Comparison of the ScreenFire and Xpert HPV assays for the detection of human papillomavirus and cervical precancer among women living with HIV in Malawi.

Background: The World Health Organization (WHO) recommends human papillomavirus (HPV) testing for primary cervical cancer screening, including among women living with HIV (WLWH). Low-and-middle-income countries (LMICs) account for 85% of the cervical cancer burden globally, yet have limited access to HPV-based screening, largely due to cost. This study aims to compare the performance of a rapid, isothermal amplification HPV assay (ScreenFire) to that of the Xpert HPV assay for the detection of HPV and cervical precancer among WLWH in Malawi. Methods: We utilized stored self- and provider-collected specimens from a prospective cohort study of WLWH in Malawi from July 2020 to February 2022. Specimens were tested with both Xpert and ScreenFire HPV assays. The overall and within-channel non-hierarchical agreement between ScreenFire and Xpert was determined for both self- and provider-collected specimens. Hierarchical ScreenFire HPV positivity by channel was compared to Xpert for each histological diagnosis - cervical intraepithelial neoplasia grade 2 or worse (CIN2+) compared to

Calendar-period trends in cervical precancer and cancer diagnoses since the introduction of human papillomavirus and cytology co-testing into routine cervical cancer screening at Kaiser Permanente Northern California.

OBJECTIVES: The longer-term impact of introducing human papillomavirus (HPV) testing into routine cervical cancer screening on precancer and cancer rates by histologic type has not been well described. Calendar trends in diagnoses were examined using data from Kaiser Permanente Northern California, which introduced triennial HPV and cytology co-testing in 2003 for women aged ≥30 years. METHODS: We examined trends in cervical precancer (cervical intraepithelial neoplasia grade 3 [CIN3] and adenocarcinoma in situ [AIS]) and cancer (squamous cell carcinoma [SCC] and adenocarcinoma [ADC]) diagnoses per 1000 screened during 2003-2018. We examined ratios of squamous vs. glandular diagnoses (SCC:ADC and CIN3:AIS). RESULTS: CIN3 and AIS diagnoses increased approximately 2% and 3% annually, respectively (ptrend < 0.001 for both). While SCC diagnoses decreased by 5% per annually (ptrend < 0.001), ADC diagnoses did not change. These patterns were generally observed within each age group (30-39, 40-49, and 50-64 years). ADC diagnoses per 1000 screened did not change even among those who underwent co-testing starting in 2003-2006. SCC:ADC decreased from approximately 2.5:1 in 2003-2006 to 1.3:1 in 2015-2018 while the CIN3:AIS remained relatively constant, ∼10:1. CONCLUSIONS: Since its introduction at KPNC, co-testing increased the detection of CIN3 over time, which likely caused a subsequent reduction of SCC. However, there has been no observed decrease in ADC. One possible explanation for lack of effectiveness against ADC is the underdiagnosis of AIS. Novel strategies to identify and treat women at high risk of ADC need to be developed and clinically validated.

Squamocolumnar junction visibility, age, and implications for cervical cancer screening programs.

Visual assessment is currently used for primary screening or triage of screen-positive individuals in cervical cancer screening programs. Most guidelines recommend screening and triage up to at least age 65 years old. We examined cervical images from participants in three National Cancer Institute funded cervical cancer screening studies: ALTS (2864 participants recruited between 1996 to 1998) in the United States (US), NHS (7548 in 1993) in Costa Rica, and the Biopsy study (684 between 2009 to 2012) in the US. Specifically, we assessed the visibility of the squamocolumnar junction (SCJ), which is the susceptible zone for precancer/cancer by age, as reported by colposcopist reviewers either at examination or review of cervical images. The visibility of the SCJ declined substantially with age: by the late 40s the majority of people screened had at most partially visible SCJ. On longitudinal analysis, the change in SCJ visibility from visible to not visible was largest for participants from ages 40-44 in ALTS and 50-54 in NHS. Of note, in the Biopsy study, the live colposcopic exam resulted in significantly higher SCJ visibility as compared to review of static images (Weighted kappa 0.27 (95% Confidence Interval: 0.21, 0.33), Asymmetry chi-square P-value<0.001). Lack of SCJ visibility leads to increased difficulty in diagnosis and management of cervical precancers. Therefore, cervical cancer screening programs reliant on visual assessment might consider lowering the upper age limit for screening if there are not adequately trained personnel and equipment to evaluate and manage participants with inadequately visible SCJ.

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.

Agreement on Lesion Presence and Location at Colposcopy.

OBJECTIVES/PURPOSE: The reproducibility and sensitivity of image-based colposcopy is low, but agreement on lesion presence and location remains to be explored. Here, we investigate the interobserver agreement on lesions on colposcopic images by evaluating and comparing marked lesions on digitized colposcopic images between colposcopists. METHODS: Five colposcopists reviewed images from 268 colposcopic examinations. Cases were selected based on histologic diagnosis, i.e., normal/cervical intraepithelial neoplasia (CIN)1 ( n = 50), CIN2 ( n = 50), CIN3 ( n = 100), adenocarcinoma in situ ( n = 53), and cancer ( n = 15). We obtained digitized time-series images every 7-10 seconds from before acetic acid application to 2 minutes after application. Colposcopists were instructed to digitally annotate all areas with acetowhitening or suspect of lesions. To estimate the agreement on lesion presence and location, we assessed the proportion of images with annotations and the proportion of images with overlapping annotated area by at least 4 (4+) colposcopists, respectively. RESULTS: We included images from 241 examinations (1 image from each) with adequate annotations. The proportion with a least 1 lesion annotated by 4+ colposcopists increased by severity of histologic diagnosis. Among the CIN3 cases, 84% had at least 1 lesion annotated by 4+ colposcopists, whereas 54% of normal/CIN1 cases had a lesion annotated. Notably, the proportion was 70% for adenocarcinoma in situ and 71% for cancer. Regarding lesion location, there was no linear association with severity of histologic diagnosis. CONCLUSION: Despite that 80% of the CIN2 and CIN3 cases were annotated by 4+ colposcopists, we did not find increasing agreement on lesion location with histology severity. This underlines the subjective nature of colposcopy.

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

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.

2019 ASCCP Risk-Based Management Consensus Guidelines: Updates Through 2023.

ABSTRACT: This Research Letter summarizes all updates to the 2019 Guidelines through September 2023, including: endorsement of the 2021 Opportunistic Infections guidelines for HIV+ or immunosuppressed patients; clarification of use of human papillomavirus testing alone for patients undergoing observation for cervical intraepithelial neoplasia 2; revision of unsatisfactory cytology management; clarification that 2012 guidelines should be followed for patients aged 25 years and older screened with cytology only; management of patients for whom colposcopy was recommended but not completed; clarification that after treatment for cervical intraepithelial neoplasia 2+, 3 negative human papillomavirus tests or cotests at 6, 18, and 30 months are recommended before the patient can return to a 3-year testing interval; and clarification of postcolposcopy management of minimally abnormal results.

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

Deepening 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.

Reproducible and clinically translatable deep neural networks for cervical screening.

Cervical cancer is a leading cause of cancer mortality, with approximately 90% of the 250,000 deaths per year occurring in low- and middle-income countries (LMIC). Secondary prevention with cervical screening involves detecting and treating precursor lesions; however, scaling screening efforts in LMIC has been hampered by infrastructure and cost constraints. Recent work has supported the development of an artificial intelligence (AI) pipeline on digital images of the cervix to achieve an accurate and reliable diagnosis of treatable precancerous lesions. In particular, WHO guidelines emphasize visual triage of women testing positive for human papillomavirus (HPV) as the primary screen, and AI could assist in this triage task. In this work, we implemented a comprehensive deep-learning model selection and optimization study on a large, collated, multi-geography, multi-institution, and multi-device dataset of 9462 women (17,013 images). We evaluated relative portability, repeatability, and classification performance. The top performing model, when combined with HPV type, achieved an area under the Receiver Operating Characteristics (ROC) curve (AUC) of 0.89 within our study population of interest, and a limited total extreme misclassification rate of 3.4%, on held-aside test sets. Our model also produced reliable and consistent predictions, achieving a strong quadratic weighted kappa (QWK) of 0.86 and a minimal %2-class disagreement (% 2-Cl. D.) of 0.69%, between image pairs across women. Our work is among the first efforts at designing a robust, repeatable, accurate and clinically translatable deep-learning model for cervical screening.

Validation in Zambia of a cervical screening strategy including HPV genotyping and artificial intelligence (AI)-based automated visual evaluation.

BACKGROUND: WHO has recommended HPV testing for cervical screening where it is practical and affordable. If used, it is important to both clarify and implement the clinical management of positive results. We estimated the performance in Lusaka, Zambia of a novel screening/triage approach combining HPV typing with visual assessment assisted by a deep-learning approach called automated visual evaluation (AVE). METHODS: In this well-established cervical cancer screening program nested inside public sector primary care health facilities, experienced nurses examined women with high-quality digital cameras; the magnified illuminated images permit inspection of the surface morphology of the cervix and expert telemedicine quality assurance. Emphasizing sensitive criteria to avoid missing precancer/cancer, ~ 25% of women screen positive, reflecting partly the high HIV prevalence. Visual screen-positive women are treated in the same visit by trained nurses using either ablation (~ 60%) or LLETZ excision, or referred for LLETZ or more extensive surgery as needed. We added research elements (which did not influence clinical care) including collection of HPV specimens for testing and typing with BD Onclarity™ with a five channel output (HPV16, HPV18/45, HPV31/33/52/58, HPV35/39/51/56/59/66/68, human DNA control), and collection of triplicate cervical images with a Samsung Galaxy J8 smartphone camera™ that were analyzed using AVE, an AI-based algorithm pre-trained on a large NCI cervical image archive. The four HPV groups and three AVE classes were crossed to create a 12-level risk scale, ranking participants in order of predicted risk of precancer. We evaluated the risk scale and assessed how well it predicted the observed diagnosis of precancer/cancer. RESULTS: HPV type, AVE classification, and the 12-level risk scale all were strongly associated with degree of histologic outcome. The AVE classification showed good reproducibility between replicates, and added finer predictive accuracy to each HPV type group. Women living with HIV had higher prevalence of precancer/cancer; the HPV-AVE risk categories strongly predicted diagnostic findings in these women as well. CONCLUSIONS: These results support the theoretical efficacy of HPV-AVE-based risk estimation for cervical screening. If HPV testing can be made affordable, cost-effective and point of care, this risk-based approach could be one management option for HPV-positive women.

Design of the HPV-Automated Visual Evaluation (PAVE) Study: Validating a Novel Cervical Screening Strategy.

Objective: To describe the HPV-Automated Visual Evaluation (PAVE) Study, an international, multi-centric study designed to evaluate a novel cervical screen-triage-treat strategy for resource-limited settings as part of a global strategy to reduce cervical cancer burden. The PAVE strategy involves: 1) screening with self-sampled HPV testing; 2) triage of HPV-positive participants with a combination of extended genotyping and visual evaluation of the cervix assisted by deep-learning-based automated visual evaluation (AVE); and 3) treatment with thermal ablation or excision (Large Loop Excision of the Transformation Zone). The PAVE study has two phases: efficacy (2023-2024) and effectiveness (planned to begin in 2024-2025). The efficacy phase aims to refine and validate the screen-triage portion of the protocol. The effectiveness phase will examine acceptability and feasibility of the PAVE strategy into clinical practice, cost-effectiveness, and health communication within the PAVE sites. Study design: Phase 1 Efficacy: Around 100,000 nonpregnant women, aged 25-49 years, without prior hysterectomy, and irrespective of HIV status, are being screened at nine study sites in resource-limited settings. Eligible and consenting participants perform self-collection of vaginal specimens for HPV testing using a FLOQSwab (Copan). Swabs are transported dry and undergo testing for HPV using a newly-redesigned isothermal DNA amplification HPV test (ScreenFire HPV RS), which has been designed to provide HPV genotyping by hierarchical risk groups: HPV16, else HPV18/45, else HPV31/33/35/52/58, else HPV39/51/56/59/68. HPV-negative individuals are considered negative for precancer/cancer and do not undergo further testing. HPV-positive individuals undergo pelvic examination with collection of cervical images and targeted biopsies of all acetowhite areas or endocervical sampling in the absence of visible lesions. Accuracy of histology diagnosis is evaluated across all sites. Cervical images are used to refine a deep learning AVE algorithm that classifies images as normal, indeterminate, or precancer+. AVE classifications are validated against the histologic endpoint of high-grade precancer determined by biopsy. The combination of HPV genotype and AVE classification is used to generate a risk score that corresponds to the risk of precancer (lower, medium, high, highest). During the efficacy phase, clinicians and patients within the PAVE sites will receive HPV testing results but not AVE results or risk scores. Treatment during the efficacy phase will be performed per local standard of care: positive Visual Inspection with Acetic Acid impression, high-grade colposcopic impression or CIN2+ on colposcopic biopsy, HPV positivity, or HPV 16,18/45 positivity. Follow up of triage negative patients and post treatment will follow standard of care protocols. The sensitivity of the PAVE strategy for detection of precancer will be compared to current SOC at a given level of specificity.Phase 2 Effectiveness: The AVE software will be downloaded to the new dedicated image analysis and thermal ablation devices (Liger Iris) into which the HPV genotype information can be entered to provide risk HPV-AVE risk scores for precancer to clinicians in real time. The effectiveness phase will examine clinician use of the PAVE strategy in practice, including feasibility and acceptability for clinicians and patients, cost-effectiveness, and health communication within the PAVE sites. Conclusion: The goal of the PAVE study is to validate a screen-triage-treat protocol using novel biomarkers to provide an accurate, feasible, cost-effective strategy for cervical cancer prevention in resource-limited settings. If validated, implementation of PAVE at larger scale can be encouraged. 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. Date of protocol latest review: September 24 th 2023.

Cervical Cancer Screening: A Review.

Importance: Each year in the US, approximately 100 000 people are treated for cervical precancer, 14 000 people are diagnosed with cervical cancer, and 4000 die of cervical cancer. Observations: Essentially all cervical cancers worldwide are caused by persistent infections with one of 13 carcinogenic human papillomavirus (HPV) genotypes: 16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, and 68. HPV vaccination at ages 9 through 12 years will likely prevent more than 90% of cervical precancers and cancers. In people with a cervix aged 21 through 65 years, cervical cancer is prevented by screening for and treating cervical precancer, defined as high-grade squamous intraepithelial lesions of the cervix. High-grade lesions can progress to cervical cancer if not treated. Cervicovaginal HPV testing is 90% sensitive for detecting precancer. In the general population, the risk of precancer is less than 0.15% over 5 years following a negative HPV test result. Among people with a positive HPV test result, a combination of HPV genotyping and cervical cytology (Papanicolaou testing) can identify the risk of precancer. For people with current precancer risks of less than 4%, repeat HPV testing is recommended in 1, 3, or 5 years depending on 5-year precancer risk. For people with current precancer risks of 4% through 24%, such as those with low-grade cytology test results (atypical squamous cells of undetermined significance [ASC-US] or low-grade squamous intraepithelial lesion [LSIL]) and a positive HPV test of unknown duration, colposcopy is recommended. For patients with precancer risks of less than 25% (eg, cervical intraepithelial neoplasia grade 1 [CIN1] or histologic LSIL), treatment-related adverse effects, including possible association with preterm labor, can be reduced by repeating colposcopy to monitor for precancer and avoiding excisional treatment. For patients with current precancer risks of 25% through 59% (eg, high-grade cytology results of ASC cannot exclude high-grade lesion [ASC-H] or high-grade squamous intraepithelial lesion [HSIL] with positive HPV test results), management consists of colposcopy with biopsy or excisional treatment. For those with current precancer risks of 60% or more, such as patients with HPV-16-positive HSIL, proceeding directly to excisional treatment is preferred, but performing a colposcopy first to confirm the need for excisional treatment is acceptable. Clinical decision support tools can facilitate correct management. Conclusions and Relevance: Approximately 100 000 people are treated for cervical precancer each year in the US to prevent cervical cancer. People with a cervix should be screened with HPV testing, and if HPV-positive, genotyping and cytology testing should be performed to assess the risk of cervical precancer and determine the need for colposcopy or treatment. HPV vaccination in adolescence will likely prevent more than 90% of cervical precancers and cancers.

Squamocolumnar junction visibility among cervical cancer screening population in Peru might influence upper age for screening programs.

Cervical cancer screening and treatment of screen positives is an important and effective strategy to reduce cervical cancer morbidity and mortality. In order to have an accurate cervical cancer screening and evaluation of positives, the entire Squamocolumnar Junction (SCJ) must be visible. Throughout the life course, the position of the SCJ changes and affects its visibility. SCJ visibility was analyzed among participants screened at the League Against Cancer Clinic in Lima, Peru. Of the 4247 participants screened, the SCJ was fully visible in 49.7% of participants, partially visible in 23.1%, and not visible in 27.2%. Visibility decreased with age, and by age 45 years old, the SCJ was not fully visible in over 50% of participants. Our results show that a high percentage of participants at ages still recommended for screening do not have totally visible SCJ, and we may need to reconsider the upper age limit for screening and find new strategies for evaluation of those with a positive screening test and non-visible SCJ.

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

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]).

The combined finding of HPV 16, 18, or 45 and cytologic Atypical Glandular Cells (AGC) indicates a greatly elevated risk of in situ and invasive cervical adenocarcinoma.

BACKGROUND: Cervical screening has not effectively controlled cervical adenocarcinoma (AC). Human papillomavirus (HPV) testing is recommended for cervical screening but the optimal management of HPV-positive individuals to prevent AC remains a question. Cytology and HPV typing are two triage options to predict the risk of AC. We combined two potential biomarkers (atypical glandular cell, AGC, cytology and HPV-types 16, 18, or 45) to assess their joint effect on detecting AC. METHODS: Kaiser Permanente Northern California (KPNC) used triennial co-testing with cytology and HPV testing (positive/negative) for routine cervical screening between 2003 and 2020. HPV typing of a sample of residual HPV test specimens was performed on a separate cohort selected from KPNC (Persistence and Progression, PaP, cohort). We compared risk of prevalent and incident histologic AC/AIS (adenocarcinoma in situ) associated with preceding combinations of cytologic results and HPV typing. Risk of squamous cell cancer (SCC)/cervical intraepithelial neoplasia grade 3 (CIN3) (SCC/CIN3) was also included for comparison. RESULTS: Among HPV-positive individuals in PaP cohort, 99% of prevalent AC and 96% of AIS were linked to HPV-types 16, 18, or 45 (denoted HPV 16/18/45). Although rare (0.09% of screening population), the concurrent detection of HPV 16/18/45 with AGC cytology predicted a highly elevated relative risk of underlying histologic AC/AIS; the absolute risk of diagnosing AC/AIS was 12% and odds ratio (OR) was 1341 (95%CI:495-3630) compared to patients with other high-risk HPV types and normal cytology. Cumulatively (allowing non-concurrent results), approximately one-third of the AC/AIS cases ever had HPV 16/18/45 and AGC cytology (OR = 1785; 95%CI:872-3656). AGC was not as strongly associated with SCC/CIN3. CONCLUSION: Detection of HPV 16/18/45 positivity elevates risk of adenocarcinoma, particularly if AGC cytology is also found.

Estimating human papillomavirus vaccine efficacy from a single-arm trial: proof-of-principle in the Costa Rica Vaccine Trial.

BACKGROUND: The World Health Organization recommends a 1- or 2-dose human papillomavirus (HPV) vaccination schedule for females aged 9 to 20 years. Studies confirming the efficacy of a single dose and vaccine modifications are needed, but randomized controlled trials are costly and face logistical and ethical challenges. We propose a resource-efficient single-arm trial design that uses untargeted and unaffected HPV types as controls. METHODS: We estimated HPV vaccine efficacy (VE) from a single arm by comparing 2 ratios: the ratio of the rate of persistent incident infection with vaccine-targeted HPV 16 and 18 (HPV 16/18) and cross-protected types HPV 31, 33, and 45 (HPV 31/33/45) to vaccine-unaffected types HPV 35, 39, 51, 52, 56, 58, 59, and 66 (HPV 35/39/51/52/56/58/59/66) vs the ratio of prevalence of these types at the time of trial enrollment. We compare VE estimates using only data from the bivalent HPV 16/18 vaccine arm of the Costa Rica Vaccine Trial with published VE estimates that used both the vaccine and control arms. RESULTS: Our single-arm approach among 3727 women yielded VE estimates against persistent HPV 16/18 infections similar to published 2-arm estimates from the trial (according-to-protocol cohort: 91.0% , 95% CI = 82.9% to 95.3% [single-arm] vs 90.9% , 95% CI = 82.0% to 95.9% [2-arm]; intention-to-treat cohort: 41.7%, 95% CI = 32.4% to 49.8% [single-arm] vs 49.0% , 95% CI = 38.1% to 58.1% [2-arm]). VE estimates were also similar in analytic subgroups (number of doses received; baseline HPV serology status). CONCLUSIONS: We demonstrate that a single-arm design yields valid VE estimates with similar precision to a randomized controlled trial. Single-arm studies can reduce the sample size and costs of future HPV vaccine trials while avoiding concerns related to unvaccinated control groups. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT00128661.

Use of risk-based cervical screening programs in resource-limited settings.

Cervical cancer screening and management in the U.S. has adopted a risk-based approach. However, the majority of cervical cancer cases and deaths occur in resource-limited settings, where screening and management are not widely available. We describe a conceptual model that optimizes cervical cancer screening and management in resource-limited settings by utilizing a risk-based approach. The principles of risk-based screening and management in resource limited settings include (1) ensure that the screening method effectively separates low-risk from high-risk patients; (2) directing resources to populations at the highest cancer risk; (3) screen using HPV testing via self-sampling; (4) utilize HPV genotyping to improve risk stratification and better determine who will benefit from treatment, and (5) automated visual evaluation with artificial intelligence may further improve risk stratification. Risk-based screening and management in resource limited settings can optimize prevention by focusing triage and treatment resources on the highest risk patients while minimizing interventions in lower risk patients.

Opportunities for Achieving the Cancer Moonshot Goal of a 50% Reduction in Cancer Mortality by 2047.

On February 2, 2022, President Biden and First Lady Dr. Biden reignited the Cancer Moonshot, setting a new goal to reduce age-standardized cancer mortality rates by at least 50% over the next 25 years in the United States. We estimated trends in U.S. cancer mortality during 2000 to 2019 for all cancers and the six leading types (lung, colorectum, pancreas, breast, prostate, liver). Cancer death rates overall declined by 1.4% per year from 2000 to 2015, accelerating to 2.3% per year during 2016 to 2019, driven by strong declines in lung cancer mortality (-4.7%/year, 2014 to 2019). Recent declines in colorectal (-2.0%/year, 2010-2019) and breast cancer death rates (-1.2%/year, 2013-2019) also contributed. However, trends for other cancer types were less promising. To achieve the Moonshot goal, progress against lung, colorectal, and breast cancer deaths needs to be maintained and/or accelerated, and new strategies for prostate, liver, pancreatic, and other cancers are needed. We reviewed opportunities to prevent, detect, and treat these common cancers that could further reduce population-level cancer death rates and also reduce disparities. SIGNIFICANCE: We reviewed opportunities to prevent, detect, and treat common cancers, and show that to achieve the Moonshot goal, progress against lung, colorectal, and breast cancer deaths needs to be maintained and/or accelerated, and new strategies for prostate, liver, pancreatic, and other cancers are needed. See related commentary by Bertagnolli et al., p. 1049. This article is highlighted in the In This Issue feature, p. 1027.

REPRODUCIBLE AND CLINICALLY TRANSLATABLE DEEP NEURAL NETWORKS FOR CANCER SCREENING.

Cervical cancer is a leading cause of cancer mortality, with approximately 90% of the 250,000 deaths per year occurring in low- and middle-income countries (LMIC). Secondary prevention with cervical screening involves detecting and treating precursor lesions; however, scaling screening efforts in LMIC has been hampered by infrastructure and cost constraints. Recent work has supported the development of an artificial intelligence (AI) pipeline on digital images of the cervix to achieve an accurate and reliable diagnosis of treatable precancerous lesions. In particular, WHO guidelines emphasize visual triage of women testing positive for human papillomavirus (HPV) as the primary screen, and AI could assist in this triage task. Published AI reports have exhibited overfitting, lack of portability, and unrealistic, near-perfect performance estimates. To surmount recognized issues, we implemented a comprehensive deep-learning model selection and optimization study on a large, collated, multi-institutional dataset of 9,462 women (17,013 images). We evaluated relative portability, repeatability, and classification performance. The top performing model, when combined with HPV type, achieved an area under the Receiver Operating Characteristics (ROC) curve (AUC) of 0.89 within our study population of interest, and a limited total extreme misclassification rate of 3.4%, on held-aside test sets. Our work is among the first efforts at designing a robust, repeatable, accurate and clinically translatable deep-learning model for cervical screening.