Pantanowitz L. Comparison of glass slides and various digital-slide modalities for cytopathology screening and interpretation. Cancer Cytopathol. 2017; 125:701–9.

21. Xu C, Li M, Li G, Zhang Y, Sun C, Bai N. Cervical cell/clumps detection in cytology images using transfer learning. Diagnostics (Basel). 2022; 12:2477.

22. Hipp JD, Sica J, McKenna B, et al. The need for the pathology community to sponsor a whole slide imaging repository with technical guidance from the pathology informatics community. J Pathol Inform. 2011; 2:31.

23. Hartman DJ, Van Der Laak J, Gurcan MN, Pantanowitz L. Value of public challenges for the development of pathology deep learning algorithms. J Pathol Inform. 2020; 11:7.

24. Thakur N, Alam MR, Abdul-Ghafar J, Chong Y. Recent application of artificial intelligence in non-gynecological cancer cytopathology: a systematic review. Cancers (Basel). 2022; 14:3529.