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Breast cancer outco...
Breast cancer outcome prediction with tumour tissue images and machine learning
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- Turkki, Riku (författare)
- Univ Helsinki, Inst Mol Med Finland FIMM, Helsinki, Finland;Karolinska Inst, Sci Life Lab SciLifeLab, Solna, Sweden
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- Byckhov, Dmitrii (författare)
- Univ Helsinki, Inst Mol Med Finland FIMM, Helsinki, Finland
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- Lundin, Mikael (författare)
- Univ Helsinki, Inst Mol Med Finland FIMM, Helsinki, Finland
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- Isola, Jorma (författare)
- Univ Tampere, Dept Canc Biol, BioMediTech, Tampere, Finland
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- Nordling, Stig (författare)
- Univ Helsinki, Dept Pathol, Medicum, Helsinki, Finland
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- Kovanen, Panu E. (författare)
- Helsinki Univ Hosp, HUSLAB, Ctr Canc, Helsinki, Finland;Helsinki Univ Hosp, Medicum, Ctr Canc, Helsinki, Finland;Univ Helsinki, Helsinki, Finland
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- Verrill, Clare (författare)
- Univ Oxford, Nuffield Dept Surg Sci, Oxford, England;NIHR Oxford Biomed Res Ctr, Oxford, England
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- von Smitten, Karl (författare)
- Eira Hosp, Helsinki, Finland
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- Joensuu, Heikki (författare)
- Univ Helsinki, Helsinki, Finland;Helsinki Univ Hosp, Dept Oncol, Helsinki, Finland
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- Lundin, Johan (författare)
- Karolinska Institutet
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- Linder, Nina (författare)
- Uppsala universitet,Internationell mödra- och barnhälsovård (IMCH),Univ Helsinki, Inst Mol Med Finland FIMM, Helsinki, Finland
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(creator_code:org_t)
- 2019-05-22
- 2019
- Engelska.
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Ingår i: Breast Cancer Research and Treatment. - : SPRINGER. - 0167-6806 .- 1573-7217. ; 177:1, s. 41-52
- Relaterad länk:
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https://doi.org/10.1...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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http://kipublication...
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Abstract
Ämnesord
Stäng
- Purpose: Recent advances in machine learning have enabled better understanding of large and complex visual data. Here, we aim to investigate patient outcome prediction with a machine learning method using only an image of tumour sample as an input.Methods: Utilising tissue microarray (TMA) samples obtained from the primary tumour of patients (N=1299) within a nationwide breast cancer series with long-term-follow-up, we train and validate a machine learning method for patient outcome prediction. The prediction is performed by classifying samples into low or high digital risk score (DRS) groups. The outcome classifier is trained using sample images of 868 patients and evaluated and compared with human expert classification in a test set of 431 patients.Results: In univariate survival analysis, the DRS classification resulted in a hazard ratio of 2.10 (95% CI 1.33-3.32, p=0.001) for breast cancer-specific survival. The DRS classification remained as an independent predictor of breast cancer-specific survival in a multivariate Cox model with a hazard ratio of 2.04 (95% CI 1.20-3.44, p=0.007). The accuracy (C-index) of the DRS grouping was 0.60 (95% CI 0.55-0.65), as compared to 0.58 (95% CI 0.53-0.63) for human expert predictions based on the same TMA samples.Conclusions: Our findings demonstrate the feasibility of learning prognostic signals in tumour tissue images without domain knowledge. Although further validation is needed, our study suggests that machine learning algorithms can extract prognostically relevant information from tumour histology complementing the currently used prognostic factors in breast cancer.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Cancer och onkologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cancer and Oncology (hsv//eng)
Nyckelord
- Breast cancer
- Machine learning
- Deep learning
- Outcome prediction
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- art (ämneskategori)
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Turkki, Riku
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Byckhov, Dmitrii
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Lundin, Mikael
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Isola, Jorma
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Nordling, Stig
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Kovanen, Panu E.
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Verrill, Clare
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von Smitten, Kar ...
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Joensuu, Heikki
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Lundin, Johan
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Linder, Nina
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- MEDICIN OCH HÄLSOVETENSKAP
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Breast Cancer Re ...
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Uppsala universitet
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Karolinska Institutet