How dependent are CT radiomic features on CT scan parameters?

Hyeongmin Jin; Jong Hyo Kim

doi:10.1117/12.2521488

27 March 2019 How dependent are CT radiomic features on CT scan parameters?

Hyeongmin Jin, Jong Hyo Kim

Proceedings Volume 11050, International Forum on Medical Imaging in Asia 2019; 110500D (2019) https://doi.org/10.1117/12.2521488
Event: 2019 Joint International Workshop on Advanced Image Technology (IWAIT) and International Forum on Medical Imaging in Asia (IFMIA), 2019, Singapore, Singapore

Abstract

Radiomics is attracting research interests for characterization of the tumor phenotype as well as for prediction of patient outcome. However, many radiomic features are known to be affected by a multitude of variability sources, such as CT acquisition parameters, which might lead to false discovery if unknowingly used. Therefore, in order to avoid such pitfalls, the appropriate selection of robust features is an essential task in radiomic studies. We investigate the variability of CT imaging features which were previously reported as radiomic markers in non- small cell lung cancer (NSCLC). We scanned a standardized phantom with 64-slice multi-detector CT scanner with various scan conditions. We extracted forty-seven radiomic features including two texture features and first order statistics. Feature variability index was measured to evaluate the feature robustness depending on the scan parameters. The proportion of feature less effect on kernel was observed to only 32%. Our study revealed a high variability of CT image features depending on technical parameters. These characteristics should be considered in the feature extraction procedure when different protocols are used in the patient dataset. Use of the same CT protocol is preferred. Otherwise, the application of kernel normalization techniques is necessary for the radiomic study.

Citation Download Citation

Hyeongmin Jin and Jong Hyo Kim "How dependent are CT radiomic features on CT scan parameters?", Proc. SPIE 11050, International Forum on Medical Imaging in Asia 2019, 110500D (27 March 2019); https://doi.org/10.1117/12.2521488

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