{"id":"https://openalex.org/W4362673323","doi":"https://doi.org/10.1117/12.2654476","title":"Radiomics-based classification of autosomal dominant polycystic kidney disease (ADPKD) Mayo imaging classification (MIC) and the effect of gray-level discretization","display_name":"Radiomics-based classification of autosomal dominant polycystic kidney disease (ADPKD) Mayo imaging classification (MIC) and the effect of gray-level discretization","publication_year":2023,"publication_date":"2023-04-06","ids":{"openalex":"https://openalex.org/W4362673323","doi":"https://doi.org/10.1117/12.2654476"},"language":"en","primary_location":{"id":"doi:10.1117/12.2654476","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2654476","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2023: 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/A5020994752","display_name":"Linnea E. Kremer","orcid":null},"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":true,"raw_author_name":"Linnea E. Kremer","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/A5045814429","display_name":"Boris Fosso","orcid":null},"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":"Boris Fosso","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/A5032638466","display_name":"Lucy Groothuis","orcid":null},"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":"Lucy Groothuis","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/A5011537086","display_name":"Arlene B. Chapman","orcid":"https://orcid.org/0000-0003-4538-4565"},"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":"Arlene Chapman","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/A5000694161","display_name":"Samuel G. Armato","orcid":"https://orcid.org/0000-0002-5428-7997"},"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":"Samuel G. Armato","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"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5020994752"],"corresponding_institution_ids":["https://openalex.org/I40347166"],"apc_list":null,"apc_paid":null,"fwci":1.8924,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.85358472,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"107","last_page":"107"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11885","display_name":"MRI in cancer diagnosis","score":0.9994999766349792,"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/T11885","display_name":"MRI in cancer diagnosis","score":0.9994999766349792,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9986000061035156,"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/T10449","display_name":"Renal cell carcinoma treatment","score":0.9909999966621399,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"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/receiver-operating-characteristic","display_name":"Receiver operating characteristic","score":0.6860496997833252},{"id":"https://openalex.org/keywords/autosomal-dominant-polycystic-kidney-disease","display_name":"Autosomal dominant polycystic kidney disease","score":0.6759127378463745},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5524628162384033},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.5152919292449951},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.43443387746810913},{"id":"https://openalex.org/keywords/kidney","display_name":"Kidney","score":0.3664739429950714},{"id":"https://openalex.org/keywords/nuclear-medicine","display_name":"Nuclear medicine","score":0.3631739020347595},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.3281189203262329},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.2699729800224304},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.24458637833595276}],"concepts":[{"id":"https://openalex.org/C58471807","wikidata":"https://www.wikidata.org/wiki/Q327120","display_name":"Receiver operating characteristic","level":2,"score":0.6860496997833252},{"id":"https://openalex.org/C2776266639","wikidata":"https://www.wikidata.org/wiki/Q2732398","display_name":"Autosomal dominant polycystic kidney disease","level":3,"score":0.6759127378463745},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5524628162384033},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.5152919292449951},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.43443387746810913},{"id":"https://openalex.org/C2780091579","wikidata":"https://www.wikidata.org/wiki/Q9377","display_name":"Kidney","level":2,"score":0.3664739429950714},{"id":"https://openalex.org/C2989005","wikidata":"https://www.wikidata.org/wiki/Q214963","display_name":"Nuclear medicine","level":1,"score":0.3631739020347595},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.3281189203262329},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.2699729800224304},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.24458637833595276}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2654476","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2654476","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2023: Computer-Aided Diagnosis","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3","score":0.4399999976158142}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W2026616100","https://openalex.org/W2767128594","https://openalex.org/W2922239950","https://openalex.org/W2963883833","https://openalex.org/W2998789541","https://openalex.org/W3036323505","https://openalex.org/W3040659673","https://openalex.org/W3045086986","https://openalex.org/W3204595827","https://openalex.org/W4213273622"],"related_works":["https://openalex.org/W3003976835","https://openalex.org/W4213045027","https://openalex.org/W4385649027","https://openalex.org/W2074914780","https://openalex.org/W2087639140","https://openalex.org/W2332546246","https://openalex.org/W2975325791","https://openalex.org/W4388176858","https://openalex.org/W2765285283","https://openalex.org/W3152544876"],"abstract_inverted_index":{"Radiomics":[0],"has":[1],"shown":[2],"predictive":[3],"utility":[4],"in":[5,22,55,242],"kidney":[6,15,106,174,193,205,248,262],"function":[7],"decline":[8],"for":[9,62,138,260],"patients":[10,81,255],"with":[11,141,213],"autosomal":[12],"dominant":[13],"polycystic":[14],"disease":[16,37],"(ADPKD),":[17],"but":[18],"a":[19,42,163,210],"limiting":[20],"factor":[21],"the":[23,29,58,104,110,179,191,229,244],"clinical":[24],"use":[25],"of":[26,31,48,67],"radiomics":[27],"is":[28,41],"standardization":[30],"pre-processing":[32,60,221],"parameters,":[33],"which":[34],"may":[35,251],"be":[36],"specific.":[38],"Currently,":[39],"there":[40,234],"need":[43],"to":[44,94,167],"identify":[45,254],"texture-based":[46,236],"differences":[47,237],"riskstratified":[49],"Mayo":[50],"Imaging":[51],"Classification":[52],"(MIC)":[53],"groups":[54],"ADPKD":[56],"and":[57,70,76,82,108,121,130,145,190,217,246],"optimal":[59],"parameters":[61],"feature":[63,139],"extraction.":[64],"A":[65],"cohort":[66],"128":[68],"age-":[69],"gender-matched":[71],"low/intermediate":[72],"(MIC":[73,78],"classes":[74,79,241],"1A-1B)":[75],"high-risk":[77],"1C-1E)":[80],"their":[83],"respective":[84],"T2-weighted":[85],"fat":[86],"saturated":[87],"MRI":[88],"representative":[89],"coronal":[90],"images":[91],"were":[92,136,218],"used":[93,137],"classify":[95,168],"MIC":[96,170,240],"risk":[97,259],"using":[98,125,153],"radiomic":[99],"features":[100,161,226],"extracted":[101,227],"from":[102,188,201,228],"(1)":[103,126],"non-cystic":[105,173,204,245],"parenchyma":[107,206,249],"(2)":[109,131],"entire":[111,192,230,247],"kidney.":[112,231],"Graylevel":[113],"discretization":[114],"across":[115,220],"8,":[116],"16,":[117],"32,":[118],"64,":[119],"128,":[120],"256":[122],"gray":[123,215],"levels":[124,216],"fixed":[127,132],"bin":[128,133],"size":[129],"number":[134],"methods":[135,222],"extraction":[140],"up-sampling":[142],"(1.0&times;1.0":[143],"mm<sup>2</sup>)":[144,148],"down-sampling":[146],"(2.0&times;2.0":[147],"pixel":[149],"resampling.":[150],"Feature":[151],"selection":[152],"least":[154],"absolute":[155],"shrinkage":[156],"operator":[157],"(LASSO)":[158],"combined":[159],"relevant":[160],"into":[162],"logistic":[164],"regression":[165],"model":[166],"risk-stratified":[169,239],"classes.":[171],"The":[172,203],"classification":[175,195],"yielded":[176,196],"area":[177],"under":[178],"receiver":[180],"operating":[181],"characteristic":[182],"curve":[183],"(AUC)":[184],"values":[185,198,208],"that":[186,199,250],"ranged":[187,200],"0.68-0.84,":[189],"texture":[194],"AUC":[197,207],"0.83-0.88.":[202],"had":[209],"decreasing":[211],"trend":[212],"increasing":[214],"sensitive":[219],"more":[223],"so":[224],"than":[225],"Results":[232],"suggest":[233],"are":[235,257],"among":[238],"both":[243],"help":[252],"better":[253],"who":[256],"at":[258],"end-stage":[261],"disease.":[263]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2023,"cited_by_count":5}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
