Deep learning can predict lymph node status directly from histology in colorectal cancer
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...
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| Hauptverfasser: | , , , , , , , , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
11 October 2021
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| In: |
European journal of cancer
Year: 2021, Jahrgang: 157, Pages: 464-473 |
| ISSN: | 1879-0852 |
| DOI: | 10.1016/j.ejca.2021.08.039 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1016/j.ejca.2021.08.039 Verlag, lizenzpflichtig, Volltext: https://www.sciencedirect.com/science/article/pii/S0959804921005700 
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| Verfasserangaben: | 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 |
| Zusammenfassung: | 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. |
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| Beschreibung: | Gesehen am 22.01.2022 |
| Beschreibung: | Online Resource |
| ISSN: | 1879-0852 |
| DOI: | 10.1016/j.ejca.2021.08.039 |