Identification of kidney stone types by deep learning integrated with radiomics features_Journal of Biomedical Engineering

Authors：

SUN Chao ¹ , NI Jun ¹ , LIU Jianhe ² , LI Huafeng ³ ,  TAO Dapeng ¹

1. School of Information, Yunnan University, Kunming 650500, P. R. China;
2. Department of Urology, The Second Affiliated Hospital of Kunming Medical University, Kunming 650500, P. R. China;
3. Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, P. R. China;

Corresponding author：

TAO Dapeng, Email: dptao@ynu.edu.cn

Keywords：

Image classification; Infectious kidney stones; Deep learning; Machine learning; Radiomics

DOI：

10.7507/1001-5515.202310043

Video：

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Abstract Full text Figures/Tables Video References Cited by

Currently, the types of kidney stones before surgery are mainly identified by human beings, which directly leads to the problems of low classification accuracy and inconsistent diagnostic results due to the reliance on human knowledge. To address this issue, this paper proposes a framework for identifying types of kidney stones based on the combination of radiomics and deep learning, aiming to achieve automated preoperative classification of kidney stones with high accuracy. Firstly, radiomics methods are employed to extract radiomics features released from the shallow layers of a three-dimensional (3D) convolutional neural network, which are then fused with the deep features of the convolutional neural network. Subsequently, the fused features are subjected to regularization, least absolute shrinkage and selection operator (LASSO) processing. Finally, a light gradient boosting machine (LightGBM) is utilized for the identification of infectious and non-infectious kidney stones. The experimental results indicate that the proposed framework achieves an accuracy rate of 84.5% for preoperative identification of kidney stone types. This framework can effectively distinguish between infectious and non-infectious kidney stones, providing valuable assistance in the formulation of preoperative treatment plans and the rehabilitation of patients after surgery.

Citation： SUN Chao, NI Jun, LIU Jianhe, LI Huafeng, TAO Dapeng. Identification of kidney stone types by deep learning integrated with radiomics features. Journal of Biomedical Engineering, 2024, 41(6): 1213-1220. doi: 10.7507/1001-5515.202310043 Copy

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1. Coe F L, Evan A, Worcester E. Kidney stone disease. J Clin Invest, 2005, 115(10): 2598-2608.
2. Evan A P, Worcester E M, Coe F L, et al. Mechanisms of human kidney stone formation. Urolithiasis, 2015, 43(1): 19-32.
3. Lambin P, Leijenaar R T H, Deist T M, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nature Reviews Clinical Oncology, 2017, 14(12): 749-762.
4. Mayerhoefer M E, Materka A, Langs G, et al. Introduction to radiomics. Journal of Nuclear Medicine, 2020, 61(4): 488-495.
5. Kumar V, Gu Y, Basu S, et al. Radiomics: the process and the challenges. Magnetic Resonance Imaging, 2012, 30(9): 1234-1248.
6. Yip S S, Aerts H J. Applications and limitations of radiomics. Physics in Medicine & Biology, 2016, 61(13): R150.
7. Huynh E, Coroller T P, Narayan V, et al. CT-based radiomic analysis of stereotactic body radiation therapy patients with lung cancer. Radiotherapy and Oncology, 2016, 120(2): 258-266.
8. 李琼, 柏正尧, 刘莹芳. 糖尿病性视网膜图像的深度学习分类方法. 中国图象图形学报, 2018, 23(10): 1594-1603.
9. Zheng J, Yu H, Batur J, et al. A multicenter study to develop a non-invasive radiomic model to identify urinary infection stone in vivo using machine-learning. Kidney International, 2021, 100(4): 870-880.
10. Shen D, Wu G, Suk H I. Deep learning in medical image analysis. Annual Review of Biomedical Engineering, 2017, 19: 221-248.
11. 施俊, 汪琳琳, 王珊珊, 等. 深度学习在医学影像中的应用综述. 中国图象图形学报, 2020, 25(10): 1953-1981.
12. Litjens G, Kooi T, Bejnordi B E, et al. A survey on deep learning in medical image analysis. Medical Image Analysis, 2017, 42: 60-88.
13. Razzak M I, Naz S, Zaib A. Deep learning for medical image processing: overview, challenges and the future. Classification in BioApps: Automation of Decision Making, 2018, 26: 323-350.
14. 何雪英, 韩忠义, 魏本征. 基于深度学习的乳腺癌病理图像自动分类. 计算机工程与应用, 2018, 54(12): 121-125.
15. Billones C D, Demetria O J L D, Hostallero D E D, et al. DemNet: a convolutional neural network for the detection of Alzheimer's disease and mild cognitive impairment//2016 IEEE Region 10 Conference (TENCON). Marina Bay Sands: IEEE, 2016: 3724-3727.
16. Gao X W, Hui R, Tian Z. Classification of CT brain images based on deep learning networks. Computer Methods and Programs in Biomedicine, 2017, 138: 49-56.
17. Tibshirani R. Regression shrinkage and selection via the Lasso. Journal of the Royal Statistical Society Series B: Statistical Methodology, 1996, 58(1): 267-288.
18. Ke G, Meng Q, Finley T, et al. Lightgbm: a highly efficient gradient boosting decision tree//Proceedings of the 31st International Conference on Neural Information Processing Systems. New York: ACM, 2017, 3149–3157.
19. Wright S J. Coordinate descent algorithms. Mathematical Programming, 2015, 151(1): 3-34.
20. Myers L, Sirois M J. Spearman correlation coefficients, differences between. Encyclopedia of Statistical Sciences, 2006. DOI: 10.1002/0471667196.ess5050.
21. Wold S, Esbensen K, Geladi P. Principal component analysis. Chemometrics and Intelligent Laboratory Systems, 1987, 2(1-3): 37-52.
22. Grunkemeier G L, Wu Y, Furnary A P. What is the value of a p value?. Ann Thorac Surg, 2009, 87(5): 1337-1343.
23. Rigatti S J. Random forest. Journal of Insurance Medicine, 2017, 47(1): 31-39.
24. Geurts P, Ernst D, Wehenkel L. Extremely randomized tree. Machine Learning, 2006, 63(1): 3-42.
25. Chen T, He T, Benesty M, et al. Xgboost: extreme gradient boosting. R Package Version 0.4-2, 2015, 1(4): 1-4.
26. Huo X, Sun G, Tian S, et al. HiFuse: hierarchical multi-scale feature fusion network for medical image classification. Biomedical Signal Processing and Control, 2024, 87: 105534.
27. Xu J, Pan Y, Pan X, et al. RegNet: self-regulated network for image classification. IEEE Transactions on Neural Networks and Learning Systems, 2022, 34(11): 9562-9567.
28. Wang F, Jiang M, Qian C, et al. Residual attention network for image classification//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Hawaii: IEEE, 2017: 3156-3164.
29. Tatsunami Y, Taki M. Sequencer: deep lstm for image classification//Proceedings of the 36th International Conference on Neural Information Processing Systems, New York: ACM, 2022, 38204-38217.
30. Chen C F R, Fan Q, Panda R. Crossvit: cross-attention multi-scale vision transformer for image classification//Proceedings of the IEEE/CVF International Conference on Computer Vision. Venice: IEEE, 2021: 357-366.

Journal of Biomedical Engineering

Identification of kidney stone types by deep learning integrated with radiomics features

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