Espressivo Aufia, Pan Z Sienna, Usher-Smith Juliet A, Harrison Hannah
Department of Public Health and Primary Care, University of Cambridge, Cambridge CB2 0SR, UK.
Cancers (Basel). 2024 Jan 31;16(3):617. doi: 10.3390/cancers16030617.
In the last 30 years, there has been an increasing incidence of oral cancer worldwide. Earlier detection of oral cancer has been shown to improve survival rates. However, given the relatively low prevalence of this disease, population-wide screening is likely to be inefficient. Risk prediction models could be used to target screening to those at highest risk or to select individuals for preventative interventions. This review (a) systematically identified published models that predict the development of oral cancer and are suitable for use in the general population and (b) described and compared the identified models, focusing on their development, including risk factors, performance and applicability to risk-stratified screening. A search was carried out in November 2022 in the Medline, Embase and Cochrane Library databases to identify primary research papers that report the development or validation of models predicting the risk of developing oral cancer (cancers of the oral cavity or oropharynx). The PROBAST tool was used to evaluate the risk of bias in the identified studies and the applicability of the models they describe. The search identified 11,222 articles, of which 14 studies (describing 23 models), satisfied the eligibility criteria of this review. The most commonly included risk factors were age ( = 20), alcohol consumption ( = 18) and smoking ( = 17). Six of the included models incorporated genetic information and three used biomarkers as predictors. Including information on human papillomavirus status was shown to improve model performance; however, this was only included in a small number of models. Most of the identified models ( = 13) showed good or excellent discrimination (AUROC > 0.7). Only fourteen models had been validated and only two of these validations were carried out in populations distinct from the model development population (external validation). Conclusions: Several risk prediction models have been identified that could be used to identify individuals at the highest risk of oral cancer within the context of screening programmes. However, external validation of these models in the target population is required, and, subsequently, an assessment of the feasibility of implementation with a risk-stratified screening programme for oral cancer.
在过去30年里，全球口腔癌的发病率一直在上升。早期发现口腔癌已被证明可提高生存率。然而，鉴于这种疾病的患病率相对较低，全人群筛查可能效率低下。风险预测模型可用于将筛查目标锁定为风险最高的人群，或选择个体进行预防性干预。本综述：(a)系统地识别已发表的预测口腔癌发生且适用于一般人群的模型；(b)描述并比较所识别的模型，重点关注其开发情况，包括风险因素、性能以及对风险分层筛查的适用性。2022年11月在Medline、Embase和Cochrane图书馆数据库中进行了检索，以识别报告预测口腔癌（口腔或口咽癌）发生风险模型的开发或验证情况的初级研究论文。使用PROBAST工具评估所识别研究中的偏倚风险及其所描述模型的适用性。检索共识别出11,222篇文章，其中14项研究（描述了23个模型）符合本综述的纳入标准。最常纳入的风险因素是年龄（n = 20）、饮酒（n = 18）和吸烟（n = 17）。纳入的模型中有6个纳入了基因信息，3个使用生物标志物作为预测因子。纳入人乳头瘤病毒状态信息可改善模型性能；然而，只有少数模型纳入了该信息。大多数所识别的模型（n = 13）显示出良好或优异的区分度（AUROC > 0.7）。只有14个模型经过了验证，其中只有2项验证是在与模型开发人群不同的人群中进行的（外部验证）。结论：已识别出几种风险预测模型，可用于在筛查项目中识别口腔癌风险最高的个体。然而，需要在目标人群中对这些模型进行外部验证，随后评估通过口腔癌风险分层筛查项目实施的可行性。
