{"id":"https://openalex.org/W3132039706","doi":"https://doi.org/10.1117/12.2581883","title":"Comparison of diagnostic performances, case-based repeatability, and operating sensitivity and specificity in classification of breast lesions using DCE-MRI","display_name":"Comparison of diagnostic performances, case-based repeatability, and operating sensitivity and specificity in classification of breast lesions using DCE-MRI","publication_year":2021,"publication_date":"2021-02-12","ids":{"openalex":"https://openalex.org/W3132039706","doi":"https://doi.org/10.1117/12.2581883","mag":"3132039706"},"language":"en","primary_location":{"id":"doi:10.1117/12.2581883","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2581883","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2021: Image Perception, Observer Performance, and Technology Assessment","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/A5101769030","display_name":"Michelle de Oliveira","orcid":"https://orcid.org/0000-0003-0386-734X"},"institutions":[{"id":"https://openalex.org/I73236664","display_name":"Wheaton College - Illinois","ror":"https://ror.org/0581k0452","country_code":"US","type":"education","lineage":["https://openalex.org/I73236664"]},{"id":"https://openalex.org/I40347166","display_name":"University of Chicago","ror":"https://ror.org/024mw5h28","country_code":"US","type":"education","lineage":["https://openalex.org/I40347166"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Michelle de Oliveira","raw_affiliation_strings":["The Univ. of Chicago (United States)","Wheaton College (United States)"],"affiliations":[{"raw_affiliation_string":"The Univ. of Chicago (United States)","institution_ids":["https://openalex.org/I40347166"]},{"raw_affiliation_string":"Wheaton College (United States)","institution_ids":["https://openalex.org/I73236664"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024321936","display_name":"Karen Drukker","orcid":"https://orcid.org/0000-0001-6544-3476"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Karen Drukker","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012915209","display_name":"Michael Vieceli","orcid":null},"institutions":[{"id":"https://openalex.org/I73236664","display_name":"Wheaton College - Illinois","ror":"https://ror.org/0581k0452","country_code":"US","type":"education","lineage":["https://openalex.org/I73236664"]},{"id":"https://openalex.org/I40347166","display_name":"University of Chicago","ror":"https://ror.org/024mw5h28","country_code":"US","type":"education","lineage":["https://openalex.org/I40347166"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Michael Vieceli","raw_affiliation_strings":["The Univ. of Chicago (United States)","Wheaton College (United States)"],"affiliations":[{"raw_affiliation_string":"The Univ. of Chicago (United States)","institution_ids":["https://openalex.org/I40347166"]},{"raw_affiliation_string":"Wheaton College (United States)","institution_ids":["https://openalex.org/I73236664"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025334715","display_name":"Hiroyuki Ab\u00e9","orcid":"https://orcid.org/0000-0002-3568-1462"},"institutions":[{"id":"https://openalex.org/I40347166","display_name":"University of Chicago","ror":"https://ror.org/024mw5h28","country_code":"US","type":"education","lineage":["https://openalex.org/I40347166"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hiroyuki Abe","raw_affiliation_strings":["The Univ. of Chicago (United States)"],"affiliations":[{"raw_affiliation_string":"The Univ. of Chicago (United States)","institution_ids":["https://openalex.org/I40347166"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049042648","display_name":"Maryellen L. Giger","orcid":"https://orcid.org/0000-0001-5482-9728"},"institutions":[{"id":"https://openalex.org/I40347166","display_name":"University of Chicago","ror":"https://ror.org/024mw5h28","country_code":"US","type":"education","lineage":["https://openalex.org/I40347166"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Maryellen L. Giger","raw_affiliation_strings":["The Univ. of Chicago (United States)"],"affiliations":[{"raw_affiliation_string":"The Univ. of Chicago (United States)","institution_ids":["https://openalex.org/I40347166"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032409695","display_name":"Heather M. Whitney","orcid":"https://orcid.org/0000-0002-7258-1102"},"institutions":[{"id":"https://openalex.org/I73236664","display_name":"Wheaton College - Illinois","ror":"https://ror.org/0581k0452","country_code":"US","type":"education","lineage":["https://openalex.org/I73236664"]},{"id":"https://openalex.org/I40347166","display_name":"University of Chicago","ror":"https://ror.org/024mw5h28","country_code":"US","type":"education","lineage":["https://openalex.org/I40347166"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Heather M. Whitney","raw_affiliation_strings":["The Univ. of Chicago (United States)","Wheaton College (United States)"],"affiliations":[{"raw_affiliation_string":"The Univ. of Chicago (United States)","institution_ids":["https://openalex.org/I40347166"]},{"raw_affiliation_string":"Wheaton College (United States)","institution_ids":["https://openalex.org/I73236664"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5101769030"],"corresponding_institution_ids":["https://openalex.org/I40347166","https://openalex.org/I73236664"],"apc_list":null,"apc_paid":null,"fwci":0.4124,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.60119058,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"22","last_page":"22"},"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":0.9998999834060669,"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":0.9998999834060669,"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":0.9995999932289124,"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/T10862","display_name":"AI in cancer detection","score":0.9857000112533569,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/repeatability","display_name":"Repeatability","score":0.7413321733474731},{"id":"https://openalex.org/keywords/receiver-operating-characteristic","display_name":"Receiver operating characteristic","score":0.7154327034950256},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6457785367965698},{"id":"https://openalex.org/keywords/quadratic-classifier","display_name":"Quadratic classifier","score":0.6143226027488708},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6082562208175659},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.568610668182373},{"id":"https://openalex.org/keywords/confidence-interval","display_name":"Confidence interval","score":0.5341635346412659},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.5152173638343811},{"id":"https://openalex.org/keywords/margin-classifier","display_name":"Margin classifier","score":0.50687175989151},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.505500316619873},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.46599021553993225},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.45343226194381714},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.45139217376708984},{"id":"https://openalex.org/keywords/magnetic-resonance-imaging","display_name":"Magnetic resonance imaging","score":0.43472421169281006},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.32795387506484985},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2601722478866577},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.20708611607551575},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.1980314552783966}],"concepts":[{"id":"https://openalex.org/C154020017","wikidata":"https://www.wikidata.org/wiki/Q520171","display_name":"Repeatability","level":2,"score":0.7413321733474731},{"id":"https://openalex.org/C58471807","wikidata":"https://www.wikidata.org/wiki/Q327120","display_name":"Receiver operating characteristic","level":2,"score":0.7154327034950256},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6457785367965698},{"id":"https://openalex.org/C52620605","wikidata":"https://www.wikidata.org/wiki/Q7268357","display_name":"Quadratic classifier","level":3,"score":0.6143226027488708},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6082562208175659},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.568610668182373},{"id":"https://openalex.org/C44249647","wikidata":"https://www.wikidata.org/wiki/Q208498","display_name":"Confidence interval","level":2,"score":0.5341635346412659},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.5152173638343811},{"id":"https://openalex.org/C173102733","wikidata":"https://www.wikidata.org/wiki/Q6760396","display_name":"Margin classifier","level":3,"score":0.50687175989151},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.505500316619873},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.46599021553993225},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.45343226194381714},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.45139217376708984},{"id":"https://openalex.org/C143409427","wikidata":"https://www.wikidata.org/wiki/Q161238","display_name":"Magnetic resonance imaging","level":2,"score":0.43472421169281006},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32795387506484985},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2601722478866577},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.20708611607551575},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.1980314552783966}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2581883","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2581883","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2021: Image Perception, Observer Performance, and Technology Assessment","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.49000000953674316}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2113454941","https://openalex.org/W2951122819","https://openalex.org/W4288315282","https://openalex.org/W2088711785","https://openalex.org/W2411585343","https://openalex.org/W2753804328","https://openalex.org/W2085106164","https://openalex.org/W2089282851","https://openalex.org/W2999226773","https://openalex.org/W2092457881"],"abstract_inverted_index":{"Understanding":[0],"repeatability":[1,142],"of":[2,9,21,62,101,130,149,224],"classification":[3,11,55,58],"by":[4,152,182,218,231],"classifier":[5,150,155,213,225,232,261],"in":[6,133,195,233],"the":[7,92,99,111,125,131,164,190,200,210],"context":[8],"overall":[10],"performance":[12],"and":[13,60,65,68,81,174,187,238,241,247],"operating":[14,94,179],"points":[15],"can":[16],"contribute":[17],"to":[18,189,235],"improved":[19],"design":[20,252],"computer-aided":[22],"diagnosis":[23],"(CADx).":[24],"Breast":[25],"lesions":[26,103],"(243":[27],"benign,":[28],"853":[29],"malignant:":[30],"1,096":[31],"total)":[32],"were":[33,52,70,161,249],"segmented":[34],"using":[35,84,110],"a":[36,85,176,219],"fuzzy":[37],"c-means":[38],"method":[39],"from":[40,163,255],"dynamic":[41],"contrast-enhanced":[42],"magnetic":[43],"resonance":[44],"images":[45],"acquired":[46],"over":[47],"2005-2015.":[48],"Thirty-eight":[49],"radiomic":[50],"features":[51],"extracted.":[53],"Overall":[54],"performance,":[56],"case-based":[57],"repeatability,":[59],"attainment":[61],"\u2018preferred\u2019":[63],"target":[64,168,171,237],"\u2018optimal\u2019":[66,178,239],"sensitivity":[67,169],"specificity":[69,172],"investigated":[71],"for":[72,98,136,167,175],"three":[73,201],"classifiers:":[74],"linear":[75],"discriminant":[76],"analysis,":[77],"support":[78],"vector":[79],"machine,":[80],"random":[82,211],"forest":[83,212],"1000-iteration":[86],"0.632":[87],"bootstrap.":[88],"The":[89],"area":[90],"under":[91],"receiver":[93],"characteristic":[95],"curve":[96],"(AUC)":[97],"task":[100],"classifying":[102],"as":[104,216],"malignant":[105],"or":[106],"benign":[107],"was":[108,116,122,143,197,206,214],"determined":[109,144,162,181],"0.632+":[112],"bootstrap":[113],"correction.":[114],"AUC":[115,134,196],"compared":[117],"between":[118,199],"classifiers;":[119],"statistical":[120],"significance":[121],"indicated":[123,217],"when":[124,209,258],"98.33%":[126],"confidence":[127],"interval":[128],"(CI)":[129],"difference":[132,194],"(corrected":[135],"multiple":[137],"comparisons)":[138],"excluded":[139],"zero.":[140],"Classifier":[141,158,203],"through":[145],"95%":[146,221],"CI":[147,222],"width":[148,223],"output":[151,156,159,226],"case":[153],"across":[154],"range.":[157],"thresholds":[160],"training":[165],"folds":[166],"(95%),":[170,173],"selected":[177],"point":[180],"minimizing":[183],"(1-sensitivity)<sup>2</sup>":[184],"+":[185],"(1-specificity)<sup>2</sup>":[186],"applied":[188],"test":[191],"folds.":[192],"No":[193],"observed":[198],"classifiers.":[202],"output,":[204],"however,":[205],"more":[207],"repeatable":[208],"used":[215],"lower":[220],"overall.":[227],"Moreover,":[228],"limited":[229],"differences":[230],"threshold":[234],"attain":[236],"sensitivities":[240,246],"specificities":[242,248],"along":[243],"with":[244],"attained":[245],"observed.":[250],"CADx":[251],"may":[253],"benefit":[254],"these":[256],"considerations":[257],"selecting":[259],"which":[260],"is":[262],"used.":[263]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}