Radiomics-driven spectral profiling of six kidney stone types with monoenergetic CT reconstructions in photon-counting CT
Objectives: Urolithiasis, a common and painful urological condition, is influenced by factors such as lifestyle, genetics, and medication. Differentiating between different types of kidney stones is crucial for personalized therapy. The purpose of this study is to investigate the use of photon-count...
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
12 December 2024
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| In: |
European radiology
Year: 2024, Pages: 1-11 |
| ISSN: | 1432-1084 |
| DOI: | 10.1007/s00330-024-11262-w |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.1007/s00330-024-11262-w
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| Author Notes: | Alexander Hertel, Matthias F. Froelich, Daniel Overhoff, Tim Nestler, Sebastian Faby, Markus Jürgens, Bernhard Schmidt, Abhinay Vellala, Albrecht Hesse, Dominik Nörenberg, Rico Stoll, Hans Schmelz, Stefan O. Schoenberg and Stephan Waldeck |
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| 245 | 1 | 0 | |a Radiomics-driven spectral profiling of six kidney stone types with monoenergetic CT reconstructions in photon-counting CT |c Alexander Hertel, Matthias F. Froelich, Daniel Overhoff, Tim Nestler, Sebastian Faby, Markus Jürgens, Bernhard Schmidt, Abhinay Vellala, Albrecht Hesse, Dominik Nörenberg, Rico Stoll, Hans Schmelz, Stefan O. Schoenberg and Stephan Waldeck |
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| 520 | |a Objectives: Urolithiasis, a common and painful urological condition, is influenced by factors such as lifestyle, genetics, and medication. Differentiating between different types of kidney stones is crucial for personalized therapy. The purpose of this study is to investigate the use of photon-counting computed tomography (PCCT) in combination with radiomics and machine learning to develop a method for automated and detailed characterization of kidney stones. This approach aims to enhance the accuracy and detail of stone classification beyond what is achievable with conventional computed tomography (CT) and dual-energy CT (DECT). Materials and methods: In this ex vivo study, 135 kidney stones were first classified using infrared spectroscopy. All stones were then scanned in a PCCT embedded in a phantom. Various monoenergetic reconstructions were generated, and radiomics features were extracted. Statistical analysis was performed using Random Forest (RF) classifiers for both individual reconstructions and a combined model. Results: The combined model, using radiomics features from all monoenergetic reconstructions, significantly outperformed individual reconstructions and SPP parameters, with an AUC of 0.95 and test accuracy of 0.81 for differentiating all six stone types. Feature importance analysis identified key parameters, including NGTDM_Strength and wavelet-LLH_firstorder_Variance. Conclusion: This ex vivo study demonstrates that radiomics-driven PCCT analysis can improve differentiation between kidney stone subtypes. The combined model outperformed individual monoenergetic levels, highlighting the potential of spectral profiling in PCCT to optimize treatment through image-based strategies. | ||
| 650 | 4 | |a Kidney stones | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Medical Imaging | |
| 650 | 4 | |a Photon-counting CT | |
| 650 | 4 | |a Radiomics | |
| 650 | 4 | |a Spectral profiling | |
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