Research progress on artificial intelligence in diagnosis of lung cancer_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

YANG Ning ^1,2 , JIN Dacheng ^1,2 , CHEN Meng ^1,2 , WANG Bing ^1,2 , HE Xiaoyang ^1,2 , ZHANG Siyuan ^1,2 ,  GOU Yunjiu ²

1. Gansu University of Traditional Chinese Medicine, Lanzhou, 730000, P.R.China;
2. The First Department of Thoracic Surgery, Gansu Provincial Hospital, Lanzhou, 730000, P.R.China;

Corresponding author：

GOU Yunjiu, Email: gouyunjiu@163.com

Keywords：

Lung cancer; artificial intelligence; deep learning; neural network

DOI：

10.7507/1007-4848.202005014

Video：

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The early diagnosis of lung cancer and the corresponding treatment measures are crucial factors to reduce mortality rate. As an emerging technology, artificial intelligence has developed rapidly and it is used in the medical field to provide new ideas for the early diagnosis of lung cancer, which has achieved remarkable results. Artificial intelligence greatly eases the pressure of clinical work, changes the current medical model, and is expected to make doctors as a decision-maker. This article mainly describes the research progress on artificial intelligence in the identification of benign and malignant lung nodules, pathological typing, determination of markers, and detection of plasma circulating tumor DNA.

Citation： YANG Ning, JIN Dacheng, CHEN Meng, WANG Bing, HE Xiaoyang, ZHANG Siyuan, GOU Yunjiu. Research progress on artificial intelligence in diagnosis of lung cancer. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2020, 27(12): 1466-1471. doi: 10.7507/1007-4848.202005014 Copy

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7.	Taguchi A, Arenberg D. Harnessing immune response to malignant lung nodules. Promise and challenges. Am J Respir Crit Care Med, 2019, 199(10): 1184-1186.
8.	Firmino M, Morais AH, Mendoça RM, et al. Computer-aided detection system for lung cancer in computed tomography scans: review and future prospects. Biomed Eng Online, 2014, 13: 41.
9.	Dou Q, Chen H, Yu L, et al. Multilevel contextual 3-D CNNs for false positive reduction in pulmonary nodule detection. IEEE Trans Biomed Eng, 2017, 64(7): 1558-1567.
10.	S R SC, Rajaguru H. Lung cancer detection using probabilistic neural network with modified crow-search algorithm. Asian Pac J Cancer Prev, 2019, 20(7): 2159-2166.
11.	Sayed G, Hassanien AE, Azar AT. Feature selection via a novel chaotic crow search algorithm. Neu Comput Appl, 2019, 31(1): 171-188.
12.	Nasrullah N, Sang J, Alam MS, et al. Automated lung nodule detection and classification using deep learning combined with multiple strategies. Sensors (Basel), 2019, 19(17): 3722.
13.	Setio AAA, 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.
14.	Dellios N, Teichgraeber U, Chelaru R, et al. Computer-aided detection fidelity of pulmonary nodules in chest radiograph. J Clin Imaging Sci, 2017, 7: 8.
15.	Cha MJ, Chung MJ, Lee JH, et al. Performance of deep learning model in detecting operable lung cancer with chest radiographs. J Thorac Imaging, 2019, 34(2): 86-91.
16.	Ribas A, Wolchok JD. Cancer immunotherapy using checkpoint blockade. Science, 2018, 359(6382): 1350-1355.
17.	Kichenadasse G, Miners JO, Mangoni AA, et al. Association between body mass index and overall survival with immune checkpoint inhibitor therapy for advanced non-small cell lung cancer. JAMA Oncol, 2020, 6(4): 512-518.
18.	Ling MY, Guang YT, Lei Z, et al. Prediction of pathologic stage in non-small cell lung cancer using machine learning algorithm based on CT image feature analysis. BMC Cancer, 2019, 19(1): 464.
19.	Hyun SH, Ahn MS, Koh YW, et al. A machine-learning approach using pet-based radiomics to predict the histological subtypes of lung cancer. Clin Nucl Med, 2019.[Epub ahead of print].
20.	Bi WL, Hosny A, Schabath MB, et al. Artificial intelligence in cancer imaging: Clinical challenges and applications. CA Cancer J Clin, 2019, 69(2): 127-157.
21.	Feng PH, Lin YT, Lo CM. A machine learning texture model for classifying lung cancer subtypes using preliminary bronchoscopic findings. Med Phys, 2018, 45(12): 5509-5514.
22.	Xie W, Yuan S, Sun Z, et al. Long noncoding and circular RNAs in lung cancer: advances and perspectives. Epigenomics, 2016, 8(9): 1275-1287.
23.	Wang Y, Fu J, Wang Z, et al. Screening key lncRNAs for human lung adenocarcinoma based on machine learning and weighted gene co-expression network analysis. Cancer Biomark, 2019, 25(4): 313-324.
24.	Jiang N, Xu XR. Exploring the survival prognosis of lung adenocarcinoma based on the cancer genome atlas database using artificial neural network. Medicine (Baltimore), 2019, 98(20): e15642.
25.	Chen WJ, Tang RX, He RQ, et al. Clinical roles of the aberrantly expressed lncRNAs in lung squamous cell carcinoma: a study based on RNA-sequencing and microarray data mining. Oncotarget, 2017, 8(37): 61282-61304.
26.	Geread RS, Morreale P, Dony RD, et al. IHC color histograms for unsupervised Ki67 proliferation index calculation. Front Bioeng Biotechnol, 2019, 7: 226.
27.	Miller I, Min M, Yang C, et al. Ki67 is a graded rather than a binary marker of proliferation versus quiescence. Cell Rep, 2018, 24(5): 1105-1112.
28.	Tang C, Hobbs B, Amer A, et al. Development of an immune-pathology informed radiomics model for non-small cell lung cancer. Sci Rep, 2018, 8(1): 1922.
29.	Ahn HK, Jung M, Ha SY, et al. Clinical significance of Ki-67 and p53 expression in curatively resected non-small cell lung cancer. Tumour Biol, 2014, 35(6): 5735-5740.
30.	Gu Q, Feng Z, Liang Q, et al. Machine learning-based radiomics strategy for prediction of cell proliferation in non-small cell lung cancer. Eur J Radiol, 2019, 118: 32-37.
31.	Scaglia NC, Chatkin JM, Pinto JA, et al. Role of gender in the survival of surgical patients with nonsmall cell lung cancer. Ann Thorac Med, 2013, 8(3): 142-147.
32.	Cristiano S, Leal A, Phallen J, et al. Genome-wide cell-free DNA fragmentation in patients with cancer. Nature, 2019, 570(7761): 385-389.
33.	Chabon JJ, Hamilton EG, Kurtz DM, et al. Integrating genomic features for non-invasive early lung cancer detection. Nature, 2020, 580(7802): 245-251.
34.	Yazdani Charati J, Janbabaei G, Alipour N, et al. Survival prediction of gastric cancer patients by artificial neural network model. Gastroenterol Hepatol Bed Bench, 2018, 11(2): 110-117.
35.	Afshar S, Afshar S, Warden E, et al. Application of artificial neural network in miRNA biomarker selection and precise diagnosis of colorectal cancer. Iran Biomed J, 2019, 23(3): 175-183.
36.	Hu Z, Tang J, Wang Z, et al. Deep learning for image-based cancer detection and diagnosis-A survey. Pattern Recognition, 2018, 83: 134-149.
37.	Vogel L. Rise of medical AI poses new legal risks for doctors. CMAJ, 2019, 191(42): E1173-E1174.

1. Motono N, Funasaki A, Sekimura A, et al. Prognostic value of epidermal growth factor receptor mutations and histologic subtypes with lung adenocarcinoma. Med Oncol, 2018, 35(3): 22.
2. Global Burden of Disease Cancer Collaboration, Fitzmaurice C, Allen C, et al. Global, regional, and national cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life-years for 32 cancer groups, 1990 to 2015: A systematic analysis for the global burden of disease study. JAMA Oncol, 2017, 3(4): 524-548.
3. Lin HT, Liu FC, Wu CY, et al. Epidemiology and survival outcomes of lung cancer: A population-based study. Biomed Res Int, 2019, 2019: 8148156.
4. National Lung Screening Trial Research Team, Aberle DR, Adams AM, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med, 2011, 365(5): 395-409.
5. Hart GR, Roffman DA, Decker Roy, et al. A multi-parameterized artificial neural network for lung cancer risk prediction. PLoS One, 2018, 13(10): e0205264.
6. Hosny A, Aerts HJWL. Artificial intelligence for global health. Science, 2019, 366(6468): 955-956.
7. Taguchi A, Arenberg D. Harnessing immune response to malignant lung nodules. Promise and challenges. Am J Respir Crit Care Med, 2019, 199(10): 1184-1186.
8. Firmino M, Morais AH, Mendoça RM, et al. Computer-aided detection system for lung cancer in computed tomography scans: review and future prospects. Biomed Eng Online, 2014, 13: 41.
9. Dou Q, Chen H, Yu L, et al. Multilevel contextual 3-D CNNs for false positive reduction in pulmonary nodule detection. IEEE Trans Biomed Eng, 2017, 64(7): 1558-1567.
10. S R SC, Rajaguru H. Lung cancer detection using probabilistic neural network with modified crow-search algorithm. Asian Pac J Cancer Prev, 2019, 20(7): 2159-2166.
11. Sayed G, Hassanien AE, Azar AT. Feature selection via a novel chaotic crow search algorithm. Neu Comput Appl, 2019, 31(1): 171-188.
12. Nasrullah N, Sang J, Alam MS, et al. Automated lung nodule detection and classification using deep learning combined with multiple strategies. Sensors (Basel), 2019, 19(17): 3722.
13. Setio AAA, 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.
14. Dellios N, Teichgraeber U, Chelaru R, et al. Computer-aided detection fidelity of pulmonary nodules in chest radiograph. J Clin Imaging Sci, 2017, 7: 8.
15. Cha MJ, Chung MJ, Lee JH, et al. Performance of deep learning model in detecting operable lung cancer with chest radiographs. J Thorac Imaging, 2019, 34(2): 86-91.
16. Ribas A, Wolchok JD. Cancer immunotherapy using checkpoint blockade. Science, 2018, 359(6382): 1350-1355.
17. Kichenadasse G, Miners JO, Mangoni AA, et al. Association between body mass index and overall survival with immune checkpoint inhibitor therapy for advanced non-small cell lung cancer. JAMA Oncol, 2020, 6(4): 512-518.
18. Ling MY, Guang YT, Lei Z, et al. Prediction of pathologic stage in non-small cell lung cancer using machine learning algorithm based on CT image feature analysis. BMC Cancer, 2019, 19(1): 464.
19. Hyun SH, Ahn MS, Koh YW, et al. A machine-learning approach using pet-based radiomics to predict the histological subtypes of lung cancer. Clin Nucl Med, 2019.[Epub ahead of print].
20. Bi WL, Hosny A, Schabath MB, et al. Artificial intelligence in cancer imaging: Clinical challenges and applications. CA Cancer J Clin, 2019, 69(2): 127-157.
21. Feng PH, Lin YT, Lo CM. A machine learning texture model for classifying lung cancer subtypes using preliminary bronchoscopic findings. Med Phys, 2018, 45(12): 5509-5514.
22. Xie W, Yuan S, Sun Z, et al. Long noncoding and circular RNAs in lung cancer: advances and perspectives. Epigenomics, 2016, 8(9): 1275-1287.
23. Wang Y, Fu J, Wang Z, et al. Screening key lncRNAs for human lung adenocarcinoma based on machine learning and weighted gene co-expression network analysis. Cancer Biomark, 2019, 25(4): 313-324.
24. Jiang N, Xu XR. Exploring the survival prognosis of lung adenocarcinoma based on the cancer genome atlas database using artificial neural network. Medicine (Baltimore), 2019, 98(20): e15642.
25. Chen WJ, Tang RX, He RQ, et al. Clinical roles of the aberrantly expressed lncRNAs in lung squamous cell carcinoma: a study based on RNA-sequencing and microarray data mining. Oncotarget, 2017, 8(37): 61282-61304.
26. Geread RS, Morreale P, Dony RD, et al. IHC color histograms for unsupervised Ki67 proliferation index calculation. Front Bioeng Biotechnol, 2019, 7: 226.
27. Miller I, Min M, Yang C, et al. Ki67 is a graded rather than a binary marker of proliferation versus quiescence. Cell Rep, 2018, 24(5): 1105-1112.
28. Tang C, Hobbs B, Amer A, et al. Development of an immune-pathology informed radiomics model for non-small cell lung cancer. Sci Rep, 2018, 8(1): 1922.
29. Ahn HK, Jung M, Ha SY, et al. Clinical significance of Ki-67 and p53 expression in curatively resected non-small cell lung cancer. Tumour Biol, 2014, 35(6): 5735-5740.
30. Gu Q, Feng Z, Liang Q, et al. Machine learning-based radiomics strategy for prediction of cell proliferation in non-small cell lung cancer. Eur J Radiol, 2019, 118: 32-37.
31. Scaglia NC, Chatkin JM, Pinto JA, et al. Role of gender in the survival of surgical patients with nonsmall cell lung cancer. Ann Thorac Med, 2013, 8(3): 142-147.
32. Cristiano S, Leal A, Phallen J, et al. Genome-wide cell-free DNA fragmentation in patients with cancer. Nature, 2019, 570(7761): 385-389.
33. Chabon JJ, Hamilton EG, Kurtz DM, et al. Integrating genomic features for non-invasive early lung cancer detection. Nature, 2020, 580(7802): 245-251.
34. Yazdani Charati J, Janbabaei G, Alipour N, et al. Survival prediction of gastric cancer patients by artificial neural network model. Gastroenterol Hepatol Bed Bench, 2018, 11(2): 110-117.
35. Afshar S, Afshar S, Warden E, et al. Application of artificial neural network in miRNA biomarker selection and precise diagnosis of colorectal cancer. Iran Biomed J, 2019, 23(3): 175-183.
36. Hu Z, Tang J, Wang Z, et al. Deep learning for image-based cancer detection and diagnosis-A survey. Pattern Recognition, 2018, 83: 134-149.
37. Vogel L. Rise of medical AI poses new legal risks for doctors. CMAJ, 2019, 191(42): E1173-E1174.

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Research progress on artificial intelligence in diagnosis of lung cancer

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