人工神经网络在肺癌相关研究中的应用现状
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摘要
肺癌是目前全世界发病率、死亡率最高的肿瘤,目前的诊疗手段效果有限,精准医学的全面开展为提高肺癌诊疗水平带来了新的契机。但临床医生很难对精准医学需要的多维度多角度的资料(生物组学、临床检测指标以及非生物的环境背景资料等)进行有效的整合和利用,难以为患者选择最优的诊治方案。借助计算机技术的发展,以人工神经网络(artificial neural networks, ANNs)为代表的人工智能具有高容错性、智能性和具有自我学习能力的特点,其强大的信息整合能力可以对精准医学的发展与应用起到很大的帮助,在肺癌的基础研究和临床实践中发挥巨大的作用。本文对肺癌领域ANNs应用的现状进行综述。
Lung cancer is the most common and fatal tumor in the world with limited diagnostic and treatment methods. The development of precision medicine has brought new opportunities for the improvement of diagnosis and treatment of lung cancer. However, various kinds of information required by precision medicine(such as biometrics, clinical test indicators and non-biological environmental background information) are difficult for clinicians to integrate and utilize effectively. With the development of computer technology, artificial neural networks(ANNs), which has the characteristic of high fault tolerance, intelligence and self-learning ability. Its powerful information integration ability can solve many problems in the development and application of precision medicine, which can play a key role in basic research and clinical practice associated with lung cancer. This article reviewed the application of artificial neural network in the field of lung cancer.
Lung cancer is the most common and fatal tumor in the world with limited diagnostic and treatment methods. The development of precision medicine has brought new opportunities for the improvement of diagnosis and treatment of lung cancer. However, various kinds of information required by precision medicine(such as biometrics, clinical test indicators and non-biological environmental background information) are difficult for clinicians to integrate and utilize effectively. With the development of computer technology, artificial neural networks(ANNs), which has the characteristic of high fault tolerance, intelligence and self-learning ability. Its powerful information integration ability can solve many problems in the development and application of precision medicine, which can play a key role in basic research and clinical practice associated with lung cancer. This article reviewed the application of artificial neural network in the field of lung cancer.
引文
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3 Mcculloch WS,Pitts W.A logical calculus of the ideas immanent in nervous activity.Bull Math Biophys,1943,5(4):115-133.doi:10.1007/BF02478259
4 Clark G.The organization of behavior:A neuropsychological theory.D.O.Hebb.John Wiley And Sons,Inc.New York,1949:335.
5 Rosenblatt F.The perceptron:a probabilistic model for information storage and organization in the brain.Psychol Rev,1958,65(6):386-408.
6 Hopfield JJ.Neural networks and physical systems with emergent collective computational abilities.Proc Natl Acad Sci U S A,1982,79(8):2554-2558.doi:10.1073/pnas.79.8.2554
7 Rumelhart DE,Hinton GE,Williams RJ.Learning representations by back-propagating errors.Nature,1986,323:533.doi:10.1016/B978-1-4832-1446-7.50035-2
8 Hinton GE,Salakhutdinov RR.Reducing the dimensionality of data with neural networks.Science,2006,313(5786):504-507.doi:10.1126/science.1127647
9 Arel I,Rose DC,Karnowski TP.Deep machine learning-a new frontier in artificial intelligence research[Research Frontier].IEEEComput Intell M,2010,5(4):13-18.doi:10.1109/MCI.2010.938364
10 Lecun Y,Bengio Y,Hinton G.Deep learning.Nature,2015:436-444.doi:10.1038/Nature14539
11 Baxt WG.Application of artificial neural networks to clinical medicine.Lancet,1995,346(8983):1135-1138.doi:10.1016/S0140-6736(95)91804-3
12 Berner ES,Lande TJ.Overview of clinical decision support systems.Springer New York,2007:3-22.
13 Esteva A,Kuprel B,Novoa RA,et al.Dermatologist-level classification of skin cancer with deep neural networks.Nature,2017,542(7639):115-118.doi:10.1038/nature21056
14 Huys QJ,Maia TV,Frank MJ.Computational psychiatry as a bridge from neuroscience to clinical applications.Nat Neurosci,2016,19(3):404-413.doi:10.1038/nn.4238
15 Kermany DS,Goldbaum M,Cai W,et al.Identifying medical diagnoses and treatable diseases by image-based deep learning.Cell,2018,172(5):1122-1131.doi:10.1016/j.cell.2018.02.010
16 Hirasawa T,Aoyama K,Tanimoto T,et al.Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images.Gastric Cancer,2018.doi:10.1007/s10120-018-0793-2
17 Tiwari AK.Prediction of lung cancer using image processing techniques:a review.ACII,2016,3(1):1-9.
18 Kourou K,Exarchos TP,Exarchos KP,et al.Machine learning applications in cancer prognosis and prediction.Comput Struct Biotec,2015,13:8-17.doi:10.1016/j.csbj.2014.11.005
19 Jha S,Topol EJ.Adapting to Artificial Intelligence:Radiologists and Pathologists as Information Specialists.JAMA,2016,316(22):2353-2354.doi:10.1001/jama.2016.17438
20 Shen W,Zhou M,Yang F,et al.Multi-scale convolutional neural networks for lung nodule classification.IPMI,2015,24:588-599.doi:10.1007/978-3-319-19992-4_46
21 Liu X,Hou F,Qin H,et al.A CADe system for nodule detection in thoracic CT images based on artificial neural network.Sci China Inform Sci,2017,60(7):72106.doi:10.1007/s11432-016-9008-0
22 Toney LK,Vesselle HJ.Neural networks for nodal staging of non-small cell lung cancer with FDG PET and CT:importance of combining uptake values and sizes of nodes and primary tumor.Radiology,2014,270(1):91-98.doi:10.1148/radiol.13122427
23 Duan X,Yang Y,Tan S,et al.Application of artificial neural network model combined with four biomarkers in auxiliary diagnosis of lung cancer.Med Biol Eng Comput,2017,55(8):1239-1248.doi:10.1007/s11517-016-1585-7
24 Butcher JB,Rutter AV,Wootton AJ,et al.Artificial neural network analysis of volatile organic compounds for the detection of lung cancer.ACII,2017:183-190.
25 Ligor T,Pater L,Buszewski B.Application of an artificial neural network model for selection of potential lung cancer biomarkers.JBreath Res,2015,9(2):27106.doi:10.1088/1752-7155/9/2/027106
26 O’Shea K,Cameron SJS,Lewis KE,et al.Metabolomic-based biomarker discovery for non-invasive lung cancer screening:A case study.BBA-BIOMEMBRANES,2016,1860(11,Part B):2682-2687.doi:10.1016/j.bbagen.2016.07.007
27 Alzubaidi K,Sideseq FB,Faeq A,et al.Computer aided diagnosis in digital pathology application:Review and perspective approach in lung cancer classification.NTICT,2017:219-224.doi:10.1109/NTICT.2017.7976109
28 Xie NN,Hu L,Li TH.Lung cancer risk prediction method based on feature selection and artificial neural network.Asian Pac J Cancer Prev,2014,15(23):10539-10542.
29 Hsia T,Chiang H,Chiang D,et al.Prediction of survival in surgical unresectable lung cancer by artificial neural networks including genetic polymorphisms and clinical parameters.J Clin Lab Anal,2003,17(6):229-234.doi:10.1002/jcla.10102
30 Paul R,Hawkins SH,Hall LO,et al.Combining deep neural network and traditional image features to improve survival prediction accuracy for lung cancer patients from diagnostic CT.2016 IEEE International Conference on Systems,2016:002570-002575.2017.doi:10.1109/SMC.2016.7844626
31 Chatzimichail E,Matthaios D,Bouros D,et al.Gamma-H2AX:Anovel prognostic marker in a prognosis prediction model of patients with early operable non-small cell lung cancer.Int J Genomics,2014,2014:160236.doi:10.1155/2014/160236
32 Santos-Garcia G,Varela G,Novoa N,et al.Prediction of postoperative morbidity after lung resection using an artificial neural network ensemble.Artif Intell Med,2004,30(1):61-69.doi:10.1016/S0933-3657(03)00059-9
33 Chen J,Chen J,Ding HY,et al.Use of an artificial neural network to construct a model of predicting deep fungal infection in lung cancer patients.Asian Pac J Cancer Prev,2015,16(12):5095-5099.doi:10.7314/APJCP.2015.16.12.5095
34 Chen Y,Chang Y,Ke W,et al.Cancer adjuvant chemotherapy strategic classification by artificial neural network with gene expression data:An example for non-small cell lung cancer.JBiomed Inform,2015,56:1-7.doi:10.1016/j.jbi.2015.05.006
35 Liu C,Liu X,Wu F,et al.Using artificial intelligence(watson for oncology)for treatment recommendations amongst chinese patients with lung cancer:feasibility study.J Med Internet Res,2018,20(9):e11087.doi:10.2196/11087
36 Bertolaccini L,Solli P,Pardolesi A,et al.An overview of the use of artificial neural networks in lung cancer research.J Thorac Dis,2017, 9(4):924-931. doi:10.21037/jtd.2017.03.157
2 Tan FW,Li N,Gao SG,et al.Development of precision medicine in the surgical treatment of lung cancer.Zhongguo Fei Ai Za Zhi,2016,19(6):318-320.[谭锋维,李宁,高树庚,等.精准医学时代的肺癌外科治疗走向.中国肺癌杂志,2016,19(6):318-320.]doi:10.3779/j.issn.1009-3419.2016.06.02
3 Mcculloch WS,Pitts W.A logical calculus of the ideas immanent in nervous activity.Bull Math Biophys,1943,5(4):115-133.doi:10.1007/BF02478259
4 Clark G.The organization of behavior:A neuropsychological theory.D.O.Hebb.John Wiley And Sons,Inc.New York,1949:335.
5 Rosenblatt F.The perceptron:a probabilistic model for information storage and organization in the brain.Psychol Rev,1958,65(6):386-408.
6 Hopfield JJ.Neural networks and physical systems with emergent collective computational abilities.Proc Natl Acad Sci U S A,1982,79(8):2554-2558.doi:10.1073/pnas.79.8.2554
7 Rumelhart DE,Hinton GE,Williams RJ.Learning representations by back-propagating errors.Nature,1986,323:533.doi:10.1016/B978-1-4832-1446-7.50035-2
8 Hinton GE,Salakhutdinov RR.Reducing the dimensionality of data with neural networks.Science,2006,313(5786):504-507.doi:10.1126/science.1127647
9 Arel I,Rose DC,Karnowski TP.Deep machine learning-a new frontier in artificial intelligence research[Research Frontier].IEEEComput Intell M,2010,5(4):13-18.doi:10.1109/MCI.2010.938364
10 Lecun Y,Bengio Y,Hinton G.Deep learning.Nature,2015:436-444.doi:10.1038/Nature14539
11 Baxt WG.Application of artificial neural networks to clinical medicine.Lancet,1995,346(8983):1135-1138.doi:10.1016/S0140-6736(95)91804-3
12 Berner ES,Lande TJ.Overview of clinical decision support systems.Springer New York,2007:3-22.
13 Esteva A,Kuprel B,Novoa RA,et al.Dermatologist-level classification of skin cancer with deep neural networks.Nature,2017,542(7639):115-118.doi:10.1038/nature21056
14 Huys QJ,Maia TV,Frank MJ.Computational psychiatry as a bridge from neuroscience to clinical applications.Nat Neurosci,2016,19(3):404-413.doi:10.1038/nn.4238
15 Kermany DS,Goldbaum M,Cai W,et al.Identifying medical diagnoses and treatable diseases by image-based deep learning.Cell,2018,172(5):1122-1131.doi:10.1016/j.cell.2018.02.010
16 Hirasawa T,Aoyama K,Tanimoto T,et al.Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images.Gastric Cancer,2018.doi:10.1007/s10120-018-0793-2
17 Tiwari AK.Prediction of lung cancer using image processing techniques:a review.ACII,2016,3(1):1-9.
18 Kourou K,Exarchos TP,Exarchos KP,et al.Machine learning applications in cancer prognosis and prediction.Comput Struct Biotec,2015,13:8-17.doi:10.1016/j.csbj.2014.11.005
19 Jha S,Topol EJ.Adapting to Artificial Intelligence:Radiologists and Pathologists as Information Specialists.JAMA,2016,316(22):2353-2354.doi:10.1001/jama.2016.17438
20 Shen W,Zhou M,Yang F,et al.Multi-scale convolutional neural networks for lung nodule classification.IPMI,2015,24:588-599.doi:10.1007/978-3-319-19992-4_46
21 Liu X,Hou F,Qin H,et al.A CADe system for nodule detection in thoracic CT images based on artificial neural network.Sci China Inform Sci,2017,60(7):72106.doi:10.1007/s11432-016-9008-0
22 Toney LK,Vesselle HJ.Neural networks for nodal staging of non-small cell lung cancer with FDG PET and CT:importance of combining uptake values and sizes of nodes and primary tumor.Radiology,2014,270(1):91-98.doi:10.1148/radiol.13122427
23 Duan X,Yang Y,Tan S,et al.Application of artificial neural network model combined with four biomarkers in auxiliary diagnosis of lung cancer.Med Biol Eng Comput,2017,55(8):1239-1248.doi:10.1007/s11517-016-1585-7
24 Butcher JB,Rutter AV,Wootton AJ,et al.Artificial neural network analysis of volatile organic compounds for the detection of lung cancer.ACII,2017:183-190.
25 Ligor T,Pater L,Buszewski B.Application of an artificial neural network model for selection of potential lung cancer biomarkers.JBreath Res,2015,9(2):27106.doi:10.1088/1752-7155/9/2/027106
26 O’Shea K,Cameron SJS,Lewis KE,et al.Metabolomic-based biomarker discovery for non-invasive lung cancer screening:A case study.BBA-BIOMEMBRANES,2016,1860(11,Part B):2682-2687.doi:10.1016/j.bbagen.2016.07.007
27 Alzubaidi K,Sideseq FB,Faeq A,et al.Computer aided diagnosis in digital pathology application:Review and perspective approach in lung cancer classification.NTICT,2017:219-224.doi:10.1109/NTICT.2017.7976109
28 Xie NN,Hu L,Li TH.Lung cancer risk prediction method based on feature selection and artificial neural network.Asian Pac J Cancer Prev,2014,15(23):10539-10542.
29 Hsia T,Chiang H,Chiang D,et al.Prediction of survival in surgical unresectable lung cancer by artificial neural networks including genetic polymorphisms and clinical parameters.J Clin Lab Anal,2003,17(6):229-234.doi:10.1002/jcla.10102
30 Paul R,Hawkins SH,Hall LO,et al.Combining deep neural network and traditional image features to improve survival prediction accuracy for lung cancer patients from diagnostic CT.2016 IEEE International Conference on Systems,2016:002570-002575.2017.doi:10.1109/SMC.2016.7844626
31 Chatzimichail E,Matthaios D,Bouros D,et al.Gamma-H2AX:Anovel prognostic marker in a prognosis prediction model of patients with early operable non-small cell lung cancer.Int J Genomics,2014,2014:160236.doi:10.1155/2014/160236
32 Santos-Garcia G,Varela G,Novoa N,et al.Prediction of postoperative morbidity after lung resection using an artificial neural network ensemble.Artif Intell Med,2004,30(1):61-69.doi:10.1016/S0933-3657(03)00059-9
33 Chen J,Chen J,Ding HY,et al.Use of an artificial neural network to construct a model of predicting deep fungal infection in lung cancer patients.Asian Pac J Cancer Prev,2015,16(12):5095-5099.doi:10.7314/APJCP.2015.16.12.5095
34 Chen Y,Chang Y,Ke W,et al.Cancer adjuvant chemotherapy strategic classification by artificial neural network with gene expression data:An example for non-small cell lung cancer.JBiomed Inform,2015,56:1-7.doi:10.1016/j.jbi.2015.05.006
35 Liu C,Liu X,Wu F,et al.Using artificial intelligence(watson for oncology)for treatment recommendations amongst chinese patients with lung cancer:feasibility study.J Med Internet Res,2018,20(9):e11087.doi:10.2196/11087
36 Bertolaccini L,Solli P,Pardolesi A,et al.An overview of the use of artificial neural networks in lung cancer research.J Thorac Dis,2017, 9(4):924-931. doi:10.21037/jtd.2017.03.157