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Congress: ECR25
Poster Number: C-19104
Type: Poster: EPOS Radiologist (scientific)
Authorblock: M. Aymerich, B. González-Oblanca, A. Garcia Baizan, M. Otero Garcia; Vigo/ES
Disclosures:
Maria Aymerich: Nothing to disclose
Beatriz González-Oblanca: Nothing to disclose
Alejandra Garcia Baizan: Nothing to disclose
Milagros Otero Garcia: Nothing to disclose
Keywords: Artificial Intelligence, CT, CT-Quantitative, Computer Applications-General, Experimental investigations, Physics, Kv imaging
Conclusion

Following the described methodology, it was determined that the GLSZM, GLDM, and NGTDM groups do not contain any features that meet the established robustness criteria. In contrast, the First-Order and GLRLM groups each had two features that met the criteria in some of the analyses. Overall, radiomic features demonstrated specific repeatability and reproducibility when extracted from a phantom using fsCT. The results obtained are consistent with the reliability of these features in pCT. Additionally, this methodology can be applied to dual-energy CT using other technologies.

Limitations

This study presents several limitations that should be considered. First, all acquisitions were performed using a single CT model with a specific technology. Additionally, only one platform was analyzed for radiomic feature extraction, which could influence the robustness of these features, as indicated in previous studies such as Zhong et al. [10].

GALLERY