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Comparing quantitative image parameters between animal and clinical CT-scanners: a translational phantom study analysis

<sec id="sec1"><title>Purpose</title><p>This study compares phantom-based variability of extracted radiomics features from scans on a photon counting CT (PCCT) and an experimental animal PET/CT-scanner (Albira II) to investigate the potential of radiomics for transl...

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Main Authors: Vellala, Abhinay K. (Author) , Mogler, Carolin (Author) , Haag, Florian (Author) , Tollens, Fabian (Author) , Rudolf, Henning (Author) , Pietsch, Friedrich L. (Author) , Wängler, Carmen (Author) , Wängler, Björn (Author) , Schönberg, Stefan (Author) , Froelich, Matthias F. (Author) , Hertel, Alexander (Author)
Format: Article (Journal)
Language:English
Published: 05 June 2024
In: Frontiers in medicine
Year: 2024, Volume: 11, Pages: 1-9
ISSN:2296-858X
DOI:10.3389/fmed.2024.1407235
Online Access:Verlag, kostenfrei, Volltext: https://doi.org/10.3389/fmed.2024.1407235
Verlag, kostenfrei, Volltext: https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2024.1407235/full
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Author Notes:Abhinay Vellala, Carolin Mogler, Florian Haag, Fabian Tollens, Henning Rudolf, Friedrich Pietsch, Carmen Wängler, Björn Wängler, Stefan O. Schoenberg, Matthias F. Froelich and Alexander Hertel
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Summary:<sec id="sec1"><title>Purpose</title><p>This study compares phantom-based variability of extracted radiomics features from scans on a photon counting CT (PCCT) and an experimental animal PET/CT-scanner (Albira II) to investigate the potential of radiomics for translation from animal models to human scans. While oncological basic research in animal PET/CT has allowed an intrinsic comparison between PET and CT, but no 1:1 translation to a human CT scanner due to resolution and noise limitations, Radiomics as a statistical and thus scale-independent method can potentially close the critical gap.</p></sec><sec id="sec2"><title>Methods</title><p>Two phantoms were scanned on a PCCT and animal PET/CT-scanner with different scan parameters and then the radiomics parameters were extracted. A Principal Component Analysis (PCA) was conducted. To overcome the limitation of a small dataset, a data augmentation technique was applied. A Ridge Classifier was trained and a Feature Importance- and Cluster analysis was performed.</p></sec><sec id="sec3"><title>Results</title><p>PCA and Cluster Analysis shows a clear differentiation between phantom types while emphasizing the comparability of both scanners. The Ridge Classifier exhibited a strong training performance with 93% accuracy, but faced challenges in generalization with a test accuracy of 62%.</p></sec><sec id="sec4"><title>Conclusion</title><p>These results show that radiomics has great potential as a translational tool between animal models and human routine diagnostics, especially using the novel photon counting technique. This is another crucial step towards integration of radiomics analysis into clinical practice.</p></sec>
Item Description:Gesehen am 11.11.2024
Physical Description:Online Resource
ISSN:2296-858X
DOI:10.3389/fmed.2024.1407235

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