| |  Online-Ressource |
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| Verfasst von: | Jung, Jin-On [VerfasserIn]  |
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| | Pisula, Juan I. [VerfasserIn] |
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| | Beyerlein, Xenia [VerfasserIn] |
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| | Lukomski, Leandra [VerfasserIn] |
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| | Knipper, Karl [VerfasserIn] |
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| | Abu Hejleh, Aram P. [VerfasserIn] |
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| | Fuchs, Hans F. [VerfasserIn] |
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| | Tolkach, Yuri [VerfasserIn] |
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| | Chon, Seung-Hun [VerfasserIn] |
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| | Nienhüser, Henrik [VerfasserIn] |
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| | Büchler, Markus W. [VerfasserIn] |
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| | Bruns, Christiane J. [VerfasserIn] |
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| | Quaas, Alexander [VerfasserIn] |
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| | Bozek, Katarzyna [VerfasserIn] |
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| | Popp, Felix [VerfasserIn] |
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| | Schmidt, Thomas [VerfasserIn] |
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| Titel: | Deep learning histology for prediction of lymph node metastases and tumor regression after neoadjuvant FLOT therapy of gastroesophageal adenocarcinoma |
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| Verf.angabe: | Jin-On Jung, Juan I. Pisula, Xenia Beyerlein, Leandra Lukomski, Karl Knipper, Aram P. Abu Hejleh, Hans F. Fuchs, Yuri Tolkach, Seung-Hun Chon, Henrik Nienhüser, Markus W. Büchler, Christiane J. Bruns, Alexander Quaas, Katarzyna Bozek, Felix Popp and Thomas Schmidt |
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| E-Jahr: | 2024 |
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| Jahr: | 3 July 2024 |
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| Umfang: | 12 S. |
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| Illustrationen: | Illustrationen |
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| Fussnoten: | Gesehen am 09.12.2024 |
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| Titel Quelle: | Enthalten in: Cancers |
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| Ort Quelle: | Basel : MDPI, 2009 |
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| Jahr Quelle: | 2024 |
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| Band/Heft Quelle: | 16(2024), 13, Artikel-ID 2445, Seite 1-12 |
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| ISSN Quelle: | 2072-6694 |
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| Abstract: | Background: The aim of this study was to establish a deep learning prediction model for neoadjuvant FLOT chemotherapy response. The neural network utilized clinical data and visual information from whole-slide images (WSIs) of therapy-naïve gastroesophageal cancer biopsies. Methods: This study included 78 patients from the University Hospital of Cologne and 59 patients from the University Hospital of Heidelberg used as external validation. Results: After surgical resection, 33 patients from Cologne (42.3%) were ypN0 and 45 patients (57.7%) were ypN+, while 23 patients from Heidelberg (39.0%) were ypN0 and 36 patients (61.0%) were ypN+ (p = 0.695). The neural network had an accuracy of 92.1% to predict lymph node metastasis and the area under the curve (AUC) was 0.726. A total of 43 patients from Cologne (55.1%) had less than 50% residual vital tumor (RVT) compared to 34 patients from Heidelberg (57.6%, p = 0.955). The model was able to predict tumor regression with an error of ±14.1% and an AUC of 0.648. Conclusions: This study demonstrates that visual features extracted by deep learning from therapy-naïve biopsies of gastroesophageal adenocarcinomas correlate with positive lymph nodes and tumor regression. The results will be confirmed in prospective studies to achieve early allocation of patients to the most promising treatment. |
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| DOI: | doi:10.3390/cancers16132445 |
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| 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.
kostenfrei: Volltext: https://doi.org/10.3390/cancers16132445 |
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| | kostenfrei: Volltext: https://www.mdpi.com/2072-6694/16/13/2445 |
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| | DOI: https://doi.org/10.3390/cancers16132445 |
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| Datenträger: | Online-Ressource |
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| Sprache: | eng |
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| Sach-SW: | artificial intelligence |
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| | chemotherapy response |
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| | deep learning |
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| | FLOT therapy |
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| | gastroesophageal cancer |
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| | neural network |
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| | prediction algorithm |
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| K10plus-PPN: | 1911204432 |
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| Verknüpfungen: | → Zeitschrift |
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Deep learning histology for prediction of lymph node metastases and tumor regression after neoadjuvant FLOT therapy of gastroesophageal adenocarcinoma / Jung, Jin-On [VerfasserIn]; 3 July 2024 (Online-Ressource)
