{"id":"https://openalex.org/W2587447325","doi":"https://doi.org/10.1109/smc.2016.7844845","title":"Modelling xenograft tumor growth under antiangiogenic inhibitation with mixed-effects models","display_name":"Modelling xenograft tumor growth under antiangiogenic inhibitation with mixed-effects models","publication_year":2016,"publication_date":"2016-10-01","ids":{"openalex":"https://openalex.org/W2587447325","doi":"https://doi.org/10.1109/smc.2016.7844845","mag":"2587447325"},"language":"en","primary_location":{"id":"doi:10.1109/smc.2016.7844845","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc.2016.7844845","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5019524154","display_name":"Tam\u00e1s Ferenci","orcid":"https://orcid.org/0000-0001-6791-3080"},"institutions":[{"id":"https://openalex.org/I103356709","display_name":"Obuda University","ror":"https://ror.org/00ax71d21","country_code":"HU","type":"education","lineage":["https://openalex.org/I103356709"]}],"countries":["HU"],"is_corresponding":true,"raw_author_name":"Tamas Ferenci","raw_affiliation_strings":["Physiological Controls Group, \u00d3buda University, Budapest, Hungary"],"affiliations":[{"raw_affiliation_string":"Physiological Controls Group, \u00d3buda University, Budapest, Hungary","institution_ids":["https://openalex.org/I103356709"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055196164","display_name":"Johanna S\u00e1pi","orcid":"https://orcid.org/0000-0002-9072-1965"},"institutions":[{"id":"https://openalex.org/I103356709","display_name":"Obuda University","ror":"https://ror.org/00ax71d21","country_code":"HU","type":"education","lineage":["https://openalex.org/I103356709"]}],"countries":["HU"],"is_corresponding":false,"raw_author_name":"Johanna Sapi","raw_affiliation_strings":["Physiological Controls Group, \u00d3buda University, Budapest, Hungary"],"affiliations":[{"raw_affiliation_string":"Physiological Controls Group, \u00d3buda University, Budapest, Hungary","institution_ids":["https://openalex.org/I103356709"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021051122","display_name":"Levente Kov\u00e1cs","orcid":"https://orcid.org/0000-0002-3188-0800"},"institutions":[{"id":"https://openalex.org/I103356709","display_name":"Obuda University","ror":"https://ror.org/00ax71d21","country_code":"HU","type":"education","lineage":["https://openalex.org/I103356709"]}],"countries":["HU"],"is_corresponding":false,"raw_author_name":"Levente Kovacs","raw_affiliation_strings":["Physiological Controls Group, \u00d3buda University, Budapest, Hungary"],"affiliations":[{"raw_affiliation_string":"Physiological Controls Group, \u00d3buda University, Budapest, Hungary","institution_ids":["https://openalex.org/I103356709"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5019524154"],"corresponding_institution_ids":["https://openalex.org/I103356709"],"apc_list":null,"apc_paid":null,"fwci":0.2344,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.65095882,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"27","issue":null,"first_page":"003912","last_page":"003917"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11829","display_name":"Mathematical Biology Tumor Growth","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2611","display_name":"Modeling and Simulation"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11829","display_name":"Mathematical Biology Tumor Growth","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2611","display_name":"Modeling and Simulation"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10422","display_name":"Angiogenesis and VEGF in Cancer","score":0.9833999872207642,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10336","display_name":"Cancer Cells and Metastasis","score":0.9764999747276306,"subfield":{"id":"https://openalex.org/subfields/2730","display_name":"Oncology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/gompertz-function","display_name":"Gompertz function","score":0.6899324655532837},{"id":"https://openalex.org/keywords/heteroscedasticity","display_name":"Heteroscedasticity","score":0.6464606523513794},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.5932298302650452},{"id":"https://openalex.org/keywords/bevacizumab","display_name":"Bevacizumab","score":0.5732477903366089},{"id":"https://openalex.org/keywords/covariate","display_name":"Covariate","score":0.5367739200592041},{"id":"https://openalex.org/keywords/oncology","display_name":"Oncology","score":0.5000400543212891},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.47736644744873047},{"id":"https://openalex.org/keywords/outcome","display_name":"Outcome (game theory)","score":0.46163249015808105},{"id":"https://openalex.org/keywords/autocorrelation","display_name":"Autocorrelation","score":0.45542803406715393},{"id":"https://openalex.org/keywords/extrapolation","display_name":"Extrapolation","score":0.4363757371902466},{"id":"https://openalex.org/keywords/magnetic-resonance-imaging","display_name":"Magnetic resonance imaging","score":0.42636242508888245},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.36749327182769775},{"id":"https://openalex.org/keywords/urology","display_name":"Urology","score":0.3536180555820465},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3334934711456299},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.27160194516181946},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.15646591782569885},{"id":"https://openalex.org/keywords/chemotherapy","display_name":"Chemotherapy","score":0.14957529306411743}],"concepts":[{"id":"https://openalex.org/C134463574","wikidata":"https://www.wikidata.org/wiki/Q1011785","display_name":"Gompertz function","level":2,"score":0.6899324655532837},{"id":"https://openalex.org/C101104100","wikidata":"https://www.wikidata.org/wiki/Q1063540","display_name":"Heteroscedasticity","level":2,"score":0.6464606523513794},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.5932298302650452},{"id":"https://openalex.org/C2777802072","wikidata":"https://www.wikidata.org/wiki/Q413299","display_name":"Bevacizumab","level":3,"score":0.5732477903366089},{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.5367739200592041},{"id":"https://openalex.org/C143998085","wikidata":"https://www.wikidata.org/wiki/Q162555","display_name":"Oncology","level":1,"score":0.5000400543212891},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.47736644744873047},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.46163249015808105},{"id":"https://openalex.org/C5297727","wikidata":"https://www.wikidata.org/wiki/Q786970","display_name":"Autocorrelation","level":2,"score":0.45542803406715393},{"id":"https://openalex.org/C132459708","wikidata":"https://www.wikidata.org/wiki/Q744069","display_name":"Extrapolation","level":2,"score":0.4363757371902466},{"id":"https://openalex.org/C143409427","wikidata":"https://www.wikidata.org/wiki/Q161238","display_name":"Magnetic resonance imaging","level":2,"score":0.42636242508888245},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.36749327182769775},{"id":"https://openalex.org/C126894567","wikidata":"https://www.wikidata.org/wiki/Q105650","display_name":"Urology","level":1,"score":0.3536180555820465},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3334934711456299},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.27160194516181946},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.15646591782569885},{"id":"https://openalex.org/C2776694085","wikidata":"https://www.wikidata.org/wiki/Q974135","display_name":"Chemotherapy","level":2,"score":0.14957529306411743},{"id":"https://openalex.org/C99454951","wikidata":"https://www.wikidata.org/wiki/Q932068","display_name":"Environmental health","level":1,"score":0.0},{"id":"https://openalex.org/C144237770","wikidata":"https://www.wikidata.org/wiki/Q747534","display_name":"Mathematical economics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/smc.2016.7844845","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc.2016.7844845","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"},{"id":"pmh:oai:real.mtak.hu:45560","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4306400081","display_name":"Repository of the Academy's Library (Library of the Hungarian Academy of Sciences)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210140733","host_organization_name":"Library and Information Centre of the Hungarian Academy of Sciences","host_organization_lineage":["https://openalex.org/I4210140733"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"","raw_type":"Book Section"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4300000071525574,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W200633739","https://openalex.org/W1587094587","https://openalex.org/W1980911127","https://openalex.org/W1984383995","https://openalex.org/W2012976204","https://openalex.org/W2068047481","https://openalex.org/W2070779353","https://openalex.org/W2074642772","https://openalex.org/W2081084899","https://openalex.org/W2099998237","https://openalex.org/W2101961850","https://openalex.org/W2111663010","https://openalex.org/W2148501415","https://openalex.org/W2150515509","https://openalex.org/W2223825899","https://openalex.org/W2243404975","https://openalex.org/W2582743722","https://openalex.org/W2804842145","https://openalex.org/W3086315876","https://openalex.org/W3090529871","https://openalex.org/W4206439534","https://openalex.org/W4237685485","https://openalex.org/W4399568367","https://openalex.org/W6634990337","https://openalex.org/W6645621514","https://openalex.org/W6675433768","https://openalex.org/W6681733904","https://openalex.org/W6733261796","https://openalex.org/W6869296237"],"related_works":["https://openalex.org/W3142026086","https://openalex.org/W152192128","https://openalex.org/W193081795","https://openalex.org/W1987826597","https://openalex.org/W4242816516","https://openalex.org/W4234317657","https://openalex.org/W2783283813","https://openalex.org/W2066188834","https://openalex.org/W1984038448","https://openalex.org/W2950890709"],"abstract_inverted_index":{"Antiangiogenic":[0],"inhibitors":[1],"offer":[2],"a":[3],"promising":[4],"new":[5],"treatment":[6],"modality":[7],"in":[8,144],"oncology.":[9],"However,":[10],"the":[11,20,29,34,72,75,79,131,134,150,153],"optimal":[12],"administration":[13,80],"regime":[14],"is":[15,137],"often":[16],"not":[17],"well-established,":[18],"despite":[19],"fact":[21],"that":[22,130],"it":[23,141,147],"might":[24],"have":[25],"substantial":[26],"impact":[27],"on":[28,115],"outcome.":[30],"The":[31],"aim":[32],"of":[33,68,133,152],"present":[35],"study":[36],"was":[37,83,95],"to":[38,113],"investigate":[39],"this":[40],"issue.":[41],"Eight":[42],"weeks":[43],"old":[44],"male":[45],"C57Bl/6":[46],"mice":[47],"were":[48,55,100,124],"implanted":[49],"with":[50,102],"C38":[51],"colon":[52],"adenocarcinoma,":[53],"and":[54,91,109,126],"given":[56],"either":[57],"daily":[58],"(n":[59,64],"=":[60,65],"9)":[61],"or":[62],"single":[63],"5)":[66],"dose":[67,74],"bevacizumab;":[69],"both":[70,89],"receiving":[71],"same":[73],"only":[76],"difference":[77],"being":[78],"pattern.":[81],"Outcome":[82],"measured":[84],"by":[85],"tracking":[86],"tumor":[87],"volume;":[88],"caliper":[90],"magnetic":[92],"resonance":[93],"imaging":[94],"employed.":[96],"Longitudinal":[97],"growth":[98,119],"curves":[99],"modelled":[101],"mixed-effects":[103],"models":[104,120],"(with":[105],"correction":[106],"for":[107],"autocorrelation":[108],"heteroscedasticity,":[110],"where":[111],"necessary)":[112],"infer":[114],"population-level.":[116],"Several":[117],"different":[118],"(exponential,":[121],"logistic,":[122],"Gompertz)":[123],"applied":[125],"compared.":[127],"Results":[128],"show":[129],"estimation":[132],"exponential":[135],"model":[136],"very":[138],"reliable,":[139],"but":[140],"prevents":[142],"extrapolation":[143],"time.":[145],"Nevertheless,":[146],"clearly":[148],"established":[149],"advantage":[151],"continuous":[154],"regime.":[155]},"counts_by_year":[{"year":2017,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}