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Deep learning based tissue analysis predicts outcome in colorectal cancer

Scientific reports, 2018-02, Vol.8 (1), p.3395-11, Article 3395 [Peer Reviewed Journal]

2018. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;The Author(s) 2018 ;ISSN: 2045-2322 ;EISSN: 2045-2322 ;DOI: 10.1038/s41598-018-21758-3 ;PMID: 29467373

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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
Subjects: Aged ; Algorithms ; Classification ; Colorectal cancer ; Colorectal carcinoma ; Colorectal Neoplasms - pathology ; Deep Learning ; Eosine Yellowish-(YS) - administration & dosage ; Female ; Hematoxylin - administration & dosage ; Humans ; Image Processing, Computer-Assisted - methods ; Information processing ; Learning algorithms ; Machine Learning ; Male ; Medicin och hälsovetenskap ; Middle Aged ; Prognosis ; Retrospective Studies ; Risk groups ; Tumors
Is Part Of: Scientific reports, 2018-02, Vol.8 (1), p.3395-11, Article 3395
Description: 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.
Publisher: England: Nature Publishing Group
Language: English
Identifier: ISSN: 2045-2322
EISSN: 2045-2322
DOI: 10.1038/s41598-018-21758-3
PMID: 29467373
Source: MEDLINE
PubMed Central
SWEPUB Freely available online
ProQuest Central
DOAJ Directory of Open Access Journals

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Deep learning based tissue analysis predicts outcome in colorectal cancer

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