人工智能阅片技术在老年早期肺癌辅助诊断中的应用价值

doi:10.3969/j.issn.1671-7171.2022.10.043

摘要
图/表
参考文献
相关文章 (15)

全文: PDF (1085 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要【目的】探讨人工智能阅片技术在老年早期肺癌辅助诊断中的应用价值。【方法】对两院收治的142例经胸腔镜手术治疗后病理检查确诊为早期肺癌的老年患者,采用人工智能阅片技术检查患者胸部CT,并与医师阅片方法进行比较,以常规病理检查为金标准,采用Kappa检验评定阅片方法的一致性。【结果】142例患者中常规病理诊断检测出阳性131例,其中118例人工智能检测结果与常规病理检测相符,阳性符合率为90.08%(118/131),阴性符合率为27.27%(3/11),总符合率为85.21%(121/142),经Kappa一致性检验,两种检测方法具有较好的一致性。医师阅片检测出113例阳性患者,其中104例与常规病理检测相符,阳性符合率为79.39%(104/131),阴性符合率为18.18%(2/11),总符合率为74.65%(106/142),经Kappa一致性检验,两种检测方法一致性一般。人工智能阅片技术检测准确率为85.21%(121/142),高于医师阅片的74.65%(106/142),且差异有统计学意义(P<0.05)。【结论】人工智能阅片技术用于老年早期肺癌诊断准确性较高,可辅助医生进行诊断工作。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	雷哲锋
	王黄震

关键词 ：肺肿瘤/影像诊断, 人工智能, 老年人

收稿日期: 2022-01-25

中图分类号:

R734.2

通讯作者: *E-mail:whz8968169@163.com

引用本文:

雷哲锋, 王黄震. 人工智能阅片技术在老年早期肺癌辅助诊断中的应用价值[J]. 医学临床研究, 2022, 39(10): 1590-1592.

链接本文:

http://journal07.magtech.org.cn/yxlcyj/CN/10.3969/j.issn.1671-7171.2022.10.043 或 http://journal07.magtech.org.cn/yxlcyj/CN/Y2022/V39/I10/1590

[1] GIROUX-LEPRIEUR E,COSTANTINI A,DING V W,et al.Hedgehog signaling in lung cancer:from oncogenesis to cancer treatment resistance[J].Int J Mol Sci,2018,19(9):2835-2844.
[2] LEI Z,LOU J,BAO L,et al. Contrast-enhanced ultrasound for needle biopsy of central lung cancer with atelectasis [J].J Med Ultrason,2018,45(3):461-467.
[3] NAWA T. Low-dose CT screening for lung cancer reduced lung cancer mortality in Hitachi City [J].Int J Radiat Biol,2019,95(10):1441-1446.
[4] TERAMOTO A,YAMADA A,TSUKAMOTO T,et al. Decision support system for lung cancer using PET/CT and microscopic images [J].Adv Exp Med Biol,2020,13(12):73-94.
[5] CHRISTIE JR,LANG P,ZELLKO LM,et al. Artificial intelligence in lung cancer:bridging the gap between computational power and clinical decision-making [J].Can Assoc Radiol J,2021,72(1):86-97.
[6] 中华医学会,中华医学会肿瘤学分会,中华医学会杂志社.中华医学会肺癌临床诊疗指南(2018版)[J].中华肿瘤杂志,2018,40(12):935-964.
[7] WHITTAKER BROWN S A,PADILLA M,MHANGO G,et al. Interstitial lung abnormalities and lung cancer risk in the national lung screening trial [J].Chest,2019,156(6):1195-1203.
[8] WALTER J E,HEUVELMANS M A,de BOCK G H,et al. Relationship between the number of new nodules and lung cancer probability in incidence screening rounds of CT lung cancer screening:The NELSON study[J].Lung Cancer,2018,125(14):103-108.
[9] LI X,HU B,LI H,et al. Application of artificial intelligence in the diagnosis of multiple primary lung cancer [J].Thorac Cancer,2019,10(11):2168-2174.
[10] RABBANI M,KANEVSKY J,KAFI K,et al. Role of artificial intelligence in the care of patients with nonsmall cell lung cancer [J].Eur J Clin Invest,2018,48(4):36-42.
[11] ATHER S,KADIR T,GLEESON F. Artificial intelligence and radiomics in pulmonary nodule management:current status and future applications [J].Clin Radiol,2020,75(1):13-19.
[12] 毕珂,王茵,张婷婷,等. 基于深度学习算法人工智能肺癌病理诊断模型的开发和应用[J].第二军医大学学报,2020,41(11):59-65.
[13] HEEKE S,DELINGETTE H,FANJAT Y,et al. The age of artificial intelligence in lung cancer pathology:Between hope,gloom and perspectives [J].Ann Pathol,2019,39(2):130-136.