%0 Journal Article
%A Kiehl, Lennard
%A Kuntz, Sara
%A Höhn, Julia
%A Jutzi, Tanja
%A Krieghoff-Henning, Eva
%A Kather, Jakob N
%A Holland-Letz, Tim
%A Kopp-Schneider, Annette
%A Chang-Claude, Jenny
%A Brobeil, Alexander
%A von Kalle, Christof
%A Fröhling, Stefan
%A Alwers, Elizabeth
%A Brenner, Hermann
%A Hoffmeister, Michael
%A Brinker, Titus
%T Deep learning can predict lymph node status directly from histology in colorectal cancer.
%J European journal of cancer
%V 157
%@ 0959-8049
%C Amsterdam [u.a.]
%I Elsevier
%M DKFZ-2021-02261
%P 464 - 473
%D 2021
%Z #EA:C140#LA:C140#
%X 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
%K CNN (Other)
%K Clinical data (Other)
%K Colorectal cancer (Other)
%K Deep learning (Other)
%K Lymph node status (Other)
%F PUB:(DE-HGF)16
%9 Journal Article
%$ pmid:34649117
%R 10.1016/j.ejca.2021.08.039
%U https://inrepo02.dkfz.de/record/177033