Deep learning can predict lymph node status directly from histology in colorectal cancer

• Verfasst von: Kiehl, Lennard [VerfasserIn]
• Verfasst von: Kuntz, Sara [VerfasserIn]
• Verfasst von: Höhn, Julia [VerfasserIn]
• Verfasst von: Jutzi, Tanja [VerfasserIn]
• Verfasst von: Krieghoff-Henning, Eva [VerfasserIn]
• Verfasst von: Kather, Jakob Nikolas [VerfasserIn]
• Verfasst von: Holland-Letz, Tim [VerfasserIn]
• Verfasst von: Kopp-Schneider, Annette [VerfasserIn]
• Verfasst von: Chang-Claude, Jenny [VerfasserIn]
• Verfasst von: Brobeil, Alexander [VerfasserIn]
• Verfasst von: Kalle, Christof von [VerfasserIn]
• Verfasst von: Fröhling, Stefan [VerfasserIn]
• Verfasst von: Alwers, Elizabeth [VerfasserIn]
• Verfasst von: Brenner, Hermann [VerfasserIn]
• Verfasst von: Hoffmeister, Michael [VerfasserIn]
• Verfasst von: Brinker, Titus Josef [VerfasserIn]
• Titel: Deep learning can predict lymph node status directly from histology in colorectal cancer
• Verf.angabe: Lennard Kiehl, Sara Kuntz, Julia Höhn, Tanja Jutzi, Eva Krieghoff-Henning, Jakob N. Kather, Tim Holland-Letz, Annette Kopp-Schneider, Jenny Chang-Claude, Alexander Brobeil, Christof von Kalle, Stefan Fröhling, Elizabeth Alwers, Hermann Brenner, Michael Hoffmeister, Titus J. Brinker
• E-Jahr: 2021
• Jahr: 11 October 2021
• Umfang: 10 S.
• Fussnoten: Gesehen am 22.01.2022
• Titel Quelle: Enthalten in: European journal of cancer
• Ort Quelle: Amsterdam [u.a.] : Elsevier, 1992
• Jahr Quelle: 2021
• Band/Heft Quelle: 157(2021), Seite 464-473
• ISSN Quelle: 1879-0852
• DOI: doi:10.1016/j.ejca.2021.08.039
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Clinical data
• Sach-SW: CNN
• Sach-SW: Colorectal cancer
• Sach-SW: Deep learning
• Sach-SW: Lymph node status
• K10plus-PPN: 1786951630

Abstract

Background - Lymph node status is a prognostic marker and strongly influences therapeutic decisions in colorectal cancer (CRC). - Objectives - The objective of the study is to investigate whether image features extracted by a deep learning model from routine histological slides and/or clinical data can be used to predict CRC lymph node metastasis (LNM). - Methods - Using histological whole slide images (WSIs) of primary tumours of 2431 patients in the DACHS cohort, we trained a convolutional neural network to predict LNM. In parallel, we used clinical data derived from the same cases in logistic regression analyses. Subsequently, the slide-based artificial intelligence predictor (SBAIP) score was included in the regression. WSIs and data from 582 patients of the TCGA cohort were used as the external test set. - Results - On the internal test set, the SBAIP achieved an area under receiver operating characteristic (AUROC) of 71.0%, the clinical classifier achieved an AUROC of 67.0% and a combination of the two classifiers yielded an improvement to 74.1%. Whereas the clinical classifier's performance remained stable on the TCGA set, performance of the SBAIP dropped to an AUROC of 61.2%. Performance of the clinical classifier depended strongly on the T stage. - Conclusion - Deep learning-based image analysis may help predict LNM of patients with CRC using routine histological slides. Combination with clinical data such as T stage might be useful. Strategies to increase performance of the SBAIP on external images should be investigated.