{"id":"https://openalex.org/W3010764538","doi":"https://doi.org/10.1117/12.2549962","title":"Machine learning-powered prediction of recurrence in patients with non-small cell lung cancer using quantitative clinical and radiomic biomarkers","display_name":"Machine learning-powered prediction of recurrence in patients with non-small cell lung cancer using quantitative clinical and radiomic biomarkers","publication_year":2020,"publication_date":"2020-03-16","ids":{"openalex":"https://openalex.org/W3010764538","doi":"https://doi.org/10.1117/12.2549962","mag":"3010764538"},"language":"en","primary_location":{"id":"doi:10.1117/12.2549962","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2549962","pdf_url":null,"source":{"id":"https://openalex.org/S4306519512","display_name":"Medical Imaging 2020: 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 2020: 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/A5003020149","display_name":"Sehwa Moon","orcid":null},"institutions":[{"id":"https://openalex.org/I138925566","display_name":"Ewha Womans University","ror":"https://ror.org/053fp5c05","country_code":"KR","type":"education","lineage":["https://openalex.org/I138925566"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Sehwa Moon","raw_affiliation_strings":["Ewha Womans Univ. (Korea, Republic of)"],"affiliations":[{"raw_affiliation_string":"Ewha Womans Univ. (Korea, Republic of)","institution_ids":["https://openalex.org/I138925566"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037156175","display_name":"Dahim Choi","orcid":"https://orcid.org/0000-0001-7583-1333"},"institutions":[{"id":"https://openalex.org/I138925566","display_name":"Ewha Womans University","ror":"https://ror.org/053fp5c05","country_code":"KR","type":"education","lineage":["https://openalex.org/I138925566"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Dahim Choi","raw_affiliation_strings":["Ewha Womans Univ. (Korea, Republic of)"],"affiliations":[{"raw_affiliation_string":"Ewha Womans Univ. (Korea, Republic of)","institution_ids":["https://openalex.org/I138925566"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100405491","display_name":"Ji-Yeon Lee","orcid":"https://orcid.org/0000-0001-7057-9044"},"institutions":[{"id":"https://openalex.org/I138925566","display_name":"Ewha Womans University","ror":"https://ror.org/053fp5c05","country_code":"KR","type":"education","lineage":["https://openalex.org/I138925566"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Ji-Yeon Lee","raw_affiliation_strings":["Ewha Womans Univ. (Korea, Republic of)"],"affiliations":[{"raw_affiliation_string":"Ewha Womans Univ. (Korea, Republic of)","institution_ids":["https://openalex.org/I138925566"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109249505","display_name":"Myoung-Hee Kim","orcid":null},"institutions":[{"id":"https://openalex.org/I138925566","display_name":"Ewha Womans University","ror":"https://ror.org/053fp5c05","country_code":"KR","type":"education","lineage":["https://openalex.org/I138925566"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Myoung-Hee Kim","raw_affiliation_strings":["Ewha Womans Univ. (Korea, Republic of)"],"affiliations":[{"raw_affiliation_string":"Ewha Womans Univ. (Korea, Republic of)","institution_ids":["https://openalex.org/I138925566"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035033921","display_name":"Helen Hong","orcid":"https://orcid.org/0000-0001-5044-7909"},"institutions":[{"id":"https://openalex.org/I98600543","display_name":"Seoul Women's University","ror":"https://ror.org/04b2fhx54","country_code":"KR","type":"education","lineage":["https://openalex.org/I98600543"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Helen Hong","raw_affiliation_strings":["Seoul Women's Univ. (Korea, Republic of)"],"affiliations":[{"raw_affiliation_string":"Seoul Women's Univ. (Korea, Republic of)","institution_ids":["https://openalex.org/I98600543"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112528377","display_name":"Bong-Seog Kim","orcid":null},"institutions":[{"id":"https://openalex.org/I4210101595","display_name":"Seoul Veterans Hospital","ror":"https://ror.org/00xhz2q61","country_code":"KR","type":"healthcare","lineage":["https://openalex.org/I2801339556","https://openalex.org/I4210101595","https://openalex.org/I4210129722"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Bong-Seog Kim","raw_affiliation_strings":["Veterans Health Service Medical Ctr. (Korea, Republic of)"],"affiliations":[{"raw_affiliation_string":"Veterans Health Service Medical Ctr. (Korea, Republic of)","institution_ids":["https://openalex.org/I4210101595"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5083618878","display_name":"Jang\u2010Hwan Choi","orcid":"https://orcid.org/0000-0001-9273-034X"},"institutions":[{"id":"https://openalex.org/I138925566","display_name":"Ewha Womans University","ror":"https://ror.org/053fp5c05","country_code":"KR","type":"education","lineage":["https://openalex.org/I138925566"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jang-Hwan Choi","raw_affiliation_strings":["Ewha Womans Univ. (Korea, Republic of)"],"affiliations":[{"raw_affiliation_string":"Ewha Womans Univ. (Korea, Republic of)","institution_ids":["https://openalex.org/I138925566"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5003020149"],"corresponding_institution_ids":["https://openalex.org/I138925566"],"apc_list":null,"apc_paid":null,"fwci":0.3304,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.61270804,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"32","last_page":"32"},"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":0.9994000196456909,"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":0.9994000196456909,"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/T10202","display_name":"Lung Cancer Diagnosis and Treatment","score":0.9664999842643738,"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"}},{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9192000031471252,"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/lung-cancer","display_name":"Lung cancer","score":0.5379942059516907},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4984138011932373},{"id":"https://openalex.org/keywords/radiomics","display_name":"Radiomics","score":0.4504179358482361},{"id":"https://openalex.org/keywords/biomarker","display_name":"Biomarker","score":0.41873639822006226},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4147256910800934},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3769576847553253},{"id":"https://openalex.org/keywords/oncology","display_name":"Oncology","score":0.31339967250823975},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.30472439527511597},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.07853227853775024}],"concepts":[{"id":"https://openalex.org/C2776256026","wikidata":"https://www.wikidata.org/wiki/Q47912","display_name":"Lung cancer","level":2,"score":0.5379942059516907},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4984138011932373},{"id":"https://openalex.org/C2778559731","wikidata":"https://www.wikidata.org/wiki/Q23808793","display_name":"Radiomics","level":2,"score":0.4504179358482361},{"id":"https://openalex.org/C2781197716","wikidata":"https://www.wikidata.org/wiki/Q864574","display_name":"Biomarker","level":2,"score":0.41873639822006226},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4147256910800934},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3769576847553253},{"id":"https://openalex.org/C143998085","wikidata":"https://www.wikidata.org/wiki/Q162555","display_name":"Oncology","level":1,"score":0.31339967250823975},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.30472439527511597},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.07853227853775024},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2549962","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2549962","pdf_url":null,"source":{"id":"https://openalex.org/S4306519512","display_name":"Medical Imaging 2020: 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 2020: Computer-Aided Diagnosis","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Good health and well-being","score":0.699999988079071,"id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"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/W4385221818","https://openalex.org/W3030796519","https://openalex.org/W4383892348","https://openalex.org/W4390866092"],"abstract_inverted_index":{"Lung":[0],"cancer":[1,9,49],"is":[2,24,64,88,259],"a":[3,102,132,256],"fatal":[4],"disease,":[5],"non-small":[6],"cell":[7],"lung":[8,103],"(NSCLC)":[10],"being":[11,129,260],"the":[12,18,65,94,97,125,135,143,199,211,229,234,239,242,263],"most":[13,66],"prevalent":[14],"type.":[15],"One":[16],"of":[17,21,48,70,96,182,189,213,218,228,265],"main":[19],"purposes":[20],"researching":[22],"NSCLC":[23],"identifying":[25],"patients":[26],"at":[27],"high":[28],"risk":[29,81],"for":[30,44,262],"recurrence":[31,178,190,215],"after":[32],"surgical":[33,183],"resection":[34],"so":[35],"that":[36,54,203,217,220],"specific":[37],"and":[38,60,73,112,117,121,142,165,207,246,258,267,274],"suitable":[39],"treatments":[40],"can":[41,83],"be":[42,84],"found":[43],"them.":[45],"The":[46],"classification":[47],"by":[50,56,131],"anatomic":[51],"disease":[52],"extent,":[53],"is,":[55],"tumor-size":[57],"(T":[58],"stage)":[59],"nodal-involvement":[61],"(N":[62],"stage),":[63],"widely":[67],"accepted":[68],"determinant":[69],"appropriate":[71],"treatment":[72],"prognosis":[74],"among":[75],"practicing":[76],"clinicians.":[77],"However,":[78],"TN":[79,126,223],"stage-based":[80],"prediction":[82,105,212],"inaccurate,":[85],"as":[86],"there":[87],"moderate":[89],"observer":[90],"variability":[91],"when":[92],"reporting":[93],"size":[95],"lesion.":[98],"Here,":[99],"we":[100],"propose":[101],"cancer\u2013recurrence":[104],"model":[106,251],"using":[107],"principal":[108,136],"component":[109],"analysis":[110],"(PCA)":[111],"machine":[113],"learning":[114],"(ML)":[115],"techniques":[116],"considering":[118],"radiomic":[119,145,205],"features":[120,146,206],"clinical":[122,208],"data,":[123],"including":[124,138],"stage.":[127],"After":[128],"filtered":[130],"statistical":[133],"model,":[134],"components,":[137],"Tand":[139],"N-stage":[140],"data":[141,209,225],"handcrafted":[144],"from":[147],"CT":[148],"images,":[149],"were":[150],"applied":[151],"to":[152],"various":[153],"ML":[154],"models":[155,219],"(i.e.,":[156],"random":[157],"forests,":[158],"support":[159],"vector":[160],"machines,":[161],"naive":[162],"Bayesian":[163],"classifiers,":[164],"both":[166,197],"boosting).":[167],"We":[168],"conducted":[169],"this":[170,193,250],"study,":[171],"not":[172],"only":[173,221],"on":[174],"recurrence,":[175],"but":[176],"also":[177],"within":[179,192],"two":[180],"years":[181],"resection,":[184],"since":[185],"more":[186],"than":[187],"80%":[188],"occurs":[191],"time":[194],"frame.":[195],"In":[196],"cases,":[198],"experimental":[200],"results":[201],"showed":[202],"combining":[204],"improves":[210],"lung-cancer":[214],"over":[216],"use":[222],"stage":[224],"in":[226,255,276],"terms":[227],"5-fold":[230],"cross-validation":[231],"accuracy":[232],"mean,":[233],"receiver":[235],"operating":[236],"characteristic":[237],"(ROC),":[238],"area":[240],"under":[241],"ROC":[243],"curve":[244],"(AUC),":[245],"Kaplan-Meier":[247],"curves.":[248],"Finally,":[249],"has":[252],"been":[253],"embedded":[254],"website":[257],"prepared":[261],"Ministry":[264],"Food":[266],"Drug":[268],"Safety":[269],"(MFDS)":[270],"medical":[271],"device":[272],"registration":[273],"approval":[275],"South":[277],"Korea.":[278]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
