@article{ibrahim_radiomics_2019,
	series = {From {Basic} {Science} to {Clinical} {Imaging}},
	title = {Radiomics analysis for clinical decision support in nuclear medicine},
	volume = {49},
	issn = {0001-2998},
	url = {http://www.sciencedirect.com/science/article/pii/S0001299819300546},
	doi = {10.1053/j.semnuclmed.2019.06.005},
	abstract = {Radiomics – the high-throughput computation of quantitative image features extracted from medical imaging modalities- can be used to aid clinical decision support systems in order to build diagnostic, prognostic, and predictive models, which could ultimately improve personalized management based on individual characteristics. Various tools for radiomic features extraction are available, and the field gained a substantial scientific momentum for standardization and validation. Radiomics analysis of molecular imaging is expected to provide more comprehensive description of tissues than that of currently used parameters. We here review the workflow of radiomics, the challenges the field currently faces, and its potential for inclusion in clinical decision support systems to maximize disease characterization, and to improve clinical decision-making. We also present guidelines for standardization and implementation of radiomics in order to facilitate its transition to clinical use.},
	language = {en},
	number = {5},
	urldate = {2020-04-09},
	journal = {Seminars in Nuclear Medicine},
	author = {Ibrahim, Abdalla and Vallieres, Martin and Woodruff, Henry and Primakov, Sergey and Beheshti, Mohsen and Keek, Simon and Refaee, Turkey and Sanduleanu, Sebastian and Walsh, Sean and Morin, Olivier and Lambin, Philippe and Hustinx, Roland and Mottaghy, Felix M.},
	month = sep,
	year = {2019},
	note = {Number: 5
Reporter: Seminars in Nuclear Medicine},
	keywords = {Journal Article},
	pages = {438--449},
}

{"_id":"rZmvT48HFTsRFN2rm","bibbaseid":"ibrahim-vallieres-woodruff-primakov-beheshti-keek-refaee-sanduleanu-etal-radiomicsanalysisforclinicaldecisionsupportinnuclearmedicine-2019","author_short":["Ibrahim, A.","Vallieres, M.","Woodruff, H.","Primakov, S.","Beheshti, M.","Keek, S.","Refaee, T.","Sanduleanu, S.","Walsh, S.","Morin, O.","Lambin, P.","Hustinx, R.","Mottaghy, F. M."],"bibdata":{"bibtype":"article","type":"article","series":"From Basic Science to Clinical Imaging","title":"Radiomics analysis for clinical decision support in nuclear medicine","volume":"49","issn":"0001-2998","url":"http://www.sciencedirect.com/science/article/pii/S0001299819300546","doi":"10.1053/j.semnuclmed.2019.06.005","abstract":"Radiomics – the high-throughput computation of quantitative image features extracted from medical imaging modalities- can be used to aid clinical decision support systems in order to build diagnostic, prognostic, and predictive models, which could ultimately improve personalized management based on individual characteristics. Various tools for radiomic features extraction are available, and the field gained a substantial scientific momentum for standardization and validation. Radiomics analysis of molecular imaging is expected to provide more comprehensive description of tissues than that of currently used parameters. We here review the workflow of radiomics, the challenges the field currently faces, and its potential for inclusion in clinical decision support systems to maximize disease characterization, and to improve clinical decision-making. We also present guidelines for standardization and implementation of radiomics in order to facilitate its transition to clinical use.","language":"en","number":"5","urldate":"2020-04-09","journal":"Seminars in Nuclear Medicine","author":[{"propositions":[],"lastnames":["Ibrahim"],"firstnames":["Abdalla"],"suffixes":[]},{"propositions":[],"lastnames":["Vallieres"],"firstnames":["Martin"],"suffixes":[]},{"propositions":[],"lastnames":["Woodruff"],"firstnames":["Henry"],"suffixes":[]},{"propositions":[],"lastnames":["Primakov"],"firstnames":["Sergey"],"suffixes":[]},{"propositions":[],"lastnames":["Beheshti"],"firstnames":["Mohsen"],"suffixes":[]},{"propositions":[],"lastnames":["Keek"],"firstnames":["Simon"],"suffixes":[]},{"propositions":[],"lastnames":["Refaee"],"firstnames":["Turkey"],"suffixes":[]},{"propositions":[],"lastnames":["Sanduleanu"],"firstnames":["Sebastian"],"suffixes":[]},{"propositions":[],"lastnames":["Walsh"],"firstnames":["Sean"],"suffixes":[]},{"propositions":[],"lastnames":["Morin"],"firstnames":["Olivier"],"suffixes":[]},{"propositions":[],"lastnames":["Lambin"],"firstnames":["Philippe"],"suffixes":[]},{"propositions":[],"lastnames":["Hustinx"],"firstnames":["Roland"],"suffixes":[]},{"propositions":[],"lastnames":["Mottaghy"],"firstnames":["Felix","M."],"suffixes":[]}],"month":"September","year":"2019","note":"Number: 5 Reporter: Seminars in Nuclear Medicine","keywords":"Journal Article","pages":"438–449","bibtex":"@article{ibrahim_radiomics_2019,\n\tseries = {From {Basic} {Science} to {Clinical} {Imaging}},\n\ttitle = {Radiomics analysis for clinical decision support in nuclear medicine},\n\tvolume = {49},\n\tissn = {0001-2998},\n\turl = {http://www.sciencedirect.com/science/article/pii/S0001299819300546},\n\tdoi = {10.1053/j.semnuclmed.2019.06.005},\n\tabstract = {Radiomics – the high-throughput computation of quantitative image features extracted from medical imaging modalities- can be used to aid clinical decision support systems in order to build diagnostic, prognostic, and predictive models, which could ultimately improve personalized management based on individual characteristics. Various tools for radiomic features extraction are available, and the field gained a substantial scientific momentum for standardization and validation. Radiomics analysis of molecular imaging is expected to provide more comprehensive description of tissues than that of currently used parameters. We here review the workflow of radiomics, the challenges the field currently faces, and its potential for inclusion in clinical decision support systems to maximize disease characterization, and to improve clinical decision-making. We also present guidelines for standardization and implementation of radiomics in order to facilitate its transition to clinical use.},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2020-04-09},\n\tjournal = {Seminars in Nuclear Medicine},\n\tauthor = {Ibrahim, Abdalla and Vallieres, Martin and Woodruff, Henry and Primakov, Sergey and Beheshti, Mohsen and Keek, Simon and Refaee, Turkey and Sanduleanu, Sebastian and Walsh, Sean and Morin, Olivier and Lambin, Philippe and Hustinx, Roland and Mottaghy, Felix M.},\n\tmonth = sep,\n\tyear = {2019},\n\tnote = {Number: 5\nReporter: Seminars in Nuclear Medicine},\n\tkeywords = {Journal Article},\n\tpages = {438--449},\n}\n\n","author_short":["Ibrahim, A.","Vallieres, M.","Woodruff, H.","Primakov, S.","Beheshti, M.","Keek, S.","Refaee, T.","Sanduleanu, S.","Walsh, S.","Morin, O.","Lambin, P.","Hustinx, R.","Mottaghy, F. M."],"key":"ibrahim_radiomics_2019","id":"ibrahim_radiomics_2019","bibbaseid":"ibrahim-vallieres-woodruff-primakov-beheshti-keek-refaee-sanduleanu-etal-radiomicsanalysisforclinicaldecisionsupportinnuclearmedicine-2019","role":"author","urls":{"Paper":"http://www.sciencedirect.com/science/article/pii/S0001299819300546"},"keyword":["Journal Article"],"metadata":{"authorlinks":{}}},"bibtype":"article","biburl":"https://api.zotero.org/groups/2298129/items?key=Di2aUrSQXpXlWmYdUvNGDXyv&format=bibtex&limit=100","dataSources":["3byWrivAb63dcxozq"],"keywords":["journal article"],"search_terms":["radiomics","analysis","clinical","decision","support","nuclear","medicine","ibrahim","vallieres","woodruff","primakov","beheshti","keek","refaee","sanduleanu","walsh","morin","lambin","hustinx","mottaghy"],"title":"Radiomics analysis for clinical decision support in nuclear medicine","year":2019}