Division of Biotherapy, Cancer Center, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China.
Department of Thoracic Surgery, West China Hospital, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China.
Semin Cancer Biol. 2022 Nov;86(Pt 2):146-159. doi: 10.1016/j.semcancer.2022.08.002. Epub 2022 Aug 11.
Lung cancer accounts for the main proportion of malignancy-related deaths and most patients are diagnosed at an advanced stage. Immunotherapy and targeted therapy have great advances in application in clinics to treat lung cancer patients, yet the efficacy is unstable. The response rate of these therapies varies among patients. Some biomarkers have been proposed to predict the outcomes of immunotherapy and targeted therapy, including programmed cell death-ligand 1 (PD-L1) expression and oncogene mutations. Nevertheless, the detection tests are invasive, time-consuming, and have high demands on tumor tissue. The predictive performance of conventional biomarkers is also unsatisfactory. Therefore, novel biomarkers are needed to effectively predict the outcomes of immunotherapy and targeted therapy. The application of artificial intelligence (AI) can be a possible solution, as it has several advantages. AI can help identify features that are unable to be used by humans and perform repetitive tasks. By combining AI methods with radiomics, pathology, genomics, transcriptomics, proteomics, and clinical data, the integrated model has shown predictive value in immunotherapy and targeted therapy, which significantly improves the precision treatment of lung cancer patients. Herein, we reviewed the application of AI in predicting the outcomes of immunotherapy and targeted therapy in lung cancer patients, and discussed the challenges and future directions in this field.
肺癌占恶性肿瘤相关死亡的主要比例，大多数患者在晚期被诊断出来。免疫疗法和靶向治疗在临床上治疗肺癌患者方面取得了很大进展，但疗效不稳定。这些疗法在患者中的反应率各不相同。一些生物标志物已被提出用于预测免疫疗法和靶向治疗的结果，包括程序性细胞死亡配体 1（PD-L1）表达和致癌基因突变。然而，检测试验具有侵袭性、耗时且对肿瘤组织要求高。传统生物标志物的预测性能也不理想。因此，需要新型生物标志物来有效预测免疫疗法和靶向治疗的结果。人工智能（AI）的应用可能是一个可行的解决方案，因为它具有几个优势。AI 可以帮助识别人类无法使用的特征并执行重复任务。通过将 AI 方法与放射组学、病理学、基因组学、转录组学、蛋白质组学和临床数据相结合，该综合模型在免疫疗法和靶向治疗中显示出了预测价值，显著提高了肺癌患者的精准治疗水平。本文综述了 AI 在预测肺癌患者免疫疗法和靶向治疗结果中的应用，并讨论了该领域的挑战和未来方向。
