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Cost-effectiveness of artificial intelligence-assisted liquid-based cytology testing for cervical cancer screening in China

  • Mingwang Shen
  • , Zhuoru Zou
  • , Heling Bao
  • , Christopher K. Fairley
  • , Karen Canfell
  • , Jason J. Ong
  • , Jane Hocking
  • , Eric P.F. Chow
  • , Guihua Zhuang
  • , Linhong Wang
  • , Lei Zhang
  • Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province
  • Xi'an Jiaotong University
  • Chinese Academy of Medical Sciences
  • Chinese Center for Disease Control and Prevention
  • Melbourne Sexual Health Centre
  • Monash University
  • Cancer Council NSW
  • University of New South Wales
  • The University of Sydney
  • Melbourne School of Population and Global Health

Research output: Contribution to journal › Article › peer-review

48 Scopus citations

Abstract

Background: The 2021 World Health Organization (WHO) guidelines for cervical cancer screening recommend human papillomavirus (HPV) DNA or mRNA testing. Artificial intelligence (AI)-assisted liquid-based cytology (LBC) systems also have the potential to facilitate rapid scale-up of cervical cancer screening. We aimed to evaluate the cost-effectiveness of AI-assisted LBC testing, compared with the manual LBC and HPV-DNA testing, for primary cervical cancer screening in China. Methods: We developed a Markov model for a cohort of 100,000 women aged 30 years over a lifetime to simulate the natural history of cervical cancer progression. We evaluated the incremental cost-effectiveness ratios (ICER) of 18 screening strategies (a combination of the three screening methods with six screening frequencies) from a healthcare provider's perspective. The willingness-to-pay threshold (US$30,828) was chosen as three times the Chinese per-capita gross domestic product in 2019. Univariate and probabilistic sensitivity analyses were performed to examine the robustness of the results. Findings: Compared with no screening, all 18 screening strategies were cost-effective, with an ICER of $622–24,482 per quality-adjusted life-year (QALY) gained. If HPV testing after scaling up to population level screening costs $10.80 or more, screening once every 5 years using AI-assisted LBC would be the most cost-effective strategy with an ICER of $8790/QALY gained compared with the lower-cost non-dominated strategy on the cost-effectiveness frontier. Its probability of being cost-effective was higher (55.4%) than other strategies. Sensitivity analyses showed that the most cost-effective strategy would become AI-assisted LBC testing once every 3 years if the sensitivity (74.1%) and specificity (95.6%) of this method were both reduced by ≥10%. The most cost-effective strategy would become HPV-DNA testing once every 5 years if the cost of AI-assisted LBC was more expensive than manual LBC or if the HPV-DNA test cost is slightly reduced (from $10.8 to <$9.4). Interpretation: AI-assisted LBC screening once every 5 years could be more cost-effective than manually-read LBC. Using AI-assisted LBC could have comparable cost-effectiveness to HPV DNA screening, but the relative pricing of HPV DNA testing is critical in this result.

Original languageEnglish
Article number100726
JournalThe Lancet Regional Health - Western Pacific
Volume34
DOIs
StatePublished - May 2023

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • AI-assisted LBC
  • Cervical cancer screening
  • Cost-effectiveness
  • HPV DNA testing
  • Manual LBC

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