Deep learning based tissue analysis predicts outcome in colorectal cancer

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Bychkov , D , Linder , N , Turkki , R , Nordling , S , Kovanen , P E , Verrill , C , Walliander , M , Lundin , M , Haglund , C & Lundin , J 2018 , ' Deep learning based tissue analysis predicts outcome in colorectal cancer ' , Scientific Reports , vol. 8 , 3395 . https://doi.org/10.1038/s41598-018-21758-3

Title: Deep learning based tissue analysis predicts outcome in colorectal cancer
Author: Bychkov, Dmitrii; Linder, Nina; Turkki, Riku; Nordling, Stig; Kovanen, Panu E.; Verrill, Clare; Walliander, Margarita; Lundin, Mikael; Haglund, Caj; Lundin, Johan
Other contributor: University of Helsinki, Helsinki Institute for Information Technology
University of Helsinki, Institute for Molecular Medicine Finland
University of Helsinki, Johan Edvard Lundin / Principal Investigator
University of Helsinki, Medicum
University of Helsinki, HUSLAB
University of Helsinki, Institute for Molecular Medicine Finland
University of Helsinki, Johan Edvard Lundin / Principal Investigator
University of Helsinki, Translational Cancer Biology (TCB) Research Programme
University of Helsinki, Johan Edvard Lundin / Principal Investigator













Date: 2018-02-21
Language: eng
Number of pages: 11
Belongs to series: Scientific Reports
ISSN: 2045-2322
DOI: https://doi.org/10.1038/s41598-018-21758-3
URI: http://hdl.handle.net/10138/233833
Abstract: Image-based machine learning and deep learning in particular has recently shown expert-level accuracy in medical image classification. In this study, we combine convolutional and recurrent architectures to train a deep network to predict colorectal cancer outcome based on images of tumour tissue samples. The novelty of our approach is that we directly predict patient outcome, without any intermediate tissue classification. We evaluate a set of digitized haematoxylin-eosin-stained tumour tissue microarray (TMA) samples from 420 colorectal cancer patients with clinicopathological and outcome data available. The results show that deep learning-based outcome prediction with only small tissue areas as input outperforms (hazard ratio 2.3; CI 95% 1.79-3.03; AUC 0.69) visual histological assessment performed by human experts on both TMA spot (HR 1.67; CI 95% 1.28-2.19; AUC 0.58) and whole-slide level (HR 1.65; CI 95% 1.30-2.15; AUC 0.57) in the stratification into low-and high-risk patients. Our results suggest that state-of-the-art deep learning techniques can extract more prognostic information from the tissue morphology of colorectal cancer than an experienced human observer.
Subject: NEURAL-NETWORKS
SURVIVAL PREDICTION
CLASSIFICATION
EXPRESSION
MARKERS
3122 Cancers
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