Application and development of artificial intelligence in detection and diagnosis of pulmonary nodules

Abstract

Abstract: The mortality rate of lung cancer is very high. The detection of lung nodules by chest CT is of great significance for the early diagnosis and treatment of lung cancer. Computer-aided detection and diagnostic systems can help radiologists reduce workload and improve diagnostic accuracy. With the development of deep learning, computer-aided detection and diagnosis systems based on deep learning are also growing. This article reviews the application and development of artificial intelligence in the detection and diagnosis of pulmonary nodules.

Key words: Artificial intelligence, Pulmonary nodules, Deep learning, Convolutional neural network, Computer-aided detection

LI xin-ling, WANG Ying. Application and development of artificial intelligence in detection and diagnosis of pulmonary nodules[J]. Electronic Journal of Emerging Infectious Diseases, 2019, 4(3): 185-189.

References

[1] Sui Y, Wei Y, Zhao D.Computer-aided lung nodule recognition by SVM classifier based on combination of random undersampling and SMOTE[J].Comput Math Methods Med, 2015, 2015(p1): 368674.
[2] 曹恩涛, 范丽, 肖湘生.CT计算机辅助检测与诊断对肺癌早期诊断的应用与进展[J].国际医学放射学杂志, 2016, 39(1):55-60.
[3] . Sahiner B, Chan HP, Hadjiiski LM, et al.Effect of CAD on radiologists' detection of lung nodules on thoracic CT scans: analysis of an observer performance study by nodule size.[J]. Acad Radiol, 2009, 16(12):1518-1530.
[4] Xia Y, Lu SH, Wen Ling-feng, et al.Automated Identification of Dementia Using FDG-PET Imaging[J].Biomed Res Int, 2014, 2014(5):421743.
[5] Zhou T, Lu HL, Zhang JJ, et al.Pulmonary Nodule Detection Model Based on SVM and CT Image Feature-Level Fusion with Rough Sets[J]. Biomed Res Int, 2016, 2016(18):8052436.
[6] Tartar A, Akan A, Kilic N.A novel approach to malignant-benign classification of pulmonary nodules by using ensemble learning classifiers[J]. Conf Proc IEEE Eng Med Biol Soc, 2014, 2014(36): 4651-4654.
[7] 胡伟俭, 陈为, 冯浩哲, 等.应用于平扫CT图像肺结节检测的深度学习方法综述[J].浙江大学学报(理学版), 2017, 44(4):379-384.
[8] Lecun Y, Bengio Y, Hinton G.Deep learning[J]. Nature, 2015, 521(7553):436-444.
[9] Naqi S M, Sharif M, Yasmin M.Multistage segmentation model and SVM-ensemble for precise lung nodule detection[J]. Int J Comput Assist Radiol Surg, 2018, 13(7):1083-1095.
[10] 张鹏, 徐欣楠, 王洪伟, 等.基于深度学习的计算机辅助肺癌诊断方法[J].计算机辅助设计与图形学学报, 2018, 30(1):90-99.
[11] Lee M C, Boroczky L, Sungur-Stasik K, et al.Computer-aided diagnosis of pulmonary nodules using a two-step approach for feature selection and classifier ensemble construction[J]. Artif Intell Med, 2010, 50(1):43-53.
[12] Orozco HM, Villegas OOV, Vianey G C.et al.Automated system for lung nodules classification based on wavelet feature descriptor and support vector machine[J]. Biomed Eng Online, 2015;14(1):9.
[13] Kuruvilla J, Gunavathi K.Lung cancer classification using neural networks for CT images[J]. Comput Methods Programs Biomed, 2014, 113(1):202-209.
[14] Murphy K, Van GB, Schilham AM, et al.A large-scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification[J]. Med Image Anal, 2009, 13(5):757-770.
[15] Lee SL, Kouzani AZ, Hu EJ.Random forest based lung nodule classification aided by clustering[J]. Comput Med Imaging Graph, 2010, 34(7):535-542.
[16] Li W, Cao P, Zhao D, et al.Pulmonary Nodule Classification with Deep Convolutional Neural Networks on Computed Tomography Images[J].Comput Math Methods Med, 2016, 2016:6215085.
[17] Setio AA, Ciompi F, Litjens G, et al.Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks[J]. IEEE Trans Med Imaging, 2016, 35(5):1160-1169.
[18] Jin H, Li Z, Tong R, et al.A deep 3D residual CNN for false-positive reduction in pulmonary nodule detection[J]. Med Phys, 2018, 45(5):2097-2107.
[19] Aberle D R, Adams A M, Berg C D, et al.Reduced lung-cancer mortality with low-dose computed tomographic screening[J]. N Engl J Med, 2011, 365(5):395-409.
[20] Van Riel SJ, Sánchez, Clara I, et al.Observer Variability for Classification of Pulmonary Nodules on Low-Dose CT Images and Its Effect on Nodule Management[J]. Radiology, 2015, 277(3):863-871.
[21] Jacobs C, Van Rikxoort EM, Scholten ET, et al.Solid, part-solid, or non-solid?: classification of pulmonary nodules in low-dose chest computed tomography by a computer-aided diagnosis system[J]. Invest Radiol, 2015, 50(3):168-173.
[22] Shaukat F, Raja G, Gooya A, et al.Fully automatic detection of lung nodules in CT images using a hybrid feature set[J]. Med Phys, 2017, 44(7):3615-3629.
[23] Niehaus R, Stan Raicu D, Furst J, et al.Toward Understanding the Size Dependence of Shape Features for Predicting Spiculation in Lung Nodules for Computer-Aided Diagnosis[J]. J Digit Imaging, 2015, 28(6):704-717.
[24] Mei X, Wang R, Yang W, et al.Predicting malignancy of pulmonary ground-glass nodules and their invasiveness by random forest[J]. J Thorac Dis, 2018, 10(1):458-463.
[25] Wang H, Zhao T, Li L C, et al.A hybrid CNN feature model for pulmonary nodule malignancy risk differentiation[J]. J Xray Sci Technol, 2018, 26(2):171-187.
[26] Sun W, Zheng B, Qian W.Automatic feature learning using multichannel ROI based on deep structured algorithms for computerized lung cancer diagnosis[J]. Comput Biol Med, 2017, 89(11):530-539.
[27] Shen W, Zhou M, Yang F, et al.Multi-scale Convolutional Neural Networks for Lung Nodule Classification[J]. Inf Process Med Imaging, 2015, 24(12):588-599.
[28] Jeon KN, Goo JM, Lee CH, et al.Computer-aided nodule detection and volumetry to reduce variability between radiologists in the interpretation of lung nodules at low-dose screening computed tomography[J]. Invest Radiol, 2012, 47(8):457-461.
[29] Zheng B, Hardesty LA, Poller WR, et al.Mammography with computer-aided detection: reproducibility assessment initial experience[J]. Radiology, 2003, 228(1):58-62.
[30] Hua KL, Hsu CH, Hidayati SC, et al.Computer-aided classification of lung nodules on computed tomography images via deep learning technique[J]. Onco Targets Ther, 2015, 8(22):2015-2022.
[31] Ciompi F, Chung K, Riel SJV, et al.Towards automatic pulmonary nodule management in lung cancer screening with deep learning[J]. Sci Rep, 2016, 7:46479.
[32] Kang G, Liu K, Hou B, et al.3D multi-view convolutional neural networks for lung nodule classification[J]. PLoS One, 2017, 12(11):e0188290.
[33] Wang S, Zhou M, Gevaert O, et al.A multi-view deep convolutional neural networks for lung nodule segmentation[J]. Conf Proc IEEE Eng Med Biol Soc, 2017, 2017:1752-1755.
[34] Liu S, Xie Y, Jirapatnakul A, et al.Pulmonary nodule classification in lung cancer screening with three-dimensional convolutional neural networks[J]. J Med Imaging (Bellingham), 2017, 4(4):041308.
[35] Nibali A, He Z, Wollersheim D.Wollersheim, Pulmonary nodule classification with deep residual networks[J]. Int J Comput Assist Radiol Surg, 2017, 12(10): 1799-1808.
[36] Lin H, Chen Z, Wang W.A pulmonary nodule view system for the Lung Image Database Consortium (LIDC)[J]. Acad Radiol, 2011, 18(9):1181-1185.