Lung cancer is the primary cause of cancer-related mortality, claiming approximately 1.79 million lives globally in 2020, with an estimated 2.21 million new cases diagnosed within the same period. Among these, Non-Small Cell Lung Cancer (NSCLC) is the predominant subtype, characterized by a notably bleak prognosis and low overall survival rate of approximately 25% over five years across all disease stages. However, survival outcomes vary considerably based on the stage at diagnosis and the therapeutic interventions administered. Recent advancements in artificial intelligence (AI) have revolutionized the landscape of lung cancer prognosis. AI-driven methodologies, including machine learning and deep learning algorithms, have shown promise in enhancing survival prediction accuracy by efficiently analyzing complex multi-omics data and integrating diverse clinical variables. By leveraging AI techniques, clinicians can harness comprehensive prognostic insights to tailor personalized treatment strategies, ultimately improving patient outcomes in NSCLC. Overviewing AI-driven data processing can significantly help bolster the understanding and provide better directions for using such systems.

肺癌是2020年全球癌症相关死亡的首要原因，其中不可小觑的病例为非小细胞肺癌，在所有疾病阶段的综合存活率约为25％，但预后依疾病阶段和治疗干预而异。最近，人工智能的进展在肺癌预后方面发挥了革命性作用，包括机器学习和深度学习算法在内的AI驱动方法通过高效分析复杂的多组学数据和整合多样的临床变量，提高了生存预测准确性。通过利用AI技术，临床医生可以获得全面的预后洞察，量身定制个性化治疗策略，最终提高非小细胞肺癌患者的预后。总而言之，AI驱动的数据处理方法能够大大增进对此类系统的理解，提供更好的指导方向。

基于人工智能的肺癌预后