Summary
Cervical cancer remains a major public health challenge in low- and middle-income countries (LMICs) due to financial and logistical issues. The World Health Organization (WHO) recommendation for cervical cancer screening in LMICs includes Human Papillomavirus (HPV) testing as primary screening followed by visual inspection with acetic acid (VIA) and treatment. However, VIA is a subjective procedure dependent on the healthcare provider's experience. Therefore, an objective approach based on quantitative diagnostic algorithms is desirable to improve performance of VIA. With this objective and in a collaboration between the Gynecology and Obstetrics Department of the Geneva University Hospital (HUG) and the Swiss Institute of Technology (EPFL), our group started the development of an automated smartphone-based image classification device called AVC (for Automatic VIA Classifier). Two-minute videos of the cervix are recorded during VIA and classified using an artificial neural network (ANN) and image processing techniques to differentiate precancer and cancer from non-neoplastic cervical tissue. The result is displayed on the smartphone screen with a delimitation map of the lesions when appropriate. The key feature used for classification is the dynamic of cervical acetowhitening during the 120 second following the application of acetic acid. Precancerous and cancerous cells whiten more rapidly than non-cancerous ones and their whiteness persists stronger overtime. Our aim is to assess the diagnostic performance of the AVC and to compare it with the performance of current triage tests (VIA and cytology). Histopathological examination will serve as reference standard. Participants' and providers' acceptability will also be considered as part of the study. The study will be nested in an ongoing cervical cancer screening program called "3T-approach" (for Test, Triage and Treat) which includes HPV self-sampling for women aged 30 to 49 years, followed by VIA triage and treatment if needed. The AVC will be evaluated in this context. The study's risk category is A according to swiss ethical guidelines. This decision is based on the fact that the planned measures for sampling biological material or collecting personal data entail only minimal risks and burdens.
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Inclusion Criteria: * Free and informed consent to take part in the study on a voluntary basis Exclusion Criteria: * No initiation of sexual intercourse * Pregnancy at the screening consultation * Any condition altering the cervix visualization at the screening consultation (e.g. heavy vaginal bleeding) * History of anogenital cancer or known anogenital cancer at the screening consultation * Previous hysterectomy * Not sufficiently healthy to participate in the study