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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| Hauptverfasser: | , , , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
05 June 2024
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| In: |
Frontiers in medicine
Year: 2024, Jahrgang: 11, Pages: 1-9 |
| ISSN: | 2296-858X |
| DOI: | 10.3389/fmed.2024.1407235 |
| Online-Zugang: | 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 |
| Verfasserangaben: | 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 |
| Zusammenfassung: | <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> |
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| Beschreibung: | Gesehen am 11.11.2024 |
| Beschreibung: | Online Resource |
| ISSN: | 2296-858X |
| DOI: | 10.3389/fmed.2024.1407235 |