Abstract
The accuracy of glioma segmentation is significantly affected by the radiomics-based prediction model for grading glioma. This study proposed a robust feature selection method that can select stable and insensitive features to the segmentation of the region of interest (ROI). The method consists of three main steps. First, stable features are selected from 360 radiomics features based on the Pearson correlation coefficient. Then, an unsupervised K-means algorithm is introduced to remove redundant features from those selected in the first step and obtain sets of K group candidate features. Finally, by using these K group feature sets to train four prediction models, the final feature set and final prediction models that have the best prediction performance are selected. Experiments were conducted on 156 glioma samples from Henan Provincial People’s Hospital between 2012 and 2016, and 11 robust features were selected. The results demonstrated excellent accuracy, sensitivity, specificity, and AUC (0.88, 0.94, 0.88, and 0.85, respectively). Compare with the performance without feature selection, a 5% increase in accuracy, sensitivity, and AUC and 13% increase in specificity were observed. The proposed feature selection method can reduce the training time by 94.04%.
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Acknowledgements
This study was funded by the National Natural Science Foundation of China (Grant 81772009), Scientific and Technological Research Project of Henan Province (Grant 182102310162).
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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.
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Informed consent was obtained from all individual participants included in the study.
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Wu, Y. et al. (2019). Robust Feature Selection Method of Radiomics for Grading Glioma. In: Wu, C., Chyu, MC., Lloret, J., Li, X. (eds) Proceedings of the 2nd International Conference on Healthcare Science and Engineering . ICHSE 2018. Lecture Notes in Electrical Engineering, vol 536. Springer, Singapore. https://doi.org/10.1007/978-981-13-6837-0_2
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DOI: https://doi.org/10.1007/978-981-13-6837-0_2
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