Preoperative Grading of Brain Gliomas Using 3D-ResNet18 Based on Multimodal MRI and Attention Mechanism
DOI:
https://doi.org/10.12974/2313-1047.2024.11.02Keywords:
Glioma classification, 3D-residual networks, Magnetic resonance imaging, Deep learning, Multimodal magnetic resonance imagingAbstract
In response to the prevalent challenge of imprecise preoperative glioma grading prediction, a multimodal Magnetic Resonance Imaging (MRI) image with attention mechanism prediction model based on Residual Network (ResNet) is proposed for accurate prediction. To achieve this, publicly accessible datasets from BraTS 2019 and BraTS 2020 were employed. MRI images across three modalities underwent preprocessing, cropping, and subsequent stacking to generate comprehensive multimodal representations. Meanwhile, An Efficient Channel Attention (ECA) mechanism based on 3D data (3D-ECA) is proposed and introduced to reduce the problem of slow network convergence and overfitting. Notably, experiments demonstrate that the 3D-ECA attention mechanism added to the model improves the network training speed and the classification accuracy of the model, and the classification accuracy reached 88.1%, with an impressive area under the subject working characteristic curve of 0.918. Therefore, the 3D-ResNet18 deep learning model, incorporating multimodal MRI and 3D-ECA attention mechanisms, demonstrates high accuracy and robustness in distinguishing between high-grade and low-grade gliomas. Thus, showcasing significant clinical potential for glioma classification and diagnosis.
References
Jiang T, Nam D H, Ram Z, et al. Clinical practice guidelines for the management of adult diffuse gliomas[J]. Cancer Lett, 2021, 499:60-72. https://doi.org/10.1016/j.canlet.2020.10.050
Louis D N, Perry A, Reifenberger G, et al. The 2016 World Health Organization Classification of Tumors of the Central Nervous System:a summary[J]. Acta Neuropathol, 2016, 131(6): 803-820. https://doi.org/10.1007/s00401-016-1545-1
Su C Q, Lu S S, Han Q Y, et al. Intergrating conventional MRI,texture analysis of dynamic contrast-enhanced MRI, and
susceptibility weighted imaging for glioma grading[J]. Acta Radiol, 2019, 60(6): 777-787. https://doi.org/10.1177/0284185118801127
Zeineldin, R. A., Karar, M. E., Coburger, J., Wirtz, C. R. & Burgert, O. Deep Seg: deep neural network framework for automatic brain tumor segmentation using magnetic resonance FLAIR images. Int. J. Comput. Assist Radiol. Surg. 15, 909-920. https://doi.org/10.1007/s11548-020-02186-z
Ahammed Muneer K V, Rajendran V R, K P J. Glioma Tumor Grade Identification Using Artificial Intelligent Techniques[J]. J Med Syst, 2019, 43(5): 113. https://doi.org/10.1007/s10916-019-1228-2
Naser MA, Deen MJ. Brain tumor segmentation and grading of lowergrade glioma using deep learning in MRI images[J]. Comput Biol Med, 2020, 121:103758. https://doi.org/10.1016/j.compbiomed.2020.103758
He K M,Zhang X Y,Ren S Q,et al.Deep residual learning for image recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).June 27-30,2016,Las Vegas,NV,USA.IEEE,2016:770-778. https://doi.org/10.1109/CVPR.2016.90
Wang Q, Wu B, Zhu P, et al. ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks.[J]. CoRR,2019,abs/1910.03151. https://doi.org/10.1109/CVPR42600.2020.01155
Zha C, Meng X, LI L, et al. Neutrophil extracellular traps mediate the crosstalk between glioma progression and the tumor microenvironment via the HMGB1/RAGE/IL-8 axis[J]. Cancer Biol Med, 2020, 17(1): 154-168. https://doi.org/10.20892/j.issn.2095-3941.2019.0353
Mzoughi H, Njeh I, Wali A, et al. Deep Multi-Scale 3D Convolutional Neural Network(CNN)for MRI Gliomas Brain Tumor Classification[J]. J Digit Imaging, 2020, 33(4): 903-915. https://doi.org/10.1007/s10278-020-00347-9
Singh G, Manjila S, Sakla N, et al. Radiomics and radiogenomics in gliomas:a contemporary update[J]. Br J Cancer, 2021, 125(5): 641-657. https://doi.org/10.1038/s41416-021-01387-w
Zhuge Y, Ning H, Mathen P, et al. Automated glioma grading on conventional MRI images using deep convolutional neural networks[J].Med Phys, 2020, 47(7): 3044-3053. https://doi.org/10.1002/mp.14168
Yang Y, Yan L F, Zhang X, et al. Glioma Grading on Conventional MR Images:A Deep Learning Study With Transfer Learning[J]. Front Neurosci, 2018, 12: 804. https://doi.org/10.3389/fnins.2018.00804
LI Y, Wei D, Liu X, et al. Molecular subtyping of diffuse gliomas using magnetic resonance imaging:comparison and correlation between radiomics and deep learning[J]. Eur Radiol, 2022, 32(2): 747-758. https://doi.org/10.1007/s00330-021-08237-6
Shin I, Kim H, Ahn S S, et al. Development and Validation of a Deep Learning-Based Model to Distinguish Glioblastoma from Solitary Brain Metastasis Using Conventional MR Images[J]. AJNR Am J Neuroradiol, 2021, 42(5): 838-844. https://doi.org/10.3174/ajnr.A7003
Bangalore Yogananda C G, Shah B R, Vejdani-Jahromi M, et al. A novel fully automated MRI-based deep-learning method for classification of IDH mutation status in brain gliomas[J]. Neuro Oncol, 2020, 22(3): 402-411. https://doi.org/10.1093/neuonc/noz199
YU J, Yang B, Wang J, et al. 2D CNN versus 3D CNN for false-positive reduction in lung cancer screening[J]. J Med Imaging(Bellingham), 2020, 7(5): 051202. https://doi.org/10.1117/1.JMI.7.5.051202
Zeineldin, R.A., Karar, M.E., Elshaer, Z. et al. Explainable hybrid vision transformers and convolutional network for multimodal glioma segmentation in brain MRI. Sci Rep 14, 3713 (2024). https://doi.org/10.1038/s41598-024-54186-7
Babu Vimala, B., Srinivasan, S., Mathivanan, S.K. et al. Detection and classification of brain tumor using hybrid deep learning models. Sci Rep 13, 23029 (2023). https://doi.org/10.1038/s41598-023-50505-6
Ranjbarzadeh, R., Bagherian Kasgari, A., Jafarzadeh Ghoushchi, S. et al. Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images. Sci Rep 11: 10930 (2021). https://doi.org/10.1038/s41598-021-90428-8