Deep-Learning–Based Evaluation of Dual Stain Cytology for Cervical Cancer Screening: A New Paradigm
Menée à partir de données histologiques portant sur 602 femmes ayant subi une colposcopie, 3 333 patientes infectées par le papillomavirus humain et 318 hommes ayant subi une anoscopie, cette étude évalue la performance d'un système automatisé, utilisant un algorithme d'apprentissage automatique qui analyse les images de lames histologiques avec double marquage (p16/Ki67), pour détecter des lésions cervicales intraépithéliales de haut grade
Cervical cancer is diagnosed in more than 13 000 women, of whom 30% will die in the United States each year (1). Worldwide, more than 3% of both the global burden of cancer incidence and death is attributable to this preventable disease with an annual estimated 570 000 cases and 311 400 deaths (2). Prevention of cervical cancer can be primary through human papillomavirus (HPV) vaccination or secondary through repetitive screening during a woman’s reproductive years. The challenge of screening has been to capture true positives and reduce the false positives that lead to over-testing and increased medical costs. The current recommendation of combined cytology with HPV testing of high-risk subtypes at 5-year intervals has improved the positive predictive value of screening but still leads to over-referrals for colposcopy because of the high prevalence of transient HPV infections (3). An additional challenge for resource-limited regions is the need for a sophisticated infrastructure requiring materials for cytology and HPV testing; processing instruments; highly trained cytopathologists; systems for reporting, tracking, and follow-up; and the lack of health-care insurance in many regions requiring out-of-pocket pay by patients.
Journal of the National Cancer Institute , éditorial en libre accès, 2019