Materials and Methods
W. Chen, R. Zheng, P.D. Baade, S. Zhang, H. Zeng, F. Bray, A. Jemal, X.Q. Yu, J. He, Cancer statistics in China, 2015, CA: a cancer journal for clinicians 66(2) (2016) 115-32.
- Clinical characteristics and work-up of small to intermediate-sized pulmonary nodules in a Chinese dedicated cancer hospital.Cancer Biol Med. 2020; 17: 199-207
- European position statement on lung cancer screening.The Lancet Oncology. 2017; 18: e754-e766
W.J. Yang, F.F. Qian, J.J. Teng, H.M. Wang, C. Manegold, L.R. Pilz, W. Voigt, Y.W. Zhang, J.D. Ye, Q.H. Chen, B.H. Han, A.M.E.T.S. Collaborative, Community-based lung cancer screening with low-dose CT in China: Results of the baseline screening, Lung Cancer 117 (2018) 20-26.
- Demonstration program of population-based lung cancer screening in C hina: Rationale and study design.Thoracic cancer. 2014; 5: 197-203
N.L.S.T.R. Team, Reduced lung-cancer mortality with low-dose computed tomographic screening, N. Engl. J. Med. 365(5) (2011) 395-409.
- ESR/ERS statement paper on lung cancer screening.Eur. Radiol. 2020; 55: 1900506https://doi.org/10.1183/13993003.00506-201910.1183/13993003.00506-2019.Supp110.1183/13993003.00506-2019.Shareable1
- Methods of computed tomography screening and management of lung cancer in Tianjin: design of a population-based cohort study.Cancer biology & medicine. 2019; 16: 181
- Risk-based selection from the general population in a screening trial: selection criteria, recruitment and power for the Dutch-Belgian randomised lung cancer multi-slice CT screening trial (NELSON).Int. J. Cancer. 2007; 120: 868-874
- Lung cancer LDCT screening and mortality reduction—evidence, pitfalls and future perspectives.Nature Reviews Clinical Oncology. 2021; 18: 135-151
- Large scale validation of the M5L lung CAD on heterogeneous CT datasets.Med. Phys. 2015; 42: 1477-1489
- The utility of computer-aided detection (CAD) for lung cancer screening using low-dose CT.International Congress Series, Elsevier. 2005; 1281: 1137-1142
N.L.S.T.R. Team, Results of initial low-dose computed tomographic screening for lung cancer, N. Engl. J. Med. 368(21) (2013) 1980-1991.
- Performance of computer-aided detection of pulmonary nodules in low-dose CT: comparison with double reading by nodule volume.Eur. Radiol. 2012; 22: 2076-2084
K.N. Jeon, J.M. Goo, C.H. Lee, Y. Lee, J.Y. Choo, N.K. Lee, M.-S. Shim, I.S. Lee, K.G. Kim, D.S.J.I.r. Gierada, Computer-aided nodule detection and volumetry to reduce variability between radiologists in the interpretation of lung nodules at low-dose screening CT, 47(8) (2012) 457.
E.J. Hwang, C.M.J.K.J.o.R. Park, Clinical Implementation of Deep Learning in Thoracic Radiology: Potential Applications and Challenges, 21(5) (2020) 511.
- Shape-based computer-aided detection of lung nodules in thoracic CT images.IEEE Transactions on Biomedical Engineering. 2009; 56: 1810-1820
K. Murphy, A. Schilham, H. Gietema, M. Prokop, B. van Ginneken, Automated detection of pulmonary nodules from low-dose computed tomography scans using a two-stage classification system based on local image features, Medical Imaging 2007: Computer-Aided Diagnosis, International Society for Optics and Photonics, 2007, p. 651410.
- 3DFPN-HS $$^ $$: 3D Feature Pyramid Network Based High Sensitivity and Specificity Pulmonary Nodule Detection.International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer. 2019; : 513-521
- The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans.Med. Phys. 2011; 38: 915-931
The Cancer Imaging Archive. TCIA collections. . http://www.cancerimagingarchive.net/. 02.08.2016).
- Phased searching with NEAT in a time-scaled framework: experiments on a computer-aided detection system for lung nodules.Artif. Intell. Med. 2013; 59: 157-167
B. Van Ginneken, A.A. Setio, C. Jacobs, F. Ciompi, Off-the-shelf convolutional neural network features for pulmonary nodule detection in computed tomography scans, 2015 IEEE 12th International symposium on biomedical imaging (ISBI), IEEE, 2015, pp. 286-289.
P.-P. Ypsilantis, G. Montana, Recurrent convolutional networks for pulmonary nodule detection in CT imaging, arXiv preprint arXiv:1609.09143 (2016).
- Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks.IEEE Trans. Med. Imaging. 2016; 35: 1160-1169
- Computer-aided detection (CADe) and diagnosis (CADx) system for lung cancer with likelihood of malignancy.Biomedical engineering online. 2016; 15: 2
- Multilevel contextual 3-D CNNs for false positive reduction in pulmonary nodule detection.IEEE Trans. Biomed. Eng. 2017; 64: 1558-1567
- 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
- An automatic detection system of lung nodule based on multigroup patch-based deep learning network.IEEE journal of biomedical and health informatics. 2018; 22: 1227-1237
- NODULe: Combining constrained multi-scale LoG filters with densely dilated 3D deep convolutional neural network for pulmonary nodule detection.Neurocomputing. 2018; 317: 159-167
- Automatic Pulmonary Nodule Detection in CT Scans Using Convolutional Neural Networks Based on Maximum Intensity Projection.IEEE Trans Med Imaging. 2020; 39: 797-805
Lung CT Screening Reporting & Data System (Lung-RADS), 2019. https://www.acr.org/Clinical-Resources/Reporting-and-Data-Systems/Lung-Rads.
- A free response approach to the measurement and characterization of radiographic observer performance, Application of Optical Instrumentation in Medicine VI.International Society for Optics and Photonics. 1977; : 124-135
- Area under the free-response ROC curve (FROC) and a related summary index.Biometrics. 2009; 65: 247-256
- On combining computer-aided detection systems.IEEE Trans. Med. Imaging. 2011; 30: 215-223
- Pulmonary nodule detection on MDCT images: evaluation of diagnostic performance using thin axial images, maximum intensity projections, and computer-assisted detection.European radiology. 2007; 17: 3148-3156
- End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. 2019; 25: 954-961
- Computer-aided detection of pulmonary nodules: a comparative study using the public LIDC/IDRI database.Eur. Radiol. 2016; 26: 2139-2147
- Evaluating the performance of a deep learning-based computer-aided diagnosis (DL-CAD) system for detecting and characterizing lung nodules: Comparison with the performance of double reading by radiologists.Thoracic cancer. 2019; 10: 183-192
- 3D shape analysis to reduce false positives for lung nodule detection systems.Med Biol Eng Comput. 2017; 55: 1199-1213
- Automatic detection of lung nodules in CT datasets based on stable 3D mass–spring models. 2012; 42: 1098-1109
- Automatic detection of pulmonary nodules at spiral CT: clinical application of a computer-aided diagnosis system.Eur. Radiol. 2002; 12: 1052-1057
- Lung micronodules: automated method for detection at thin-section CT—initial experience.Radiology. 2003; 226: 256-262
- Lung cancer: performance of automated lung nodule detection applied to cancers missed in a CT screening program.Radiology. 2002; 225: 685-692
- A review of lung cancer screening and the role of computer-aided detection.Clin. Radiol. 2017; 72: 433-442