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Deep learning based...
Deep learning based tissue analysis predicts outcome in colorectal cancer
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- Bychkov, D (författare)
- Univ Helsinki, Inst Mol Med Finland FIMM, Helsinki Inst Life Sci HiLIFE, Helsinki, Finland.
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- Linder, Nina (författare)
- Uppsala universitet,Internationell barnhälsa och nutrition,Univ Helsinki, Inst Mol Med Finland FIMM, Helsinki Inst Life Sci HiLIFE, Helsinki, Finland.
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- Turkki, R (författare)
- Univ Helsinki, Inst Mol Med Finland FIMM, Helsinki Inst Life Sci HiLIFE, Helsinki, Finland.
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- Nordling, S (författare)
- Univ Helsinki, Dept Pathol, Med, Helsinki, Finland.
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- Kovanen, PE (författare)
- Univ Helsinki, Dept Pathol, Helsinki, Finland.;Helsinki Univ Hosp, HUSLAB, Helsinki, Finland.
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- Verrill, C (författare)
- Univ Oxford, Nuffield Dept Surg Sci, NIHR Oxford Biomed Res Ctr, Oxford, England.
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- Walliander, M (författare)
- Univ Helsinki, Inst Mol Med Finland FIMM, Helsinki Inst Life Sci HiLIFE, Helsinki, Finland.
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- Lundin, M (författare)
- Univ Helsinki, Inst Mol Med Finland FIMM, Helsinki Inst Life Sci HiLIFE, Helsinki, Finland.
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- Haglund, C (författare)
- Univ Helsinki, Dept Surg, Helsinki, Finland.;Helsinki Univ Hosp, Helsinki, Finland.;Univ Helsinki, Res Programs Unit, Translat Canc Biol, Helsinki, Finland.
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- Lundin, J (författare)
- Karolinska Institutet,Univ Helsinki, Inst Mol Med Finland FIMM, Helsinki Inst Life Sci HiLIFE, Helsinki, Finland.;Karolinska Inst, Dept Publ Hlth Sci, Global Hlth IHCAR, Stockholm, Sweden.
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Univ Helsinki, Inst Mol Med Finland FIMM, Helsinki Inst Life Sci HiLIFE, Helsinki, Finland Internationell barnhälsa och nutrition (creator_code:org_t)
- 2018-02-21
- 2018
- Engelska.
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Ingår i: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 8:1, s. 3395-
- Relaterad länk:
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https://www.nature.c...
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https://doi.org/10.1...
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https://uu.diva-port... (primary) (Raw object)
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http://kipublication...
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https://doi.org/10.1...
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https://urn.kb.se/re...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Cancer och onkologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cancer and Oncology (hsv//eng)
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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- Av författaren/redakt...
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Bychkov, D
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Linder, Nina
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Turkki, R
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Nordling, S
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Kovanen, PE
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Verrill, C
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Walliander, M
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Lundin, M
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Haglund, C
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Lundin, J
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- Om ämnet
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- MEDICIN OCH HÄLSOVETENSKAP
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MEDICIN OCH HÄLS ...
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och Klinisk medicin
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och Cancer och onkol ...
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Scientific repor ...
- Av lärosätet
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Karolinska Institutet
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Uppsala universitet