高分辨率CT对实性孤立性肺结节良恶性的鉴别价值分析

doi:10.3969/j.issn.1001-8131.2023.06.018

Abstract
Figure/Table
References
Related Citation (3)

Download: PDF (387 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract Objective To investigate the value of high-resolution CT in differentiating benign and malignant solid solitary pulmonary nodules（SPN）. Methods A total of 60 patients with solid SPN admitted were retrospectively selected as research objects and divided into benign group（n=22）and malignant group（n=38）according to the Resultsof pathology/puncture examination.Both groups underwent high-resolution CT to analyze the differential value of high-resolution CT in the benign and malignant diagnosis of solid SPN. Results The coincidence rates of high-resolution CT in the diagnosis of benign solid SPN and malignant solid SPN were 86.36% and 73.68%, respectively.There was no significant difference in the coincidence rates between the two groups（P> 0.05）.There were statistically significant differences between benign group and malignant group in boundary characteristics, cavity, tractive emphysema and vessel convergence sign（P< 0.05）.Binary logistic regression analysis showed that lobulation sign, non tractive emphysema and vessel convergence sign were all independent factors influencing the benign and malignant diagnosis of solid SPN by high-resolution CT（P< 0.05）. Conclusions High-resolution CT is of great value in the differential diagnosis of benign and malignant solid SPN.It can be used to assist in the differential diagnosis of solid SPN according to the boundary characteristics, tractive emphysema, and vessel convergence sign.

Key words： high-resolution CT SPN benign and malignant

Received: 10 December 2022

PACS:

R734.2

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	He Lu
	Lv Xiaoting

Cite this article:

He Lu,Lv Xiaoting. The Value of High-Resolution CT in Differentiating Benign and Malignant Solid Solitary Pulmonary Nodules[J]. journal1, 2023, 43(6): 49-51.

URL:

http://www.hrbyybjb.org.cn/EN/10.3969/j.issn.1001-8131.2023.06.018 OR http://www.hrbyybjb.org.cn/EN/Y2023/V43/I6/49

[1] Bade B C, Cruz C S D.Lung cancer 2020:epidemiology, etiology, and prevention[J].Clinics in chest medicine, 2020, 41(1):1-24.
[2] Cao M, Chen W.Epidemiology of lung cancer in China[J].Thoracic cancer, 2019, 10(1):3-7.
[3] Deshpand R, Chandra M, Rauthan A.Evolving trends in lung cancer:Epidemiology, diagnosis, and management[J].Indian Journal of Cancer, 2022, 59(5):90.
[4] 李梦乾, 张晓梅, 胡家蕊, 等.孤立性肺结节良恶性预测模型的系统评价[J].肿瘤预防与治疗, 2022, 35(4):318-328.
[5] Jia Y, Gong W, Zhang Z, et al.Comparing the diagnostic value of 18F-FDG-PET/CT versus CT for differentiating benign and malignant solitary pulmonary nodules:A meta-analysis[J].Journal of Thoracic Disease, 2019, 11(5):2082-2098.
[6] Zhao J, Chai Y, Zhou J, et al.Energy spectrum computed tomography improves the differentiation between benign and malignant solitary pulmonary nodules[J].Clinical and Investigative Medicine, 2019, 42(3):E40-E46.
[7] 盛翠云, 陈斌, 高徳宏, 等.孤立性肺结节患者血清CEA, NSE, Cyfra21-1水平与结节直径的关系及联合高分辨率CT的诊断价值研究[J].现代生物医学进展, 2021, 21(14):2708-2712.
[8] 周清华, 范亚光, 王颖, 等.中国肺部结节分类、诊断与治疗指南(2016年版)[J].中国肺癌杂志, 2016, 19(12):793-798.
[9] 王昭珏, 赵静, 王孟昭.孤立性肺结节恶性概率预测模型的研究进展及临床应用[J].中国肺癌杂志, 2021, 24(9):660-667.
[10] 邱钱赛, 冯峰.孤立性肺结节影像学异质性的研究进展[J].放射学实践, 2019, 34(2):212-215.
[11] 冯洋洋, 徐兴祥.孤立性肺结节诊断模型研究进展[J].现代肿瘤医学, 2020, 28(11):1989-1993.
[12] 冯旭霞, 张梅, 吴丙琳, 等.高分辨CT联合血清CEA, NSE在鉴别诊断良恶性孤立性肺结节中的应用价值[J].中国CT和MRI杂志, 2020, 18(6):38-40.
[13] 李红英, 胡鑫, 宋瑞祥.高分辨CT影像学特征对孤立性肺结节良恶性的鉴别诊断效能[J].海南医学, 2022, 33(19):2540-2543.
[14] Wen Q, Yue Y, Shang J, et al.The application of dual-layer spectral detector computed tomography in solitary pulmonary nodule identification[J].Quantitative Imaging in Medicine and Surgery, 2021, 11(2):521-532.
[15] 古超. HRCT联合肿瘤标志物在诊断孤立性肺结节中的临床价值[J].河北医科大学学报, 2019, 40(4):461-465.
[16] 孔令重, 姜壮国, 曹小江, 等.Logistic回归分析高分辨率CT的纹理特征对孤立性肺结节诊断价值[J].新疆医科大学学报, 2019, 42(2):198-202.
[17] 孔芳, 段艳华, 宋学林, 等.高分辨率CT鉴别诊断良, 恶性实性孤立性肺结节[J].中国医学影像技术, 2021, 37(8):1168-1172.