Issanov Alpamys, Aravindakshan Atul, Puil Lorri, Tammemägi Martin C, Lam Stephen, Dummer Trevor J B
School of Population and Public Health, University of British Columbia, Vancouver, BC, V6T 1Z3, Canada.
Faculty of Applied Health Sciences, Brock University, St. Catharines, ON, Canada.
Diagn Progn Res. 2024 Feb 13;8(1):3. doi: 10.1186/s41512-024-00166-4.
Lung cancer is one of the most commonly diagnosed cancers and the leading cause of cancer-related death worldwide. Although smoking is the primary cause of the cancer, lung cancer is also commonly diagnosed in people who have never smoked. Currently, the proportion of people who have never smoked diagnosed with lung cancer is increasing. Despite this alarming trend, this population is ineligible for lung screening. With the increasing proportion of people who have never smoked among lung cancer cases, there is a pressing need to develop prediction models to identify high-risk people who have never smoked and include them in lung cancer screening programs. Thus, our systematic review is intended to provide a comprehensive summary of the evidence on existing risk prediction models for lung cancer in people who have never smoked.
Electronic searches will be conducted in MEDLINE (Ovid), Embase (Ovid), Web of Science Core Collection (Clarivate Analytics), Scopus, and Europe PMC and Open-Access Theses and Dissertations databases. Two reviewers will independently perform title and abstract screening, full-text review, and data extraction using the Covidence review platform. Data extraction will be performed based on the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies (CHARMS). The risk of bias will be evaluated independently by two reviewers using the Prediction model Risk-of-Bias Assessment Tool (PROBAST) tool. If a sufficient number of studies are identified to have externally validated the same prediction model, we will combine model performance measures to evaluate the model's average predictive accuracy (e.g., calibration, discrimination) across diverse settings and populations and explore sources of heterogeneity.
The results of the review will identify risk prediction models for lung cancer in people who have never smoked. These will be useful for researchers planning to develop novel prediction models, and for clinical practitioners and policy makers seeking guidance for clinical decision-making and the formulation of future lung cancer screening strategies for people who have never smoked.
This protocol has been registered in PROSPERO under the registration number CRD42023483824.
肺癌是最常被诊断出的癌症之一，也是全球癌症相关死亡的主要原因。尽管吸烟是该癌症的主要病因，但肺癌也常见于从不吸烟的人群中。目前，被诊断出患有肺癌的从不吸烟者的比例正在增加。尽管有这一令人担忧的趋势，但这一人群不符合肺癌筛查的条件。随着肺癌病例中从不吸烟者的比例不断增加，迫切需要开发预测模型，以识别从不吸烟的高危人群，并将他们纳入肺癌筛查项目。因此，我们的系统评价旨在全面总结关于从不吸烟人群中现有肺癌风险预测模型的证据。
将在MEDLINE（Ovid）、Embase（Ovid）、科学网核心合集（科睿唯安）、Scopus以及欧洲生物医学与健康科学数据库和开放获取学位论文数据库中进行电子检索。两名评审员将使用Covidence评审平台独立进行标题和摘要筛选、全文评审以及数据提取。数据提取将基于预测建模研究系统评价的关键评估和数据提取清单（CHARMS）进行。两名评审员将使用预测模型偏倚风险评估工具（PROBAST）独立评估偏倚风险。如果确定有足够数量的研究对同一预测模型进行了外部验证，我们将合并模型性能指标，以评估该模型在不同环境和人群中的平均预测准确性（例如，校准、区分度），并探索异质性来源。
该评价的结果将识别从不吸烟人群中肺癌的风险预测模型。这些模型将有助于计划开发新型预测模型的研究人员，以及寻求临床决策指导和制定未来从不吸烟人群肺癌筛查策略指导的临床医生和政策制定者。
本方案已在PROSPERO注册，注册号为CRD42023483824。
