切换至 "中华医学电子期刊资源库"

中华消化病与影像杂志(电子版) ›› 2022, Vol. 12 ›› Issue (06) : 348 -350. doi: 10.3877/cma.j.issn.2095-2015.2022.06.005

论著

人工智能与放射科医师判读肺实性肿块CT征象的一致性研究
韩雷1, 陈小宇1,(), 王楠楠1   
  1. 1. 223001 江苏淮安,徐州医科大学附属淮安医院影像科
  • 收稿日期:2022-02-28 出版日期:2022-12-01
  • 通信作者: 陈小宇

Study on the consistency between artificial intelligence and radiologist in interpreting CT signs of solid lung masses

Lei Han1, Xiaoyu Chen1,(), Nannan Wang1   

  1. 1. Department of Imaging, Huai′an Hospital Affiliated to Xuzhou Medical University, Huai′an 223001, China
  • Received:2022-02-28 Published:2022-12-01
  • Corresponding author: Xiaoyu Chen
引用本文:

韩雷, 陈小宇, 王楠楠. 人工智能与放射科医师判读肺实性肿块CT征象的一致性研究[J/OL]. 中华消化病与影像杂志(电子版), 2022, 12(06): 348-350.

Lei Han, Xiaoyu Chen, Nannan Wang. Study on the consistency between artificial intelligence and radiologist in interpreting CT signs of solid lung masses[J/OL]. Chinese Journal of Digestion and Medical Imageology(Electronic Edition), 2022, 12(06): 348-350.

目的

分析人工智能与放射科医师判读肺内实性肿块CT征象的一致性,探索人工智能在临床应用中的价值。

方法

回顾性分析徐州医科大学附属淮安医院2020年1月至2022年1月经病理确诊的58例肺内实性肿块,重建CT薄层数据包并使用人工智能软件分析肿块的征象(包括分叶征、毛刺征、空泡征、血管集束征、胸膜凹陷征),同时两位放射科医师分别分析肿块的以上特征,采用一致性检验分析放射科医师与人工智能判读结果的一致性。

结果

放射科医师两次判读肿块是否存在分叶征、毛刺征、空泡征、血管集束征、胸膜凹陷征、支气管充气征的一致性优(Kappa值0.862~0.965,P均<0.001),两名放射科医师判读结果的一致性优(Kappa值0.8262~0.928,P均<0.001)。放射科医师与人工智能判读的肿块CT征象一致性优(Kappa值0.770~0.906,P均<0.001),其中分叶征的一致性最高(Kappa=0.906),血管集束征的一致性最低(Kappa=0.770)。

结论

人工智能可以可靠的评估肺内实性肿块的CT征象,并提高放射科医师的阅片效率。

Objective

To analyze the consistency between artificial intelligence and radiologists in interpreting CT signs of solid lung masses, and explore the value of artificial intelligence in clinical application.

Methods

A retrospective analysis was performed on 58 cases of solid lung masses confirmed by pathology in Huai ′an Hospital Affiliated to Xuzhou Medical University from January 2020 to January 2022.CT thin layer data package was reconstructed and the tumor signs(including lobulation sign, burr sign, vacuole sign, vascular cluster sign and pleural depression sign)were analyzed by artificial intelligence software.At the same time, two radiologists analyzed the above characteristics of the tumors respectively.Consistency test was used to analyze the consistency of interpretation results by radiologists and artificial intelligence.

Results

The consistency of the radiologist′s two interpretation of whether the tumors had lobulation sign, burr sign, vacuole sign, vascular convergence sign, pleural depression sign and bronchial inflation sign was excellent(Kappa value ranged from 0.862 to 0.965, all P<0.001), and the consistency of the two radiologists′ interpretation results was excellent(Kappa value ranged from 0.826 to 0.928, all P<0.001). The consistency of CT signs of tumors interpreted by radiologists and artificial intelligence was excellent(Kappa value ranged from 0.770 to 0.906, all P<0.001), among which the consistency of lobulation sign was the highest(Kappa=0.906), and the consistency of vascular bundle sign was the lowest(Kappa=0.770).

Conclusion

Artificial intelligence can reliably evaluate the CT signs of solid lung masses, and improve the efficiency of radiologists.

图1 人工智能软件分析肺内实性肿块注:1A患者男性,72岁,左肺上叶下舌段段鳞癌,人工智能分析肿块CT具有分叶征、毛刺征、胸膜凹陷征、支气管充气征四种恶性征象,提示高风险。1B患者男性,52岁,右肺下叶背段腺癌,人工智能分析肿块CT具有分叶征、胸膜凹陷征、支气管充气征三种恶性征象,提示高风险
表1 一名放射科医师两次判读肿块CT特征的一致性
表2 两名放射科医师判读肿块CT特征的一致性
表3 人工智能与放射科医师判读肿块CT特征的一致性
1
Deshpand RChandra MRauthan A.Evolving trends in lung cancer:Epidemiology,diagnosis,and management[J].Indian J Cancer202259(Supplement):S90-S105.
2
Zhang JIjzerman MJOberoi J,et al.Time to diagnosis and treatment of lung cancer:A systematic overview of risk factors,interventions and impact on patient outcomes[J].Lung Cancer2022166:27-39.
3
舒静,张涵,赵振国,等.肺炎性假瘤、周围型肺癌CT征象特征及其鉴别诊断价值[J].中国CT和MRI杂志202119(5):35-37.
4
Tao GZhu LChen Q,et al.Prediction of future imagery of lung nodule as growth modeling with follow-up computed tomography scans using deep learning:a retrospective cohort study[J].Transl Lung Cancer Res202211(2):250-262.
5
梁雪,潘金彬,丁建民,等.超声造影LI-RADS与增强CT/MRI LI-RADS对肝微小病灶分类的一致性及差异性研究[J].中华超声影像学杂志202130(11):938-943.
6
温艳,于连政,杜灵彬,等.中国3省城市癌症早诊早治项目地区肺癌高危人群的低剂量螺旋CT筛查依从性及相关因素分析[J].中华预防医学杂志202155(5):633-639.
7
孟瑞瑞,刘圆圆,青浩渺,等.肺癌高危人群低剂量螺旋CT筛查的临床分析[J].放射学实践202136(1):71-75.
8
Gao YHua MLv J,et al.Reproducibility of radiomic features of pulmonary nodules between low-dose CT and conventional-dose CT[J].Quant Imaging Med Surg202212(4):2368-2377.
9
Lan CCHsieh MSHsiao JK,et al.Deep Learning-based Artificial Intelligence Improves Accuracy of Error-prone Lung Nodules[J].Int J Med Sci202219(3):490-498.
10
段秀杰,李福元,付玉存.周围型非小细胞肺癌CT征象与临床病理分型的关系[J].现代肿瘤医学202028(15):2622-2626.
11
Wei SShi BZhang J,et al.Differentiating mass-like tuberculosis from lung cancer based on radiomics and CT features[J]Transl Cancer Res202110(10):4454-4463.
12
Manickavasagam RSelvan SSelvan Mary.CAD system for lung nodule detection using deep learning with CNN[J].Med Biol Eng Comput202260(1):221-228.
13
Potter ALRosenstein ALKiang MV,et al.Association of computed tomography screening with lung cancer stage shift and survival in the United States:quasi-experimental study[J].BMJ2022376:e069008.
14
Han XLuo NXu L,et al.Artificial intelligence stenosis diagnosis in coronary CTA:effect on the performance and consistency of readers with less cardiovascular experience[J].BMC Med Imaging202222(1):28.
15
Lyu SYZhang YZhang MW,et al.Diagnostic value of artificial intelligence automatic detection systems for breast BI-RADS 4 nodules[J].World J Clin Cases202210(2):518-527.
16
Deng JZhao MLi Q,et al.Implementation of artificial intelligence in the histological assessment of pulmonary subsolid nodules[J].Transl Lung Cancer Res202110(2):4574-4586.
17
Zhang HMeng DCai S,et al.The application of artificial intelligence in lung cancer:a narrative review[J].Transl Cancer Res202110(5):2478-2487.
18
Huang GWei XTang H,et al.A systematic review and meta-analysis of diagnostic performance and physicians′ perceptions of artificial intelligence(AI)-assisted CT diagnostic technology for the classification of pulmonary nodules[J].J Thorac Dis202113(8):4797-4811.
19
Lancaster HLZheng SAleshina OO,et al.Outstanding negative prediction performance of solid pulmonary nodule volume AI for ultra-LDCT baseline lung cancer screening risk stratification[J].Lung Cancer2022165:133-140.
20
于广浩,李为民,高杨,等.人工智能系统在CT肺小结节筛查中的准确率及检出时间分析[J].中国医药科学202111(21):193-195,208.
[1] 陶宏宇, 叶菁菁, 俞劲, 杨秀珍, 钱晶晶, 徐彬, 徐玮泽, 舒强. 右心声学造影在儿童右向左分流相关疾病中的评估价值[J/OL]. 中华医学超声杂志(电子版), 2024, 21(10): 959-965.
[2] 农云洁, 黄小桂, 黄裕兰, 农恒荣. 超声在多重肺部感染诊断中的临床应用价值[J/OL]. 中华医学超声杂志(电子版), 2024, 21(09): 872-876.
[3] 李洋, 蔡金玉, 党晓智, 常婉英, 巨艳, 高毅, 宋宏萍. 基于深度学习的乳腺超声应变弹性图像生成模型的应用研究[J/OL]. 中华医学超声杂志(电子版), 2024, 21(06): 563-570.
[4] 邱琳, 刘锦辉, 组木热提·吐尔洪, 马悦心, 冷晓玲. 超声影像组学对致密型乳腺背景中非肿块型乳腺癌的诊断价值[J/OL]. 中华乳腺病杂志(电子版), 2024, 18(06): 353-360.
[5] 叶莉, 杜宇. 深度学习在牙髓根尖周病临床诊疗中的应用[J/OL]. 中华口腔医学研究杂志(电子版), 2024, 18(06): 351-356.
[6] 熊鹰, 林敬莱, 白奇, 郭剑明, 王烁. 肾癌自动化病理诊断:AI离临床还有多远?[J/OL]. 中华腔镜泌尿外科杂志(电子版), 2024, 18(06): 535-540.
[7] 李伟, 宋子健, 赖衍成, 周睿, 吴涵, 邓龙昕, 陈锐. 人工智能应用于前列腺癌患者预后预测的研究现状及展望[J/OL]. 中华腔镜泌尿外科杂志(电子版), 2024, 18(06): 541-546.
[8] 黄俊龙, 李文双, 李晓阳, 刘柏隆, 陈逸龙, 丘惠平, 周祥福. 基于盆底彩超的人工智能模型在女性压力性尿失禁分度诊断中的应用[J/OL]. 中华腔镜泌尿外科杂志(电子版), 2024, 18(06): 597-605.
[9] 袁园园, 岳乐淇, 张华兴, 武艳, 李全海. 间充质干细胞在呼吸系统疾病模型中肺组织分布及治疗机制的研究进展[J/OL]. 中华细胞与干细胞杂志(电子版), 2024, 14(06): 374-381.
[10] 陈倩倩, 袁晨, 刘基, 尹婷婷. 多层螺旋CT 参数、癌胚抗原、错配修复基因及病理指标对结直肠癌预后的影响[J/OL]. 中华消化病与影像杂志(电子版), 2024, 14(06): 507-511.
[11] 张立俊, 孙存杰, 胡春峰, 孟冲, 张辉. MSCT、DCE-MRI 评估术前胃癌TNM 分期的准确性研究[J/OL]. 中华消化病与影像杂志(电子版), 2024, 14(06): 519-523.
[12] 贾玲玲, 滕飞, 常键, 黄福, 刘剑萍. 心肺康复在各种疾病中应用的研究进展[J/OL]. 中华临床医师杂志(电子版), 2024, 18(09): 859-862.
[13] 孙铭远, 褚恒, 徐海滨, 张哲. 人工智能应用于多发性肺结节诊断的研究进展[J/OL]. 中华临床医师杂志(电子版), 2024, 18(08): 785-790.
[14] 李茂军, 唐彬秩, 吴青, 阳倩, 梁小明, 邹福兰, 黄蓉, 陈昌辉. 新生儿呼吸窘迫综合征的管理:多国指南/共识及RDS-NExT workshop 共识陈述简介和评价[J/OL]. 中华临床医师杂志(电子版), 2024, 18(07): 607-617.
[15] 闫维, 张二明, 张克, 安欣华, 向平超. 北京市石景山区40岁及以上居民早期慢性阻塞性肺疾病异质性及影响因素分析[J/OL]. 中华临床医师杂志(电子版), 2024, 18(06): 533-540.
阅读次数
全文


摘要