Enduring Consensus Guidelines for Cervical Cancer Screening and Management: Introduction to the Scope and Process.

OBJECTIVES: The Enduring Consensus Cervical Cancer Screening and Management Guidelines (Enduring Guidelines) effort is a standing committee to continuously evaluate new technologies and approaches to cervical cancer screening, management, and surveillance. METHODS AND RESULTS: The Enduring Guidelines process will selectively incorporate new technologies and approaches with adequate supportive data to more effectively improve cancer prevention for high-risk individuals and decrease unnecessary procedures in low-risk individuals. This manuscript describes the structure, process, and methods of the Enduring Guidelines effort. Using systematic literature reviews and primary data sources, risk of precancer will be estimated and recommendations will be made based on risk estimates in the context of established risk-based clinical action thresholds. The Enduring Guidelines process will consider health equity and health disparities by assuring inclusion of diverse populations in the evidence review and risk assessment and by developing recommendations that provide a choice of well-validated strategies that can be adapted to different settings. CONCLUSIONS: The Enduring Guidelines process will allow updating existing cervical cancer screening and management guidelines rapidly when new technologies are approved or new scientific evidence becomes available.

Recommendations for Use of p16/Ki67 Dual Stain for Management of Individuals Testing Positive for Human Papillomavirus.

OBJECTIVES: The Enduring Consensus Cervical Cancer Screening and Management Guidelines Committee developed recommendations for dual stain (DS) testing with CINtec PLUS Cytology for use of DS to triage high-risk human papillomavirus (HPV)-positive results. METHODS: Risks of cervical intraepithelial neoplasia grade 3 or worse were calculated according to DS results among individuals testing HPV-positive using data from the Kaiser Permanente Northern California cohort and the STudying Risk to Improve DisparitiES study in Mississippi. Management recommendations were based on clinical action thresholds developed for the 2019 American Society for Colposcopy and Cervical Pathology Risk-Based Management Consensus Guidelines. Resource usage metrics were calculated to support decision-making. Risk estimates in relation to clinical action thresholds were reviewed and used as the basis for draft recommendations. After an open comment period, recommendations were finalized and ratified through a vote by the Consensus Stakeholder Group. RESULTS: For triage of positive HPV results from screening with primary HPV testing (with or without genotyping) or with cytology cotesting, colposcopy is recommended for individuals testing DS-positive. One-year follow-up with HPV-based testing is recommended for individuals testing DS-negative, except for HPV16- and HPV18-positive results, or high-grade cytology in cotesting, where immediate colposcopy referral is recommended. Risk estimates were similar between the Kaiser Permanente Northern California and STudying Risk to Improve DisparitiES populations. In general, resource usage metrics suggest that compared with cytology, DS requires fewer colposcopies and detects cervical intraepithelial neoplasia grade 3 or worse earlier. CONCLUSIONS: Dual stain testing with CINtec PLUS Cytology is acceptable for triage of HPV-positive test results. Risk estimates are portable across different populations.

Two-phase designs with failure time processes subject to nonsusceptibility.

Epidemiological studies based on 2-phase designs help ensure efficient use of limited resources in situations where certain covariates are prohibitively expensive to measure for a full cohort. Typically, these designs involve 2 steps: In phase I, data on an outcome and inexpensive covariates are acquired, and in phase II, a subsample is chosen in which the costly variable of interest is measured. For right-censored data, 2-phase designs have been primarily based on the Cox model. We develop efficient 2-phase design strategies for settings involving a fraction of long-term survivors due to nonsusceptibility. Using mixture models accommodating a nonsusceptible fraction, we consider 3 regression frameworks, including (a) a logistic "cure" model, (b) a proportional hazards model for those who are susceptible, and (c) regression models for susceptibility and failure time in those susceptible. Importantly, we introduce a novel class of bivariate residual-dependent designs to address the unique challenges presented in scenario (c), which involves 2 parameters of interest. Extensive simulation studies demonstrate the superiority of our approach over various phase II subsampling schemes. We illustrate the method through applications to the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial.

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.

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.

Absolute lung cancer risk increases among individuals with >15 quit-years: Analyses to inform the update of the American Cancer Society lung cancer screening guidelines.

BACKGROUND: This report quantifies counteracting effects of quit-years and concomitant aging on lung cancer risk, especially on exceeding 15 quit-years, when the US Preventive Services Task Force (USPSTF) recommends curtailing lung-cancer screening. METHODS: Cox models were fitted to estimate absolute lung cancer risk among Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO) and National Lung Screening Trial (NLST) participants who ever smoked. Absolute lung cancer risk and gainable years of life from screening for individuals aged 50 to 80 in the US-representative National Health Interview Survey (NHIS) 2015-2018 who ever smoked were projected. Relaxing USPSTF recommendations to 20/25/30 quit-years versus augmenting USPSTF criteria with individuals whose estimated gain in life expectancy from screening exceeded 16.2 days according to the Life Years From Screening-CT (LYFS-CT) prediction model was compared. RESULTS: Absolute lung cancer risk increased by 8.7%/year (95% CI, 7.7%-9.7%; p < .001) as individuals aged beyond 15 quit-years in the PLCO, with similar results in NHIS and NLST. For example, mean 5-year lung cancer risk for those aged 65 years with 15 quit-years = 1.47% (95% CI, 1.35%-1.59%) versus 1.76% (95% CI, 1.62%-1.90%) for those aged 70 years with 20 quit-years in the PLCO. Removing the quit-year criterion would make 4.9 million more people eligible and increase the proportion of preventable lung cancer deaths prevented (sensitivity) from 63.7% to 74.2%. Alternatively, augmentation using LYFS-CT would make 1.7 million more people eligible while increasing the lung cancer death sensitivity to 74.0%. CONCLUSIONS: Because of aging, absolute lung cancer risk increases beyond 15 quit-years, which does not support exemption from screening or curtailing screening once it has been initiated. Compared with relaxing the USPSTF quit-year criterion, augmentation using LYFS-CT could prevent most of the deaths at substantially superior efficiency, while also preventing deaths among individuals who currently smoke with low intensity or long duration.

Inflammatory Tongue Conditions and Risk of Oral Tongue Cancer Among the US Elderly Individuals.

PURPOSE: The incidence of oral tongue cancers has increased since the 1980s among US men and women for unknown reasons. We investigated associations of inflammatory tongue conditions with risk of cancers of the oral tongue, other oral cavity, and oropharynx among the US elderly individuals (age 65 years or older). METHODS: We conducted a case-control study (2,534 oral tongue cancers, 6,832 other oral cavity cancers, 9,373 oropharyngeal cancers, and 200,000 controls) within the SEER-Medicare data set (1992-2013). Medicare records were used to identify patients with clinically diagnosed inflammatory tongue conditions (glossitis, benign migratory glossitis, median rhomboid glossitis, atrophic glossitis, glossodynia, other specified conditions [eg, atrophy and hypertrophy], and other unspecified conditions) and oral precancer (leukoplakia/erythroplakia). Only conditions preceding cancer/control selection by >12 months were included. RESULTS: The prevalence of inflammatory tongue conditions was significantly higher in patients with tongue cancer than controls (6.0% v 0.6%; odds ratios [ORs], adjusted for age, sex, race, Medicare utilization, and precancer, 5.8 [95% CI, 4.7 to 7.2]). This overall association primarily arose from glossitis, 5.6 (95% CI, 4.4 to 7.2); other specified conditions, 9.1 (95% CI, 5.5 to 15.2); and other unspecified conditions, 13.7 (95% CI, 8.0 to 23.7). These associations remained strongly elevated >5 years preceding tongue cancer (arguing against reverse causation), for conditions diagnosed by a specialist (arguing against misclassification), and among patients who received an oral biopsy (arguing against missed cancer). During 2013, an estimated 1 in 11 patients with oral tongue cancer had a preceding diagnosis of inflammatory tongue conditions. Associations of inflammatory tongue conditions were relatively weak for other oral cavity cancers (ORs, 1.8 [95% CI, 1.5 to 2.3]) and oropharyngeal cancer (OR, 1.3 [95% CI, 1.0 to 1.6]) and were observed only closest to cancer diagnosis. CONCLUSION: Inflammatory tongue conditions were associated with strongly increased risks of oral tongue cancers and preceded cancer diagnosis by several years, underscoring the need for increased clinical surveillance among patients with such apparently benign diagnoses.

Untreated cervical intraepithelial neoplasia grade 2 and subsequent risk of cervical cancer: population based cohort study.

OBJECTIVE: To describe the long term risk of cervical cancer in women with untreated (that is, undergoing active surveillance) or immediately treated cervical intraepithelial neoplasia grade 2 (CIN2). DESIGN: Nationwide population based historical cohort study. SETTING: Danish healthcare registries. PARTICIPANTS: Women with CIN2 diagnosed in 1998-2020 and aged 18-40 years at diagnosis, who had either active surveillance or immediate treatment with large loop excision of the transformation zone (LLETZ). Women with a previous record of CIN2 or worse or LLETZ were excluded. MAIN OUTCOME MEASURE: A Weibull survival model for interval censored time-to-event data was used to estimate the cumulative risk of cervical cancer. Inverse probability treatment weighting was used to adjust estimates for age, index cytology, calendar year, and region of residence. RESULTS: The cohort included 27 524 women with CIN2, of whom 12 483 (45%) had active surveillance and 15 041 (55%) had immediate LLETZ. During follow-up, 104 cases of cervical cancer were identified-56 (54%) in the active surveillance group and 48 (46%) in the LLETZ group. The cumulative risk of cervical cancer was comparable across the two groups during the active surveillance period of two years. Thereafter, the risk increased in the active surveillance group, reaching 2.65% (95% confidence interval 2.07% to 3.23%) after 20 years, whereas it remained stable in the LLETZ group at 0.76% (0.58% to 0.95%). CONCLUSIONS: Undergoing active surveillance for CIN2, thereby leaving the lesion untreated, was associated with increased long term risk of cervical cancer compared with immediate LLETZ. These findings show the importance of continued follow-up of women having active surveillance.

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.

Methods for Using Race and Ethnicity in Prediction Models for Lung Cancer Screening Eligibility.

Importance: Using race and ethnicity in clinical prediction models can reduce or inadvertently increase racial and ethnic disparities in medical decisions. Objective: To compare eligibility for lung cancer screening in a contemporary representative US population by refitting the life-years gained from screening-computed tomography (LYFS-CT) model to exclude race and ethnicity vs a counterfactual eligibility approach that recalculates life expectancy for racial and ethnic minority individuals using the same covariates but substitutes White race and uses the higher predicted life expectancy, ensuring that historically underserved groups are not penalized. Design, Setting, and Participants: The 2 submodels composing LYFS-CT NoRace were refit and externally validated without race and ethnicity: the lung cancer death submodel in participants of a large clinical trial (recruited 1993-2001; followed up until December 31, 2009) who ever smoked (n = 39 180) and the all-cause mortality submodel in the National Health Interview Survey (NHIS) 1997-2001 participants aged 40 to 80 years who ever smoked (n = 74 842, followed up until December 31, 2006). Screening eligibility was examined in NHIS 2015-2018 participants aged 50 to 80 years who ever smoked. Data were analyzed from June 2021 to September 2022. Exposure: Including and removing race and ethnicity (African American, Asian American, Hispanic American, White) in each LYFS-CT submodel. Main Outcomes and Measures: By race and ethnicity: calibration of the LYFS-CT NoRace model and the counterfactual approach (ratio of expected to observed [E/O] outcomes), US individuals eligible for screening, predicted days of life gained from screening by LYFS-CT. Results: The NHIS 2015-2018 included 25 601 individuals aged 50 to 80 years who ever smoked (2769 African American, 649 Asian American, 1855 Hispanic American, and 20 328 White individuals). Removing race and ethnicity from the submodels underestimated lung cancer death risk (expected/observed [E/O], 0.72; 95% CI, 0.52-1.00) and all-cause mortality (E/O, 0.90; 95% CI, 0.86-0.94) in African American individuals. It also overestimated mortality in Hispanic American (E/O, 1.08, 95% CI, 1.00-1.16) and Asian American individuals (E/O, 1.14, 95% CI, 1.01-1.30). Consequently, the LYFS-CT NoRace model increased Hispanic American and Asian American eligibility by 108% and 73%, respectively, while reducing African American eligibility by 39%. Using LYFS-CT with the counterfactual all-cause mortality model better maintained calibration across groups and increased African American eligibility by 13% without reducing eligibility for Hispanic American and Asian American individuals. Conclusions and Relevance: In this study, removing race and ethnicity miscalibrated LYFS-CT submodels and substantially reduced African American eligibility for lung cancer screening. Under counterfactual eligibility, no one became ineligible, and African American eligibility increased, demonstrating the potential for maintaining model accuracy while reducing disparities.

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.

Recalibration of a Deep Learning Model for Low-Dose Computed Tomographic Images to Inform Lung Cancer Screening Intervals.

Importance: Annual low-dose computed tomographic (LDCT) screening reduces lung cancer mortality, but harms could be reduced and cost-effectiveness improved by reusing the LDCT image in conjunction with deep learning or statistical models to identify low-risk individuals for biennial screening. Objective: To identify low-risk individuals in the National Lung Screening Trial (NLST) and estimate, had they been assigned a biennial screening, how many lung cancers would have been delayed 1 year in diagnosis. Design, Setting, and Participants: This diagnostic study included participants with a presumed nonmalignant lung nodule in the NLST between January 1, 2002, and December 31, 2004, with follow-up completed on December 31, 2009. Data were analyzed for this study from September 11, 2019, to March 15, 2022. Exposures: An externally validated deep learning algorithm that predicts malignancy in current lung nodules using LDCT images (Lung Cancer Prediction Convolutional Neural Network [LCP-CNN]; Optellum Ltd) was recalibrated to predict 1-year lung cancer detection by LDCT for presumed nonmalignant nodules. Individuals with presumed nonmalignant lung nodules were hypothetically assigned annual vs biennial screening based on the recalibrated LCP-CNN model, Lung Cancer Risk Assessment Tool (LCRAT + CT [a statistical model combining individual risk factors and LDCT image features]), and the American College of Radiology recommendations for lung nodules, version 1.1 (Lung-RADS). Main Outcomes and Measures: Primary outcomes included model prediction performance, the absolute risk of a 1-year delay in cancer diagnosis, and the proportion of people without lung cancer assigned a biennial screening interval vs the proportion of cancer diagnoses delayed. Results: The study included 10 831 LDCT images from patients with presumed nonmalignant lung nodules (58.7% men; mean [SD] age, 61.9 [5.0] years), of whom 195 were diagnosed with lung cancer from the subsequent screen. The recalibrated LCP-CNN had substantially higher area under the curve (0.87) than LCRAT + CT (0.79) or Lung-RADS (0.69) to predict 1-year lung cancer risk (P < .001). If 66% of screens with nodules were assigned to biennial screening, the absolute risk of a 1-year delay in cancer diagnosis would have been lower for recalibrated LCP-CNN (0.28%) than LCRAT + CT (0.60%; P = .001) or Lung-RADS (0.97%; P < .001). To delay only 10% of cancer diagnoses at 1 year, more people would have been safely assigned biennial screening under LCP-CNN than LCRAT + CT (66.4% vs 40.3%; P < .001). Conclusions and Relevance: In this diagnostic study evaluating models of lung cancer risk, a recalibrated deep learning algorithm was most predictive of 1-year lung cancer risk and had least risk of 1-year delay in cancer diagnosis among people assigned biennial screening. Deep learning algorithms could prioritize people for workup of suspicious nodules and decrease screening intensity for people with low-risk nodules, which may be vital for implementation in health care systems.

Upper age limits for US male human papillomavirus vaccination for oropharyngeal cancer prevention: a microsimulation-based modeling study.

BACKGROUND: Human papillomavirus (HVP)-positive oropharyngeal cancer is the most common HPV-associated cancer in the United States. The age at acquisition of oral HPV infections that cause oropharyngeal cancer (causal infections) is unknown; consequently, the benefit of vaccination of US men aged 27-45 years remains uncertain. METHODS: We developed a microsimulation-based, individual-level, state-transition model of oral HPV16 and HPV16-positive oropharyngeal cancer among heterosexual US men aged 15-84 years, calibrated to population-level data. We estimated the benefit of vaccination of men aged 27-45 years for prevention of oropharyngeal cancer, accounting for direct- and indirect effects (ie, herd effects) of male and female vaccination. RESULTS: In the absence of vaccination, most (70%) causal oral HPV16 infections are acquired by age 26 years, and 29% are acquired between ages 27 and 45 years. Among men aged 15-45 years in 2021 (1976-2006 birth cohorts), status quo vaccination of men through age 26 years is estimated to prevent 95% of 153 450 vaccine-preventable cancers. Assuming 100% vaccination in 2021, extending the upper age limit to 30, 35, 40, or 45 years for men aged 27-45 years (1976-1994 cohorts) is estimated to yield small benefits (3.0%, 4.2%, 5.1%, and 5.6% additional cancers prevented, respectively). Importantly, status quo vaccination of men through age 26 years is predicted to result in notable declines in HPV16-positive oropharyngeal cancer incidence in young men by 2035 (51% and 24% declines at ages 40-44 years and 45-49 years, respectively) and noticeable declines (12%) overall by 2045. CONCLUSION: Most causal oral HPV16 infections in US men are acquired by age 26 years, underscoring limited benefit from vaccination of men aged 27-45 years for prevention of HPV16-positive oropharyngeal cancers.

Absolute and relative risk estimation in the presence of outcome ascertainment gaps and competing risks.

Incomplete coverage by cancer registries can lead to an underreporting of cancers and a resulting bias in risk estimates. When registries are defined by geographic region, gaps in observation can arise for individuals who reside outside of or migrate from the total registry catchment area. Moreover, the exact periods of non-observation for an individual may be unknown due to intermittent reporting of residential histories. The motivating example for this work is the U.S. Radiologic Technologist (USRT) study which ascertained cancer outcomes for a national cohort through 43 state/regional registries; similar gaps in outcome ascertainment can appear in other registry or electronic health record- based cohort studies. We propose a two-step procedure for estimating relative and absolute risk in these settings. First, using a mover stayer model fitted to individuals' known residential history, we obtain individual posterior probabilities of residing outside the registry catchment area each year. Second, we incorporate these probabilities in the survival data likelihood for competing risks to account for unobserved events. We assess the performance of the proposed method in extensive simulation studies. Compared to several simple alternative approaches, the proposed method reduces bias and improves efficiency. Finally, we apply the proposed method to a study of first primary lung cancers in the USRT cohort.

Variation in Human Papillomavirus Vaccination Effectiveness in the US by Age at Vaccination.

This survey study assesses the status and timing of HPV vaccination as self-reported by female participants in the National Health and Nutrition Examination Survey from 2011 to 2018.

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.

Multistate models for the natural history of cancer progression.

BACKGROUND: Multistate models can be effectively used to characterise the natural history of cancer. Inference from such models has previously been useful for setting screening policies. METHODS: We introduce the basic elements of multistate models and the challenges of applying these models to cancer data. Through simulation studies, we examine (1) the impact of assuming time-homogeneous Markov transition intensities when the intensities depend on the time since entry to the current state (i.e., the process is time-inhomogenous semi-Markov) and (2) the effect on precancer risk estimation when observation times depend on an unmodelled intermediate disease state. RESULTS: In the settings we examined, we found that misspecifying a time-inhomogenous semi-Markov process as a time-homogeneous Markov process resulted in biased estimates of the mean sojourn times. When screen-detection of the intermediate disease leads to more frequent future screening assessments, there was minimal bias induced compared to when screen-detection of the intermediate disease leads to less frequent screening. CONCLUSIONS: Multistate models are useful for estimating parameters governing the process dynamics in cancer such as transition rates, sojourn time distributions, and absolute and relative risks. As with most statistical models, to avoid incorrect inference, care should be given to use the appropriate specifications and assumptions.