[1] 陈典, 隆寰宇, 张丛溪, 等. 2025年GOLD慢性阻塞性肺疾病诊断、治疗、管理及预防全球策略更新要点解读[J]. 中国全科医学, 2025, 28(16): 1937-1949. [2] CHRISTENSON S A, SMITH B M, BAFADHEL M, et al. Chronic obstructive pulmonary disease[J]. Lancet, 2022, 399(10342):2227-2242. [3] WANG C, XU J, YANG L, et al. Prevalence and risk factors of chronic obstructive pulmonary disease in China (the China Pulmonary Health [CPH] study): a national cross-sectional study[J]. Lancet, 2018, 391(10131): 1706-1717. [4] 李艳, 窦彤, 王肖潇, 等. 胸部CT对老年重度慢性阻塞性肺疾病病人的评估价值[J]. 实用老年医学, 2025, 39(1): 46-50. [5] MACHIDA H, INOUE S, SHIBATA Y, et al. The incidence and risk analysis of lung cancer development in patients with chronic obstructive pulmonary disease: possible effectiveness of annual CT-screening[J]. Int J Chron Obstruct Pulmon Dis, 2021, 16: 739-749. [6] LIN X, ZHANG Z, ZHOU T, et al. The role of computed tomography and artificial intelligence in evaluating the comorbidities of chronic obstructive pulmonary disease: a one-stop CT scanning for lung cancer screening[J]. Int J Chron Obstruct Pulmon Dis, 2025, 20: 1395-1406. [7] LEE D, YOON S N. Application of artificial intelligence-based technologies in the healthcare industry: opportunities and challenges[J]. Int J Environ Res Public Health, 2021, 18(1): 271. [8] WU Y, XIA S, LIANG Z, et al. Artificial intelligence in COPD CT images: identification, staging, and quantitation[J]. Respir Res, 2024, 25(1): 319. [9] 吴漫, 吴健卫, 徐非洲, 等. 慢性阻塞性肺疾病合并肺癌现状调查及预后初探[J]. 临床肺科杂志, 2025, 30(2): 219-225. [10] DE TORRES J P, MARÍN J M, CASANOVA C, et al. Lung cancer in patients with chronic obstructive pulmonary disease: incidence and predicting factors[J]. Am J Respir Crit Care Med, 2011, 184(8): 913-919. [11] DAI J, YANG P, COX A, et al. Lung cancer and chronic obstructive pulmonary disease:from a clinical perspective[J]. Oncotarget, 2017, 8(11): 18513-18524. [12] SHEN J, GAO C, LOU X, et al. The association between emphysema detected on computed tomography and increased risk of lung cancer: a systematic review and meta-analysis[J]. Quant Imaging Med Surg, 2025, 15(3): 2193-2208. [13] 杨晨, 杨中卫, 李威. 探索慢性阻塞性肺疾病临床异质性、肺气肿亚型与肺癌的关系[J]. 中国医学工程, 2023, 31(11): 33-38. [14] LIU W, WU Y, ZHENG Z, et al. Enhancing diagnostic accuracy of lung nodules in chest computed tomography using artificial intelligence: retrospective analysis[J]. J Med Internet Res, 2025, 27: e64649. [15] WEI Y, ZHOU Q, WU J, et al. Review of artificial intelligence in lung nodule risk assessment[J]. IEEE Rev Biomed Eng, 2025. DOI: 10.1109/RBME.2025.3528946. [16] DURANTI L, TAVECCHIO L, ROLLI L, et al. New perspectives on lung cancer screening and artificial intelligence[J]. Life: Basel, 2025, 15(3): 498. [17] ZHANG L, YANG D, YE X, et al. Successful application of artificial intelligence-assisted analysis of invasive pulmonary adenocarcinoma less than 6 mm in size: a case report and literature review[J]. Clin Respir J, 2025, 19(5): e70073. [18] ZHAO Y, DE BOCK G H, VLIEGENTHART R, et al. Performance of computer-aided detection of pulmonary nodules in low-dose CT: comparison with double reading by nodule volume[J]. Eur Radiol, 2012, 22(10): 2076-2084. [19] HUANG D, LI Z, JIANG T, et al. Artificial intelligence in lung cancer: current applications, future perspectives, and challenges[J]. Front Oncol, 2024, 14: 1486310. [20] WANG C, CHEN B, LIANG S, et al. China protocol for early screening, precise diagnosis, and individualized treatment of lung cancer[J]. Signal Transduct Target Ther, 2025, 10(1):175. [21] GRENIER P A. COPD: artificial intelligence detects and quantifies anomalies on chest CT enabling prediction of disease severity[J]. Eur Radiol, 2024, 34(7): 4376-4378. [22] BERMEJO-PELÁEZ D, SAN JOSÉ ESTÉPAR R, LEDESMA-CARBAYO M J. Emphysema classification using a multi-view convolutional network[J]. Proc IEEE Int Symp Biomed Imaging, 2018, 2018: 519-522. [23] SHIN H J, KWAK S H, KIM K Y, et al. Effectiveness of artificial intelligence for detecting operable lung cancer on chest radiographs[J]. Transl Lung Cancer Res, 2024, 13(12): 3473-3485. [24] LOCOCO F, GHALY G, FLAMINI S, et al. Artificial intelligence applications in personalizing lung cancer management: state of the art and future perspectives[J]. J Thorac Dis, 2024, 16(10): 7096-7110. [25] WU Q, HUANG Y, WANG S, et al. Artificial intelligence in lung cancer screening: detection, classification, prediction, and prognosis[J]. Cancer Med, 2024, 13(7): e7140. [26] KAHNERT K, JÖRRES R A, BEHR J, et al. The diagnosis and treatment of COPD and its comorbidities[J]. Dtsch Arztebl Int, 2023, 120(25): 434-444. [27] JIANG L, ZHOU Y, MIAO W, et al. Artificial intelligence-assisted quantitative CT parameters in predicting the degree of risk of solitary pulmonary nodules[J]. Ann Med, 2024, 56(1): 2405075. [28] LI Y, DENG J, MA X, et al. Diagnostic accuracy of CT and PET/CT radiomics inpredicting lymph node metastasis in non-small cell lung cancer[J]. Eur Radiol, 2025, 35(4): 1966-1979. [29] 郑莉莉, 陈恒屹. 慢性阻塞性肺疾病合并肺癌的发病机制与治疗研究进展[J]. 检验医学与临床, 2024, 21(19): 2935-2940. [30] SHEN J, WANG S, GUAN H, et al. Artificial intelligence in automatic image segmentation system for exploring recurrence patterns in small cell carcinoma of the lung[J]. Front Oncol, 2025, 15: 1534740. [31] CLAUDIO QUIROS A, COUDRAY N, YEATON A, et al. Mapping the landscape of histomorphological cancer phenotypes using self-supervised learning on unannotated pathology slides[J]. Nat Commun, 2024, 15(1): 4596. [32] AYASA Y, ALAJRAMI D, IDKEDEK M, et al. The impact of artificial intelligence on lung cancer diagnosis and personalized treatment[J]. Int J Mol Sci, 2025, 26(17): 8472. |