Artificial intelligence-based prediction of clinical outcome in immunotherapy and targeted therapy of lung cancer

免疫疗法 靶向治疗 医学 肺癌 癌症 肿瘤科 恶性肿瘤 内科学 无线电技术 放射科
作者
Xiaomeng Yin,Hu Liao,Yun Hong,Nan Lin,Shen Li,Yu Xiang,Xuelei Ma
出处
期刊:Seminars in Cancer Biology [Elsevier]
卷期号:86: 146-159 被引量:45
标识
DOI:10.1016/j.semcancer.2022.08.002
摘要

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.
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