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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Abstract Full text Figures/Tables Video References Cited by

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. 基于深度學習的人工智能胸部CT肺結節檢測效能評估. 中國肺癌雜志, 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.	董來東, 黃果. 基于CT影像的人工智能輔助診斷系統對肺癌診斷價值4 771例的系統評價與Meta分析. 中國胸心血管外科臨床雜志, 2021. [Epub ahead of print].
21.	黃雙雙, 張勝男, 葉君如, 等. 經皮肺活檢組織病理、微生物培養及快速現場評價對肺部感染性疾病的診斷價值. 中華醫學雜志, 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, 朱曉雷, 等. 人工智能肺部結節輔助診療系統預測肺結節的良惡性及浸潤情況. 中國胸心血管外科臨床雜志, 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. 張遜. 人工智能輔助肺癌診療一體化解決方案的臨床實踐與展望. 中國胸心血管外科臨床雜志, 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. 基于深度學習的人工智能胸部CT肺結節檢測效能評估. 中國肺癌雜志, 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. 董來東, 黃果. 基于CT影像的人工智能輔助診斷系統對肺癌診斷價值4 771例的系統評價與Meta分析. 中國胸心血管外科臨床雜志, 2021. [Epub ahead of print].
21. 黃雙雙, 張勝男, 葉君如, 等. 經皮肺活檢組織病理、微生物培養及快速現場評價對肺部感染性疾病的診斷價值. 中華醫學雜志, 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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