The advent of personalized medicine and novel therapeutic strategies has transformed the treatment landscape of non-small cell lung cancer (NSCLC), significantly improving patient survival. However, only a minority of patients experience a durable benefit, as intrinsic or acquired resistance remains a major challenge. Understanding the complex mechanisms of resistance—linked to tumor biology, the tumor microenvironment (TME), and host factors—is crucial to overcoming these barriers. Recent innovations in diagnostics, including artificial intelligence and liquid biopsy, offer promising tools to refine therapeutic decisions. Machine Learning and Deep Learning provide predictive algorithms that enhance diagnostic accuracy and prognostic assessment. Techniques like single-cell RNA sequencing and pathomics offer deeper insights into the role of the TME. Liquid biopsy, as a minimally invasive method, enables real-time detection of circulating tumor components, facilitating the identification of predictive and prognostic biomarkers and illuminating tumor heterogeneity. These translational research advances are revolutionizing the understanding of cancer biology and are key to optimizing personalized treatment strategies. This review highlights emerging tools aimed at improving diagnostic and therapeutic precision in NSCLC, underscoring their role in decoding the interplay between tumor cells, the TME, and the host to ultimately improve patient outcomes.
个体化医疗及新型治疗策略的出现，改变了非小细胞肺癌（NSCLC）的治疗格局，显著提升了患者生存率。然而，由于内在或获得性耐药仍是主要挑战，仅有少数患者能获得持久疗效。理解与肿瘤生物学、肿瘤微环境（TME）及宿主因素相关的复杂耐药机制，对于突破这些治疗障碍至关重要。近期诊断技术的创新，包括人工智能与液体活检，为优化治疗决策提供了前景广阔的工具。机器学习与深度学习提供的预测算法可提升诊断准确性与预后评估能力。单细胞RNA测序和病理组学等技术为深入解析肿瘤微环境的作用提供了新视角。液体活检作为一种微创检测手段，能够实时监测循环肿瘤成分，有助于识别预测性和预后性生物标志物，并揭示肿瘤异质性。这些转化研究的进展正在革新对癌症生物学的理解，是优化个体化治疗策略的关键。本综述重点介绍了旨在提升NSCLC诊疗精准度的新兴工具，强调其在解析肿瘤细胞、肿瘤微环境与宿主之间相互作用机制中的重要作用，最终为改善患者预后提供支持。
肿瘤(癌症)患者之家