Mark Schiffman

The Orderly Incorporation of Continuing Technologic Advances Into Cervical Cancer Screening

Treatment of Cervical Precancers is the Major Remaining Challenge in Cervical Screening Research

AbstractDeepening understanding of cervical cancer pathogenesis has yielded one-dose prophylactic human papillomavirus (HPV) vaccines and accurate HPV-based cervical screening tests. Knowing the heterogeneous carcinogenic potential of the individual high-risk HPV types permits prioritization of vaccination and screening strategies. However, “correct” (i.e., safe and effective) treatment of women found to have precancer is still undefined, forcing reliance on one or more rounds of untargeted destructive/excisional treatment. Both over-treatment and under-treatment are common results. Until safe and effective anti-HPV therapies are invented, defining optimal destructive/excisional treatment of precancer remains a fundamental and under-researched challenge, especially in resource-constrained settings.See related article by King et al., p. 681

A novel human papillomavirus and host DNA methylation score and detection of cervical adenocarcinoma

Abstract Background The widespread introduction of Pap testing in the 1960s was followed by substantial reductions in the incidence of cervical squamous cell cancer (SCC). However, the incidence of cervical adenocarcinoma (ADC) did not decrease, likely because of low Pap test sensitivity for ADC and adenocarcinoma in situ (AIS). This study assessed a novel human papillomavirus (HPV) and host DNA Methylation Score for AIS and ADC screening. Methods We measured methylation levels at CpG sites in the L2/L1 open reading frames of HPV16, HPV18, and HPV45—as well as 2 human loci, DCC and HS3ST2. Specifically, we tested exfoliated cervicovaginal cells from women in the HPV Persistence and Progression (PaP) cohort who were positive for 1 of HPV16, 18, or 45, including: 1) 176 with AIS/ADC, 2) 353 with cervical intraepithelial neoplasia–3 (CIN3) or SCC, and 3) controls who either cleared (HPV-Clearers; n = 579) or had persistent HPV16, 18, or 45 infection (HPV-Persisters; n = 292). CpG site–specific methylation percentages were measured using our reported next-generation methods. The Methylation Score was the average methylation percentage across all 35 CpG sites tested. Results Each individual CpG site had higher methylation percentages in exfoliated cervicovaginal cells collected from patients with AIS/ADC, and as well as those with CIN3/SCC, relative to either control group (weakest P  = .004). The Methylation Score for AIS/ADC had a sensitivity of 74% and specificity of 89%. The multivariate odds ratio (OR) between the Methylation Score (4th vs 1st quartile) for AIS/ADC was ORq4-q1 = 49.01 (PBenjamini-Hochberg = 4.64E-12), using HPV-Clearers as controls. CIN3/SCC had similar, albeit weaker, associations with the Methylation Score. Conclusions HPV16/18/45-infected women with Methylation Scores in the highest quartile had very high odds of AIS/ADC, suggesting they may warrant careful histologic evaluation of the cervical transition zone (eg, conization or loop electrosurgical excision procedure [LEEP]).

Design of the HPV-automated visual evaluation (PAVE) study: Validating a novel cervical screening strategy

Background: The HPV-automated visual evaluation (PAVE) Study is an extensive, multinational initiative designed to advance cervical cancer prevention in resource-constrained regions. Cervical cancer disproportionally affects regions with limited access to preventive measures. PAVE aims to assess a novel screening-triage-treatment strategy integrating self-sampled HPV testing, deep-learning-based automated visual evaluation (AVE), and targeted therapies. Methods: Phase 1 efficacy involves screening up to 100,000 women aged 25–49 across nine countries, using self-collected vaginal samples for hierarchical HPV evaluation: HPV16, else HPV18/45, else HPV31/33/35/52/58, else HPV39/51/56/59/68 else negative. HPV-positive individuals undergo further evaluation, including pelvic exams, cervical imaging, and biopsies. AVE algorithms analyze images, assigning risk scores for precancer, validated against histologic high-grade precancer. Phase 1, however, does not integrate AVE results into patient management, contrasting them with local standard care. Phase 2 effectiveness focuses on deploying AVE software and HPV genotype data in real-time clinical decision-making, evaluating feasibility, acceptability, cost-effectiveness, and health communication of the PAVE strategy in practice. Results: Currently, sites have commenced fieldwork, and conclusive results are pending. Conclusions: The study aspires to validate a screen-triage-treat protocol utilizing innovative biomarkers to deliver an accurate, feasible, and cost-effective strategy for cervical cancer prevention in resource-limited areas. Should the study validate PAVE, its broader implementation could be recommended, potentially expanding cervical cancer prevention worldwide. Funding: The consortial sites are responsible for their own study costs. Research equipment and supplies, and the NCI-affiliated staff are funded by the National Cancer Institute Intramural Research Program including supplemental funding from the Cancer Cures Moonshot Initiative. No commercial support was obtained. Brian Befano was supported by NCI/ NIH under Grant T32CA09168.

Artificial intelligence–based image analysis in clinical testing: lessons from cervical cancer screening

Abstract Novel screening and diagnostic tests based on artificial intelligence (AI) image recognition algorithms are proliferating. Some initial reports claim outstanding accuracy followed by disappointing lack of confirmation, including our own early work on cervical screening. This is a presentation of lessons learned, organized as a conceptual step-by-step approach to bridge the gap between the creation of an AI algorithm and clinical efficacy. The first fundamental principle is specifying rigorously what the algorithm is designed to identify and what the test is intended to measure (eg, screening, diagnostic, or prognostic). Second, designing the AI algorithm to minimize the most clinically important errors. For example, many equivocal cervical images cannot yet be labeled because the borderline between cases and controls is blurred. To avoid a misclassified case-control dichotomy, we have isolated the equivocal cases and formally included an intermediate, indeterminate class (severity order of classes: case>indeterminate>control). The third principle is evaluating AI algorithms like any other test, using clinical epidemiologic criteria. Repeatability of the algorithm at the borderline, for indeterminate images, has proven extremely informative. Distinguishing between internal and external validation is also essential. Linking the AI algorithm results to clinical risk estimation is the fourth principle. Absolute risk (not relative) is the critical metric for translating a test result into clinical use. Finally, generating risk-based guidelines for clinical use that match local resources and priorities is the last principle in our approach. We are particularly interested in applications to lower-resource settings to address health disparities. We note that similar principles apply to other domains of AI-based image analysis for medical diagnostic testing.

Initial evaluation of a new cervical screening strategy combining human papillomavirus genotyping and automated visual evaluation: the Human Papillomavirus–Automated Visual Evaluation Consortium

Abstract The HPV-Automated Visual Evaluation Consortium is validating a cervical screening strategy enabling accurate cervical screening in resource-limited settings. A rapid, low-cost human papillomavirus (HPV) assay permits sensitive HPV testing of self-collected vaginal specimens; HPV-negative women are reassured. Triage of positive participants combines HPV genotyping (4 groups in order of cancer risk) and visual inspection assisted by automated cervical visual evaluation that classifies cervical appearance as severe, indeterminate, or normal. Together, the combination predicts which women have precancer, permitting targeted management to those most needing treatment. We analyzed CIN3+ yield for each HPV-Automated Visual Evaluation risk level (HPV genotype crossed by automated cervical visual evaluation classification) from 9 clinical sites (Brazil, Cambodia, Dominican Republic, El Salvador, Eswatini, Honduras, Malawi, Nigeria, and Tanzania). Data from 1832 HPV-positive participants confirmed that HPV genotype and automated cervical visual evaluation classification strongly and independently predict risk of histologic CIN3+. The combination of these low-cost tests provided excellent risk stratification, warranting pre-implementation demonstration projects.

Validation of a Lab-free Low-cost Screening Test for Prevention of Cervical Cancer

The purpose of this study is to validate Automated Visual Evaluation (AVE), specifically the CINFinder version developed by DL Analytics, a point-of-care screening and triage diagnostic tool for cervical cancer based on the assessment of digital images through artificial intelligence. Several teams around the world have developed versions of AVE as a triage technology but none as a screening tool.