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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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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DOI: https://doi.org/10.1007/s11307-020-01507-7