In two independent populations, antibodies against prior/current C. trachomatis (Pgp3) were associated with a doubling in ovarian cancer risk, whereas markers of other infectious agents were unrelated. These findings lend support for an association between PID and ovarian cancer.

Background High-risk human papillomavirus (hrHPV) testing on self-collected samples has potential as a primary screening tool in cervical screening, but real-world evidence on its accuracy in hrHPV-based screening programmes is lacking. Methods In the Netherlands, women aged 30–60 years invited for cervical screening can choose between sampling at the clinician's office (Cervex Brush) or self-sampling at home (Evalyn Brush). HrHPV testing is performed using Roche Cobas 4800. We collected screening test results between January 2017 and March 2018 and histological follow-up until August 2019. The main outcome measures were mean cycle threshold (Ct) value, cervical intraepithelial neoplasia (CIN) grade 3 or cancer (CIN3+) and CIN grade 2 or worse (CIN2+). Findings 30,808 women had a self-collected and 456,207 had a clinician-collected sample. In hrHPV-positive women with adequate cytology, Ct values were higher for self-collection than clinician-collection with a mean Ct difference of 1·25 (95% CI 0·98–1·52) in women without CIN2+, 2·73 (1·75–3·72) in CIN2 and 3·59 (3·03–4·15) in CIN3+. The relative sensitivity for detecting CIN3+ was 0·94 (0·90–0·97) for self-collection versus clinician-collection and the relative specificity was 1·02 (1·02–1·02). Interpretation The clinical accuracy of hrHPV testing on a self-collected sample for detection of CIN3+ is high and supports its use as a primary screening test for all invited women. Because of the slightly lower sensitivity of hrHPV testing on a self-collected compared to a clinician-collected sample, an evaluation of the workflow procedure to optimise clinical performance seems warranted. Funding National Institute for Public Health and the Environment (the Netherlands) and the European Commission.

Cervical cancer is a leading cause of cancer mortality, with approximately 90% of the 250,000 deaths per year occurring in low- and middle-income countries (LMIC). Secondary prevention with cervical screening involves detecting and treating precursor lesions; however, scaling screening efforts in LMIC has been hampered by infrastructure and cost constraints. Recent work has supported the development of an artificial intelligence (AI) pipeline on digital images of the cervix to achieve an accurate and reliable diagnosis of treatable precancerous lesions. In particular, WHO guidelines emphasize visual triage of women testing positive for human papillomavirus (HPV) as the primary screen, and AI could assist in this triage task. Published AI reports have exhibited overfitting, lack of portability, and unrealistic, near-perfect performance estimates. To surmount recognized issues, we implemented a comprehensive deep-learning model selection and optimization study on a large, collated, multi-institutional dataset of 9,462 women (17,013 images). We evaluated relative portability, repeatability, and classification performance. The top performing model, when combined with HPV type, achieved an area under the Receiver Operating Characteristics (ROC) curve (AUC) of 0.89 within our study population of interest, and a limited total extreme misclassification rate of 3.4%, on held-aside test sets. Our work is among the first efforts at designing a robust, repeatable, accurate and clinically translatable deep-learning model for cervical screening.