Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD.
Kaiser Permanente Northern California Regional Laboratory, Berkeley, CA.
J Natl Cancer Inst. 2018 Nov 1;110(11):1222-1228. doi: 10.1093/jnci/djy044.
State-of-the-art cervical cancer prevention includes human papillomavirus (HPV) vaccination among adolescents and screening/treatment of cervical precancer (CIN3/AIS and, less strictly, CIN2) among adults. HPV testing provides sensitive detection of precancer but, to reduce overtreatment, secondary "triage" is needed to predict women at highest risk. Those with the highest-risk HPV types or abnormal cytology are commonly referred to colposcopy; however, expert cytology services are critically lacking in many regions.
To permit completely automatable cervical screening/triage, we designed and validated a novel triage method, a cytologic risk score algorithm based on computer-scanned liquid-based slide features (FocalPoint, BD, Burlington, NC). We compared it with abnormal cytology in predicting precancer among 1839 women testing HPV positive (HC2, Qiagen, Germantown, MD) in 2010 at Kaiser Permanente Northern California (KPNC). Precancer outcomes were ascertained by record linkage. As additional validation, we compared the algorithm prospectively with cytology results among 243 807 women screened at KPNC (2016-2017). All statistical tests were two-sided.
Among HPV-positive women, the algorithm matched the triage performance of abnormal cytology. Combined with HPV16/18/45 typing (Onclarity, BD, Sparks, MD), the automatable strategy referred 91.7% of HPV-positive CIN3/AIS cases to immediate colposcopy while deferring 38.4% of all HPV-positive women to one-year retesting (compared with 89.1% and 37.4%, respectively, for typing and cytology triage). In the 2016-2017 validation, the predicted risk scores strongly correlated with cytology (P < .001).
High-quality cervical screening and triage performance is achievable using this completely automated approach. Automated technology could permit extension of high-quality cervical screening/triage coverage to currently underserved regions.
最先进的宫颈癌预防措施包括为青少年接种人乳头瘤病毒（HPV）疫苗，以及为成年女性筛查/治疗宫颈癌前病变（CIN3/AIS，以及不太严格的 CIN2）。HPV 检测可以灵敏地检测出癌前病变，但为了减少过度治疗，需要进行二次“分流”以预测风险最高的女性。那些 HPV 高危型别或异常细胞学的患者通常会被转诊行阴道镜检查；然而，在许多地区，专家细胞学服务严重匮乏。
为了实现完全自动化的宫颈筛查/分流，我们设计并验证了一种新的分流方法，即基于计算机扫描液基制片特征的细胞学风险评分算法（FocalPoint，BD，Burlington，NC）。我们在 2010 年对 Kaiser Permanente Northern California（KPNC）的 1839 名 HPV 阳性（HC2，Qiagen，Germantown，MD）女性进行了这项研究，并将其与异常细胞学检测在预测宫颈癌前病变方面的表现进行了比较。通过病历记录来确定癌前病变的结局。作为进一步的验证，我们前瞻性地将该算法与 2016-2017 年在 KPNC 接受筛查的 243807 名女性的细胞学结果进行了比较。所有的统计检验均为双侧检验。
在 HPV 阳性女性中，该算法与异常细胞学检测的分流性能相当。结合 HPV16/18/45 分型（Onclarity，BD，Sparks，MD），该自动化策略将 91.7%的 HPV 阳性 CIN3/AIS 病例转诊至立即行阴道镜检查，同时将 38.4%的 HPV 阳性女性推迟至一年后复查（与 HPV 分型和细胞学分流的 89.1%和 37.4%相比）。在 2016-2017 年的验证中，预测风险评分与细胞学结果高度相关（P <.001）。
使用这种完全自动化的方法可以实现高质量的宫颈筛查和分流性能。自动化技术可以将高质量的宫颈筛查/分流覆盖范围扩大到目前服务不足的地区。
