| Citation: |
PEI Changjun, SUN Xueli, WANG Xin, HUANG Wei, LI Mei, SHEN Zhimei. Application of artificial intelligence combined with multi-slice spiral CT scanning in the screening of pulmonary nodules in health examination population in government departments[J]. Journal of Clinical Medicine in Practice, 2023, 27(24): 89-92. DOI: 10.7619/jcmp.20232282
|
To explore the application value of artificial intelligence (AI) combined with multi-slice spiral CT in screening pulmonary nodules in health examination population in government departments.
A retrospective analysis was conducted on the chest CT screening data of health examination population in government departments who had at least diameter of one nodule ≥3 mm. All data were analyzed using AI technology combined with manual film reading. The detection rate of pulmonary nodules was recorded. Based on the risk values predicted by AI technology, the nodules were divided into different risk groups, and the basic characteristics of the nodules were analyzed and compared.
The detection rate of pulmonary nodules was 60.4%. High-risk patients were more likely to present as pure ground glass nodules or mixed ground glass nodules. The average age of individuals with pulmonary nodules was higher than those without(P<0.05). There were significant differences in the basic characteristics of pulmonary nodules, including nature, morphology, internal signs, and external signs among different risk groups (P<0.05).
Multi-slice spiral CT examination of the chest scan is helpful for screening pulmonary nodules in large-scale physical examination populations. The combination of AI technology and manual film reading can improve the accuracy of pulmonary nodule screening.
| [1] |
SHAO Q, LI J B, LI F X, et al. Clinical investigation into the initial diagnosis and treatment of 1, 168 lung cancer patients[J]. Oncol Lett, 2015, 9(2): 563-568. doi: 10.3892/ol.2014.2777
|
| [2] |
WEI S H, TIAN J T, SONG X P, et al. Causes of death and competing risk analysis of the associated factors for non-small cell lung cancer using the Surveillance, Epidemiology, and End Results database[J]. J Cancer Res Clin Oncol, 2018, 144(1): 145-155. doi: 10.1007/s00432-017-2522-3
|
| [3] |
TANOUE L T. Clinical features of 5, 628 primary lung cancer patients: experience at mayo clinic from 1997 to 2003[J]. Yearb Pulm Dis, 2007, 2007: 102-104. doi: 10.1016/S8756-3452(08)70346-3
|
| [4] |
OUDKERK M, DEVARAJ A, VLIEGENTHART R, et al. European position statement on lung cancer screening[J]. Lancet Oncol, 2017, 18(12): e754-e766. doi: 10.1016/S1470-2045(17)30861-6
|
| [5] |
The National Lung Screening Trial Research Team. Reduced lung-cancer mortality with low-dose computed tomographic screening[J]. N Engl J Med, 2011, 365(5): 395-409. doi: 10.1056/NEJMoa1102873
|
| [6] |
中华医学会呼吸病学分会肺癌学组, 中国肺癌防治联盟专家组. 肺部结节诊治中国专家共识[J]. 中华结核和呼吸杂志, 2015, 38(4): 249-254. doi: 10.3760/cma.j.issn.1001-0939.2015.04.004
|
| [7] |
中国食品药品检定研究院, 中华医学会放射学分会心胸学组, 任海萍, 等. 胸部CT肺结节数据标注与质量控制专家共识(2018)[J]. 中华放射学杂志, 2019, 53(1): 9-15. doi: 10.3760/cma.j.issn.1005-1201.2019.01.004
|
| [8] |
金晨望, 郭佑民. 人工智能辅助诊断技术在低剂量CT肺结节筛查中的应用及质控[J]. 中华放射学杂志, 2019, 53(1): 6-8.
|
| [9] |
ZHOU M G, WANG H D, ZHU J, et al. Cause-specific mortality for 240 causes in China during 1990-2013: a systematic subnational analysis for the Global Burden of Disease Study 2013[J]. Lancet, 2016, 387(10015): 251-272. doi: 10.1016/S0140-6736(15)00551-6
|
| [10] |
CHEN W Q, ZHENG R S, BAADE P D, et al. Cancer statistics in China, 2015[J]. CA A Cancer J Clinicians, 2016, 66(2): 115-132. doi: 10.3322/caac.21338
|
| [11] |
WANG J B, FAN Y G, JIANG Y, et al. Attributable causes of lung cancer incidence and mortality in China[J]. Thorac Cancer, 2011, 2(4): 156-163. doi: 10.1111/j.1759-7714.2011.00067.x
|
| [12] |
CHEN W Q, ZHENG R S, ZENG H M, et al. Epidemiology of lung cancer in China[J]. Thorac Cancer, 2015, 6(2): 209-215. doi: 10.1111/1759-7714.12169
|
| [13] |
CHURCH T, BLACK W, ABERLE D, et al. Results of initial low-dose computed tomographic screening for lung cancer[J]. N Engl J Med, 2013, 368(21): 1980-1991. doi: 10.1056/NEJMoa1209120
|
| [14] |
YOUSAF-KHAN U, VAN DER AALST C, DE JONG P A, et al. Final screening round of the NELSON lung cancer screening trial: the effect of a 2. 5-year screening interval[J]. Thorax, 2017, 72(1): 48-56. doi: 10.1136/thoraxjnl-2016-208655
|
| [15] |
DU Y H, ZHAO Y R, GRIGORY S, et al. Methods of computed tomography screening and management of lung cancer in Tianjin: design of a population-based cohort study[J]. Cancer Biol Med, 2019, 16(1): 181. doi: 10.20892/j.issn.2095-3941.2018.0237
|
| [16] |
ZHAO Y R, 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. doi: 10.1007/s00330-012-2437-y
|
| [17] |
李娟娟, 刘敏, 杨斌, 等. 人工智能辅助肺结节筛查及定性诊断的应用研究[J]. 实用临床医药杂志, 2022, 26(8): 8-12. doi: 10.7619/jcmp.20214698
|
| [18] |
BENZAKOUN J, BOMMART S, COSTE J, et al. Computer-aided diagnosis (CAD) of subsolid nodules: evaluation of a commercial CAD system[J]. Eur J Radiol, 2016, 85(10): 1728-1734. doi: 10.1016/j.ejrad.2016.07.011
|
| 1. |
宋惠芳，王敏，李俊杰，冯宪梅. 共情共赢心理护理联合愉悦因子输入对有机磷农药中毒患者心理状况的影响. 医学理论与实践. 2024(10): 1760-1762 .
| |
| 2. |
徐香港，汪杏. 重症有机磷农药中毒患者的急诊急救护理效果. 名医. 2024(01): 36-38 .
| |
| 3. |
饶巧，张志坚，程春梅. 有机磷农药中毒康复期患者自我隐瞒现状及其影响因素. 中国工业医学杂志. 2024(03): 298-300 .
| |
| 4. |
周创宇，兰浩云，张增强，李海峰. PSS、APACHE-Ⅱ评分对急性有机磷农药中毒患者近期预后不良预测价值比较. 现代医药卫生. 2024(22): 3849-3854 .
| |
| 5. |
张丽平. 急性有机磷农药中毒康复期患者负性情绪现状调查及其影响因素分析. 心理月刊. 2024(23): 214-216 .
|
© 2020 《实用临床医药杂志》编辑部
Address: 江苏省扬州市江阳中路136号，扬州大学江阳路北校区14号楼201室China Pos: 225009Tel: 0514-87978917、87978989、87978807
Supported by:
Beijing Renhe Information Technology Co., Ltd.
苏公网安备 32100302010246号
DownLoad: