A rapid, high-volume cervical screening project using self-sampling and isothermal PCR HPV testing

Infect Agent Cancer. 2020 Oct 22:15:64. doi: 10.1186/s13027-020-00329-0. eCollection 2020.

Abstract

Objective: Rapid, high-volume screening programs are needed as part of cervical cancer prevention in China.

Methods: In a 5-day screening project in Inner Mongolia, 3345 women volunteered following a community awareness campaign, and self-swabbed to permit rapid HPV testing. Two AmpFire™ HPV detection systems (Atila Biosystems) were sufficient to provide pooled 15-HPV type data within an hour. HPV+ patients had same-day digital colposcopy (DC) performed by 1 of 6 physicians, using the EVA™ system (MobileODT). Digital images were obtained and, after biopsy of suspected lesions for later confirmatory diagnosis, women were treated immediately based on colposcopic impression. Suspected low- grade lesions were offered treatment with thermal ablation (Wisap), and suspected high-grade lesions were treated with LLETZ.

Results: Of 3345 women screened, 624 (18.7%) were HPV+. Of these, 88.5% HPV+ women underwent same-day colposcopy and 78 were treated. Later consensus histology results obtained on 197 women indicated 20 CIN2+, of whom 15 were detected and treated/referred at screening (10 by thermal ablation, 4 by LLETZ, 1 by referral).

Conclusions: Global control of cervical cancer will require both vaccination and screening of a huge number of women. This study illustrates a cervical screening strategy that can be used to screen-and-treat large numbers of women. HPV self-sampling facilitates high-volume screening. Specimens can be tested rapidly, promoting minimal loss-to-follow-up. Specifically, the AmpFire™ system used in this study is highly portable, simple, rapid (92 specimens per 65 min per unit), and economical. Visual triage can be performed on HPV+ women with a portable digital colposcope that provides magnification, lighting, and a recorded image. Diagnosis and appropriate treatment remain the most subjective elements. The digital image is under study for deep-learning based automated evaluation that could assist the management decision, either by itself or combined with HPV typing.