{"id":"W2103004421","doi":"10.1038/ncomms5006","title":"Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach","year":2014,"lang":"en","type":"article","venue":"Nature Communications","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":5145,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; University Health Network; Princess Margaret Cancer Centre","funders":"Interreg; National Cancer Institute; National Institutes of Health; Innovative Medicines Initiative; European Federation of Pharmaceutical Industries and Associations; Center for Translational Molecular Medicine; KWF Kankerbestrijding","keywords":"Radiomics; Radiogenomics; Lung cancer; Medicine; Head and neck cancer; Phenotype; Head and neck; Medical imaging; Cancer; Pathology; Radiology; Bioinformatics; Gene; Biology; Internal medicine; Surgery","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null}