Background: Lung cancer remains the leading cause of cancer-related mortality globally, largely due to delayed diagnosis in its early stages. While conventional diagnostic tools like low-dose CT and tissue biopsy are routinely used, they suffer from limitations including invasiveness, radiation exposure, cost, and limited sensitivity for early-stage detection. Liquid biopsy, a minimally invasive alternative that captures circulating tumor-derived biomarkers such as ctDNA, cfRNA, and exosomes from body fluids, offers promising diagnostic potential—yet its sensitivity in early disease remains suboptimal. Recent advances in Artificial Intelligence (AI) and radiomics are poised to bridge this gap. Objective: This review aims to explore how AI, in combination with radiomics, enhances the diagnostic capabilities of liquid biopsy for early detection of lung cancer and facilitates personalized monitoring strategies. Content Overview: We begin by outlining the molecular heterogeneity of lung cancer, emphasizing the need for earlier, more accurate detection strategies. The discussion then transitions into liquid biopsy and its key analytes, followed by an in-depth overview of AI techniques—including machine learning (e.g., SVMs, Random Forest) and deep learning models (e.g., CNNs, RNNs, GANs)—that enable robust pattern recognition across multi-omics datasets. The role of radiomics, which quantitatively extracts spatial and morphological features from imaging modalities such as CT and PET, is explored in conjunction with AI to provide an integrative, multimodal approach. This convergence supports the broader vision of precision medicine by integrating omics data, imaging, and electronic health records. Discussion: The synergy between AI, liquid biopsy, and radiomics signifies a shift from traditional diagnostics toward dynamic, patient-specific decision-making. Radiomics contributes spatial information, while AI improves pattern detection and predictive modeling. Despite these advancements, challenges remain—including data standardization, limited annotated datasets, the interpretability of deep learning models, and ethical considerations. A push toward rigorous validation and multimodal AI frameworks is necessary to facilitate clinical adoption. Conclusion: The integration of AI with liquid biopsy and radiomics holds transformative potential for early lung cancer detection. This non-invasive, scalable, and individualized diagnostic paradigm could significantly reduce lung cancer mortality through timely and targeted interventions. As technology and regulatory pathways mature, collaborative research is crucial to standardize methodologies and translate this innovation into routine clinical practice.
背景：肺癌仍是全球癌症相关死亡的主要原因，很大程度上归因于其早期诊断的延迟。虽然低剂量CT和组织活检等常规诊断工具已常规应用，但它们存在诸多局限性，包括侵入性、辐射暴露、成本高昂以及对早期检测的敏感性有限。液体活检作为一种微创替代方案，可从体液中捕获循环肿瘤来源的生物标志物（如ctDNA、cfRNA和外泌体），展现出良好的诊断潜力，但其在早期疾病中的敏感性仍不理想。人工智能与影像组学的最新进展有望弥补这一不足。目的：本综述旨在探讨人工智能如何结合影像组学，提升液体活检在肺癌早期检测中的诊断能力，并促进个体化监测策略的实施。内容概述：首先概述肺癌的分子异质性，强调对更早期、更准确检测策略的需求。随后讨论转向液体活检及其关键分析物，并深入综述人工智能技术——包括机器学习（如支持向量机、随机森林）和深度学习模型（如卷积神经网络、循环神经网络、生成对抗网络）——这些技术能够实现对多组学数据集的稳健模式识别。同时探讨影像组学的作用，其从CT、PET等影像模态中定量提取空间与形态学特征，并与人工智能结合，提供一种整合的多模态方法。这种融合通过整合组学数据、影像学和电子健康记录，支持了精准医学的更广阔愿景。讨论：人工智能、液体活检与影像组学之间的协同作用，标志着从传统诊断向动态、患者特异性决策的转变。影像组学提供空间信息，而人工智能则提升模式检测和预测建模能力。尽管取得这些进展，仍面临诸多挑战，包括数据标准化、标注数据集有限、深度学习模型的可解释性以及伦理考量。推动严格验证和多模态人工智能框架的发展，对于促进临床转化至关重要。结论：人工智能与液体活检、影像组学的整合，对肺癌早期检测具有变革性潜力。这种非侵入性、可扩展且个体化的诊断范式，通过及时、有针对性的干预，有望显著降低肺癌死亡率。随着技术与监管路径的成熟，合作研究对于标准化方法并将此创新转化为常规临床实践至关重要。
肿瘤(癌症)患者之家