The need for forward-looking decision analyses to guide cervical cancer prevention

The development of "automated visual evaluation" for cervical cancer screening: The promise and challenges in adapting deep-learning for clinical testing

There is limited access to effective cervical cancer screening programs in many resource‐limited settings, resulting in continued high cervical cancer burden. Human papillomavirus (HPV) testing is increasingly recognized to be the preferable primary screening approach if affordable due to superior long‐term reassurance when negative and adaptability to self‐sampling. Visual inspection with acetic acid (VIA) is an inexpensive but subjective and inaccurate method widely used in resource‐limited settings, either for primary screening or for triage of HPV‐positive individuals. A deep learning (DL)‐based automated visual evaluation (AVE) of cervical images has been developed to help improve the accuracy and reproducibility of VIA as assistive technology. However, like any new clinical technology, rigorous evaluation and proof of clinical effectiveness are required before AVE is implemented widely. In the current article, we outline essential clinical and technical considerations involved in building a validated DL‐based AVE tool for broad use as a clinical test.

An updated natural history model of cervical cancer: derivation of model parameters

Mathematical models of cervical cancer have been widely used to evaluate the comparative effectiveness and cost-effectiveness of preventive strategies. Major advances in the understanding of cervical carcinogenesis motivate the creation of a new disease paradigm in such models. To keep pace with the most recent evidence, we updated a previously developed microsimulation model of human papillomavirus (HPV) infection and cervical cancer to reflect 1) a shift towards health states based on HPV rather than poorly reproducible histological diagnoses and 2) HPV clearance and progression to precancer as a function of infection duration and genotype, as derived from the control arm of the Costa Rica Vaccine Trial (2004-2010). The model was calibrated leveraging empirical data from the New Mexico Surveillance, Epidemiology, and End Results Registry (1980-1999) and a state-of-the-art cervical cancer screening registry in New Mexico (2007-2009). The calibrated model had good correspondence with data on genotype- and age-specific HPV prevalence, genotype frequency in precancer and cancer, and age-specific cancer incidence. We present this model in response to a call for new natural history models of cervical cancer intended for decision analysis and economic evaluation at a time when global cervical cancer prevention policy continues to evolve and evidence of the long-term health effects of cervical interventions remains critical.