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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: Gotta, Jennifer (VerfasserIn) , Grünewald, Leon David (VerfasserIn) , Martin, Simon (VerfasserIn) , Booz, Christian (VerfasserIn) , Eichler, Katrin (VerfasserIn) , Mahmoudi, Scherwin (VerfasserIn) , Özdemir-Rezazadeh, Canan (VerfasserIn) , Reschke, Philipp (VerfasserIn) , Biciusca, Teodora (VerfasserIn) , Juergens, Lisa-Joy (VerfasserIn) , Mader, Christoph (VerfasserIn) , Hammerstingl, Renate (VerfasserIn) , Sommer, Christof-Matthias (VerfasserIn) , Vogl, Thomas J. (VerfasserIn) , Koch, Vitali (VerfasserIn)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: 14 February 2024
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
Volltext
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
Beschreibung
Zusammenfassung: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.
Beschreibung:Gesehen am 27.05.2024
Beschreibung:Online Resource
ISSN:1098-1098
DOI:10.1002/ima.23037

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