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Breast cancer outcome prediction with tumour tissue images and machine learning
Univ Helsinki, Inst Mol Med Finland FIMM, Helsinki, Finland;Karolinska Inst, Sci Life Lab SciLifeLab, Solna, Sweden.ORCID iD: 0000-0002-8690-6983
Univ Helsinki, Inst Mol Med Finland FIMM, Helsinki, Finland.
Univ Helsinki, Inst Mol Med Finland FIMM, Helsinki, Finland.
Univ Tampere, Dept Canc Biol, BioMediTech, Tampere, Finland.
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2019 (English)In: Breast Cancer Research and Treatment, ISSN 0167-6806, E-ISSN 1573-7217, Vol. 177, no 1, p. 41-52Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
SPRINGER , 2019. Vol. 177, no 1, p. 41-52
Keywords [en]
Breast cancer, Machine learning, Deep learning, Outcome prediction
National Category
Cancer and Oncology
Identifiers
URN: urn:nbn:se:uu:diva-390386DOI: 10.1007/s10549-019-05281-1ISI: 000475783500005PubMedID: 31119567OAI: oai:DiVA.org:uu-390386DiVA, id: diva2:1341575
Available from: 2019-08-09 Created: 2019-08-09 Last updated: 2019-08-09Bibliographically approved

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