{"id":"https://openalex.org/W2791554584","doi":"https://doi.org/10.1117/12.2293661","title":"Deriving stable multi-parametric MRI radiomic signatures in the presence of inter-scanner variations: survival prediction of glioblastoma via imaging pattern analysis and machine learning techniques","display_name":"Deriving stable multi-parametric MRI radiomic signatures in the presence of inter-scanner variations: survival prediction of glioblastoma via imaging pattern analysis and machine learning techniques","publication_year":2018,"publication_date":"2018-02-27","ids":{"openalex":"https://openalex.org/W2791554584","doi":"https://doi.org/10.1117/12.2293661","mag":"2791554584"},"language":"en","primary_location":{"id":"doi:10.1117/12.2293661","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2293661","pdf_url":null,"source":{"id":"https://openalex.org/S4306519508","display_name":"Medical Imaging 2018: Computer-Aided Diagnosis","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2018: Computer-Aided Diagnosis","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/A5043497448","display_name":"Spyridon Bakas","orcid":"https://orcid.org/0000-0001-8734-6482"},"institutions":[{"id":"https://openalex.org/I79576946","display_name":"University of Pennsylvania","ror":"https://ror.org/00b30xv10","country_code":"US","type":"education","lineage":["https://openalex.org/I79576946"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Spyridon Bakas","raw_affiliation_strings":["Perelman School of Medicine, Univ. of Pennsylvania (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Perelman School of Medicine, Univ. of Pennsylvania (United States)","institution_ids":["https://openalex.org/I79576946"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083616879","display_name":"Hamed Akbari","orcid":"https://orcid.org/0000-0001-9786-3707"},"institutions":[{"id":"https://openalex.org/I79576946","display_name":"University of Pennsylvania","ror":"https://ror.org/00b30xv10","country_code":"US","type":"education","lineage":["https://openalex.org/I79576946"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hamed Akbari","raw_affiliation_strings":["Perelman School of Medicine, Univ. of Pennsylvania (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Perelman School of Medicine, Univ. of Pennsylvania (United States)","institution_ids":["https://openalex.org/I79576946"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115592529","display_name":"Garima Shukla","orcid":"https://orcid.org/0000-0002-9673-3107"},"institutions":[{"id":"https://openalex.org/I149251103","display_name":"Thomas Jefferson University","ror":"https://ror.org/00ysqcn41","country_code":"US","type":"education","lineage":["https://openalex.org/I149251103"]},{"id":"https://openalex.org/I79576946","display_name":"University of Pennsylvania","ror":"https://ror.org/00b30xv10","country_code":"US","type":"education","lineage":["https://openalex.org/I79576946"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Gaurav Shukla","raw_affiliation_strings":["Perelman School of Medicine, Univ. of Pennsylvania (United States)","Thomas Jefferson Univ. (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Perelman School of Medicine, Univ. of Pennsylvania (United States)","institution_ids":["https://openalex.org/I79576946"]},{"raw_affiliation_string":"Thomas Jefferson Univ. (United States)","institution_ids":["https://openalex.org/I149251103"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068958391","display_name":"Martin Rozycki","orcid":null},"institutions":[{"id":"https://openalex.org/I79576946","display_name":"University of Pennsylvania","ror":"https://ror.org/00b30xv10","country_code":"US","type":"education","lineage":["https://openalex.org/I79576946"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Martin Rozycki","raw_affiliation_strings":["Perelman School of Medicine, Univ. of Pennsylvania (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Perelman School of Medicine, Univ. of Pennsylvania (United States)","institution_ids":["https://openalex.org/I79576946"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034999945","display_name":"Christos Davatzikos","orcid":"https://orcid.org/0000-0002-1025-8561"},"institutions":[{"id":"https://openalex.org/I79576946","display_name":"University of Pennsylvania","ror":"https://ror.org/00b30xv10","country_code":"US","type":"education","lineage":["https://openalex.org/I79576946"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Christos Davatzikos","raw_affiliation_strings":["Perelman School of Medicine, Univ. of Pennsylvania (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Perelman School of Medicine, Univ. of Pennsylvania (United States)","institution_ids":["https://openalex.org/I79576946"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102723166","display_name":"Saima Rathore","orcid":"https://orcid.org/0000-0003-4752-2298"},"institutions":[{"id":"https://openalex.org/I79576946","display_name":"University of Pennsylvania","ror":"https://ror.org/00b30xv10","country_code":"US","type":"education","lineage":["https://openalex.org/I79576946"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Saima Rathore","raw_affiliation_strings":["Perelman School of Medicine, Univ. of Pennsylvania (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Perelman School of Medicine, Univ. of Pennsylvania (United States)","institution_ids":["https://openalex.org/I79576946"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5553,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.68255549,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"60","issue":null,"first_page":"8","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11885","display_name":"MRI in cancer diagnosis","score":0.9941999912261963,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10129","display_name":"Glioma Diagnosis and Treatment","score":0.9925000071525574,"subfield":{"id":"https://openalex.org/subfields/2716","display_name":"Genetics"},"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/glioblastoma","display_name":"Glioblastoma","score":0.796403706073761},{"id":"https://openalex.org/keywords/scanner","display_name":"Scanner","score":0.7927983999252319},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6684268116950989},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6269105672836304},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.541577160358429},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.5355002880096436},{"id":"https://openalex.org/keywords/radiomics","display_name":"Radiomics","score":0.4469267725944519},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.33467310667037964},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.11353179812431335},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.11193928122520447},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11023679375648499}],"concepts":[{"id":"https://openalex.org/C2776194525","wikidata":"https://www.wikidata.org/wiki/Q282142","display_name":"Glioblastoma","level":2,"score":0.796403706073761},{"id":"https://openalex.org/C2779751349","wikidata":"https://www.wikidata.org/wiki/Q1474480","display_name":"Scanner","level":2,"score":0.7927983999252319},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6684268116950989},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6269105672836304},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.541577160358429},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.5355002880096436},{"id":"https://openalex.org/C2778559731","wikidata":"https://www.wikidata.org/wiki/Q23808793","display_name":"Radiomics","level":2,"score":0.4469267725944519},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.33467310667037964},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.11353179812431335},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.11193928122520447},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11023679375648499},{"id":"https://openalex.org/C502942594","wikidata":"https://www.wikidata.org/wiki/Q3421914","display_name":"Cancer research","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2293661","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2293661","pdf_url":null,"source":{"id":"https://openalex.org/S4306519508","display_name":"Medical Imaging 2018: Computer-Aided Diagnosis","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2018: Computer-Aided Diagnosis","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Good health and well-being","score":0.6899999976158142,"id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W33787415","https://openalex.org/W1836087675","https://openalex.org/W2028569674","https://openalex.org/W2037316890","https://openalex.org/W2098812986","https://openalex.org/W2099164578","https://openalex.org/W2115862526","https://openalex.org/W2124793236","https://openalex.org/W2148726987","https://openalex.org/W2221563957","https://openalex.org/W2237916396","https://openalex.org/W2299407206","https://openalex.org/W2412027445","https://openalex.org/W2465688795","https://openalex.org/W2588003010","https://openalex.org/W2751069891","https://openalex.org/W2768026820","https://openalex.org/W2768079112","https://openalex.org/W2783923767","https://openalex.org/W2977883299","https://openalex.org/W3120421331","https://openalex.org/W4213201865","https://openalex.org/W4256188663","https://openalex.org/W6714719069"],"related_works":["https://openalex.org/W3000891326","https://openalex.org/W4205100762","https://openalex.org/W2734724112","https://openalex.org/W2582997534","https://openalex.org/W3009210156","https://openalex.org/W4388577230","https://openalex.org/W4321251649","https://openalex.org/W3128847470","https://openalex.org/W2909044173","https://openalex.org/W2555360349"],"abstract_inverted_index":{"There":[0],"is":[1,41],"mounting":[2],"evidence":[3],"that":[4,113,223],"assessment":[5],"of":[6,25,60,78,86,89,114,235],"multi-parametric":[7],"magnetic":[8],"resonance":[9],"imaging":[10],"(mpMRI)":[11],"profiles":[12],"can":[13],"noninvasively":[14],"predict":[15],"survival":[16,130,160,236],"in":[17,36,52,151,156,161,172,193],"many":[18],"cancers,":[19],"including":[20],"glioblastoma.":[21],"The":[22,95,119,132,158,199],"clinical":[23],"adoption":[24],"mpMRI":[26,77,239],"as":[27,66,166],"a":[28,37,57,167],"prognostic":[29],"biomarker,":[30],"however,":[31],"depends":[32],"on":[33,238],"its":[34],"applicability":[35],"multicenter":[38,76],"setting,":[39],"which":[40],"hampered":[42],"by":[43,106,124,140,241],"inter-scanner":[44],"variations.":[45],"This":[46],"concept":[47],"has":[48],"not":[49],"been":[50],"addressed":[51],"existing":[53],"studies.":[54],"We":[55],"developed":[56],"comprehensive":[58],"set":[59],"within-patient":[61],"normalized":[62],"tumor":[63,72,109],"features":[64,121],"such":[65],"intensity":[67,110],"profile,":[68,111],"shape,":[69],"volume,":[70],"and":[71,94,146,154,174,188,191,195,210,216],"location,":[73],"extracted":[74,120],"from":[75],"two":[79,141],"large":[80],"(n<sub>patients</sub>=353)":[81],"cohorts,":[82],"comprising":[83],"the":[84,87,108,115],"Hospital":[85],"University":[88],"Pennsylvania":[90],"(HUP,":[91],"n<sub>patients</sub>=252,":[92],"n<sub>scanners</sub>=3)":[93],"Cancer":[96],"Imaging":[97],"Archive":[98],"(TCIA,":[99],"n<sub>patients</sub>=101,":[100],"n<sub>scanners</sub>=8).":[101],"Inter-scanner":[102],"harmonization":[103],"was":[104,135,164,186,206],"conducted":[105],"normalizing":[107],"with":[112,228],"contralateral":[116],"healthy":[117],"tissue.":[118],"were":[122],"integrated":[123],"support":[125],"vector":[126],"machines":[127],"to":[128,169],"derive":[129],"predictors.":[131],"predictors\u2019":[133],"generalizability":[134],"evaluated":[136],"within":[137],"each":[138,162],"cohort,":[139],"cross-validation":[142],"configurations:":[143],"i)":[144],"pooled/scanner-agnostic,":[145],"ii)":[147],"across":[148],"scanners":[149,153],"(training":[150],"multiple":[152,242],"testing":[155],"one).":[157],"median":[159],"configuration":[163],"used":[165],"cut-off":[168],"divide":[170],"patients":[171],"long-":[173,180],"short-survivors.":[175],"Accuracy":[176],"(ACC)":[177],"for":[178,183,214],"predicting":[179],"versus":[181],"short-survivors,":[182],"these":[184],"configurations":[185],"ACC<sub>pooled</sub>=79.06%":[187],"ACC<sub>pooled</sub>=84.7%,":[189],"ACC<sub>across</sub>=73.55%":[190],"ACCacross=74.76%,":[192],"HUP":[194,215],"TCIA":[196,217],"datasets,":[197,218],"respectively.":[198,219],"hazard":[200],"ratio":[201],"at":[202],"95%":[203],"confidence":[204],"interval":[205],"3.87":[207],"(2.87\u20135.20,":[208],"P&lt;0.001)":[209,213],"6.65":[211],"(3.57-12.36,":[212],"Our":[220],"findings":[221],"suggest":[222],"adequate":[224],"data":[225],"normalization":[226],"coupled":[227],"machine":[229],"learning":[230],"classification":[231],"allows":[232],"robust":[233],"prediction":[234],"estimates":[237],"acquired":[240],"scanners.":[243]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
