{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Huang SY"],"funding":["U.S. Department of Defense","NCI NIH HHS","Center for Strategic Scientific Initiatives, National Cancer Institute"],"pagination":["24"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC6095872"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["4"],"pubmed_abstract":["Radiomics is an emerging technology for imaging biomarker discovery and disease-specific personalized treatment management. This paper aims to determine the benefit of using multi-modality radiomics data from PET and MR images in the characterization breast cancer phenotype and prognosis. Eighty-four features were extracted from PET and MR images of 113 breast cancer patients. Unsupervised clustering based on PET and MRI radiomic features created three subgroups. These derived subgroups were statistically significantly associated with tumor grade (p = 2.0 × 10-6), tumor overall stage (p = 0.037), breast cancer subtypes (p = 0.0085), and disease recurrence status (p = 0.0053). The PET-derived first-order statistics and gray level co-occurrence matrix (GLCM) textural features were discrimina"],"journal":["NPJ breast cancer"],"pubmed_title":["Exploration of PET and MRI radiomic features for decoding breast cancer phenotypes and prognosis."],"pmcid":["PMC6095872"],"funding_grant_id":["R01 CA154561","W81XWH-17-1-0033"],"pubmed_authors":["Behr SC","Kornak J","Huang SY","Hylton NM","Esserman L","Jones EF","Copeland TP","Mitra D","Harnish RJ","Price ER","Seo Y","Franc BL","Arasu VA","Liu G"],"additional_accession":[]},"is_claimable":false,"name":"Exploration of PET and MRI radiomic features for decoding breast cancer phenotypes and prognosis.","description":"Radiomics is an emerging technology for imaging biomarker discovery and disease-specific personalized treatment management. This paper aims to determine the benefit of using multi-modality radiomics data from PET and MR images in the characterization breast cancer phenotype and prognosis. Eighty-four features were extracted from PET and MR images of 113 breast cancer patients. Unsupervised clustering based on PET and MRI radiomic features created three subgroups. These derived subgroups were statistically significantly associated with tumor grade (p = 2.0 × 10-6), tumor overall stage (p = 0.037), breast cancer subtypes (p = 0.0085), and disease recurrence status (p = 0.0053). The PET-derived first-order statistics and gray level co-occurrence matrix (GLCM) textural features were discrimina","dates":{"release":"2018-01-01T00:00:00Z","publication":"2018","modification":"2025-04-21T17:43:38.048Z","creation":"2019-03-26T23:52:18Z"},"accession":"S-EPMC6095872","cross_references":{"pubmed":["30131973"],"doi":["10.1038/s41523-018-0078-2"]}}