Volume 36, Issue 3 pp. 250-258

ORIGINAL ARTICLE

Digital Cytology Combined With Artificial Intelligence Compared to Conventional Microscopy for Anal Cytology: A Preliminary Study

Renê Gerhard,

Corresponding Author

Renê Gerhard

[email protected]

orcid.org/0009-0003-4382-9791

Centre National de Pathologie, Laboratoire National de Santé, Dudelange, Luxembourg

Correspondence:

Renê Gerhard ([email protected])

Search for more papers by this author

Cioly Rivero Colmenarez,

Cioly Rivero Colmenarez

Centre National de Pathologie, Laboratoire National de Santé, Dudelange, Luxembourg

Search for more papers by this author

Corinne Selle,

Corinne Selle

Centre National de Pathologie, Laboratoire National de Santé, Dudelange, Luxembourg

Search for more papers by this author

Gaël Paul Hammer,

Gaël Paul Hammer

orcid.org/0000-0002-5798-9548

Centre National de Pathologie, Laboratoire National de Santé, Dudelange, Luxembourg

Search for more papers by this author

Renê Gerhard,

Corresponding Author

Renê Gerhard

[email protected]

orcid.org/0009-0003-4382-9791

Centre National de Pathologie, Laboratoire National de Santé, Dudelange, Luxembourg

Correspondence:

Renê Gerhard ([email protected])

Search for more papers by this author

Cioly Rivero Colmenarez,

Cioly Rivero Colmenarez

Centre National de Pathologie, Laboratoire National de Santé, Dudelange, Luxembourg

Search for more papers by this author

Corinne Selle,

Corinne Selle

Centre National de Pathologie, Laboratoire National de Santé, Dudelange, Luxembourg

Search for more papers by this author

Gaël Paul Hammer,

Gaël Paul Hammer

orcid.org/0000-0002-5798-9548

Centre National de Pathologie, Laboratoire National de Santé, Dudelange, Luxembourg

Search for more papers by this author

First published: 11 March 2025

https://doi.org/10.1111/cyt.13482

Funding: The authors received no specific funding for this work.

Share a link

Email
Wechat
Bluesky

ABSTRACT

Introduction

Recent studies have shown that digital cytology (DC) coupled with artificial intelligence (AI) algorithms is a valid approach to the diagnosis of cervico-vaginal lesions using liquid-based cytology (LBC). We evaluated the use of these methods for anal LBC specimens.

Methods

A series of 124 anal LBC slides previously diagnosed by conventional microscopy (CC) were reviewed with a DC/AI system that generated a gallery of images. Diagnoses based on the selected images, according to the 2014 Bethesda System for Reporting Cervical Cytology, were compared to CC.

Results

Overall, CC and DC/AI approaches detected a similar number of abnormal (ASC-US+) cases (63 and 62 cases, respectively). We observed an exact concordance between CC and DC in 70 (57.9%) cases, corresponding to a moderate agreement between the two approaches (κ = 0.41, p < 0.001). A moderate agreement (κ = 0.48, p < 0.001) was also found when positive cases were stratified into ‘low-grade’ (ASC-US, LSIL) and ‘high-grade’ lesions (ASC-H, HSIL). The DC/AI system detected more cases of higher severity (ASC-H, HSIL: 9 and 2 cases, respectively) than CC (3 cases classified as HSIL).

Conclusions

The number of ASC-US+ cases detected by both systems was similar. The DC/AI system detected more cases of higher severity compared to the CC.

Graphical Abstract

Cytology is an accepted method of screening for anal cancer. We compared the diagnostic performance between conventional microscopy and digital cytology in a series of anal specimens. The number of ASC-US+ cases detected by both systems was similar. However, the digital cytology system detected more high-grade lesions than conventional microscopy.

This study compared the diagnostic performance between conventional microscopy (CC) and digital cytology (DC) coupled with artificial intelligence (AI) in a series of anal cytology specimens. Although the number of ASC-US+ cases detected by both systems was similar, the DC/AI system detected more high-grade lesions than CC.

Conflicts of Interest

The authors declare no conflicts of interest.

Open Research

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

1G. M. Clifford, D. Georges, M. S. Shiels, et al., “A Meta-Analysis of Anal Cancer Incidence by Risk Group: Toward a Unified Anal Cancer Risk Scale,” International Journal of Cancer 148, no. 1 (2021): 38–47.
10.1002/ijc.33185
CAS PubMed Web of Science® Google Scholar
2C. Lin, S. Franceschi, and G. M. Clifford, “Human Papillomavirus Types From Infection to Cancer in the Anus, According to Sex and HIV Status: A Systematic Review and Meta-Analysis,” Lancet Infectious Diseases 18, no. 2 (2018): 198–206.
10.1016/S1473-3099(17)30653-9
PubMed Web of Science® Google Scholar
3M. A. Clarke and N. Wentzensen, “Strategies for Screening and Early Detection of Anal Cancers: A Narrative and Systematic Review and Meta-Analysis of Cytology, HPV Testing, and Other Biomarkers,” Cancer Cytopathology 126, no. 7 (2018): 447–460.
10.1002/cncy.22018
PubMed Web of Science® Google Scholar
4l. Spindler, I. Etienney, L. Abramowitz, et al., “Screening for Precancerous Anal Lesions Linked to Human Papillomaviruses: French Recommendations for Clinical Practice,” Techniques in Coloproctology 28, no. 1 (2024): 23.
10.1007/s10151-023-02899-8
CAS PubMed Web of Science® Google Scholar
5A. Albuquerque, E. Rios, and F. Schmitt, “Recommendations Favoring Anal Cytology as a Method for Anal Cancer Screening: A Systematic Review,” Cancers (Basel) 11, no. 12 (2019): 1942.
10.3390/cancers11121942
PubMed Web of Science® Google Scholar
6J. M. Berry, J. M. Palefsky, N. Jay, S. C. Cheng, T. M. Darragh, and P. V. Chin-Hong, “Performance Characteristics of Anal Cytology and Human Papillomavirus Testing in Patients With High-Resolution Anoscopy-Guided Biopsy of High-Grade Anal Intraepithelial Neoplasia,” Diseases of the Colon and Rectum 52, no. 2 (2009): 239–247.
10.1007/DCR.0b013e31819793d9
PubMed Web of Science® Google Scholar
7J. C. N. Gonçalves, A. C. L. Macedo, K. Madeira, et al., “Accuracy of Anal Cytology for Diagnostic of Precursor Lesions of Anal Cancer: Systematic Review and Meta-Analysis,” Diseases of the Colon and Rectum 62, no. 1 (2019): 112–120.
10.1097/DCR.0000000000001231
PubMed Web of Science® Google Scholar
8C. Zhao, A. B. Domfeh, and R. M. Austin, “Histopathologic Outcomes and Clinical Correlations for High-Risk Patients Screened With Anal Cytology,” Acta Cytologica 56, no. 1 (2012): 62–67.
10.1159/000331431
PubMed Web of Science® Google Scholar
9E. M. Betancourt, M. M. Wahbah, L. C. Been, E. Y. Chiao, D. R. Citron, and R. Lauricia, “Anal Cytology as a Predictor of Anal Intraepithelial Neoplasia in HIV-Positive Men and Women,” Diagnostic Cytopathology 41, no. 8 (2013): 697–702.
10.1002/dc.22941
PubMed Web of Science® Google Scholar
10A. Albuquerque, M. Sheaff, O. Stirrup, et al., “Performance of Anal Cytology Compared With High-Resolution Anoscopy and Histology in Women With Lower Anogenital Tract Neoplasia,” Clinical Infectious Diseases 67, no. 8 (2018): 1262–1268.
10.1093/cid/ciy273
CAS PubMed Web of Science® Google Scholar
11I. Girolami, L. Pantanowitz, S. Marletta, et al., “Diagnostic Concordance Between Whole Slide Imaging and Conventional Light Microscopy in Cytopathology: A Systematic Review,” Cancer Cytopathology 128, no. 1 (2020): 17–28.
10.1002/cncy.22195
PubMed Web of Science® Google Scholar
12E. F. Crowell, C. Bazin, F. Saunier, et al., “CytoProcessor™: A New Cervical Cancer Screening System for Remote Diagnosis,” Acta Cytologica 63, no. 3 (2019): 215–223.
10.1159/000497111
PubMed Web of Science® Google Scholar
13H. Ikenberg, S. Lieder, A. Ahr, M. Wilhelm, C. Schon, and A. Xhaja, “Comparison of the Hologic Genius Digital Diagnostics System With the ThinPrep Imaging System—A Retrospective Assessment,” Cancer Cytopathology 131, no. 7 (2023): 424–432.
10.1002/cncy.22695
PubMed Web of Science® Google Scholar
14R. L. Cantley, X. Jing, B. Smola, W. Hao, S. Harrington, and L. Pantanowitz, “Validation of AI-Assisted ThinPrep Pat Test Screening Using the Genius™ Digital Diagnostics System,” Journal of Pathology Informatics 15 (2024): 100391.
10.1016/j.jpi.2024.100391
PubMed Google Scholar
15T. M. Darragh and J. M. Palefsky, “ Anal Cytology,” in The Bethesda System for Reporting Cervical Cytology: Definitions, Criteria, and Explanatory Notes, 3rd ed., ed. R. Nayar and D. C. Wilbur (Springer, 2015), 263–285.
10.1007/978-3-319-11074-5_8
Google Scholar
16 R Core Team, R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2022), https://www.R-project.org/.
Google Scholar
17J. R. Landis and G. G. Koch, “The Measurement of Observer Agreement for Categorical Data,” Biometrics 33, no. 1 (1977): 159–174.
10.2307/2529310
CAS PubMed Web of Science® Google Scholar
18J. H. Scholefield, J. Johnson, A. Hitchcock, et al., “Guidelines for Anal Cytology—To Make Cytological Diagnosis and Follow Up Much More Reliable,” Cytopathology 9, no. 1 (1998): 15–22.
10.1046/j.1365-2303.1998.00134.x
CAS PubMed Web of Science® Google Scholar
19A. Lytwyn, I. E. Salit, J. Raboud, et al., “Interobserver Agreement in the Interpretation of Anal Intraepithelial Lesion,” Cancer 103, no. 7 (2005): 1447–1456.
10.1002/cncr.20927
CAS PubMed Web of Science® Google Scholar
20T. M. Darragh, D. Tokugawa, P. E. Castle, et al., “Interrater Agreement of Anal Cytology,” Cancer Cytopathology 121, no. 2 (2013): 72–78.
10.1002/cncy.21218
PubMed Web of Science® Google Scholar
21M. Benevolo, F. Rollo, A. Latini, et al., “Interobserver Agreement in the Interpretation of Anal Cytology,” Cancer Cytopathology 132, no. 7 (2024): 419–424.
10.1002/cncy.22807
CAS PubMed Web of Science® Google Scholar
22D. F. I. Kurtycz, P. N. Staats, D. J. Chute, et al., “Bethesda Interobserver Reproducibility Study-2 (BIRST-2): Bethesda System 2014,” Journal of the American Society of Cytopathology 6, no. 4 (2017): 131–144.
10.1016/j.jasc.2017.03.003
PubMed Google Scholar
23O. Bongaerts, C. Clevers, M. Debets, et al., “Conventional Microscopical Versus Digital Whole-Slide Imaging-Based of Thin-Layer Cervical Specimens: A Validation Study,” Journal of Pathology Informatics 9 (2018): 29.
10.4103/jpi.jpi_28_18
PubMed Google Scholar

Volume36, Issue3

May 2025

Pages 250-258