Application of machine learning algorithm in clinical diagnosis and survival prognosis analysis of lung cancer_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

XU Jiaxin ¹ , QIAN Kai ² ,  JIANG Lihong ²

1. Department of Cardiothoracic Surgery, Yan'an Hospital Affiliated to Kunming Medical University, Kunming, 650051, P. R. China;
2. Department of Thoracic Surgery, Yunnan First People's Hospital, Kunming, 650051, P. R. China;

Corresponding author：

JIANG Lihong, Email: jlh15198763375@163.com

Keywords：

Lung cancer; machine learning; neural network; support vector machine; review

DOI：

10.7507/1007-4848.202102036

Video：

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Lung cancer is one of the tumors with the highest incidence rate and mortality rate in the world. It is also the malignant tumor with the fastest growing number of patients, which seriously threatens human life. How to improve the accuracy of diagnosis and treatment of lung cancer and the survival prognosis is particularly important. Machine learning is a multi-disciplinary interdisciplinary specialty, covering the knowledge of probability theory, statistics, approximate theory and complex algorithm. It uses computer as a tool and is committed to simulating human learning methods, and divides the existing content into knowledge structures to effectively improve learning efficiency and being able to integrate computer science and statistics into medical problems. Through the introduction of algorithm to absorb the input data, and the application of computer analysis to predict the output value within the acceptable accuracy range, identify the patterns and trends in the data, and finally learn from previous experience, the development of this technology brings a new direction for the diagnosis and treatment of lung cancer. This article will review the performance and application prospects of different types of machine learning algorithms in the clinical diagnosis and survival prognosis analysis of lung cancer.

Citation： XU Jiaxin, QIAN Kai, JIANG Lihong. Application of machine learning algorithm in clinical diagnosis and survival prognosis analysis of lung cancer. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2022, 29(6): 777-781. doi: 10.7507/1007-4848.202102036 Copy

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1. Wu KG. Analysis of conditional logistic regression for risk factors of lung cancer in Dachang Tin Mine. Zhonghua Zhong Liu Za Zhi, 1989, 11(2): 124-126.
2. Liao X, Kerr D, Morales J, et al. Application of machine learning to identify clustering of cardiometabolic risk factors in U. S. adults. Diabetes Technol Ther, 2019, 21(5): 245-253.
3. Romero MP, Chang YM, Brunton LA, et al. Decision tree machine learning applied to bovine tuberculosis risk factors to aid disease control decision making. Prev Vet Med, 2020, 175: 104860.
4. Hotzy F, Theodoridou A, Hoff P, et al. Machine learning: An approach in identifying risk factors for coercion compared to binary logistic regression. Front Psychiatry, 2018, 9: 258.
5. Podolsky MD, Barchuk AA, Kuznetcov VI, et al. Evaluation of machine learning algorithm utilization for lung cancer classification based on gene expression levels. Asian Pac J Cancer Prev, 2016, 17(2): 835-838.
6. Wang W, Ding M, Duan X, et al. Diagnostic value of plasma microRNAs for lung cancer using support vector machine model. J Cancer, 2019, 10(21): 5090-5098.
7. Sherafatian M, Arjmand F. Decision tree-based classifiers for lung cancer diagnosis and subtyping using TCGA miRNA expression data. Oncol Lett, 2019, 18(2): 2125-2131.
8. Zhang X, Zhang Q, Wang X, et al. Structured sparse logistic regression with application to lung cancer prediction using breath volatile biomarkers. Stat Med, 2020, 39(7): 955-967.
9. Ligor T, Pater Ł, Buszewski B. Application of an artificial neural network model for selection of potential lung cancer biomarkers. J Breath Res, 2015, 9(2): 027106.
10. Sakumura Y, Koyama Y, Tokutake H, et al. Diagnosis by volatile organic compounds in exhaled breath from lung cancer patients using support vector machine algorithm. Sensors (Basel), 2017, 17(2): 287.
11. Lombardi C, Tassi GF, Pizzocolo G, et al. Clinical significance of a multiple biomarker assay in patients with lung cancer. A study with logistic regression analysis. Chest, 1990, 97(3): 639-644.
12. Xie Y, Meng WY, Li RZ, et al. Early lung cancer diagnostic biomarker discovery by machine learning methods. Transl Oncol, 2021, 14(1): 100907.
13. Zhang C, Sun X, Dang K, et al. Toward an expert level of lung cancer detection and classification using a deep convolutional neural network. Oncologist, 2019, 24(9): 1159-1165.
14. Moitra D, Mandal RK. Automated AJCC (7th edition) staging of non-small cell lung cancer (NSCLC) using deep convolutional neural network (CNN) and recurrent neural network (RNN). Health Inf Sci Syst, 2019, 7(1): 14.
15. Onishi Y, Teramoto A, Tsujimoto M, et al. Multiplanar analysis for pulmonary nodule classification in CT images using deep convolutional neural network and generative adversarial networks. Int J Comput Assist Radiol Surg, 2020, 15(1): 173-178.
16. Baldwin DR, Gustafson J, Pickup L, et al. External validation of a convolutional neural network artificial intelligence tool to predict malignancy in pulmonary nodules. Thorax, 2020, 75(4): 306-312.
17. Gao N, Tian S, Li X, et al. Three-dimensional texture feature analysis of pulmonary nodules in CT images: Lung cancer predictive models based on support vector machine classifier. J Digit Imaging, 2020, 33(2): 414-422.
18. Wang S, Wang T, Yang L, et al. ConvPath: A software tool for lung adenocarcinoma digital pathological image analysis aided by a convolutional neural network. EBioMedicine, 2019, 50: 103-110.
19. Wang S, Yang DM, Rong R, et al. Artificial intelligence in lung cancer pathology image analysis. Cancers (Basel), 2019, 11(11): 1673.
20. Feng PH, Lin YT, Lo CM. A machine learning texture model for classifying lung cancer subtypes using preliminary bronchoscopic findings. Med Phys, 2018, 45(12): 5509-5514.
21. Sim JA, Yun YH. Predicting disease-free lung cancer survival using patient reported outcome (PRO) measurements with comparisons of five machine learning techniques (MLT). Stud Health Technol Inform, 2019, 264: 1588-1589.
22. Sim JA, Kim YA, Kim JH, et al. The major effects of health-related quality of life on 5-year survival prediction among lung cancer survivors: Applications of machine learning. Sci Rep, 2020, 10(1): 10693.
23. Doppalapudi S, Qiu RG, Badr Y. Lung cancer survival period prediction and understanding: Deep learning approaches. Int J Med Inform, 2021, 148: 104371.
24. Topol EJ. High-performance medicine: The convergence of human and artificial intelligence. Nat Med, 2019, 25(1): 44-56.
25. Buch VH, Ahmed I, Maruthappu M. Artificial intelligence in medicine: Current trends and future possibilities. Br J Gen Pract, 2018, 68(668): 143-144.
26. He J, Baxter SL, Xu J, et al. The practical implementation of artificial intelligence technologies in medicine. Nat Med, 2019, 25(1): 30-36.
27. Combi C. Editorial from the new editor-in-chief: Artificial intelligence in medicine and the forthcoming challenges. Artif Intell Med, 2017, 76: 37-39.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Application of machine learning algorithm in clinical diagnosis and survival prognosis analysis of lung cancer

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