Abstract
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.
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Abbreviations
- 2D:
-
Two-dimensional
- 3D:
-
Three-dimensional
- AAPM:
-
American Association of Physicists in Medicine
- AI:
-
Artificial intelligence
- ANN:
-
Artificial neural network
- AUC:
-
Area under the receiver operating characteristic curve
- BPNN:
-
Back propagation neural network
- CAD:
-
Computer-aided/assisted diagnosis
- CANN:
-
Convolutional autoencoder neural network
- CNN:
-
Convolutional neural network
- CT:
-
Computed tomography
- CV:
-
Curvedness
- DBN:
-
Deep belief network
- DLCST:
-
Danish Lung Cancer Screening Trial
- DSB:
-
Data science bowl
- ELCAP:
-
Early Lung Cancer Action Project
- FOM:
-
Figure of merit
- EPANN:
-
Evolved Plastic Artificial Neural Network
- EU:
-
European Union
- GDPR:
-
General data protection and regulation
- GLCM:
-
Gray level co-occurrence matrix
- GPU:
-
Graphics processing unit
- HIST:
-
Histogram analysis
- IARC:
-
International Agency for Research on Cancer
- IDRI:
-
Image Database Resource Initiative
- JAFROC:
-
Jackknife alternative free-response
- KNN:
-
k-Nearest neighbors
- LASSO:
-
Least absolute shrinkage and selection operator
- LBP:
-
Local binary pattern
- LDCT:
-
Low-dose computed tomography
- LIDC:
-
Lung image database consortium
- LUNA16:
-
LUng Nodule Analysis 2016
- MC-CNN:
-
Multi-crop CNN
- MILD:
-
Multicentric Italian Lung Detection
- MTANN:
-
Massive training artificial neural network
- MTL:
-
Multi-task learning
- NCDs:
-
Noncommunicable Diseases
- NCI:
-
National Cancer Institute
- NLST:
-
National Lung Cancer Screening Trial
- NN:
-
Neural network
- PCF:
-
Prevent Cancer Foundation
- PET:
-
Positron emission tomography
- RBM:
-
Restricted Boltzmann machines
- ROC:
-
Receiver operating characteristic
- SDAE:
-
Stacked denoising autoencoder
- SIFT:
-
Scale invariant feature transform
- SI:
-
Shape index
- SPIE:
-
International Society for Optics and Photonics
- SUV:
-
Standardized uptake value
- SVM:
-
Support vector machine
- TPE:
-
Tree Parzen estimator
- WHO:
-
World Health Organization
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Acknowledgements
We thank the anonymous referees and editors for their constructive comments in advance on earlier drafts of this manuscript. We thank Emeritus Prof. M.A. Keyzer (SOW-VU, Vrije Universiteit Amsterdam, The Netherlands), Yihui Jin, Ling Wang, Xiaokui Yang (Tsinghua University), Jikun Huang (Peking University), Yubao Guan, Ying Chen, Danxia Huang (China State Key Laboratory of Respiratory Disease and The First Affiliated Hospital of Guangzhou Medical University), Jun Huang (The Sixth Affiliated Hospital of Sun Yat-Sen University), Xiao Han (The Fourth Medical College of Peking University), Rong Zhu (Department of Statistics, Columbia University), Ce Xu, Yi Zhang, Guoxue Wei (National Development and Reform Commission of China), Zhiguang Ren (National Natural Science Foundation of China), Shouyang Wang, Xiaoyi Feng, Yixin Yang, Zhengyang Li, Xin Lyu, Mengchen Ji, Xin Yun, Wenzhe Duan, Keyao Wang (Academy of Mathematics and Systems Science, Chinese Academy of Sciences), Ying Liu (Beihang University) for their insightful discussions and comments on earlier versions of this paper.
Funding
BL was funded by Key Research Program of Frontier Sciences, Chinese Academy of Sciences (QYZDB-SSW-SYS020), Major Project to Promote Development of Big Data from National Development and Reform Commission (2016-999999-65-01-000696-01). JH was sponsored by Collaboration Research Project of Guangdong Education Department (GJHZ1006 and 2014KGJHZ010). HL was supported by the Medical and Health Science and Technology Project of Guangzhou Municipal Health Commission (20161A011060), the Science and Technology Planning Project of Guangdong Province (2017A020215110), the Natural Science Foundation of Guangdong Province (2018A030313534).
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Liu, B., Chi, W., Li, X. et al. Evolving the pulmonary nodules diagnosis from classical approaches to deep learning-aided decision support: three decades’ development course and future prospect. J Cancer Res Clin Oncol 146, 153–185 (2020). https://doi.org/10.1007/s00432-019-03098-5
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DOI: https://doi.org/10.1007/s00432-019-03098-5