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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| Vydáno v: | Scientific reports Ročník 8; číslo 1; s. 3395 - 11 |
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| Hlavní autoři: | , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
London
Nature Publishing Group UK
21.02.2018
Nature Publishing Group |
| Témata: | |
| ISSN: | 2045-2322, 2045-2322 |
| On-line přístup: | Získat plný text |
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| 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. |
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| AbstractList | 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. 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.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. |
| ArticleNumber | 3395 |
| Author | Turkki, Riku Lundin, Mikael Bychkov, Dmitrii Kovanen, Panu E. Nordling, Stig Lundin, Johan Haglund, Caj Linder, Nina Verrill, Clare Walliander, Margarita |
| Author_xml | – sequence: 1 givenname: Dmitrii surname: Bychkov fullname: Bychkov, Dmitrii email: dmitrii.bychkov@helsinki.fi organization: Institute for Molecular Medicine Finland FIMM, Helsinki Institute for Life Science HiLIFE, University of Helsinki – sequence: 2 givenname: Nina surname: Linder fullname: Linder, Nina organization: Institute for Molecular Medicine Finland FIMM, Helsinki Institute for Life Science HiLIFE, University of Helsinki, Department of Women’s and Children’s Health, International Maternal and Child Health (IMCH), Uppsala University – sequence: 3 givenname: Riku orcidid: 0000-0002-8690-6983 surname: Turkki fullname: Turkki, Riku organization: Institute for Molecular Medicine Finland FIMM, Helsinki Institute for Life Science HiLIFE, University of Helsinki – sequence: 4 givenname: Stig surname: Nordling fullname: Nordling, Stig organization: Department of Pathology, Medicum, University of Helsinki – sequence: 5 givenname: Panu E. surname: Kovanen fullname: Kovanen, Panu E. organization: Department of Pathology, University of Helsinki and HUSLAB, Helsinki University Hospital – sequence: 6 givenname: Clare surname: Verrill fullname: Verrill, Clare organization: Nuffield Department of Surgical Sciences, NIHR Oxford Biomedical Research Centre, University of Oxford – sequence: 7 givenname: Margarita surname: Walliander fullname: Walliander, Margarita organization: Institute for Molecular Medicine Finland FIMM, Helsinki Institute for Life Science HiLIFE, University of Helsinki – sequence: 8 givenname: Mikael surname: Lundin fullname: Lundin, Mikael organization: Institute for Molecular Medicine Finland FIMM, Helsinki Institute for Life Science HiLIFE, University of Helsinki – sequence: 9 givenname: Caj surname: Haglund fullname: Haglund, Caj organization: Department of Surgery, University of Helsinki and Helsinki University Hospital, Research Programs Unit, Translational Cancer Biology, University of Helsinki – sequence: 10 givenname: Johan surname: Lundin fullname: Lundin, Johan organization: Institute for Molecular Medicine Finland FIMM, Helsinki Institute for Life Science HiLIFE, University of Helsinki, Department of Public Health Sciences, Global Health/IHCAR, Karolinska Institutet |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29467373$$D View this record in MEDLINE/PubMed https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-349348$$DView record from Swedish Publication Index (Uppsala universitet) http://kipublications.ki.se/Default.aspx?queryparsed=id:137722206$$DView record from Swedish Publication Index (Karolinska Institutet) |
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| Snippet | Image-based machine learning and deep learning in particular has recently shown expert-level accuracy in medical image classification. In this study, we... |
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| SubjectTerms | 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 |
| Title | Deep learning based tissue analysis predicts outcome in colorectal cancer |
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