Validation in Zambia of a cervical screening strategy including HPV genotyping and artificial intelligence (AI)-based automated visual evaluation

Groesbeck P Parham; Didem Egemen; Brian Befano; Mulindi H Mwanahamuntu; Ana Cecilia Rodriguez; Sameer Antani; Samson Chisele; Mukatimui Kalima Munalula; Friday Kaunga; Francis Musonda; Evans Malyangu; Aaron Lunda Shibemba; Silvia de Sanjose; Mark Schiffman; Vikrant V Sahasrabuddhe

doi:10.1186/s13027-023-00536-5

Validation in Zambia of a cervical screening strategy including HPV genotyping and artificial intelligence (AI)-based automated visual evaluation

Infect Agent Cancer. 2023 Oct 16;18(1):61. doi: 10.1186/s13027-023-00536-5.

Authors

Affiliations

¹ Women and Newborn Hospital, University Teaching Hospital, Lusaka, Zambia. professorparham@gmail.com.
² HPV-AVE (PAVE) Initiative, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, 20850, USA.
³ Information Management Services Inc, Calverton, MD, USA.
⁴ Women and Newborn Hospital, University Teaching Hospital, Lusaka, Zambia.
⁵ National Library of Medicine, NIH, Bethesda, MD, 20892, USA.
⁶ HPV-AVE (PAVE) Initiative, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, 20850, USA. schiffmm@exchange.nih.gov.
⁷ Division of Cancer Prevention, National Cancer Institute, Rockville, MD, 20850, USA.

Abstract

Background: WHO has recommended HPV testing for cervical screening where it is practical and affordable. If used, it is important to both clarify and implement the clinical management of positive results. We estimated the performance in Lusaka, Zambia of a novel screening/triage approach combining HPV typing with visual assessment assisted by a deep-learning approach called automated visual evaluation (AVE).

Methods: In this well-established cervical cancer screening program nested inside public sector primary care health facilities, experienced nurses examined women with high-quality digital cameras; the magnified illuminated images permit inspection of the surface morphology of the cervix and expert telemedicine quality assurance. Emphasizing sensitive criteria to avoid missing precancer/cancer, ~ 25% of women screen positive, reflecting partly the high HIV prevalence. Visual screen-positive women are treated in the same visit by trained nurses using either ablation (~ 60%) or LLETZ excision, or referred for LLETZ or more extensive surgery as needed. We added research elements (which did not influence clinical care) including collection of HPV specimens for testing and typing with BD Onclarity™ with a five channel output (HPV16, HPV18/45, HPV31/33/52/58, HPV35/39/51/56/59/66/68, human DNA control), and collection of triplicate cervical images with a Samsung Galaxy J8 smartphone camera™ that were analyzed using AVE, an AI-based algorithm pre-trained on a large NCI cervical image archive. The four HPV groups and three AVE classes were crossed to create a 12-level risk scale, ranking participants in order of predicted risk of precancer. We evaluated the risk scale and assessed how well it predicted the observed diagnosis of precancer/cancer.

Results: HPV type, AVE classification, and the 12-level risk scale all were strongly associated with degree of histologic outcome. The AVE classification showed good reproducibility between replicates, and added finer predictive accuracy to each HPV type group. Women living with HIV had higher prevalence of precancer/cancer; the HPV-AVE risk categories strongly predicted diagnostic findings in these women as well.

Conclusions: These results support the theoretical efficacy of HPV-AVE-based risk estimation for cervical screening. If HPV testing can be made affordable, cost-effective and point of care, this risk-based approach could be one management option for HPV-positive women.

Keywords: AI; Cervical cancer screening; HPV typing; Risk-based management.