Reproducibility of radiomic features using network analysis and its application in Wasserstein k-means clustering Journal Article
| Authors: | Oh, J. H.; Apte, A. P.; Katsoulakis, E.; Riaz, N.; Hatzoglou, V.; Yu, Y.; Mahmood, U.; Veeraraghavan, H.; Pouryahya, M.; Iyer, A.; Shukla-Dave, A.; Tannenbaum, A.; Lee, N. Y.; Deasy, J. O. |
| Article Title: | Reproducibility of radiomic features using network analysis and its application in Wasserstein k-means clustering |
| Abstract: | Purpose: The goal of this study is to develop innovative methods for identifying radiomic features that are reproducible over varying image acquisition settings. Approach: We propose a regularized partial correlation network to identify reliable and reproducible radiomic features. This approach was tested on two radiomic feature sets generated using two different reconstruction methods on computed tomography (CT) scans from a cohort of 47 lung cancer patients. The largest common network component between the two networks was tested on phantom data consisting of five cancer samples. To further investigate whether radiomic features found can identify phenotypes, we propose a k-means clustering algorithm coupled with the optimal mass transport theory. This approach following the regularized partial correlation network analysis was tested on CT scans from 77 head and neck squamous cell carcinoma (HNSCC) patients in the Cancer Imaging Archive (TCIA) and validated using an independent dataset. Results: A set of common radiomic features was found in relatively large network components between the resultant two partial correlation networks resulting from a cohort of lung cancer patients. The reliability and reproducibility of those radiomic features were further validated on phantom data using the Wasserstein distance. Further analysis using the network-based Wasserstein k-means algorithm on the TCIA HNSCC data showed that the resulting clusters separate tumor subsites as well as HPV status, and this was validated on an independent dataset. Conclusion: We showed that a network-based analysis enables identifying reproducible radiomic features and use of the selected set of features can enhance clustering results. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. |
| Keywords: | reproducibility; computerized tomography; medical imaging; biological organs; diseases; phantoms; k-means clustering; reconstruction method; statistical mechanics; correlation methods; head-and-neck squamous cell carcinoma; computed tomography scan; radiomics; wasserstein distance; optimal mass transport; network-based wasserstein k-means clustering; network components; clustering results; network-based analysis; partial correlation |
| Journal Title: | Journal of Medical Imaging |
| Volume: | 8 |
| Issue: | 3 |
| ISSN: | 2329-4302 |
| Publisher: | SPIE |
| Date Published: | 2021-05-01 |
| Start Page: | 031904 |
| Language: | English |
| DOI: | 10.1117/1.Jmi.8.3.031904 |
| PROVIDER: | scopus |
| PMCID: | PMC8085581 |
| PUBMED: | 33954225 |
| DOI/URL: | |
| Notes: | Article -- Export Date: 2 August 2021 -- Source: Scopus |
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