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Status: Bibliographieeintrag

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Verfasst von:Hu, Wenjia [VerfasserIn]   i
 Wen, Feifei [VerfasserIn]   i
 Zhao, Mengyu [VerfasserIn]   i
 Li, Xiangnan [VerfasserIn]   i
 Luo, Peiyuan [VerfasserIn]   i
 Jiang, Guancheng [VerfasserIn]   i
 Yang, Huizhen [VerfasserIn]   i
 Herth, Felix [VerfasserIn]   i
 Zhang, Xiaoju [VerfasserIn]   i
 Zhang, Quncheng [VerfasserIn]   i
Titel:Endobronchial ultrasound-based support vector machine model for differentiating between benign and malignant mediastinal and hilar lymph nodes
Verf.angabe:Wenjia Hu, Feifei Wen, Mengyu Zhao, Xiangnan Li, Peiyuan Luo, Guancheng Jiang, Huizhen Yang, Felix J.F. Herth, Xiaoju Zhang, Quncheng Zhang
E-Jahr:2024
Jahr:November 2024
Umfang:11 S.
Fussnoten:Online veröffentlicht: 22. Juli 2024 ; Gesehen am 18.06.2025
Titel Quelle:Enthalten in: Respiration
Ort Quelle:Basel : Karger, 1968
Jahr Quelle:2024
Band/Heft Quelle:103(2024), 11 vom: Nov., Seite 675-685
ISSN Quelle:1423-0356
Abstract:Introduction: The aim of the study was to establish an ultrasonographic radiomics machine learning model based on endobronchial ultrasound (EBUS) to assist in diagnosing benign and malignant mediastinal and hilar lymph nodes (LNs). Methods: The clinical and ultrasonographic image data of 197 patients were retrospectively analyzed. The radiomics features extracted by EBUS-based radiomics were analyzed by the least absolute shrinkage and selection operator. Then, we used a support vector machine (SVM) algorithm to establish an EBUS-based radiomics model. A total of 205 lesions were randomly divided into training (n = 143) and validation (n = 62) groups. The diagnostic efficiency was evaluated by receiver operating characteristic (ROC) curve analysis. Results: A total of 13 stable radiomics features with non-zero coefficients were selected. The SVM model exhibited promising performance in both groups. In the training group, the SVM model achieved an ROC area under the curve (AUC) of 0.892 (95% CI: 0.885-0.899), with an accuracy of 85.3%, sensitivity of 93.2%, and specificity of 79.8%. In the validation group, the SVM model had an ROC AUC of 0.906 (95% CI: 0.890-0.923), an accuracy of 74.2%, a sensitivity of 70.3%, and a specificity of 74.1%. Conclusion: The EBUS-based radiomics model can be used to differentiate mediastinal and hilar benign and malignant LNs. The SVM model demonstrated excellent potential as a diagnostic tool in clinical practice.
DOI:doi:10.1159/000540467
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

Volltext: https://doi.org/10.1159/000540467
 DOI: https://doi.org/10.1159/000540467
Datenträger:Online-Ressource
Sprache:eng
K10plus-PPN:1928521517
Verknüpfungen:→ Zeitschrift

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