Diagnostic value of artificial intelligence-assisted diagnostic system for pulmonary cancer based on CT images: A systematic review and meta-analysis of 4 771 patients_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

DONG Laidong ¹ ,  HUANG Guo ²

1. Outpatient Department, Qilu Hospital, Shandong University, Jinan, 250012, P.R.China;
2. School of Public Health, Fudan University, Shanghai, 200032, P.R.China;

Corresponding?author：

HUANG Guo, Email: 2360191844@qq.com

Keywords：

Artificial intelligence-assisted diagnostic system; diagnostic value; pulmonary cancer; CT images; systematic review/meta-analysis

DOI：

10.7507/1007-4848.202012022

Video：

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Objective To evaluate the diagnostic value of artificial intelligence (AI)-assisted diagnostic system for pulmonary cancer based on CT images.Methods Databases including PubMed, The Cochrane Library, EMbase, CNKI, WanFang Data and Chinese BioMedical Literature Database (CBM) were electronically searched to collect relevant studies on AI-assisted diagnostic system in the diagnosis of pulmonary cancer from 2010 to 2019. The eligible studies were selected according to inclusion and exclusion criteria, and the quality of included studies was assessed and the special information was identified. Then, meta-analysis was performed using RevMan 5.3, Stata 12.0 and SAS 9.4 softwares. The sensitivity, specificity, positive likelihood ratio, negative likelihood ratio and diagnostic odds ratio were pooled and the summary receiver operating characteristic (SROC) curve was drawn. Meta-regression analysis was used to explore the sources of heterogeneity.Results Totally 18 studies were included with 4 771 patients. Random effect model was used for the analysis due to the heterogeneity among studies. The results of meta-analysis showed that the pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnosis odds ratio and area under the SROC curve were 0.87 [95%CI (0.84, 0.90)], 0.89 [95%CI (0.84, 0.92)], 7.70 [95%CI (5.32, 11.15)], 0.14 [95%CI (0.11, 0.19)], 53.54 [95%CI (30.68, 93.42)] and 0.94 [95%CI (0.91, 0.95)], respectively.Conclusion AI-assisted diagnostic system based on CT images has high diagnostic value for pulmonary cancer, and thus it is worthy of clinical application. However, due to the limited quality and quantity of included studies, above results should be validated by more studies.

Citation： DONG Laidong, HUANG Guo. Diagnostic value of artificial intelligence-assisted diagnostic system for pulmonary cancer based on CT images: A systematic review and meta-analysis of 4 771 patients. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2021, 28(10): 1183-1191. doi: 10.7507/1007-4848.202012022 Copy

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16.	徐力平, 張華杰, 吳逸明. 高斯隸屬度函數模糊神經網絡在肺癌診斷中的應用. 鄭州大學學報(理學版), 2011, 43(1): 95-98.
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18.	何俊詩, 梁鵬, 羅英華, 等. 基于神經網絡的MSCT孤立肺小結節診斷模型的設計. 臨床醫學工程, 2012, 19(4): 497-499.
19.	王剛, 林森森, 姜新國, 等. 用于小細胞肺癌診斷的人工神經網絡模型. 中國衛生統計, 2013, 30(2): 257-258.
20.	張極峰, 夏旭東, 李萍, 等. 計算機輔助診斷在孤立性肺結節CT診斷的應用. 醫學影像學雜志, 2013, 23(9): 1386-1390.
21.	顧艷. 肺部孤立性結節與腫塊常規形態學、CT灌注和計算機輔助診斷對比研究. 徐州醫科大學, 2013.
22.	徐力平, 尚丹, 陳小玉. 模糊神經網絡在肺癌CT診斷中的應用. 鄭州大學學報(醫學版), 2014, 49(2): 191-194.
23.	王克全, 張義蘭, 鄺雙鑫. 肺癌CT診斷中應用模糊神經網絡輔助診斷的效果探析. 深圳中西醫結合雜志, 2015, 25(6): 70-71.
24.	張澤文, 張才擎, 王廣麗, 等. 孤立性肺結節在HDCT的計算機輔助診斷. 醫學影像學雜志, 2015, 25(6): 993-997.
25.	何霞霞, 張紅升, 李迪, 等. 基于CT影像評分的人工神經網絡模型對肺部良惡性病變的判別價值. 鄭州大學學報(醫學版), 2018, 53(6): 723-726.
26.	王霞. 基于數據挖掘技術的肺癌風險評估與診斷及組織分型系統研究. 鄭州大學, 2019.
27.	印宏坤, 黃皓, 林強, 等. 拓展醫療人工智能的新疆界. 人工智能, 2018, 11(4): 88-96.
28.	楊尚文, 胡安寧, 徐亞運, 等. CT圖像分辨率對人工智能肺結節輔助診斷系統診斷準確性的影響. 醫學影像學雜志, 2020, 30(6): 965-968.
29.	高晨, 王世威, 許茂盛. 肺癌影像的人工智能研究. 中國中西醫結合影像學雜志, 2020, 18(3): 219-223.
30.	動脈網. 聯影智能AI入駐火神山、雷神山醫院, 分析肺段以搜尋新冠病毒特征\|科技戰疫. [2021-02-24]. https://mp.ofweek.com/medical/a945693520376.
31.	Huang XF, Lei Q, Xie T, et al. Deep transfer convolutional neural network and extreme learning machine for lung nodule diagnosis on CT images. Knowledge-Based Sys, 2020, 204(27): 106230.
32.	李幼平, 主編. 實用循證醫學. 北京: 人民衛生出版社, 2018.
33.	陳長波. 肺部影像人工智能診斷系統對肺結節性質的診斷價值分析. 中國數字醫學, 2020, 15(11): 40-42, 131.
34.	蕭毅, 劉士遠. 肺結節影像人工智能技術現狀與思考. 腫瘤影像學, 2018, 27(4): 249-252.
35.	Zhang Y, Oikonomou A, Wong A, et al. Radiomics-based prognosis analysis for non-small cell lung cancer. Sci Rep, 2017, 7: 46349.
36.	Vogel L. Rise of medical AI poses new legal risks for doctors. CMAJ, 2019, 191(42): E1173-E1174.

1. Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2018, 68(6): 394-424.
2. Amir GJ, Lehmann HP. After detection: The improved accuracy of lung cancer assessment using radiologic computer-aided diagnosis. Acad Radiol, 2016, 23(2): 186-191.
3. 孫惠昕. 人工智能在癌癥篩查中的研究進展. 腫瘤預防與治療, 2020, 33(10): 898-902.
4. 藍美紅, 高明明, 侯代倫. 超高分辨率CT靶掃描與CT靶重建在肺磨玻璃樣結節定性診斷中的價值. 中國防癆雜志, 2018, 40(7): 702-706.
5. 王萬勤, 劉斌, 周勇, 等. 良惡性肺結節多排CT征象分析. 安徽醫藥, 2018, 22(8): 1491-1496.
6. 趙呈華. 人工智能輔助診斷系統聯合CT檢查肺結節的診斷價值. 實用臨床醫藥雜志, 2020, 24(19): 9-11.
7. Schlattmann P, Schuetz GM, Dewey M. Wake up and smell the PRISMA, Cochrane, and QUADAS statements. Radiology, 2011, 261(1): 325-326.
8. Bradley AP. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition, 1997, 30(7): 1145-1159.
9. Sun T, Zhang R, Wang J, et al. Computer-aided diagnosis for early-stage lung cancer based on longitudinal and balanced data. PLoS One, 2013, 8(5): e63559.
10. Wang J, Liu X, Dong D, et al. Prediction of malignant and benign of lung tumor using a quantitative radiomic method. Annu Int Conf IEEE Eng Med Biol Soc, 2016, 2016: 1272-1275.
11. Dilger SKN. The use of surrounding lung parenchyma for the automated classification of pulmonary nodules. University of Iowa, 2013.
12. Teramoto A, Tsujimoto M, Inoue T, et al. Automated classification of pulmonary nodules through a retrospective analysis of conventional CT and two-phase PET images in patients undergoing biopsy. Asia Ocean J Nucl Med Biol, 2019, 7(1): 29-37.
13. 張矗, 吳逸明, 吳擁軍, 等. 人工神經網絡技術在纖維支氣管鏡診斷肺癌中的應用. 鄭州大學學報(醫學版), 2010, 45(1): 113-115.
14. 邸曉東. 基于CT圖像的孤立性肺結節診斷模型研究. 哈爾濱理工大學, 2010.
15. 劉宗才, 駱科進. MSCT LungCARE軟件對孤立性肺結節的診斷價值. 貴州醫藥, 2010, 34(6): 539-542.
16. 徐力平, 張華杰, 吳逸明. 高斯隸屬度函數模糊神經網絡在肺癌診斷中的應用. 鄭州大學學報(理學版), 2011, 43(1): 95-98.
17. 劉燦. 遺傳算法優化模糊神經網絡在肺癌診斷中的應用. 鄭州大學, 2011.
18. 何俊詩, 梁鵬, 羅英華, 等. 基于神經網絡的MSCT孤立肺小結節診斷模型的設計. 臨床醫學工程, 2012, 19(4): 497-499.
19. 王剛, 林森森, 姜新國, 等. 用于小細胞肺癌診斷的人工神經網絡模型. 中國衛生統計, 2013, 30(2): 257-258.
20. 張極峰, 夏旭東, 李萍, 等. 計算機輔助診斷在孤立性肺結節CT診斷的應用. 醫學影像學雜志, 2013, 23(9): 1386-1390.
21. 顧艷. 肺部孤立性結節與腫塊常規形態學、CT灌注和計算機輔助診斷對比研究. 徐州醫科大學, 2013.
22. 徐力平, 尚丹, 陳小玉. 模糊神經網絡在肺癌CT診斷中的應用. 鄭州大學學報(醫學版), 2014, 49(2): 191-194.
23. 王克全, 張義蘭, 鄺雙鑫. 肺癌CT診斷中應用模糊神經網絡輔助診斷的效果探析. 深圳中西醫結合雜志, 2015, 25(6): 70-71.
24. 張澤文, 張才擎, 王廣麗, 等. 孤立性肺結節在HDCT的計算機輔助診斷. 醫學影像學雜志, 2015, 25(6): 993-997.
25. 何霞霞, 張紅升, 李迪, 等. 基于CT影像評分的人工神經網絡模型對肺部良惡性病變的判別價值. 鄭州大學學報(醫學版), 2018, 53(6): 723-726.
26. 王霞. 基于數據挖掘技術的肺癌風險評估與診斷及組織分型系統研究. 鄭州大學, 2019.
27. 印宏坤, 黃皓, 林強, 等. 拓展醫療人工智能的新疆界. 人工智能, 2018, 11(4): 88-96.
28. 楊尚文, 胡安寧, 徐亞運, 等. CT圖像分辨率對人工智能肺結節輔助診斷系統診斷準確性的影響. 醫學影像學雜志, 2020, 30(6): 965-968.
29. 高晨, 王世威, 許茂盛. 肺癌影像的人工智能研究. 中國中西醫結合影像學雜志, 2020, 18(3): 219-223.
30. 動脈網. 聯影智能AI入駐火神山、雷神山醫院, 分析肺段以搜尋新冠病毒特征|科技戰疫. [2021-02-24]. https://mp.ofweek.com/medical/a945693520376.
31. Huang XF, Lei Q, Xie T, et al. Deep transfer convolutional neural network and extreme learning machine for lung nodule diagnosis on CT images. Knowledge-Based Sys, 2020, 204(27): 106230.
32. 李幼平, 主編. 實用循證醫學. 北京: 人民衛生出版社, 2018.
33. 陳長波. 肺部影像人工智能診斷系統對肺結節性質的診斷價值分析. 中國數字醫學, 2020, 15(11): 40-42, 131.
34. 蕭毅, 劉士遠. 肺結節影像人工智能技術現狀與思考. 腫瘤影像學, 2018, 27(4): 249-252.
35. Zhang Y, Oikonomou A, Wong A, et al. Radiomics-based prognosis analysis for non-small cell lung cancer. Sci Rep, 2017, 7: 46349.
36. Vogel L. Rise of medical AI poses new legal risks for doctors. CMAJ, 2019, 191(42): E1173-E1174.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Diagnostic value of artificial intelligence-assisted diagnostic system for pulmonary cancer based on CT images: A systematic review and meta-analysis of 4 771 patients

Abstract Full text Figures/Tables Video References Cited by

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