Khalife Ghida, Nilsson Matilda, Peltola Lotta, Waris Juho, Jekunen Antti, Leskelä Riikka-Leena, Andersén Heidi, Nuutinen Mikko, Heikkilä Eija, Nurmi-Rantala Susanna, Torkki Paulus
Department of Public Health, University of Helsinki, Helsinki, Finland.
Department of Oncology, Vaasa Central Hospital, Vaasa, Finland.
Acta Oncol. 2025 May 12;64:661-671. doi: 10.2340/1651-226X.2025.42529.
Lung cancer (LC) remains the leading cause of cancer-related mortality worldwide. Early detection through targeted screening significantly improves patient outcomes. However, identifying high-risk individuals remains a critical challenge.
This systematic review evaluates externally validated LC risk prediction models to assess their performance and potential applicability in screening strategies.
Of the 11,805 initial studies, 66 met inclusion criteria and 38 published mainly between 2020 and 2024 were included in the final analysis. Model methodologies, validation approaches, and performance metrics were extracted and compared.
The review identified 18 models utilising conventional machine learning, six employing neural networks, and 14 comparing different predictive frameworks. The Prostate Lung Colorectal and Ovarian Cancer Screening Trial (PLCOm2012) demonstrated superior sensitivity across diverse populations, while newer models, such as Optimized Early Warning model for Lung cancer risk (OWL) and CanPredict, showed promising results. However, differences in population demographics and healthcare systems may limit the generalisability of these models.
While LC risk prediction models have advanced, their applicability to specific healthcare systems, such as Finland's, requires further adaptation and validation. Future research should focus on optimising these models for local contexts to improve clinical impact and cost-effectiveness in targeted screening programmes.
PROSPERO CRD42022321391.
肺癌仍然是全球癌症相关死亡的主要原因。通过靶向筛查进行早期检测可显著改善患者预后。然而，识别高危个体仍然是一项严峻挑战。
本系统评价对外验证的肺癌风险预测模型进行评估，以评估其在筛查策略中的性能和潜在适用性。
在11805项初始研究中，66项符合纳入标准，最终分析纳入了38项主要在2020年至2024年期间发表的研究。提取并比较了模型方法、验证方法和性能指标。
该评价确定了18个使用传统机器学习的模型、6个采用神经网络的模型以及14个比较不同预测框架的模型。前列腺、肺、结肠和卵巢癌筛查试验（PLCOm2012）在不同人群中表现出较高的敏感性，而较新的模型，如肺癌风险优化早期预警模型（OWL）和CanPredict，显示出有前景的结果。然而，人群人口统计学和医疗系统的差异可能会限制这些模型的通用性。
虽然肺癌风险预测模型已经取得进展，但其在特定医疗系统（如芬兰）中的适用性需要进一步调整和验证。未来的研究应侧重于针对当地情况优化这些模型，以提高靶向筛查计划中的临床影响和成本效益。
PROSPERO CRD42022321391。
