Chen Hsin-Hung, Wu Yun-Ju, Wu Fu-Zong
Department of Medical Education and Research, Kaohsiung Veterans General Hospital, Kaohsiung 813414, Taiwan.
Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung 813414, Taiwan.
Diagnostics (Basel). 2025 Jun 19;15(12):1562. doi: 10.3390/diagnostics15121562.
Lung cancer remains the leading cause of cancer-related mortality globally, largely due to late-stage diagnoses. While low-dose computed tomography (LDCT) has improved early detection and reduced mortality in high-risk populations, traditional screening strategies often adopt a one-size-fits-all approach based primarily on age and smoking history. This can lead to limitations, such as overdiagnosis, false positives, and the underrepresentation of non-smokers, which are especially prevalent in Asian populations. Precision medicine offers a transformative solution by tailoring screening protocols to individual risk profiles through the integration of clinical, genetic, environmental, and radiological data. Emerging tools, such as risk prediction models, radiomics, artificial intelligence (AI), and liquid biopsies, enhance the accuracy of screening, allowing for the identification of high-risk individuals who may not meet conventional criteria. Polygenic risk scores (PRSs) and molecular biomarkers further refine stratification, enabling more personalized and effective screening intervals. Incorporating these innovations into clinical workflows, alongside shared decision-making (SDM) and robust data infrastructure, represents a paradigm shift in lung cancer prevention. However, implementation must also address challenges related to health equity, algorithmic bias, and system integration. As precision medicine continues to evolve, it holds the promise of optimizing early detection, minimizing harm, and extending the benefits of lung cancer screening to broader and more diverse populations. This review explores the current landscape and future directions of precision medicine in lung cancer screening, emphasizing the need for interdisciplinary collaboration and population-specific strategies to realize its full potential in reducing the global burden of lung cancer.
肺癌仍然是全球癌症相关死亡的主要原因，这主要归因于晚期诊断。虽然低剂量计算机断层扫描（LDCT）改善了高危人群的早期检测并降低了死亡率，但传统的筛查策略通常主要基于年龄和吸烟史采取一刀切的方法。这可能导致一些局限性，如过度诊断、假阳性以及非吸烟者代表性不足等问题，这些在亚洲人群中尤为普遍。精准医学通过整合临床、基因、环境和放射学数据，根据个体风险概况量身定制筛查方案，提供了一种变革性的解决方案。风险预测模型、放射组学、人工智能（AI）和液体活检等新兴工具提高了筛查的准确性，能够识别出可能不符合传统标准的高危个体。多基因风险评分（PRSs）和分子生物标志物进一步优化了分层，从而实现更个性化、有效的筛查间隔。将这些创新纳入临床工作流程，同时结合共同决策（SDM）和强大的数据基础设施，代表了肺癌预防领域的范式转变。然而，实施过程还必须应对与健康公平、算法偏差和系统整合相关的挑战。随着精准医学不断发展，它有望优化早期检测、将危害降至最低，并将肺癌筛查的益处扩展到更广泛、更多样化的人群。本综述探讨了肺癌筛查中精准医学的现状和未来方向，强调需要跨学科合作和针对特定人群的策略，以充分发挥其在减轻全球肺癌负担方面的潜力。
