Division of Cancer Epidemiology & Genetics, National Cancer Institute, Rockville, Maryland, USA.
Steinbeis Transfer Center for Medical Systems Biology, Heidelberg, Germany.
Clin Infect Dis. 2022 Oct 29;75(9):1565-1572. doi: 10.1093/cid/ciac211.
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).
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.
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).
Compared with manual DS, automated DS detects the same number of precancers, with a lower HRA referral rate.
人乳头瘤病毒相关生物标志物，如 p16/Ki-67“双染”（DS）细胞学，已显示出在肛门癌筛查方面具有良好的临床性能。在这里，我们评估了自动化评估 DS 细胞学（自动 DS）在与人类免疫缺陷病毒（HIV）共存的男男性行为者（MSM）中检测肛门癌前病变的性能。
我们对 2009 年至 2010 年间接受肛门癌筛查和高分辨率肛门镜检查（HRA）的 320 名 HIV 阳性 MSM 进行了横断面分析。我们评估了基于深度学习分类器的自动 DS 与手动 DS 细胞学评估（手动 DS）相比，检测高级别上皮内瘤变（AIN2+）和高级别上皮内瘤变（AIN3）的性能。我们评估了自动方法量化的不同 DS 阳性细胞阈值，并在 HIV 阳性 MSM 的假设人群中对模型性能与其他筛查策略进行了比较。
与手动 DS 相比，自动 DS 检测 AIN2+的特异性（50.9%对 42.2%；P<.001）显著提高，敏感性（93.2%对 92.1%）相似。自动 DS 与 HPV 检测联合进行分诊，特异性显著高于自动 DS 单独检测（56.5%对 50.9%；P<.001），敏感性相同（93.2%）。在一项假设 AIN2+患病率为 20%的模型分析中，自动 DS 检测到的癌前病变多于手动 DS 和肛门细胞学（分别为 186、184 和 162），且每例 AIN2+检测到的 HRA 转诊率最低（分别为 3.1、3.5 和 3.3）。
与手动 DS 相比，自动 DS 检测到相同数量的癌前病变，且 HRA 转诊率较低。
