Impact of Screening for HPV-positive Oropharyngeal Cancers: a Microsimulation-Based Modeling Study.
Journal of the National Cancer Institute(2025)
Division of Cancer Epidemiology and Genetics | Johns Hopkins University | Harvard T. H. Chan School of Public Health | MD Anderson Cancer Center
Abstract
BACKGROUND:We estimated the impact of screening on morbidity and mortality of HPV16-positive oropharyngeal cancer among US men aged 45-79 years. METHODS:We developed an individual-level, state-transition natural history microsimulation model to estimate the impact of screening using oral HPV16 detection, HPV16-E6 antibody detection, and transcervical-ultrasound of neck/oropharynx. We compared clinical detection to counterfactual screen detection for cancer stage, single- vs multiple-modality treatment, and survival. Screening scenarios encompassed four progression speeds across cancer stages (very-slow, slow, fast, and very-fast) and four screening frequencies. RESULTS:Among US men aged 45-79 years in 2021 (N = 54,881,311), 163,958 clinically diagnosed HPV-positive oropharyngeal cancers and 32,009 deaths would occur through age 84 in the absence of screening. Assuming very-fast progression, 4%, 20%, 31%, and 60% of these cancers would be detected by one-off, 5-yearly, 3-yearly, and annual screening. Annual screening (very-fast progression) could reduce the number of cancers diagnosed at advanced stages (AJCC 7, Stages III/IV: 90.0% with no screening vs 59.1%) and treated by multiple-modalities (80.6% with no screening vs 50.6%). Cancer mortality would also be reduced by 36.2%, with a gain of 106,000 life-years. Annual screening would have a number needed to screen (NNS) of 561 per screen-detected cancer, 1,118 per additional cancer treated by single-modality, 4,740 per death prevented, and 520 per life-year gained; such high NNS reflect potential inefficiency of population-level screening. CONCLUSIONS:If proven efficacious in randomized trials and cost-effective, screening for HPV-positive oropharyngeal cancers could provide considerable population-level reductions in advanced stage cancers, treatment-related morbidities, and mortality.
MoreTranslated text
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper