人工智能在肺磨玻璃结节鉴别诊断中的研究进展  

Artificial intelligence research advances in discrimination and diagnosis of pulmonary ground-glass nodules

作　　者：李元婕 王渊 邱志新[1] Li Yuanjie;Wang Yuan;Qiu Zhixin(Department of Pulmonary and Critical Care Medicine,West China Hospital,Sichuan University,State Key Laboratory of Respiratory Health and Multimorbidity,Chengdu 610041,China;Department of Pulmonary and Critical Care Medicine/Institute of Respiratory Health,Frontiers Science Center for Disease-related Molecular Network,West China Hospital,Sichuan University,Chengdu 610041,China)

机构地区：[1]四川大学华西医院呼吸与危重症医学科,呼吸和共病全国重点实验室,成都610041 [2]四川大学华西医院呼吸与危重症医学科,呼吸健康研究所、四川大学疾病分子网络前沿科学中心,成都610041

出　　处：《中华结核和呼吸杂志》2024年第6期566-570,共5页Chinese Journal of Tuberculosis and Respiratory Diseases

摘　　要：肺磨玻璃结节(GGN)作为常见的肺部影像学表现,可见于多种病理状态,如炎症、出血、肺腺癌等。临床诊疗中,主要通过分析其影像学特征,初步预测GGN的性质,进而选择最佳的个性化诊疗方案及随访管理策略。随着信息学技术的迅速发展与普及,人工智能(AI)通过深度学习、多维度分析病例样本,构建医学影像辅助诊断模型,目前正逐步应用于GGN影像检测及预测。AI的临床应用省时省力,同时也提高了GGN预测的准确性。本文对AI在GGN良恶性、病理学亚型和EGFR突变鉴别诊断中的研究进展作一综述。Lung cancer,which accounts for about 18%of all cancer-related deaths worldwide,has a dismal 5-year survival rate of less than 20%.Survival rates for early-stage lung cancers(stages IA1,IA2,IA3,and IB,according to the TNM staging system)are significantly higher,underscoring the critical importance of early detection,diagnosis,and treatment.Ground-glass nodules(GGNs),which are commonly seen on lung imaging,can be indicative of both benign and malignant lesions.For clinicians,accurately characterizing GGNs and choosing the right management strategies present significant challenges.Artificial intelligence(AI),specifically deep learning algorithms,has shown promise in the evaluation of GGNs by analyzing complex imaging data and predicting the nature of GGNs,including their benign or malignant status,pathological subtypes,and genetic mutations such as epidermal growth factor receptor(EGFR)mutations.By integrating imaging features and clinical data,AI models have demonstrated high accuracy in distinguishing between benign and malignant GGNs and in predicting specific pathological subtypes.In addition,AI has shown promise in predicting genetic mutations such as EGFR mutations,which are critical for personalized treatment decisions in lung cancer.While AI offers significant potential to improve the accuracy and efficiency of GGN assessment,challenges remain,such as the need for extensive validation studies,standardization of imaging protocols,and improving the interpretability of AI algorithms.In summary,AI has the potential to revolutionise the management of GGNs by providing clinicians with more accurate and timely information for diagnosis and treatment decisions.However,further research and validation are needed to fully realize the benefits of AI in clinical practice.

关 键 词：肺磨玻璃结节 随访管理 EGFR突变 良恶性 医学影像 深度学习 肺腺癌 多维度分析 

分 类 号：R563[医药卫生—呼吸系统]