Evolving the pulmonary nodules diagnosis from classical approaches to deep learning-aided decision support: three decades' development course and future prospect

被引:53
作者
Liu, Bo [1 ]
Chi, Wenhao [1 ,6 ]
Li, Xinran [5 ]
Li, Peng [1 ]
Liang, Wenhua [2 ,4 ]
Liu, Haiping [3 ,4 ]
Wang, Wei [2 ,4 ]
He, Jianxing [2 ,4 ]
机构
[1] Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China
[2] Guangzhou Med Univ, Affiliated Hosp 1, Dept Thorac Surg & Oncol, Guangzhou, Guangdong, Peoples R China
[3] Guangzhou Med Univ, Affiliated Hosp 1, PET CT Ctr, Guangzhou, Guangdong, Peoples R China
[4] China State Key Lab Resp Dis, Guangzhou, Guangdong, Peoples R China
[5] Univ Wisconsin, Dept Math, Madison, WI 53706 USA
[6] Univ Chinese Acad Sci, Beijing, Peoples R China
关键词
Computer-aided diagnosis; Pulmonary nodules; Lung cancer; Deep learning; Artificial intelligence; Review; FALSE-POSITIVE REDUCTION; COMPUTED-TOMOGRAPHY IMAGES; RESOURCE INITIATIVE IDRI; LUNG-CANCER PROBABILITY; LARGE-SCALE VALIDATION; LOW-DOSE CT; AUTOMATIC DETECTION; NEURAL-NETWORKS; DATABASE CONSORTIUM; DETECTION SYSTEM;
D O I
10.1007/s00432-019-03098-5
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Purpose Lung cancer is the commonest cause of cancer deaths worldwide, and its mortality can be reduced significantly by performing early diagnosis and screening. Since the 1960s, driven by the pressing needs to accurately and effectively interpret the massive volume of chest images generated daily, computer-assisted diagnosis of pulmonary nodule has opened up new opportunities to relax the limitation from physicians' subjectivity, experiences and fatigue. And the fair access to the reliable and affordable computer-assisted diagnosis will fight the inequalities in incidence and mortality between populations. It has been witnessed that significant and remarkable advances have been achieved since the 1980s, and consistent endeavors have been exerted to deal with the grand challenges on how to accurately detect the pulmonary nodules with high sensitivity at low false-positive rate as well as on how to precisely differentiate between benign and malignant nodules. There is a lack of comprehensive examination of the techniques' development which is evolving the pulmonary nodules diagnosis from classical approaches to machine learning-assisted decision support. The main goal of this investigation is to provide a comprehensive state-of-the-art review of the computer-assisted nodules detection and benign-malignant classification techniques developed over three decades, which have evolved from the complicated ad hoc analysis pipeline of conventional approaches to the simplified seamlessly integrated deep learning techniques. This review also identifies challenges and highlights opportunities for future work in learning models, learning algorithms and enhancement schemes for bridging current state to future prospect and satisfying future demand. Conclusion It is the first literature review of the past 30 years' development in computer-assisted diagnosis of lung nodules. The challenges indentified and the research opportunities highlighted in this survey are significant for bridging current state to future prospect and satisfying future demand. The values of multifaceted driving forces and multidisciplinary researches are acknowledged that will make the computer-assisted diagnosis of pulmonary nodules enter into the main stream of clinical medicine and raise the state-of-the-art clinical applications as well as increase both welfares of physicians and patients. We firmly hold the vision that fair access to the reliable, faithful, and affordable computer-assisted diagnosis for early cancer diagnosis would fight the inequalities in incidence and mortality between populations, and save more lives.
引用
收藏
页码:153 / 185
页数:33
相关论文
共 138 条
[1]   Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening [J].
Aberle, Denise R. ;
Adams, Amanda M. ;
Berg, Christine D. ;
Black, William C. ;
Clapp, Jonathan D. ;
Fagerstrom, Richard M. ;
Gareen, Ilana F. ;
Gatsonis, Constantine ;
Marcus, Pamela M. ;
Sicks, JoRean D. .
NEW ENGLAND JOURNAL OF MEDICINE, 2011, 365 (05) :395-409
[2]   AggNet: Deep Learning From Crowds for Mitosis Detection in Breast Cancer Histology Images [J].
Albarqouni, Shadi ;
Baur, Christoph ;
Achilles, Felix ;
Belagiannis, Vasileios ;
Demirci, Stefanie ;
Navab, Nassir .
IEEE TRANSACTIONS ON MEDICAL IMAGING, 2016, 35 (05) :1313-1321
[3]  
[Anonymous], 2018, LANCET, V392, pe2, DOI DOI 10.1016/S0140-6736(18)32252-9
[4]  
[Anonymous], 2017, CANC FACT SHEET
[5]  
[Anonymous], 2018, CANC FACTS FIG 2018
[6]   Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network [J].
Anthimopoulos, Marios ;
Christodoulidis, Stergios ;
Ebner, Lukas ;
Christe, Andreas ;
Mougiakakou, Stavroula .
IEEE TRANSACTIONS ON MEDICAL IMAGING, 2016, 35 (05) :1207-1216
[7]   The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A Completed Public Database of CT Scans for Lung Nodule Analysis [J].
Armato, S. ;
McLennan, G. ;
McNitt-Gray, M. ;
Meyer, C. ;
Reeves, A. ;
Bidaut, L. ;
Zhao, B. ;
Croft, B. ;
Clarke, L. .
MEDICAL PHYSICS, 2010, 37 (06) :3416-3417
[8]   LUNGx Challenge for computerized lung nodule classification [J].
Armato, Samuel G., III ;
Drukker, Karen ;
Li, Feng ;
Hadjiiski, Lubomir ;
Tourassi, Georgia D. ;
Engelmann, Roger M. ;
Giger, Maryellen L. ;
Redmond, George ;
Farahani, Keyvan ;
Kirby, Justin S. ;
Clarke, Laurence P. .
JOURNAL OF MEDICAL IMAGING, 2016, 3 (04)
[9]   Special Section Guest Editorial: LUNGx Challenge for computerized lung nodule classification: Reflections and lessons learned [J].
Armato, Samuel G. ;
Hadjiiski, Lubomir ;
Tourassi, Georgia D. ;
Drukker, Karen ;
Giger, Maryellen L. ;
Li, Feng ;
Redmond, George ;
Farahani, Keyvan ;
Kirby, Justin S. ;
Clarke, Laurence P. .
Journal of Medical Imaging, 2015, 2 (02)
[10]   The Lung Image Database Consortium, (LIDC) and Image Database Resource Initiative (IDRI): A Completed Reference Database of Lung Nodules on CT Scans [J].
Armato, Samuel G., III ;
McLennan, Geoffrey ;
Bidaut, Luc ;
McNitt-Gray, Michael F. ;
Meyer, Charles R. ;
Reeves, Anthony P. ;
Zhao, Binsheng ;
Aberle, Denise R. ;
Henschke, Claudia I. ;
Hoffman, Eric A. ;
Kazerooni, Ella A. ;
MacMahon, Heber ;
van Beek, Edwin J. R. ;
Yankelevitz, David ;
Biancardi, Alberto M. ;
Bland, Peyton H. ;
Brown, Matthew S. ;
Engelmann, Roger M. ;
Laderach, Gary E. ;
Max, Daniel ;
Pais, Richard C. ;
Qing, David P-Y ;
Roberts, Rachael Y. ;
Smith, Amanda R. ;
Starkey, Adam ;
Batra, Poonam ;
Caligiuri, Philip ;
Farooqi, Ali ;
Gladish, Gregory W. ;
Jude, C. Matilda ;
Munden, Reginald F. ;
Petkovska, Iva ;
Quint, Leslie E. ;
Schwartz, Lawrence H. ;
Sundaram, Baskaran ;
Dodd, Lori E. ;
Fenimore, Charles ;
Gur, David ;
Petrick, Nicholas ;
Freymann, John ;
Kirby, Justin ;
Hughes, Brian ;
Casteele, Alessi Vande ;
Gupte, Sangeeta ;
Sallam, Maha ;
Heath, Michael D. ;
Kuhn, Michael H. ;
Dharaiya, Ekta ;
Burns, Richard ;
Fryd, David S. .
MEDICAL PHYSICS, 2011, 38 (02) :915-931