基于两种AI软件对肺结节的诊断价值评价研究

陈新花; 黄晓旗; 李建龙; 郭佑民

doi:10.15953/j.ctta.2022.087

基于两种AI软件对肺结节的诊断价值评价研究

doi: 10.15953/j.ctta.2022.087

1.
延安大学附属医院, 陕西延安716000
2.
西安交通大学第一附属医院, 西安710000

详细信息

作者简介:
陈新花：女，延安大学影像医学与核医学专业硕士研究生，主要从事胸部与骨关节影像诊断，E-mail：523423054@qq.com

李建龙：男，延安大学附属医院放射科主任医师、副教授、硕士生导师，主要从事胸部与骨关节影像诊断，E-mail：yaljl116@163.com

通讯作者:
男，延安大学附属医院放射科主任医师、副教授、硕士生导师，主要从事胸部与骨关节影像诊断，E-mail：yaljl116@163.com

中图分类号: R 814；TP 317
计量
- 文章访问数: 229
- HTML全文浏览量: 39
- PDF下载量: 35
- 被引次数: 0
出版历程
- 收稿日期: 2022-05-18
- 修回日期: 2022-09-28
- 录用日期: 2022-09-29
- 网络出版日期: 2022-10-25
- 刊出日期: 2023-07-31

Evaluation of the Diagnostic Value of Pulmonary Nodules Based on Two AI Software

1.
Affiliated Hospital of Yan'an University, Yan'an 716000, China
2.
The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710000, China

摘要

摘要: 目的：探讨两种AI软件在≥5 mm肺结节中的临床应用价值。方法：选取2021年6月至2021年10月延安大学附属医院体检肺结节患者92例（共483个结节）。AI软件检测到的结节由影像学医师进行评估并记录其个数及结节类型；人工阅片由两名高年资影像学医师进行视觉评估，并以此作为识别结节的金标准。计算两个软件的检出率、假阳性率和假阴性率，评价两种AI软件的结节诊断价值；卡方检验和Fisher精确检验来比较不同软件与金标准之间的差异；最后，评价两种AI软件联合对肺结节的诊断价值。结果：软件A和软件B结节检出率为92.1％和87.0％；软件A与人工阅片的吻合度一般（Kappa=0.213），软件B与人工阅片的吻合度较弱（Kappa=0.150）；软件A相比人工阅片对实性结节和钙化结节的检出有统计学差异；软件B相比人工阅片纯磨玻璃结节的检出有统计学差异；联合两种AI软件结节的检出率为97.1％，两种软件的联合与人工阅片比，结节类型的检出没有统计学差异。两种AI软件联合与人工阅片的吻合度较好（Kappa=0.439）。结论：两种AI软件联合会提高结节诊断及分类分析的能力；推荐联合诊断的方法用于临床，也为进一步提升AI软件数据集的同质化管理提供佐证。
- 人工智能 /
- 计算机体层成像 /
- 肺结节 /
- 检测
Abstract: Objective: To explore the clinical value of two kinds of AI detection software in ≥5 mm pulmonary nodules. Methods: A total of 92 patients with pulmonary nodules (483 nodules) were selected from the affiliated Hospital of Yan'an University between June and October 2021. The nodules detected by AI software were evaluated and the number and type of nodules were recorded by two senior radiologists. Two senior imaging doctors then evaluated the manual film reading, which was used as the gold standard for nodule recognition. Subsequently, the detection rate and false positive and negative rates of the two software were calculated, and the nodule detection value of the two AI software was evaluated. Additionally, the chi-square and Fisher precision tests were used to compare the differences between the different software and the gold standard. Finally, the diagnostic value of the combination of the two kinds of AI software for pulmonary nodules was calculated. Results: The detection rates of software A and software B nodules were 92.1% and 87.0%, respectively. Moreover, the coincidence degree between software A and manual reading was general (Kappa=0.213), while that between software B and manual reading was weak (Kappa=0.150). There was also a significant difference in the detection of solid nodules and calcified nodules between software A and manual reading, as well as between software B and pure ground glass nodules. The detection rate of nodules with the combined two kinds of AI software was 97.1%. However, compared with manual reading, there was no significant difference in the detection of nodule types. The combination of the two AI software had a good agreement with manual reading (Kappa=0.439). Conclusion: The combination of two kinds of AI software improved the ability of nodule detection and classification analysis. Furthermore, the method of joint diagnosis is recommended for clinical use and it provides evidence for further improving the homogenization management of AI data sets.
- artificial intelligence /
- CT /
- pulmonary nodules /
- detection

HTML全文

图 1 软件A肺结节识别界面

Figure 1. The pulmonary nodule recognition interface of software A

下载: 全尺寸图片幻灯片

图 2 软件B肺结节识别界面

Figure 2. The pulmonary nodule recognition interface of software B

下载: 全尺寸图片幻灯片

图 3 肺结节的不同类型

Figure 3. Different types of pulmonary nodules

下载: 全尺寸图片幻灯片

图 4 两种软件识别的假阳性结节

Figure 4. False positive nodules identified by the two software

下载: 全尺寸图片幻灯片

图 5 两种软件识别的假阴性结节

Figure 5. False negative nodules identified by the two software

下载: 全尺寸图片幻灯片

表 1 两种软件及两种软件联合对肺结节的检出情况

Table 1. The detection of pulmonary nodules by two separate kinds of software and their combined effect

阅片方法	检出情况
阅片方法	检出率/％	假阳性率/％	假阴性率/％
软件A	92.1	3.52	7.87
软件B	87.0	2.69	13.0
软件A联合软件B	97.1	1.04	2.91

下载: 导出CSV

表 2 两种AI软件及两种AI软件的联合对结节检出率的差异

Table 2. The differences in nodule detection rate between the two artificial intelligence software and their combined effect

软件	N（％）	统计检验
软件	N（％）	$\chi^2 $	P	Kappa
软件A-人工阅片	445（92.1）	25.519	0.000	0.213
软件B-人工阅片	420（87.0）	16.472	0.000	0.150
软件A联合软件B-人工阅片	469（97.1）	102.697	0.000	0.439

下载: 导出CSV

表 3 两种AI软件对不同类型结节的检出情况

Table 3. The detection of different types of nodules by the two artificial intelligence software

变量	N	结节检测软件		P
变量	N	软件A（％）	软件B（％）	P
实性结节	295	279（94.6）	254（86.1）	0.030
部分实性结节	6	6（100.0）	3（50.0）	0.000
纯磨玻璃结节	68	56（82.4）	63（92.6）	0.079
钙化结节	114	104（91.2）	100（87.7）	0.098

下载: 导出CSV

表 4 两种AI软件及两种AI软件联合识别不同类型结节的差异

Table 4. The differences between the two artificial intelligence software and their combined effect in identifying different types of nodules

变量	实性结节		部分实性结节		纯磨玻璃结节		钙化结节
变量	$\chi^2 $	P	$\chi^2 $	P	$\chi^2 $	P	$\chi^2 $	P
软件A-人工阅片	10.693	0.001	－	0.538	0.030	0.862	14.809	0.000
软件B-人工阅片	0.800	0.371	－	0.182	6.741	0.009	0.556	0.456
软件A联合软件B-人工阅片	0.381	0.537	－	0.464	0.271	0.603	0.000	1.000
注：－为Fisher确切概率法没有相应的统计量。

下载: 导出CSV

参考文献(19)

[1]	中华医学会肿瘤学分会, 中华医学会杂志社. 中华医学会肿瘤学分会肺癌临床诊疗指南(2021版)[J]. 中华医学杂志, 2021,101(23): 1725−1757. doi: 10.3760/cma.j.cn112137-20210207-00377
[2]	LEIGH J, COLLABORATION G B O D C. Global, regional, and national cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life years for 29 cancer groups, 1990 to 2017[J]. JAMA Oncology, 2019, 5(12): 1749−1768. DOI: 10.1001/jamaoncol.2019.2996.
[3]	YUAN S, WEI Y, ZHAO D. Computer-aided lung nodule recognition by SVM classifier based on combination of random undersampling and SMOTE[J]. Computational and Mathematical Methods in Medicine, 2015: 1−13. DOI: 10.1155/2015/368674.
[4]	AZUMA M, NAKADA H, TAKEI M, et al. Detection of acute rib fractures on CT images with convolutional neural networks: Effect of location and type of fracture and reader's experience[J]. Emergency Radiology, 2022, 29(2): 317−328. DOI: 10.1007/s10140-021-02000-6.
[5]	MASSALHA S, CLARKIN O, THORNHILL R, et al. Decision support tools, systems, and artificial intelligence in cardiac imaging[J]. Canadian Journal of Cardiology, 2018, 34(7): 827−838. DOI: 10.1016/j.cjca.2018.04.032.
[6]	SHAFFIE A, SOLIMAN A, FRAIWAN L, et al. A generalized deep learning-based diagnostic system for early diagnosis of various types of pulmonary nodules[J]. Technology in Cancer Research & Treatment, 2018, 17: 1533033818798800.
[7]	中华医学会放射学分会, 中国食品药品检定研究院, 国家卫生健康委能力建设与继续教育中心, 等. 胸部CT肺结节数据集构建及质量控制专家共识[J]. 中华放射学杂志, 2021,55(2): 104−110. DOI: 10.3760/cma.j.cn112149-20200713-00915. Chinese Society of Radiology Chinese Medical Association, National Institutes for Food and Drug Control, National Health Commission Capacity Building and Continuing Education Center, et al. Expert consensus on the construction and quality control of thoracic CT datasets for pulmonary nodules[J]. Chinese Journal of Radiology, 2021, 55(2): 104−110. DOI: 10.3760/cma.j.cn112149-20200713-00915. (in Chinese).
[8]	HASSANZADEH T, ESSAM D, SARKER R. Evolutionary deep attention convolutional neural networks for 2D and 3D medical image segmentation[J]. Journal of Digital Imaging, 2021, 34(6): 1387−1404. DOI: 10.1007/s10278-021-00526-2.
[9]	张正华, 蔡雅倩, 韩丹, 等. 人工智能与不同级别医师对肺结节检出效能的对照研究[J]. 中国医学影像学杂志, 2020,28(9): 662−665. DOI: 10.3969/j.issn.1005-5185.2020.09.005. ZHANG Z H, CAI Y Q, HAN D, et al. Artificial intelligence and various levels of physicians in detection of pulmonary nodules: A comparative study[J]. Chinese Journal of Medical Imaging, 2020, 28(9): 662−665. DOI: 10.3969/j.issn.1005-5185.2020.09.005. (in Chinese).
[10]	TSUTANI Y, MIYATA Y, NAKAYAMA H, et al. Prognostic significance of using solid versus whole tumor size on high-resolution computed tomography for predicting pathologic malignant grade of tumors in clinical stage IA lung adenocarcinoma: A multicenter study[J]. The Journal of Thoracic and Cardiovascular Surgery, 2012, 143(3): 607−612. DOI: 10.1016/j.jtcvs.2011.10.037.
[11]	LEE H J, GOO J M, LEE C H, et al. Nodular ground-glass opacities on thin-section CT: Size change during follow-up and pathological results[J]. Korean Journal of Radiology, 2007, 8(1): 22−31. DOI: 10.3348/kjr.2007.8.1.22.
[12]	KANG G X, LIU K, HOU B B, et al. 3D multi-view convolutional neural networks for lung nodule classification[J]. Plos One, 2017, 12(11): e0188290. DOI: 10.1371/journal.pone.0188290.
[13]	SHIGEFUKU S, SHIMADA Y, HAGIWARA M, et al. Prognostic significance of ground-glass opacity components in 5-year survivors with resected lung adenocarcinoma[J]. Annals of Surgical Oncology, 2021, 28(1): 148−156. DOI: 10.1245/s10434-020-09125-x.
[14]	MENG Y, LIU C L, CAI Q, et al. Contrast analysis of the relationship between the HRCT sign and new pathologic classification in small ground glass nodule-like lung adenocarcinoma[J]. Radiology Medical, 2019, 124(1): 8−13. DOI: 10.1007/s11547-018-0936-x.
[15]	刘凯, 张荣国, 涂文婷, 等. 深度学习技术对胸部X线平片亚实性结节的检测效能初探[J]. 中华放射学杂志, 2017,51(12): 918−921. DOI: 10.3760/cma.j.issn.1005-1201.2017.12.006. LIU K, ZHANG R G, TU W T, et al. A preliminary investigation on pulmonary subsolid nodule detection using deep learninb gmethods from chest X-rays[J]. Chinese Journal of Radiology, 2017, 51(12): 918−921. DOI: 10.3760/cma.j.issn.1005-1201.2017.12.006. (in Chinese).
[16]	李欣菱, 郭芳芳, 周振, 等. 基于深度学习的人工智能胸部CT肺结节检测效能评估[J]. 中国肺癌杂志, 2019,22(6): 336−340. DOI: 10.3779/j.issn.1009-3419.2019.06.02. LI X L, GUO F F, ZHOU Z, et al. Performance of deep-learning-based artificial intelligence on detection of pulmonary nodules in chest CT[J]. Chinese Journal of Lung Cancer, 2019, 22(6): 336−340. DOI: 10.3779/j.issn.1009-3419.2019.06.02. (in Chinese).
[17]	刘娜, 赵正凯, 邹佳瑜, 等. 基于人工智能的胸部CT肺结节检出及良恶性诊断效能评估[J]. CT理论与应用研究, 2021,30(6): 709−715. DOI: 10.15953/j.1004-4140.2021.30.06.06. LIU N, ZHAO Z K, ZOU J Y, et al. Evaluation of detection and diagnostic efficiency of pulmonary nodules by chest CT based on artificial intelligence[J]. CT Theory and Applications, 2021, 30(6): 709−715. DOI: 10.15953/j.1004-4140.2021.30.06.06. (in Chinese).
[18]	王祥, 李清楚, 邵影, 等. 基于三维卷积神经网络肺结节深度学习算法模临床效能初步评估[J]. 放射学实践, 2019,34(9): 942−946. DOI: 10.13609/j.cnki.1000-0313.2019.09.002. WANG X, LI Q C, SHAO Y, et al. A preliminary clinical evaluation of a 3D convolutional neural network based deep learning system[J]. Radiologic Practice, 2019, 34(9): 942−946. DOI: 10.13609/j.cnki.1000-0313.2019.09.002. (in Chinese).
[19]	左玲子, 黄艳. 人工智能在体检肺CT中检出的假阳性结节研究[J]. 中国医疗设备, 2021,36(10): 177−180. DOI: 10.3969/j.issn.1674-1633.2021.10.041. ZUO L Z, HUANG Y. Study of false positive nodules detected by artificial intelligence in lung CT examination[J]. China Medical Devices, 2021, 36(10): 177−180. DOI: 10.3969/j.issn.1674-1633.2021.10.041. (in Chinese).