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| 001 | 177033 | ||
| 005 | 20240229133729.0 | ||
| 024 | 7 | _ | |a 10.1016/j.ejca.2021.08.039 |2 doi |
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| 024 | 7 | _ | |a 0014-2964 |2 ISSN |
| 024 | 7 | _ | |a 0959-8049 |2 ISSN |
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| 024 | 7 | _ | |a (1990) |2 ISSN |
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| 024 | 7 | _ | |a (1965) |2 ISSN |
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| 037 | _ | _ | |a DKFZ-2021-02261 |
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| 100 | 1 | _ | |a Kiehl, Lennard |0 P:(DE-He78)29466f5cfe110ed866c860a358a88825 |b 0 |e First author |
| 245 | _ | _ | |a Deep learning can predict lymph node status directly from histology in colorectal cancer. |
| 260 | _ | _ | |a Amsterdam [u.a.] |c 2021 |b Elsevier |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1639480770_12182 |2 PUB:(DE-HGF) |
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| 500 | _ | _ | |a #EA:C140#LA:C140# |
| 520 | _ | _ | |a Lymph 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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| 650 | _ | 7 | |a CNN |2 Other |
| 650 | _ | 7 | |a Clinical data |2 Other |
| 650 | _ | 7 | |a Colorectal cancer |2 Other |
| 650 | _ | 7 | |a Deep learning |2 Other |
| 650 | _ | 7 | |a Lymph node status |2 Other |
| 700 | 1 | _ | |a Kuntz, Sara |0 P:(DE-He78)52f31629a970c50c559f08fddd957a3b |b 1 |
| 700 | 1 | _ | |a Höhn, Julia |0 P:(DE-He78)551f38237e85bb25b4502ba8fbb88f4f |b 2 |
| 700 | 1 | _ | |a Jutzi, Tanja |0 P:(DE-He78)23fc125c7c54492d146e72389bab5208 |b 3 |
| 700 | 1 | _ | |a Krieghoff-Henning, Eva |0 P:(DE-He78)8e2078af783ff2be822e7799c43bc86a |b 4 |
| 700 | 1 | _ | |a Kather, Jakob N |b 5 |
| 700 | 1 | _ | |a Holland-Letz, Tim |0 P:(DE-He78)457c042884c901eb0a02c18bb1d30103 |b 6 |
| 700 | 1 | _ | |a Kopp-Schneider, Annette |0 P:(DE-He78)bb6a7a70f976eb8df1769944bf913596 |b 7 |
| 700 | 1 | _ | |a Chang-Claude, Jenny |0 P:(DE-He78)c259d6cc99edf5c7bc7ce22c7f87c253 |b 8 |
| 700 | 1 | _ | |a Brobeil, Alexander |b 9 |
| 700 | 1 | _ | |a von Kalle, Christof |b 10 |
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| 700 | 1 | _ | |a Alwers, Elizabeth |0 P:(DE-He78)9b2a61b2abe4a64ca23b6783b7c4fe63 |b 12 |
| 700 | 1 | _ | |a Brenner, Hermann |0 P:(DE-He78)90d5535ff896e70eed81f4a4f6f22ae2 |b 13 |
| 700 | 1 | _ | |a Hoffmeister, Michael |0 P:(DE-He78)6c5d058b7552d071a7fa4c5e943fff0f |b 14 |
| 700 | 1 | _ | |a Brinker, Titus |0 P:(DE-He78)1e33961c8780aca9b76d776d1fdc1ebb |b 15 |e Last author |
| 773 | _ | _ | |a 10.1016/j.ejca.2021.08.039 |g Vol. 157, p. 464 - 473 |0 PERI:(DE-600)1468190-0 |p 464 - 473 |t European journal of cancer |v 157 |y 2021 |x 0959-8049 |
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