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Grading glioma by radiomics with feature selection based on mutual information

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Abstract

Grading of glioma is crucial for both treatment decisions and prognosis assessments. This study proposes a fast, simple, and accurate prediction framework for the non-invasive grading of glioma based on radiomics. The framework consists of four main steps. First, glioma images were subjected to semi-automatic segmentation to reduce the heavy workload. Then, 346 radiomics features were calculated from the segmented regions of interest. However, selecting features directly from such a large set to train the prediction model might lead to overfitting. Therefore, a de-redundancy algorithm was proposed to construct a candidate feature set based on mutual information. Finally, feature selection was executed using elastic net, and a grading model with linear regression was built. The proposed non-invasive solution for the grading of glioma can potentially hasten treatment decision, with the use of a de-redundancy algorithm that significantly improved the prediction accuracy. Experiments were conducted on 161 glioma samples from Henan Provincial People’s Hospital between 2012 and 2016, and results demonstrated the accurate grading effect and the generality of the de-redundancy algorithm. Moreover, the proposed framework exhibited desirable sensitivity (93.57%), specificity (86.53%), AUC (0.9638) and accuracy (91.30%).

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Notes

  1. ITK is an open-source, cross-platform system that provides developers with an extensive suite of software tools for image analysis. More details in https://itk.org/.

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Acknowledgements

This study was funded by National Natural Science Foundation of China (Grants 81772009, 91630206, 91330117, and 81720108021), the National Key Research and Development Program of China (Grant 2016YFB0201800), the China Postdoctoral Science Foundation (Grant 2016M590948), Social development, science and technology research projects in Shaanxi Province (Grant 2016SF-428), Henan Province Scientific and Technological Innovation Talents Project (Grant 164200510014), Henan Province Scientific and Technological Cooperation Project (Grant 152106000014).

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Authors and Affiliations

  1. School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, 710049, Shaanxi, China

    Yaping Wu & Weiguo Wu

  2. Collaborative Innovation Center for Internet Healthcare and School of Software and Applied Technology, Zhengzhou University, Zhengzhou, 450052, Henan, China

    Yaping Wu, Bo Liu, Yusong Lin & Cong Yang

  3. Department of Radiology, Zhengzhou University People’s Hospital and Henan Provincial People’s Hospital, Zhengzhou, 450003, Henan, China

    Meiyun Wang

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  1. Yaping Wu
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Correspondence to Weiguo Wu or Yusong Lin.

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Wu, Y., Liu, B., Wu, W. et al. Grading glioma by radiomics with feature selection based on mutual information. J Ambient Intell Human Comput 9, 1671–1682 (2018). https://doi.org/10.1007/s12652-018-0883-3

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