RELIABILITY OF RADIOMIC FEATURES AGAINST NOISE IN THE USE OF DAUBECHIES WAVELET DERIVED FEATURES IN CT-BASED LIVER TUMOR

Rasyida Shabihah Zukro Aini; Ira Puspasari

doi:10.36456/best.vol5.no1.8026

Authors

Rasyida Shabihah Zukro Aini Universitas PGRI Adi Buana Surabaya
Ira Puspasari Computer System Department, Dinamika University

DOI:

https://doi.org/10.36456/best.vol5.no1.8026

Keywords:

radiomic, Daubechies, wavelet, white noise, liver tumor

Abstract

There are still many challenges in the field of radiomics, especially sensitivity which is influenced by many factors, one of which is noise. The large amount of bias noise that occurs reduces the performance of the diagnosis, perhaps because it does not involve wavelet derived features. The wavelet derived feature is believed to be more resistant to noise interference in the image. So, the proposed use of wavelet derived features accompanied by a comparative review of its effectiveness with the use of traditional radiomic features is implemented in this study. In this study, two liver tumor datasets were used, namely LiTS17 and 3D-IRCADb-01 data to test the reliability of features in different data. The two data are given the same treatment, namely by adding interference to the CT image by setting the SNR indicator. The label used is limited to the tumor part, the liver parenchyma is not included, so for LiTS17 a thresholding label is done first. CT images and tumor labels extracted data from traditional radiomic features and wavelet-derived radiomic features (Daubechies level 1). The statistical approach uses the ICC method in assessing agreement between observers. As a result, of the six feature groups (First-order, GLCM, GLSZM, GLDM, NGTDM, GLRLM) only NGTDM features are more effective on wavelet derived features. Whereas in the main LiTS17 data, the wavelet derived features do not have a major effect, traditional radiomic features sufficiently show ICC values in the good to excellent category.

References

A. S. Tagliafico, M. Piana, D. Schenone, R. Lai, A. M. Massone, and N. Houssami, “Overview of radiomics in breast cancer diagnosis and prognostication,” Breast, vol. 49, pp. 74–80, 2020, doi: 10.1016/j.breast.2019.10.018.

A. Somasundaram et al., “Mitigation of noise-induced bias of PET radiomic features,” PLoS One, vol. 17, no. 8 August, 2022, doi: 10.1371/journal.pone.0272643.

G. Palareti et al., “Comparison between different D-Dimer cutoff values to assess the individual risk of recurrent venous thromboembolism: Analysis of results obtained in the DULCIS study,” Int. J. Lab. Hematol., vol. 38, no. 1, pp. 42–49, 2016, doi: 10.1111/ijlh.12426.

H. Bagher-Ebadian, C. Liu, F. Siddiqui, B. Movsas, and I. J. Chetty, “On the Impact of Smoothing and Noise on Robustness of CT and CBCT Radiomics Features for Patients with Head and Neck Cancers,” Int. J. Radiat. Oncol., vol. 99, no. 2, p. S93, 2017, doi: 10.1016/j.ijrobp.2017.06.225.

Q. Chen, L. Wang, L. Wang, Z. Deng, J. Zhang, and Y. Zhu, “Glioma Grade Prediction Using Wavelet Scattering-Based Radiomics,” IEEE Access, vol. 8, pp. 106564–106575, 2020, doi: 10.1109/ACCESS.2020.3000895.

J. Zhou et al., “Predicting the response to neoadjuvant chemotherapy for breast cancer: Wavelet transforming radiomics in MRI,” BMC Cancer, vol. 20, no. 1, pp. 1–10, 2020, doi: 10.1186/s12885-020-6523-2.

R. S. Z. Aini, “Digilib ITB.” https://digilib.itb.ac.id/index.php/gdl/view/68643 (accessed May 12, 2023).

P. Bilic et al., “The Liver Tumor Segmentation Benchmark (LiTS),” no. January, 2019, [Online]. Available: http://arxiv.org/abs/1901.04056.

L. Soler et al., “3D Image Reconstruction for Comparison of Algorithm Database: a Patient-Specific Anatomical and Medical Image Database,” p. 67091, 2010.

J. J. M. Van Griethuysen et al., “Computational radiomics system to decode the radiographic phenotype,” Cancer Res., vol. 77, no. 21, pp. e104–e107, 2017, doi: 10.1158/0008-5472.CAN-17-0339.

M. Soufi, H. Arimura, and N. Nagami, “Article Type: Research Article Identification of optimal mother wavelets in survival prediction of lung cancer patients using wavelet decomposition-based radiomic features,” doi: 10.1002/mp.13202.

Y. Meyer, “Chapter 2 Daubechies wavelets.”

T. K. Koo and M. Y. Li, “A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research,” J. Chiropr. Med., 2015, doi: 10.1016/j.jcm.2016.02.012.

F. Prinzi, C. Militello, and V. Conti, “Impact of Wavelet Kernels on Predictive Capability of Radiomic Features : A Case Study on COVID-19 Chest X-ray Images,” 2023.