Clinical application of artificial intelligence to lung nodules diagnosis in regional medical center_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

XING Yutong ¹ , LIU Jiancheng ¹ , SUN Baichen ¹ , HONG Lingling ¹ , ZHANG Xiaojun ¹ , FU Jianqian ¹ ,  GENG Guojun ^1,2 , LI Zhenlong ¹ ,  JIANG Jie ^1,2

1. Multidisciplinary Team for Lung Nodules, Xiamen Fifth Hospital, Xiamen, 361101, Fujian, P.R.China;
2. Department of Thoracic Surgery, First Affiliated Hospital of Xiamen University, Xiamen, 361000, Fujian, P.R.China;

Corresponding author：

JIANG Jie, Email: jiangjie06@126.com

Keywords：

Regional medical center; pulmonary nodule; artificial intelligence (AI); auxiliary diagnosis system; multidisciplinary team (MDT)

DOI：

10.7507/1007-4848.202102021

Video：

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Objective To explore the efficacy of artificial intelligence (AI) detection on pulmonary nodule compared with multidisciplinary team (MDT) in regional medical center.Methods We retrospectively analyzed the clinical data of 102 patients with lung nodules in the Xiamen Fifth Hospital from April to December 2020. There were 57 males and 45 females at age of 36-90 (48.8±11.6) years. The preoperative chest CT was imported into AI system to record the detected lung nodules. The detection rate of pulmonary nodules by AI system was calculated, and the sensitivity, specificity of AI in the different diagnosis of benign and malignant pulmonary was calculated and compared with manual film reading by MDT.Results A total of 322 nodules were detected by AI software system, and 305 nodules were manually detected by physicians (P<0.05). Among them, 113 pulmonary nodules were diagnosed by pathologist. Thirty-eight of 40 lung cancer nodules were AI high-risk nodules, the sensitivity was 95.0%, and 25 of 73 benign nodules were AI high-risk nodules, the specificity was 65.8%. Lung cancer nodules were correctly diagnosed by MDT, but benign nodules were still considered as lung cancer at the first diagnosis in 10 patients.Conclusion AI assisted diagnosis system has strong performance in the detection of pulmonary nodules, but it can not content itself with clinical needs in the differentiation of benign and malignant pulmonary nodules. The artificial intelligence system can be used as an auxiliary tool for MDT to detect pulmonary nodules in regional medical center.

Citation： XING Yutong, LIU Jiancheng, SUN Baichen, HONG Lingling, ZHANG Xiaojun, FU Jianqian, GENG Guojun, LI Zhenlong, JIANG Jie. Clinical application of artificial intelligence to lung nodules diagnosis in regional medical center. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2021, 28(10): 1178-1182. doi: 10.7507/1007-4848.202102021 Copy

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17.	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. 中国肺癌杂志, 2019, 22(6): 336-340.
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20.	董来东, 黄果. 基于CT影像的人工智能辅助诊断系统对肺癌诊断价值4 771例的系统评价与Meta分析. 中国胸心血管外科临床杂志, 2021. [Epub ahead of print].
21.	黄双双, 张胜男, 叶君如, 等. 经皮肺活检组织病理、微生物培养及快速现场评价对肺部感染性疾病的诊断价值. 中华医学杂志, 2019, 99(42): 3340-3344.

1. Siegel RL, Miller KD, Jemal A. Cancer statistics. 2016. CA Cancer J Clin, 2016, 66(1): 7-30.
2. Torre LA, Bray F, Siegel RL, et al. Global cancer statistics, 2012. CA Cancer J Clin, 2015, 65(2): 87-108.
3. Walter JE, Heuvelmans MA, de Jong PA, et al. Occurrence and lung cancer probability of new solid nodules at incidence screening with low-dose CT: Analysis of data from the randomised, controlled NELSON trial. Lancet Oncol, 2016, 17(7): 907-916.
4. Liang M, Tang W, Xu DM, et al. Low-dose CT screening for lung cancer: Computer-aided detection of missed lung cancers. Radiology, 2016, 281(1): 279-288.
5. Zhu WY, Tan LL, Wang Zy, et al. Clinical characteristics and advantages of primary peripheral micro-sized lung adenocarcinoma over small-sized lung adenocarcinoma. Eur J Cardiothorac Surg, 2016, 49(4): 1095-1102.
6. 曹孟昆, 姜杰, 朱晓雷, 等. 人工智能肺部结节辅助诊疗系统预测肺结节的良恶性及浸润情况. 中国胸心血管外科临床杂志, 2021, 28(3): 283-287.
7. Sui Y, Wei Y, Zhao D. Computer-aided lung nodule recognition by SVM classifier based on combination of random undersampling and SMOTE. Comput Math Methods Med, 2015: 3686-3674.
8. Walter JE, Heuvelmans MA, Ten Haaf K, et al. Persisting new nodules in incidence rounds of the NELSON CT lung cancer screening study. Thorax, 2019, 74(3): 247-253.
9. 张逊. 人工智能辅助肺癌诊疗一体化解决方案的临床实践与展望. 中国胸心血管外科临床杂志, 2019, 26(12): 1167-1170.
10. Wang S, Yang DM, Rong R, et al. Artificial intelligence in lung cancer pathology image analysis. Cancers (Basel), 2019, 11(11): 1673.
11. Espinoza JL, Dong LT. Artificial intelligence tools for refining lung cancer screening. J Clin Med, 2020, 9(12): 3860.
12. Kim MS, Park HY, Kho BG, et al. Artificial intelligence and lung cancer treatment decision: Agreement with recommendation of multidisciplinary tumor board. Transl Lung Cancer Res, 2020, 9(3): 507-514.
13. Ardila D, Kiraly AP, Bharadwaj S, et al. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med, 2019, 25(6): 954-961.
14. Zhang C, Sun X, Dang K, et al. Toward an expert level of lung cancer detection and classification using a deep convolutional neural network. Oncologist, 2019, 24(9): 1159-1165.
15. Setio AA, Ciompi F, Litjens G, et al. Pulmonary nodule detection in CT images: False positive reduction using multi-view convolutional networks. IEEE Trans Med Imaging, 2016, 35(5): 1160-1169.
16. Thiryayi SA, Rana DN, Narine N, et al. Establishment of an endobronchial ultrasound-guided transbronchial fine needle aspiration service with rapid on-site evaluation: 2 years experience of a single UK centre. Cytopathology, 2016, 27(5): 335-343.
17. 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. 中国肺癌杂志, 2019, 22(6): 336-340.
18. Walter JE, Heuvelmans MA, de Bock GH, 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. Lung Cancer, 2018, 125: 103-108.
19. Setio AA, Traverso A, de Bel T, et al. Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge. Med Image Anal, 2017, 42: 1-13.
20. 董来东, 黄果. 基于CT影像的人工智能辅助诊断系统对肺癌诊断价值4 771例的系统评价与Meta分析. 中国胸心血管外科临床杂志, 2021. [Epub ahead of print].
21. 黄双双, 张胜男, 叶君如, 等. 经皮肺活检组织病理、微生物培养及快速现场评价对肺部感染性疾病的诊断价值. 中华医学杂志, 2019, 99(42): 3340-3344.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Clinical application of artificial intelligence to lung nodules diagnosis in regional medical center

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