Tissue Phenomics for prognostic biomarker discovery in low- and intermediate-risk prostate cancer View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-12

AUTHORS

Nathalie Harder, Maria Athelogou, Harald Hessel, Nicolas Brieu, Mehmet Yigitsoy, Johannes Zimmermann, Martin Baatz, Alexander Buchner, Christian G. Stief, Thomas Kirchner, Gerd Binnig, Günter Schmidt, Ralf Huss

ABSTRACT

Tissue Phenomics is the discipline of mining tissue images to identify patterns that are related to clinical outcome providing potential prognostic and predictive value. This involves the discovery process from assay development, image analysis, and data mining to the final interpretation and validation of the findings. Importantly, this process is not linear but allows backward steps and optimization loops over multiple sub-processes. We provide a detailed description of the Tissue Phenomics methodology while exemplifying each step on the application of prostate cancer recurrence prediction. In particular, we automatically identified tissue-based biomarkers having significant prognostic value for low- and intermediate-risk prostate cancer patients (Gleason scores 6-7b) after radical prostatectomy. We found that promising phenes were related to CD8(+) and CD68(+) cells in the microenvironment of cancerous glands in combination with the local micro-vascularization. Recurrence prediction based on the selected phenes yielded accuracies up to 83% thereby clearly outperforming prediction based on the Gleason score. Moreover, we compared different machine learning algorithms to combine the most relevant phenes resulting in increased accuracies of 88% for tumor progression prediction. These findings will be of potential use for future prognostic tests for prostate cancer patients and provide a proof-of-principle of the Tissue Phenomics approach. More... »

PAGES

4470

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41598-018-22564-7

DOI

http://dx.doi.org/10.1038/s41598-018-22564-7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1101459050

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/29535336


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