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Deep learning histology for prediction of lymph node metastases and tumor regression after neoadjuvant FLOT therapy of gastroesophageal adenocarcinoma

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 incl...

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Hauptverfasser: Jung, Jin-On (VerfasserIn) , Pisula, Juan I. (VerfasserIn) , Beyerlein, Xenia (VerfasserIn) , Lukomski, Leandra (VerfasserIn) , Knipper, Karl (VerfasserIn) , Abu Hejleh, Aram P. (VerfasserIn) , Fuchs, Hans F. (VerfasserIn) , Tolkach, Yuri (VerfasserIn) , Chon, Seung-Hun (VerfasserIn) , Nienhüser, Henrik (VerfasserIn) , Büchler, Markus W. (VerfasserIn) , Bruns, Christiane J. (VerfasserIn) , Quaas, Alexander (VerfasserIn) , Bozek, Katarzyna (VerfasserIn) , Popp, Felix (VerfasserIn) , Schmidt, Thomas (VerfasserIn)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: 3 July 2024
In: Cancers
Year: 2024, Jahrgang: 16, Heft: 13, Pages: 1-12
ISSN:2072-6694
DOI:10.3390/cancers16132445
Online-Zugang:Verlag, kostenfrei, Volltext: https://doi.org/10.3390/cancers16132445
Verlag, kostenfrei, Volltext: https://www.mdpi.com/2072-6694/16/13/2445
Volltext
Verfasserangaben: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
Beschreibung
Zusammenfassung: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.
Beschreibung:Gesehen am 09.12.2024
Beschreibung:Online Resource
ISSN:2072-6694
DOI:10.3390/cancers16132445

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