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000170213 1001_ $$0P:(DE-He78)52f31629a970c50c559f08fddd957a3b$$aKuntz, Sara Andrea$$b0$$eFirst author
000170213 245__ $$aGastrointestinal cancer classification and prognostication from histology using deep learning: Systematic review.
000170213 260__ $$aAmsterdam [u.a.]$$bElsevier$$c2021
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000170213 520__ $$aGastrointestinal cancers account for approximately 20% of all cancer diagnoses and are responsible for 22.5% of cancer deaths worldwide. Artificial intelligence-based diagnostic support systems, in particular convolutional neural network (CNN)-based image analysis tools, have shown great potential in medical computer vision. In this systematic review, we summarise recent studies reporting CNN-based approaches for digital biomarkers for characterization and prognostication of gastrointestinal cancer pathology.Pubmed and Medline were screened for peer-reviewed papers dealing with CNN-based gastrointestinal cancer analyses from histological slides, published between 2015 and 2020.Seven hundred and ninety titles and abstracts were screened, and 58 full-text articles were assessed for eligibility.Sixteen publications fulfilled our inclusion criteria dealing with tumor or precursor lesion characterization or prognostic and predictive biomarkers: 14 studies on colorectal or rectal cancer, three studies on gastric cancer and none on esophageal cancer. These studies were categorised according to their end-points: polyp characterization, tumor characterization and patient outcome. Regarding the translation into clinical practice, we identified several studies demonstrating generalization of the classifier with external tests and comparisons with pathologists, but none presenting clinical implementation.Results of recent studies on CNN-based image analysis in gastrointestinal cancer pathology are promising, but studies were conducted in observational and retrospective settings. Large-scale trials are needed to assess performance and predict clinical usefulness. Furthermore, large-scale trials are required for approval of CNN-based prediction models as medical devices.
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000170213 650_7 $$2Other$$aArtificial intelligence
000170213 650_7 $$2Other$$aColorectal cancer
000170213 650_7 $$2Other$$aConvolutional neural network
000170213 650_7 $$2Other$$aDeep learning
000170213 650_7 $$2Other$$aDigital biomarker
000170213 650_7 $$2Other$$aEsophageal cancer
000170213 650_7 $$2Other$$aGastric cancer
000170213 650_7 $$2Other$$aGastrointestinal cancer
000170213 650_7 $$2Other$$aPathology
000170213 7001_ $$0P:(DE-He78)8e2078af783ff2be822e7799c43bc86a$$aKrieghoff-Henning, Eva$$b1
000170213 7001_ $$aKather, Jakob N$$b2
000170213 7001_ $$0P:(DE-He78)23fc125c7c54492d146e72389bab5208$$aJutzi, Tanja$$b3
000170213 7001_ $$0P:(DE-He78)551f38237e85bb25b4502ba8fbb88f4f$$aHöhn, Julia$$b4
000170213 7001_ $$0P:(DE-He78)29466f5cfe110ed866c860a358a88825$$aKiehl, Lennard$$b5
000170213 7001_ $$0P:(DE-He78)fe1af578a870418968c5decfd626de96$$aHekler, Achim$$b6
000170213 7001_ $$0P:(DE-He78)9b2a61b2abe4a64ca23b6783b7c4fe63$$aAlwers, Elizabeth$$b7
000170213 7001_ $$avon Kalle, Christof$$b8
000170213 7001_ $$0P:(DE-He78)f0144d171d26dbedb67c9db1df35629d$$aFröhling, Stefan$$b9$$udkfz
000170213 7001_ $$0P:(DE-He78)a229f7724466e7efadf4a1ace1ff8af3$$aUtikal, Jochen S$$b10
000170213 7001_ $$0P:(DE-He78)90d5535ff896e70eed81f4a4f6f22ae2$$aBrenner, Hermann$$b11
000170213 7001_ $$0P:(DE-He78)6c5d058b7552d071a7fa4c5e943fff0f$$aHoffmeister, Michael$$b12
000170213 7001_ $$0P:(DE-He78)1e33961c8780aca9b76d776d1fdc1ebb$$aBrinker, Titus$$b13$$eLast author
000170213 773__ $$0PERI:(DE-600)1468190-0$$a10.1016/j.ejca.2021.07.012$$gVol. 155, p. 200 - 215$$p200 - 215$$tEuropean journal of cancer$$v155$$x0959-8049$$y2021
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