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000177033 1001_ $$0P:(DE-He78)29466f5cfe110ed866c860a358a88825$$aKiehl, Lennard$$b0$$eFirst author
000177033 245__ $$aDeep learning can predict lymph node status directly from histology in colorectal cancer.
000177033 260__ $$aAmsterdam [u.a.]$$bElsevier$$c2021
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000177033 520__ $$aLymph node status is a prognostic marker and strongly influences therapeutic decisions in colorectal cancer (CRC).The objective of the study is to investigate whether image features extracted by a deep learning model from routine histological slides and/or clinical data can be used to predict CRC lymph node metastasis (LNM).Using histological whole slide images (WSIs) of primary tumours of 2431 patients in the DACHS cohort, we trained a convolutional neural network to predict LNM. In parallel, we used clinical data derived from the same cases in logistic regression analyses. Subsequently, the slide-based artificial intelligence predictor (SBAIP) score was included in the regression. WSIs and data from 582 patients of the TCGA cohort were used as the external test set.On the internal test set, the SBAIP achieved an area under receiver operating characteristic (AUROC) of 71.0%, the clinical classifier achieved an AUROC of 67.0% and a combination of the two classifiers yielded an improvement to 74.1%. Whereas the clinical classifier's performance remained stable on the TCGA set, performance of the SBAIP dropped to an AUROC of 61.2%. Performance of the clinical classifier depended strongly on the T stage.Deep learning-based image analysis may help predict LNM of patients with CRC using routine histological slides. Combination with clinical data such as T stage might be useful. Strategies to increase performance of the SBAIP on external images should be investigated.
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000177033 650_7 $$2Other$$aCNN
000177033 650_7 $$2Other$$aClinical data
000177033 650_7 $$2Other$$aColorectal cancer
000177033 650_7 $$2Other$$aDeep learning
000177033 650_7 $$2Other$$aLymph node status
000177033 7001_ $$0P:(DE-He78)52f31629a970c50c559f08fddd957a3b$$aKuntz, Sara$$b1
000177033 7001_ $$0P:(DE-He78)551f38237e85bb25b4502ba8fbb88f4f$$aHöhn, Julia$$b2
000177033 7001_ $$0P:(DE-He78)23fc125c7c54492d146e72389bab5208$$aJutzi, Tanja$$b3
000177033 7001_ $$0P:(DE-He78)8e2078af783ff2be822e7799c43bc86a$$aKrieghoff-Henning, Eva$$b4
000177033 7001_ $$aKather, Jakob N$$b5
000177033 7001_ $$0P:(DE-He78)457c042884c901eb0a02c18bb1d30103$$aHolland-Letz, Tim$$b6
000177033 7001_ $$0P:(DE-He78)bb6a7a70f976eb8df1769944bf913596$$aKopp-Schneider, Annette$$b7
000177033 7001_ $$0P:(DE-He78)c259d6cc99edf5c7bc7ce22c7f87c253$$aChang-Claude, Jenny$$b8
000177033 7001_ $$aBrobeil, Alexander$$b9
000177033 7001_ $$avon Kalle, Christof$$b10
000177033 7001_ $$0P:(DE-He78)f0144d171d26dbedb67c9db1df35629d$$aFröhling, Stefan$$b11$$udkfz
000177033 7001_ $$0P:(DE-He78)9b2a61b2abe4a64ca23b6783b7c4fe63$$aAlwers, Elizabeth$$b12
000177033 7001_ $$0P:(DE-He78)90d5535ff896e70eed81f4a4f6f22ae2$$aBrenner, Hermann$$b13
000177033 7001_ $$0P:(DE-He78)6c5d058b7552d071a7fa4c5e943fff0f$$aHoffmeister, Michael$$b14
000177033 7001_ $$0P:(DE-He78)1e33961c8780aca9b76d776d1fdc1ebb$$aBrinker, Titus$$b15$$eLast author
000177033 773__ $$0PERI:(DE-600)1468190-0$$a10.1016/j.ejca.2021.08.039$$gVol. 157, p. 464 - 473$$p464 - 473$$tEuropean journal of cancer$$v157$$x0959-8049$$y2021
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