Unmasking pancreatic cancer: advanced biomedical imaging for its detection in native versus arterial dual-energy computed tomography (DECT) scans
This study investigates the potential of a machine learning classifier using dual- energy computed tomography (DECT) radiomics to differentiate between malignant pancreatic lesions and normal pancreas tissue. A total of 100 patients who underwent third-generation DECT between November 2018 and Octob...
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| Hauptverfasser: | , , , , , , , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
14 February 2024
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| In: |
International journal of imaging systems and technology
Year: 2024, Jahrgang: 34, Heft: 2, Pages: 1-9 |
| ISSN: | 1098-1098 |
| DOI: | 10.1002/ima.23037 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1002/ima.23037 Verlag, kostenfrei, Volltext: https://onlinelibrary.wiley.com/doi/abs/10.1002/ima.23037 |
| Verfasserangaben: | Jennifer Gotta, Leon D. Gruenewald, Simon S. Martin, Christian Booz, Katrin Eichler, Scherwin Mahmoudi, Canan Özdemir Rezazadeh, Philipp Reschke, Teodora Biciusca, Lisa-Joy Juergens, Christoph Mader, Renate Hammerstingl, Christof M. Sommer, Thomas J. Vogl, Vitali Koch |
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| 245 | 1 | 0 | |a Unmasking pancreatic cancer |b advanced biomedical imaging for its detection in native versus arterial dual-energy computed tomography (DECT) scans |c Jennifer Gotta, Leon D. Gruenewald, Simon S. Martin, Christian Booz, Katrin Eichler, Scherwin Mahmoudi, Canan Özdemir Rezazadeh, Philipp Reschke, Teodora Biciusca, Lisa-Joy Juergens, Christoph Mader, Renate Hammerstingl, Christof M. Sommer, Thomas J. Vogl, Vitali Koch |
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| 520 | |a This study investigates the potential of a machine learning classifier using dual- energy computed tomography (DECT) radiomics to differentiate between malignant pancreatic lesions and normal pancreas tissue. A total of 100 patients who underwent third-generation DECT between November 2018 and October 2022 were included, with 60 patients having pancreatic cancer and 40 normal pancreatic tissue. Radiomics features were extracted from non-contrast and arterial-enhanced DECT scans with stepwise feature reduction used to identify relevant features. Thetrained machine learning classifiers achieved a diagnostic accuracy of 0.97 in the arterial-enhanced model and 0.88 in non-contrast scans with sensitivities of 0.97 and 0.96, respectively. Areas under the curve were 0.97 (95% CI, 0.92-1.0, p < 0.001) and 0.96 (95% CI, 0.90-1.0, p < 0.001), respectively with no significant differences between both models (p= 0.52). This approach shows promise in enhancing pancreatic cancer detection and improving patient diagnoses, particulary in specific patient groups. | ||
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