The latest research progress on early diagnosis of lung cancer according to CT-based computer intelligent analysis_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

JU Juan , LIN Meng , ZENG Xiangfei ,  ZHANG Jiuquan

Imaging Department, Chongqing University Cancer Hospital, Chongqing, 400030, P.R.China;

Corresponding author：

ZHANG Jiuquan, Email: 819337345@qq.com

Keywords：

Pulmonary nodules; lung cancer; CT; computer-aided diagnosis; artificial intelligence

DOI：

10.7507/1007-4848.202010054

Video：

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Lung cancer has brought tough challenges to human health due to its high incidence and mortality rate in the current practice. Nowadays, computed tomography (CT) imaging is still the most preferred diagnostic tool for early screening of lung cancer. However, a great challenge brought from accumulative CT imaging data can not meet the demand of the current clinical practice. As a novel kind of artificial intelligence technique aimed to deal with medical images, a computer-aided diagnosis has been found to provide useful auxiliary information, attenuate the workload of doctors, and significantly improve the efficiency and accuracy for clinical diagnosis of lung cancer. Therefore, an effective combination of computer-aided techniques and CT imaging has increasingly become an active area of investigation in early diagnosis of lung cancer. This review aims to summarize the latest progress on the diagnostic value of computer-aided technology with regard to early stage lung cancer from the perspectives of machine learning and deep learning.

Citation： JU Juan, LIN Meng, ZENG Xiangfei, ZHANG Jiuquan. The latest research progress on early diagnosis of lung cancer according to CT-based computer intelligent analysis. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2021, 28(3): 354-357. doi: 10.7507/1007-4848.202010054 Copy

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1. Wu F, Wang L, Zhou C. Lung cancer in China: current and prospect. Curr Opin Oncol, 2021, 33(1): 40-46.
2. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA Cancer J Clin, 2019, 69(1): 7-34.
3. Zheng S, Guo J, Cui X, et al. Automatic pulmonary nodule detection in CT scans using convolutional neural networks based on maximum intensity projection. IEEE Trans Med Imaging, 2020, 39(3): 797-805.
4. de Koning HJ, van der Aalst CM, de Jong PA, et al. Reduced lung-cancer mortality with volume CT screening in a randomized rrial. N Engl J Med, 2020, 382(6): 503-513.
5. 蔡顺达, 吴湘萍. 多排螺旋 CT 对肺小结节及早期肺癌的诊断价值. 临床医药文献电子杂志, 2019, 6(A4): 171-172.
6. 张述平, 李永立, 邢增, 等. 宝石 CT 高分辨率薄层扫描对早期肺癌的筛查. 影像研究与医学应用, 2018, 2(2): 31-32.
7. Loverdos K, Fotiadis A, Kontogianni C, et al. Lung nodules: A comprehensive review on current approach and management. Ann Thorac Med, 2019, 14(4): 226-238.
8. Massion PP, Antic S, Ather S, et al. Assessing the accuracy of a deep learning method to risk stratify indeterminate pulmonary nodules. Am J Respir Crit Care Med, 2020, 202(2): 241-249.
9. Agnes SA, Anitha J. Appraisal of deep-learning techniques on computer-aided lung cancer diagnosis with computed tomography screening. J Med Phys, 2020, 45(2): 98-106.
10. Kozuka T, Matsukubo Y, Kadoba T, et al. Efficiency of a computer-aided diagnosis (CAD) system with deep learning in detection of pulmonary nodules on 1-mm-thick images of computed tomography. Jpn J Radiol, 2020, 38(11): 1052-1061.
11. Bell L, Gandhi S. A comparison of computer-assisted detection (CAD) programs for the identification of colorectal polyps: performance and sensitivity analysis, current limitations and practical tips for radiologists. Clin Radiol, 2018, 73(6): 593.e11-593.e18.
12. Ziyad SR, Radha V, Vayyapuri T. Overview of computer aided detection and computer aided diagnosis systems for lung nodule detection in computed tomography. Curr Med Imaging Rev, 2020, 16(1): 16-26.
13. Lin CH, Wu JX, Li CM, et al. Enhancement of chest X-ray images to improve screening accuracy rate using iterated function system and multilayer fractional-order machine learning classifier. IEEE Photonics J, 2020, 12(4): 1.
14. Gu Q, Feng Z, Liang Q, et al. Machine learning-based radiomics strategy for prediction of cell proliferation in non-small cell lung cancer. Eur J Radiol, 2019, 118: 32-37.
15. Sakamoto T, Furukawa T, Lami K, et al. A narrative review of digital pathology and artificial intelligence: focusing on lung cancer. Transl Lung Cancer Res, 2020, 9(5): 2255-2276.
16. Li H, Galperin-Aizenberg M, Pryma D, et al. Unsupervised machine learning of radiomic features for predicting treatment response and overall survival of early stage non-small cell lung cancer patients treated with stereotactic body radiation therapy. Radiother Oncol, 2018, 129(2): 218-226.
17. 臧启元, 黄钢, 徐磊, 等. 基于机器学习与细胞形态学对癌细胞分类. 软件, 2019, 40(9): 81-83.
18. 王洪凯, 陈中华, 周纵苇, 等. 机器学习算法诊断 PET/CT 纵膈淋巴结性能评估. 浙江大学学报(工学版), 2018, 52(4): 788-797.
19. Hinton GE, Salakhutdinov RR. Reducing the dimensionality of data with neural networks. Science, 2006, 313(5786): 504-507.
20. Oliver A, Sheryl, Anuradha M, et al. An efficient coding network based feature extraction with support vector machine based classification model for CT lung images. J Med Imaging Health Inform, 2020, 10(11): 2628-2633.
21. Wang Y, Zhou L, Wang M, et al. Combination of generative adversarial network and convolutional neural network for automatic subcentimeter pulmonary adenocarcinoma classification. Quant Imaging Med Surg, 2020, 10(6): 1249-1264.
22. Venkatesan, Nikitha J, Nam, CS, et al. Lung nodule vlassification on CT images using deep convolutional neural network based on geometric feature extraction. Med Imeaging Health Infor, 2020, 10(9): 2042-2052.
23. Nobrega D, Raul-Victor M, Reboucas F, et al. Lung nodule malignancy classification in chest computed tomography images using transfer learning and convolutional neural networks. Neural Comput Appl, 2020, 32(15): 11065-11082.
24. Huang XF, Lei Q, Xie T, et al. Deep transfer convolutional neural network and extreme learning machine for lung nodule diagnosis on CT images. Knowledge-Based Systems, 2020, 204(27): 106230.
25. 罗嘉滢, 赵涓涓, 强彦, 等. 基于多特征广义深度自编码的肺结节诊断方法. 计算机工程与设计, 2019, 40(1): 154-160.
26. 王德才. 基于深度学习的肺癌检测方法研究. 数字技术与应用, 2020, 38(1): 85-89.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

The latest research progress on early diagnosis of lung cancer according to CT-based computer intelligent analysis

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