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Extracting and Selecting Robust Radiomic Features from PET/MR Images in Nasopharyngeal Carcinoma

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Abstract

Objectives

This work aims to study the variation, robustness, and feature redundancy of PET/MR radiomic features in the primary tumor of nasopharyngeal carcinoma (NPC).

Procedures

PET/MR scans of 21 NPC patients were used in this study. The primary tumor volumes were defined using PET, T2-weighted-MR (T2-MR), and diffusion-weighted MR (DW-MR) images. A random-dilation-erosion method was used to simulate 10 sets of tumor volumes for identifying features invariant with manual segmentation uncertainties. Feature robustness was evaluated against imaging modalities, pixel sizes, slice thickness, and grey-level bin sizes using intraclass correlation coefficient (ICC) and spearman correlation coefficient. Feature redundancy was analyzed using the hierarchical cluster analysis.

Results

Voxel size of 0.5 × 0.5 × 1.0 mm3 was found optimal for robust feature extraction from PET and MR. Normalized grey level of 64 and 128 was suggested for PET and MR, respectively. The features from wavelet-transformed images were less stable than those from the original images. The robustness analysis and volume correlation analysis identified 335 (62.04 %) PET features, 240 (44.44 %) T2-MR features, and 366 (67.78 %) DW-MR features. The cluster analysis grouped PET, T2-MR, and DW-MR features into 106, 83, and 133 representative features, respectively.

Conclusions

The present study analyzed and identified robust features extracted from tumor volumes on PET/MR, which can provide guidance and promote standardization for PET/MR radiomic studies in NPC.

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Acknowledgments

The authors thank Caineng Cao and Xiaozhong Chen, Zhejiang Cancer Hospital and Yuanfan Xu, Hangzhou Universal Medical Imagine Diagnostion for generously providing the PET/MR dataset.

Funding

This work was supported by National Key R&D Program of China (2018YFE0114800), Natural Science Foundation of China (NSFC Grant Nos. 81871351, 81827804, 81950410632), and Zhejiang Provincial Natural Science Foundation of China (Grant No. LY17E050008).

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Author notes

  1. Pengfei Yang and Lei Xu contributed equally to this work.

Authors and Affiliations

  1. Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Institute of Translational Medicine, Zhejiang University, Hangzhou, Zhejiang, China

    Pengfei Yang, Lei Xu, Yidong Wan, Yi Xue, Yangkang Jiang, Eric Yen, Chen Luo & Jing Wang

  2. College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China

    Pengfei Yang & Zuozhen Cao

  3. Department of Radiation Oncology, University of California Davis Comprehensive Cancer Center, 4501 X Street, Suite 0152, Sacramento, CA, 95817, USA

    Yi Rong

  4. Nuclear & Radiological Engineering and Medical Physics Programs, Woodruff School of Mechanical Engineering, Georgia Institute of Technology, 770 State Street, Boggs 385, Atlanta, GA, 30332-0745, USA

    Tianye Niu

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  1. Pengfei Yang
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Yang, P., Xu, L., Cao, Z. et al. Extracting and Selecting Robust Radiomic Features from PET/MR Images in Nasopharyngeal Carcinoma. Mol Imaging Biol 22, 1581–1591 (2020). https://doi.org/10.1007/s11307-020-01507-7

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