Gastrointestinal cancer classification and prognostication from histology using deep learning: Systematic review
Background - Gastrointestinal cancers account for approximately 20% of all cancer diagnoses and are responsible for 22.5% of cancer deaths worldwide. Artificial intelligence-based diagnostic support systems, in particular convolutional neural network (CNN)-based image analysis tools, have shown grea...
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| Hauptverfasser: | , , , , , , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
11 August 2021
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| In: |
European journal of cancer
Year: 2021, Jahrgang: 155, Pages: 200-215 |
| ISSN: | 1879-0852 |
| DOI: | 10.1016/j.ejca.2021.07.012 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1016/j.ejca.2021.07.012 Verlag, lizenzpflichtig, Volltext: https://www.sciencedirect.com/science/article/pii/S0959804921004603 
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| Verfasserangaben: | Sara Kuntz, Eva Krieghoff-Henning, Jakob N. Kather, Tanja Jutzi, Julia Höhn, Lennard Kiehl, Achim Hekler, Elizabeth Alwers, Christof von Kalle, Stefan Fröhling, Jochen S. Utikal, Hermann Brenner, Michael Hoffmeister, Titus J. Brinker |
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| 245 | 1 | 0 | |a Gastrointestinal cancer classification and prognostication from histology using deep learning |b Systematic review |c Sara Kuntz, Eva Krieghoff-Henning, Jakob N. Kather, Tanja Jutzi, Julia Höhn, Lennard Kiehl, Achim Hekler, Elizabeth Alwers, Christof von Kalle, Stefan Fröhling, Jochen S. Utikal, Hermann Brenner, Michael Hoffmeister, Titus J. Brinker |
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| 520 | |a Background - Gastrointestinal cancers account for approximately 20% of all cancer diagnoses and are responsible for 22.5% of cancer deaths worldwide. Artificial intelligence-based diagnostic support systems, in particular convolutional neural network (CNN)-based image analysis tools, have shown great potential in medical computer vision. In this systematic review, we summarise recent studies reporting CNN-based approaches for digital biomarkers for characterization and prognostication of gastrointestinal cancer pathology. - Methods - Pubmed and Medline were screened for peer-reviewed papers dealing with CNN-based gastrointestinal cancer analyses from histological slides, published between 2015 and 2020.Seven hundred and ninety titles and abstracts were screened, and 58 full-text articles were assessed for eligibility. - Results - Sixteen publications fulfilled our inclusion criteria dealing with tumor or precursor lesion characterization or prognostic and predictive biomarkers: 14 studies on colorectal or rectal cancer, three studies on gastric cancer and none on esophageal cancer. These studies were categorised according to their end-points: polyp characterization, tumor characterization and patient outcome. Regarding the translation into clinical practice, we identified several studies demonstrating generalization of the classifier with external tests and comparisons with pathologists, but none presenting clinical implementation. - Conclusions - Results of recent studies on CNN-based image analysis in gastrointestinal cancer pathology are promising, but studies were conducted in observational and retrospective settings. Large-scale trials are needed to assess performance and predict clinical usefulness. Furthermore, large-scale trials are required for approval of CNN-based prediction models as medical devices. | ||
| 650 | 4 | |a Artificial intelligence | |
| 650 | 4 | |a Colorectal cancer | |
| 650 | 4 | |a Convolutional neural network | |
| 650 | 4 | |a Deep learning | |
| 650 | 4 | |a Digital biomarker | |
| 650 | 4 | |a Esophageal cancer | |
| 650 | 4 | |a Gastric cancer | |
| 650 | 4 | |a Gastrointestinal cancer | |
| 650 | 4 | |a Pathology | |
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