Effects of different wavelet filters on correlation and diagnostic performance of radiomics features.
10.11817/j.issn.1672-7347.2019.03.003
- Author:
Zixuan CHENG
1
,
2
;
Yanqi HUANG
3
;
Xiaomei HUANG
3
;
Xiaomei WU
3
;
Changhong LIANG
1
,
2
;
Zaiyi LIU
1
,
2
Author Information
1. School of Medicine, South China University of Technology, Guangzhou 510006
2. Department of Radiology, Guangdong Provincial People's Hospital, Guangzhou 510080, China.
3. Department of Radiology, Guangdong Provincial People's Hospital, Guangzhou 510080, China.
- Publication Type:Journal Article
- MeSH:
Colorectal Neoplasms;
Humans;
Lymphatic Metastasis;
Retrospective Studies
- From:
Journal of Central South University(Medical Sciences)
2019;44(3):244-250
- CountryChina
- Language:Chinese
-
Abstract:
To investigate the effects of different wavelet filters on correlation and diagnostic performance of radiomics features.
Methods: A total of 143 colorectal cancer (CRC) patients (64 positive in lymph node metastasis and 79 negative) with contrast-enhanced CT examination were recruited. After labeling the tumor area by experienced radiologists, radiomics wavelets features based on 48 different wavelets were extracted using in-house software coded by Matlab. The correlation coefficients of the features with same names between different wavelets were calculated and got the distribution of high-correlation features between each wavelet. The least absolute shrinkage and selection operator (LASSO) was used to build signatures between lymph node metastasis and wavelet features data set based on different wavelets. The numbers of features in signatures and diagnostic performance were compared using Delong's test.
Results: With the difference of wavelet order increased, the number of high-correlation features between two wavelets decreased. Some features were prone to high correlation between different wavelets. When building radiomics signature based on single wavelet, signatures built from 'rbio2.2', 'sym7' and 'db7' did well in predicting lymph node metastasis. The signature based on Daubechies wavelet feature set had the highest performance in predicting lymph node metastasis, while the signature from Biorthogonal wavelet features was worst. Improvement was significant in diagnostic performance after excluding the high-correlation features in the whole features set (P=0.004).
Conclusion: In order to reduce the data redundancy of features, it is recommended to select wavelets with large differences in wavelet orders when calculating radiomics wavelet features. It is necessary to remove high correlation features for improving the diagnostic performance of radiomics signature.
