Comparing effectiveness of image perturbation and test retest imaging in improving radiomic model reliability.

Autor: Zhang J; Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Y920, Lee Shau Kee Building, Hung Hom, Kowloon, Hong Kong, China., Teng X; Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Y920, Lee Shau Kee Building, Hung Hom, Kowloon, Hong Kong, China., Zhang X; Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Y920, Lee Shau Kee Building, Hung Hom, Kowloon, Hong Kong, China., Lam SK; Department of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong, China.; Research Institute for Smart Ageing, The Hong Kong Polytechnic University, Hong Kong, China., Lin Z; Shenzhen Institute for Drug Control (Shenzhen Testing Center of Medical Devices), Shenzhen, China., Liang Y; Shenzhen Institute for Drug Control (Shenzhen Testing Center of Medical Devices), Shenzhen, China., Yu H; Institute of Biomedical and Health Engineering, Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology, Shenzhen, China., Siu SWK; Department of Clinical Oncology, The University of Hong Kong, Hong Kong, China., Chang ATY; Department of Clinical Oncology, The University of Hong Kong, Hong Kong, China., Zhang H; Beijing Linking Medical Technology Co., Ltd., Beijing, China., Kong FM; Department of Clinical Oncology, The University of Hong Kong, Hong Kong, China.; Department of Clinical Oncology, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China., Yang R; Department of Radiation Oncology, Cancer Center, Peking University Third Hospital, Beijing, China., Cai J; Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Y920, Lee Shau Kee Building, Hung Hom, Kowloon, Hong Kong, China. jing.cai@polyu.edu.hk.; Research Institute for Smart Ageing, The Hong Kong Polytechnic University, Hong Kong, China. jing.cai@polyu.edu.hk.; The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen, China. jing.cai@polyu.edu.hk.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2023 Oct 25; Vol. 13 (1), pp. 18263. Date of Electronic Publication: 2023 Oct 25.
DOI: 10.1038/s41598-023-45477-6
Abstrakt: 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.
(© 2023. Springer Nature Limited.)
Databáze: MEDLINE
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