Background: Human papillomavirus-related biomarkers such as p16/Ki-67 "dual-stain" (DS) cytology have shown promising clinical performance for anal cancer screening. Here, we assessed the performance of automated evaluation of DS cytology (automated DS) to detect anal precancer in men who have sex with men (MSM) and are living with human immunodeficiency virus (HIV).
Methods: We conducted a cross-sectional analysis of 320 MSM with HIV undergoing anal cancer screening and high-resolution anoscopy (HRA) in 2009-2010. We evaluated the performance of automated DS based on a deep-learning classifier compared to manual evaluation of DS cytology (manual DS) to detect anal intraepithelial neoplasia grade 2 or 3 (AIN2+) and grade 3 (AIN3). We evaluated different DS-positive cell thresholds quantified by the automated approach and modeled performance compared with other screening strategies in a hypothetical population of MSM with HIV.
Results: Compared with manual DS, automated DS had significantly higher specificity (50.9% vs 42.2%; P < .001) and similar sensitivity (93.2% vs 92.1%) for detection of AIN2+. Human papillomavirus testing with automated DS triage was significantly more specific than automated DS alone (56.5% vs 50.9%; P < .001), with the same sensitivity (93.2%). In a modeled analysis assuming a 20% AIN2+ prevalence, automated DS detected more precancers than manual DS and anal cytology (186, 184, and 162, respectively) and had the lowest HRA referral rate per AIN2+ case detected (3.1, 3.5, and 3.3, respectively).
Conclusions: Compared with manual DS, automated DS detects the same number of precancers, with a lower HRA referral rate.
Keywords: anal cancer; artificial intelligence; human papillomavirus; p16/Ki-67 dual stain; screening.
Published by Oxford University Press for the Infectious Diseases Society of America 2022.