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

Infectious Agents and Cancer

Research

Vikrant V. Sahasrabuddhe¹  Mark Schiffman²  Silvia de Sanjose²  Ana Cecilia Rodriguez²  Didem Egemen²  Brian Befano³  Sameer Antani⁴  Aaron Lunda Shibemba⁵  Groesbeck P. Parham⁵  Friday Kaunga⁵  Samson Chisele⁵  Francis Musonda⁵  Mukatimui Kalima Munalula⁵  Evans Malyangu⁵  Mulindi H. Mwanahamuntu⁵ 

[1] Division of Cancer Prevention, National Cancer Institute, 20850, Rockville, MD, USA;HPV-AVE (PAVE) Initiative, Division of Cancer Epidemiology and Genetics, National Cancer Institute, 20850, Rockville, MD, USA;Information Management Services Inc, Calverton, MD, USA;National Library of Medicine, NIH, 20892, Bethesda, MD, USA;Women and Newborn Hospital, University Teaching Hospital, Lusaka, Zambia;

关键词: Cervical cancer screening;    HPV typing;    AI;    Risk-based management;   

DOI  :  10.1186/s13027-023-00536-5

 received in 2023-06-02, accepted in 2023-09-13,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundWHO 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).MethodsIn 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.ResultsHPV 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.ConclusionsThese 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.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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