Deep learning-based glioma grading and feature visualization analysis

Research Article
Open access

Deep learning-based glioma grading and feature visualization analysis

Yunmeng Chai 1* , Yuehan Wang 2 , Wenqi Xue 3
  • 1 Faculty of Environment and Life, Beijing University of Technology, Beijing, 100124, China    
  • 2 School of Computer Science and Engineering, Tianjin University of Technology, Tianjin, 300382, China    
  • 3 School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou, Zhejiang, 310018, China    
  • *corresponding author chaiyunmeng@emails.bjut.edu.cn
Published on 31 January 2024 | https://doi.org/10.54254/2755-2721/31/20230173
ACE Vol.31
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-287-9
ISBN (Online): 978-1-83558-288-6

Abstract

Gliomas can be separated into high- and low-grade gliomas according to the classification method developed by the World Health Organization (WHO). Glioma classification is significantly related to prognosis, and accurate glioma classification is very important. This study aims to evaluate and verify the analytical performance of different models based on deep learning for glioma grading. Firstly, the glioma grading clinical and mutation feature data sets were included. The training cohort included 20 genes with the most common mutations and 2 clinical features in the Cancer Genome Atlas Low Grade Glioma (TCGA-LGG) and TCGA- Glioblastoma Multiforme (GBM) glioma projects. Then, four pre-trained models are used to extract deep learning features from the data. Preprocessing is used to reduce redundancy and select the most predictive value. To assess the performance, indexes, including the area under the data working curve (AUC) and the accuracy prediction value, are leveraged. Finally, the prediction performance of the test queue is compared to determine the optimal classification model

Keywords:

neural network, deep learning, glioma grading

Chai,Y.;Wang,Y.;Xue,W. (2024). Deep learning-based glioma grading and feature visualization analysis. Applied and Computational Engineering,31,296-302.
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References

[1]. Miklja, Z., Pasternak, A., Stallard, S., Nicolaides, T., Kline-Nunnally, C., Cole, et al. (2019). Molecular profiling and targeted therapy in pediatric gliomas: review and consensus recommendations. Neuro-oncology, 21(8), 968-980.

[2]. Youssef, G., & Miller, J. J. (2020). Lower grade gliomas. Current neurology and neuroscience reports, 20, 1-9.

[3]. Komori, T. (2022). Grading of adult diffuse gliomas according to the 2021 WHO Classification of Tumors of the Central Nervous System. Laboratory Investigation, 102(2), 126-133.

[4]. Mair, M. J., Geurts, M., van den Bent, M. J., & Berghoff, A. S. (2021). A basic review on systemic treatment options in WHO grade II-III gliomas. Cancer treatment reviews, 92, 102124.

[5]. Zhuge, Y., Ning, H., Mathen, P., Cheng, J. Y., Krauze, A. V., Camphausen, K., & Miller, R. W. (2020). Automated glioma grading on conventional MRI images using deep convolutional neural networks. Medical physics, 47(7), 3044-3053.

[6]. Montavon, G., Samek, W., & Müller, K. R. (2018). Methods for interpreting and understanding deep neural networks. Digital signal processing, 73, 1-15.

[7]. Wang, Z., Jensen, M. A., & Zenklusen, J. C. (2016). A practical guide to the cancer genome atlas (TCGA). Statistical Genomics: Methods and Protocols, 111-141.

[8]. Azmi, S. S., & Baliga, S. (2020). An overview of boosting decision tree algorithms utilizing AdaBoost and XGBoost boosting strategies. Int. Res. J. Eng. Technol, 7(5), 6867-6870.

[9]. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444.

[10]. Mathew, A., Amudha, P., & Sivakumari, S. (2021). Deep learning techniques: an overview. Advanced Machine Learning Technologies and Applications: Proceedings of AMLTA 2020, 599-608.

Cite this article

Chai,Y.;Wang,Y.;Xue,W. (2024). Deep learning-based glioma grading and feature visualization analysis. Applied and Computational Engineering,31,296-302.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation

ISBN：978-1-83558-287-9(Print) / 978-1-83558-288-6(Online)
Editor：Mustafa İSTANBULLU
Conference website: https://2023.confmla.org/
Conference date: 18 October 2023
Series: Applied and Computational Engineering
Volume number: Vol.31
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Miklja, Z., Pasternak, A., Stallard, S., Nicolaides, T., Kline-Nunnally, C., Cole, et al. (2019). Molecular profiling and targeted therapy in pediatric gliomas: review and consensus recommendations. Neuro-oncology, 21(8), 968-980.

[2]. Youssef, G., & Miller, J. J. (2020). Lower grade gliomas. Current neurology and neuroscience reports, 20, 1-9.

[3]. Komori, T. (2022). Grading of adult diffuse gliomas according to the 2021 WHO Classification of Tumors of the Central Nervous System. Laboratory Investigation, 102(2), 126-133.

[4]. Mair, M. J., Geurts, M., van den Bent, M. J., & Berghoff, A. S. (2021). A basic review on systemic treatment options in WHO grade II-III gliomas. Cancer treatment reviews, 92, 102124.

[5]. Zhuge, Y., Ning, H., Mathen, P., Cheng, J. Y., Krauze, A. V., Camphausen, K., & Miller, R. W. (2020). Automated glioma grading on conventional MRI images using deep convolutional neural networks. Medical physics, 47(7), 3044-3053.

[6]. Montavon, G., Samek, W., & Müller, K. R. (2018). Methods for interpreting and understanding deep neural networks. Digital signal processing, 73, 1-15.

[7]. Wang, Z., Jensen, M. A., & Zenklusen, J. C. (2016). A practical guide to the cancer genome atlas (TCGA). Statistical Genomics: Methods and Protocols, 111-141.

[8]. Azmi, S. S., & Baliga, S. (2020). An overview of boosting decision tree algorithms utilizing AdaBoost and XGBoost boosting strategies. Int. Res. J. Eng. Technol, 7(5), 6867-6870.

[9]. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444.

[10]. Mathew, A., Amudha, P., & Sivakumari, S. (2021). Deep learning techniques: an overview. Advanced Machine Learning Technologies and Applications: Proceedings of AMLTA 2020, 599-608.

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