Oropharyngeal cancer patient stratification using random forest based-learning over high-dimensional...
Oropharyngeal cancer patient stratification using random forest based-learning over high-dimensional radiomic features
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Publisher
London: Nature Publishing Group UK
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Language
English
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Publisher
London: Nature Publishing Group UK
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Scope and Contents
Contents
To improve risk prediction for oropharyngeal cancer (OPC) patients using cluster analysis on the radiomic features extracted from pre-treatment Computed Tomography (CT) scans. 553 OPC Patients randomly split into training (80%) and validation (20%), were classified into 2 or 3 risk groups by applying hierarchical clustering over the co-occurrence m...
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Full title
Oropharyngeal cancer patient stratification using random forest based-learning over high-dimensional radiomic features
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TN_cdi_doaj_primary_oai_doaj_org_article_7839f9f79de3480ebfb1a1b48ee86d6d
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_7839f9f79de3480ebfb1a1b48ee86d6d
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ISSN
2045-2322
E-ISSN
2045-2322
DOI
10.1038/s41598-021-92072-8
