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.

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.

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.

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.

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.

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.

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.

Improving the repeatability of deep learning models with Monte Carlo dropout.

The integration of artificial intelligence into clinical workflows requires reliable and robust models. Repeatability is a key attribute of model robustness. Ideal repeatable models output predictions without variation during independent tests carried out under similar conditions. However, slight variations, though not ideal, may be unavoidable and acceptable in practice. During model development and evaluation, much attention is given to classification performance while model repeatability is rarely assessed, leading to the development of models that are unusable in clinical practice. In this work, we evaluate the repeatability of four model types (binary classification, multi-class classification, ordinal classification, and regression) on images that were acquired from the same patient during the same visit. We study the each model's performance on four medical image classification tasks from public and private datasets: knee osteoarthritis, cervical cancer screening, breast density estimation, and retinopathy of prematurity. Repeatability is measured and compared on ResNet and DenseNet architectures. Moreover, we assess the impact of sampling Monte Carlo dropout predictions at test time on classification performance and repeatability. Leveraging Monte Carlo predictions significantly increases repeatability, in particular at the class boundaries, for all tasks on the binary, multi-class, and ordinal models leading to an average reduction of the 95% limits of agreement by 16% points and of the class disagreement rate by 7% points. The classification accuracy improves in most settings along with the repeatability. Our results suggest that beyond about 20 Monte Carlo iterations, there is no further gain in repeatability. In addition to the higher test-retest agreement, Monte Carlo predictions are better calibrated which leads to output probabilities reflecting more accurately the true likelihood of being correctly classified.

Image Quality Classification for Automated Visual Evaluation of Cervical Precancer.

Image quality control is a critical element in the process of data collection and cleaning. Both manual and automated analyses alike are adversely impacted by bad quality data. There are several factors that can degrade image quality and, correspondingly, there are many approaches to mitigate their negative impact. In this paper, we address image quality control toward our goal of improving the performance of automated visual evaluation (AVE) for cervical precancer screening. Specifically, we report efforts made toward classifying images into four quality categories ("unusable", "unsatisfactory", "limited", and "evaluable") and improving the quality classification performance by automatically identifying mislabeled and overly ambiguous images. The proposed new deep learning ensemble framework is an integration of several networks that consists of three main components: cervix detection, mislabel identification, and quality classification. We evaluated our method using a large dataset that comprises 87,420 images obtained from 14,183 patients through several cervical cancer studies conducted by different providers using different imaging devices in different geographic regions worldwide. The proposed ensemble approach achieved higher performance than the baseline approaches.

Comparison of accuracy and reproducibility of colposcopic impression based on a single image versus a two-minute time series of colposcopic images.

OBJECTIVE: Colposcopy is an important part of cervical screening/management programs. Colposcopic appearance is often classified, for teaching and telemedicine, based on static images that do not reveal the dynamics of acetowhitening. We compared the accuracy and reproducibility of colposcopic impression based on a single image at one minute after application of acetic acid versus a time-series of 17 sequential images over two minutes. METHODS: Approximately 5000 colposcopic examinations conducted with the DYSIS colposcopic system were divided into 10 random sets, each assigned to a separate expert colposcopist. Colposcopists first classified single two-dimensional images at one minute and then a time-series of 17 sequential images as 'normal,' 'indeterminate,' 'high grade,' or 'cancer'. Ratings were compared to histologic diagnoses. Additionally, 5 colposcopists reviewed a subset of 200 single images and 200 time series to estimate intra- and inter-rater reliability. RESULTS: Of 4640 patients with adequate images, only 24.4% were correctly categorized by single image visual assessment (11% of 64 cancers; 31% of 605 CIN3; 22.4% of 558 CIN2; 23.9% of 3412 < CIN2). Individual colposcopist accuracy was low; Youden indices (sensitivity plus specificity minus one) ranged from 0.07 to 0.24. Use of the time-series increased the proportion of images classified as normal, regardless of histology. Intra-rater reliability was substantial (weighted kappa = 0.64); inter-rater reliability was fair ( weighted kappa = 0.26). CONCLUSION: Substantial variation exists in visual assessment of colposcopic images, even when a 17-image time series showing the two-minute process of acetowhitening is presented. We are currently evaluating whether deep-learning image evaluation can assist classification.

A comparison of high-grade cervical abnormality risks in women living with and without human immunodeficiency virus undergoing routine cervical-cancer screening.

As the US moves increasingly towards using human papillomavirus (HPV) testing with or without concurrent cytology for cervical cancer screening, it is unknown what the corresponding risks are following a screening result for women living with HIV (WLWH), which will dictate the optimal clinical follow-up. Therefore, using medical records data from Kaiser Permanente Northern California, which introduced triennial HPV and cytology co-testing in women aged 30-64 years in 2003, we compared risks of cervical intraepithelial neoplasia grade 2 (CIN2) or more severe diagnoses (CIN2+) in women not known to have HIV (HIV[-] women) (n = 67,488) frequency matched 111:1 on age and year of the first co-test to the 608 WLWH (n = 608). WLWH were more likely to test HPV positive (20.2% vs. 6.5%, p < 0.001) and have non-normal cytology (14.1% vs. 4.1%, p < 0.001) than HIV[-] women. Five-year CIN2+ risks for all WLWH and HIV[-] women were 3.5% (95%CI = 2.0-5.0%) and 1.6% (95%CI = 1.5-1.8%) (p = 0.01), respectively. Five-year CIN2+ risks for WLWH with positive HPV and non-normal cytology, positive HPV and normal cytology, negative HPV and non-normal cytology, and negative HPV and normal cytology were 24.9% (95%CI = 13.4-36.4%), 3.0% (95%CI = 0.0-7.4%), 3.6 (95%CI = 0.0-9.8%) and 0.3% (95%CI = 0.0-0.8%), respectively. Corresponding 5-year CIN2+ risks for HIV[-] women were 26.6% (95%CI = 24.6-28.7%), 8.5% (95%CI = 7.2-9.9%), 1.9% (95%CI = 1.0-2.8%), and 0.5% (95%CI = 0.4-0.6%), respectively. Thus, in this healthcare setting, the main cause in overall CIN2+ risk differences between WLWH and HIV[-] women was the former was more likely to screen positive and once the screening result is known, it may be reasonable to manage both populations similarly.

Different human papillomavirus types share early natural history transitions in immunocompetent women.

Necessary stages of cervical carcinogenesis include acquisition of a carcinogenic human papillomavirus (HPV) type, persistence associated with the development of precancerous lesions, and invasion. Using prospective data from immunocompetent women in the Guanacaste HPV Natural History Study (NHS), the ASCUS-LSIL Triage Study (ALTS) and the Costa Rica HPV Vaccine Trial (CVT), we compared the early natural history of HPV types to inform transition probabilities for health decision models. We excluded women with evidence of high-grade cervical abnormalities at any point during follow-up and restricted the analysis to incident infections in all women and prevalent infections in young women (aged <30 years). We used survival approaches accounting for interval-censoring to estimate the time to clearance distribution for 20 529 HPV infections (64% were incident and 51% were carcinogenic). Time to clearance was similar across HPV types and risk classes (HPV16, HPV18/45, HPV31/33/35/52/58, HPV 39/51/56/59 and noncarcinogenic HPV types); and by age group (18-29, 30-44 and 45-54 years), among carcinogenic and noncarcinogenic infections. Similar time to clearance across HPV types suggests that relative prevalence can predict relative incidence. We confirmed that there was a uniform linear association between incident and prevalent infections for all HPV types within each study cohort. In the absence of progression to precancer, we observed similar time to clearance for incident infections across HPV types and risk classes. A singular clearance function for incident HPV infections has important implications for the refinement of microsimulation models used to evaluate the cost-effectiveness of novel prevention technologies.

Contribution of Etiologic Cofactors to CIN3+ Risk Among Women With Human Papillomavirus-Positive Screening Test Results.

OBJECTIVE: The US screening and management guidelines for cervical cancer are based on the absolute risk of precancer estimated from large clinical cohorts and trials. Given the widespread transition toward screening with human papillomavirus (HPV) testing, it is important to assess which additional factors to include in clinical risk assessment to optimize management of HPV-infected women. MATERIALS AND METHODS: We analyzed data from HPV-infected women, ages 30-65 years, in the National Cancer Institute-Kaiser Permanente Northern California Persistence and Progression study. We estimated the influence of HPV risk group, cytology result, and selected cofactors on immediate risk of cervical intraepithelial neoplasia grade 3 or higher (CIN 3+) among 16,094 HPV-positive women. Cofactors considered included, age, race/ethnicity, income, smoking, and hormonal contraceptive use. RESULTS: Human papillomavirus risk group and cytology test result were strongly correlated with CIN 3+ risk. After considering cytology and HPV risk group, other cofactors (age, race/ethnicity, income, smoking, and hormonal contraceptive use) had minimal impact on CIN 3+ risk and did not change recommended management based on accepted risk thresholds. We had insufficient data to assess the impact of long-duration heavy smoking, parity, history of sexually transmitted infection, or immunosuppression. CONCLUSIONS: In our study at the Kaiser Permanente Northern California, the risk of CIN 3+ was determined mainly by HPV risk group and cytology results, with other cofactors having limited impact in adjusted analyses. This supports the use of HPV and cytology results in risk-based management guidelines.

Cervical Precancers and Cancers Attributed to HPV Types by Race and Ethnicity: Implications for Vaccination, Screening, and Management.

BACKGROUND: Racial and ethnic variations in attribution of cervical precancer and cancer to human papillomavirus (HPV) types may result in different HPV vaccine protection, screening test coverage, and clinical management. METHODS: Pooling data from 7 US studies, we calculated the proportional attribution of precancers and cancers to HPV types using HPV DNA typing from diagnosis. All statistical tests were 2-sided. RESULTS: For all racial and ethnic groups, most cases of cervical intraepithelial neoplasia grade 3 (CIN3) (84.2%-90.8% of 5526) and squamous cell carcinoma (SCC) (90.4%-93.8% of 1138) were attributed to types targeted by the 9-valent vaccine. A higher proportion of CIN3s were attributed to nonvaccine HPV types among non-Hispanic Black women (15.8%) compared with non-Hispanic Asian or Pacific Islander (9.7%; P = .002), non-Hispanic White (9.2%; P < .001), and Hispanic (11.3%; P = .004) women. The proportion of SCCs attributed to 9-valent types was similar by race and ethnicity (P = .80). A higher proportion of CIN3s were attributed to nonvaccine HPV35 among non-Hispanic Black (9.0%) compared with non-Hispanic Asian or Pacific Islander (2.2%), non-Hispanic White (2.5%), and Hispanic (3.0%; all P < .001) women. Compared with CIN3, the proportion of SCCs attributed to HPV35 among non-Hispanic Black women (3.2%) was lower and closer to other groups (0.3%-2.1%; P = .70). CONCLUSION: The 9-valent HPV vaccine will prevent nearly all cervical precancers and invasive cancers among major racial and ethnic groups in the United States. Adding HPV35 to vaccines could prevent a small percentage of CIN3s and SCCs, with greater potential impact for CIN3s among Black women. HPV screening tests target high-risk HPV types, including HPV35. Future genotyping triage strategies could consider the importance of HPV35- and other HPV16-related types.

The development of "automated visual evaluation" for cervical cancer screening: The promise and challenges in adapting deep-learning for clinical testing: Interdisciplinary principles of automated visual evaluation in cervical screening.

There is limited access to effective cervical cancer screening programs in many resource-limited settings, resulting in continued high cervical cancer burden. Human papillomavirus (HPV) testing is increasingly recognized to be the preferable primary screening approach if affordable due to superior long-term reassurance when negative and adaptability to self-sampling. Visual inspection with acetic acid (VIA) is an inexpensive but subjective and inaccurate method widely used in resource-limited settings, either for primary screening or for triage of HPV-positive individuals. A deep learning (DL)-based automated visual evaluation (AVE) of cervical images has been developed to help improve the accuracy and reproducibility of VIA as assistive technology. However, like any new clinical technology, rigorous evaluation and proof of clinical effectiveness are required before AVE is implemented widely. In the current article, we outline essential clinical and technical considerations involved in building a validated DL-based AVE tool for broad use as a clinical test.

The Improving Risk Informed HPV Screening (IRIS) Study: Design and Baseline Characteristics.

BACKGROUND: Cervical cancer screening with high-risk human papillomavirus (HrHPV) testing is being introduced. Most HrHPV infections are transient, requiring triage tests to identify individuals at highest risk for progression to cervical cancer. Head-to-head comparisons of available strategies for screening and triage are needed. Endometrial and ovarian cancers could be amenable to similar testing. METHODS: Between 2016 and 2020, discarded cervical cancer screening specimens from women ages 25 to 65 undergoing screening at Kaiser Permanente Northern California were collected. Specimens were aliquoted, stabilized, and stored frozen. Human papillomavirus (HPV), cytology, and histopathology results as well as demographic and cofactor information were obtained from electronic medical records (EMR). Follow-up collection of specimens was conducted for 2 years, and EMR-based data collection was planned for 5 years. RESULTS: Collection of enrollment and follow-up specimens is complete, and EMR-based follow-up data collection is ongoing. At baseline, specimens were collected from 54,957 HPV-positive, 10,215 HPV-negative/Pap-positive, and 12,748 HPV-negative/Pap-negative women. Clinical history prior to baseline was available for 72.6% of individuals, of which 53.9% were undergoing routine screening, 8.6% recently had an abnormal screen, 30.3% had previous colposcopy, and 7.2% had previous treatment. As of February 2021, 55.7% had one or more colposcopies, yielding 5,563 cervical intraepithelial neoplasia grade 2 (CIN2), 2,756 cervical intraepithelial neoplasia grade 3 (CIN3), and 146 cancer histopathology diagnoses. CONCLUSIONS: This robust population-based cohort study represents all stages of cervical cancer screening, management, and posttreatment follow-up. IMPACT: The IRIS study is a unique and highly relevant resource allowing for natural history studies and rigorous evaluation of candidate HrHPV screening and triage markers, while permitting studies of biomarkers associated with other gynecologic cancers.