| |  Online-Ressource |
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| Verfasst von: | Muti, Hannah Sophie [VerfasserIn]  |
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| | Röcken, Christoph [VerfasserIn] |
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| | Behrens, Hans-Michael [VerfasserIn] |
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| | Löffler, Chiara M. L. [VerfasserIn] |
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| | Reitsam, Nic G. [VerfasserIn] |
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| | Grosser, Bianca [VerfasserIn] |
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| | Märkl, Bruno [VerfasserIn] |
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| | Stange, Daniel E. [VerfasserIn] |
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| | Jiang, Xiaofeng [VerfasserIn] |
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| | Veldhuizen, Gregory P. [VerfasserIn] |
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| | Truhn, Daniel [VerfasserIn] |
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| | Ebert, Matthias [VerfasserIn] |
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| | Grabsch, Heike I. [VerfasserIn] |
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| | Kather, Jakob Nikolas [VerfasserIn] |
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| Titel: | Deep learning trained on lymph node status predicts outcome from gastric cancer histopathology |
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| Titelzusatz: | a retrospective multicentric study |
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| Verf.angabe: | Hannah S. Muti, Christoph Röcken, Hans-Michael Behrens, Chiara M.L. Löffler, Nic G. Reitsam, Bianca Grosser, Bruno Märkl, Daniel E. Stange, Xiaofeng Jiang, Gregory P. Veldhuizen, Daniel Truhn, Matthias P. Ebert, Heike I. Grabsch, Jakob N. Kather |
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| E-Jahr: | 2023 |
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| Jahr: | November 2023 |
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| Umfang: | 12 S. |
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| Illustrationen: | Illustrationen |
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| Fussnoten: | Gesehen am 31.01.2024 ; Online verfügbar 12 September 2023, Version des Artikels 18 October 2023 |
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| Titel Quelle: | Enthalten in: European journal of cancer |
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| Ort Quelle: | Amsterdam [u.a.] : Elsevier, 1992 |
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| Jahr Quelle: | 2023 |
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| Band/Heft Quelle: | 194(2023) vom: Nov., Artikel-ID 113335, Seite 1-12 |
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| ISSN Quelle: | 1879-0852 |
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| Abstract: | Aim - Gastric cancer (GC) is a tumour entity with highly variant outcomes. Lymph node metastasis is a prognostically adverse biomarker. We hypothesised that GC primary tissue contains information that is predictive of lymph node status and patient prognosis and that this information can be extracted using deep learning (DL). - Methods - Using three patient cohorts comprising 1146 patients, we trained and validated a DL system to predict lymph node status directly from haematoxylin and eosin-stained GC tissue sections. We investigated the concordance between the DL-based prediction from the primary tumour slides (aiN score) and the histopathological lymph node status (pN). Furthermore, we assessed the prognostic value of the aiN score alone and when combined with the pN status. - Results - The aiN score predicted the pN status reaching area under the receiver operating characteristic curves of 0.71 in the training cohort and 0.69 and 0.65 in the two test cohorts. In a multivariate Cox analysis, the aiN score was an independent predictor of patient survival with hazard ratios of 1.5 in the training cohort and of 1.3 and 2.2 in the two test cohorts. A combination of the aiN score and the pN status prognostically stratified patients by survival with p-values <0.05 in logrank tests. - Conclusion - GC primary tumour tissue contains additional prognostic information that is accessible using the aiN score. In combination with the pN status, this can be used for personalised management of GC patients after prospective validation. |
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| DOI: | doi:10.1016/j.ejca.2023.113335 |
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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.
Volltext: https://doi.org/10.1016/j.ejca.2023.113335 |
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| | Volltext: https://www.sciencedirect.com/science/article/pii/S0959804923006378 |
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| | DOI: https://doi.org/10.1016/j.ejca.2023.113335 |
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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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| | Deep learning |
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| | Digital pathology |
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| | Gastric cancer |
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| | Precision oncology |
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| K10plus-PPN: | 1879629623 |
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| Verknüpfungen: | → Zeitschrift |
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Deep learning trained on lymph node status predicts outcome from gastric cancer histopathology / Muti, Hannah Sophie [VerfasserIn]; November 2023 (Online-Ressource)
