Deep learning based tissue analysis predicts outcome in colorectal cancer

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 sam...

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Veröffentlicht in:Scientific reports Jg. 8; H. 1; S. 3395 - 11
Hauptverfasser: Bychkov, Dmitrii, Linder, Nina, Turkki, Riku, Nordling, Stig, Kovanen, Panu E., Verrill, Clare, Walliander, Margarita, Lundin, Mikael, Haglund, Caj, Lundin, Johan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 21.02.2018
Nature Publishing Group
Schlagworte:
631/114/1305
692/4028/67/1504/1885
Aged
Algorithms
Classification
Colorectal cancer
Colorectal carcinoma
Colorectal Neoplasms - pathology
Deep Learning
Eosine Yellowish-(YS) - administration & dosage
Female
Hematoxylin - administration & dosage
Humanities and Social Sciences
Humans
Image Processing, Computer-Assisted - methods
Information processing
Learning algorithms
Machine Learning
Male
Middle Aged
multidisciplinary
Prognosis
Retrospective Studies
Risk groups
Science
Science (multidisciplinary)
Tissue analysis
Tumors
ISSN:2045-2322, 2045-2322
Online-Zugang:Volltext
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Zusammenfassung: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.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-018-21758-3