Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related mortality among women and men, in developed countries, despite the public health interventions including tobacco-free campaigns, screening and early detection methods, recent therapeutic advances, and ongoing intense research on novel antineoplastic modalities. Targeting oncogenic driver mutations and immune checkpoint inhibition has indeed revolutionized NSCLC treatment, yet there still remains the unmet need for robust and standardized predictive biomarkers to accurately inform clinical decisions. Artificial intelligence (AI) represents the computer-based science concerned with large datasets for complex problem-solving. Its concept has brought a paradigm shift in oncology considering its immense potential for improved diagnosis, treatment guidance, and prognosis. In this review, we present the current state of AI-driven applications on NSCLC management, with a particular focus on radiomics and pathomics, and critically discuss both the existing limitations and future directions in this field. The thoracic oncology community should not be discouraged by the likely long road of AI implementation into daily clinical practice, as its transformative impact on personalized treatment approaches is undeniable.
在发达国家，尽管已实施包括禁烟运动、筛查与早期检测方法在内的公共卫生干预措施，并取得了近期治疗进展及针对新型抗肿瘤模式的持续深入研究，非小细胞肺癌（NSCLC）仍是导致男性和女性癌症相关死亡的首要原因。针对致癌驱动基因突变和免疫检查点抑制的治疗策略虽已彻底改变了NSCLC的治疗格局，但临床决策仍缺乏稳健且标准化的预测性生物标志物作为精准指导。人工智能作为一门基于计算机处理海量数据集以解决复杂问题的科学，其理念凭借在优化诊断、治疗指导和预后评估方面的巨大潜力，正推动肿瘤学领域发生范式转变。本综述系统阐述了人工智能在NSCLC诊疗中的应用现状，重点聚焦影像组学与病理组学领域，并对该领域当前存在的局限性与未来发展方向进行批判性探讨。尽管人工智能融入日常临床实践可能仍需漫长历程，但其对个体化治疗模式产生的变革性影响毋庸置疑，胸腺肿瘤学界应对此保持信心。
Artificial Intelligence-Based Treatment Decisions: A New Era for NSCLC
肿瘤(癌症)患者之家