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Title: | Comparing effectiveness of image perturbation and test retest imaging in improving radiomic model reliability | Authors: | Zhang, J Teng, XZ Zhang, XY Lam, SK Lin, ZS Liang, YY Yu, H Siu, SWK Chang, ATY Zhang, H Kong, FM Yang, RJ Cai, J |
Issue Date: | 2023 | Source: | Scientific reports, 2023, v. 13, 18263 | Abstract: | Image perturbation is a promising technique to assess radiomic feature repeatability, but whether it can achieve the same effect as test-retest imaging on model reliability is unknown. This study aimed to compare radiomic model reliability based on repeatable features determined by the two methods using four different classifiers. A 191-patient public breast cancer dataset with 71 test-retest scans was used with pre-determined 117 training and 74 testing samples. We collected apparent diffusion coefficient images and manual tumor segmentations for radiomic feature extraction. Random translations, rotations, and contour randomizations were performed on the training images, and intra-class correlation coefficient (ICC) was used to filter high repeatable features. We evaluated model reliability in both internal generalizability and robustness, which were quantified by training and testing AUC and prediction ICC. Higher testing performance was found at higher feature ICC thresholds, but it dropped significantly at ICC = 0.95 for the test-retest model. Similar optimal reliability can be achieved with testing AUC = 0.7-0.8 and prediction ICC > 0.9 at the ICC threshold of 0.9. It is recommended to include feature repeatability analysis using image perturbation in any radiomic study when test-retest is not feasible, but care should be taken when deciding the optimal feature repeatability criteria. | Publisher: | Nature Publishing Group | Journal: | Scientific reports | EISSN: | 2045-2322 | DOI: | 10.1038/s41598-023-45477-6 | Rights: | Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. © The Author(s) 2023 The following publication Zhang, J., Teng, X., Zhang, X. et al. Comparing effectiveness of image perturbation and test retest imaging in improving radiomic model reliability. Sci Rep 13, 18263 (2023) is available at https://dx.doi.org/10.1038/s41598-023-45477-6. |
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