{"id":"https://openalex.org/W2995333296","doi":null,"title":"Quantitative Radiomic Analysis for Prognostic Medical Applications","display_name":"Quantitative Radiomic Analysis for Prognostic Medical Applications","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2995333296","mag":"2995333296"},"language":"en","primary_location":{"id":"pmh:oai:ses.library.usyd.edu.au:2123/21517","doi":null,"is_oa":false,"landing_page_url":"https://hdl.handle.net/2123/21517","pdf_url":null,"source":{"id":"https://openalex.org/S4306402414","display_name":"The Sydney eScholarship Repository (The University of Sydney)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I129604602","host_organization_name":"The University of Sydney","host_organization_lineage":["https://openalex.org/I129604602"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Thesis"},"volume":null,"type":"dissertation","is_traditional":true,"indexed_in":[],"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/A5009003205","display_name":"Chong\u2010Rui Xu","orcid":"https://orcid.org/0000-0002-3474-2809","observed_orcids":["https://orcid.org/0000-0002-3474-2809"]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Chong\u2010Rui Xu","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-3474-2809","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5009003205"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.0,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.0,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/radiomics","display_name":"radiomics","score":0.9929999709129333},{"id":"https://openalex.org/keywords/feature-selection","display_name":"feature selection","score":0.7720000147819519},{"id":"https://openalex.org/keywords/non-small-cell-lung-cancer","display_name":"non-small cell lung cancer","score":0.8100000023841858},{"id":"https://openalex.org/keywords/survival-prediction","display_name":"survival prediction","score":0.8830000162124634},{"id":"https://openalex.org/keywords/leukemia","display_name":"leukemia","score":0.5759999752044678},{"id":"https://openalex.org/keywords/breast-cancer","display_name":"breast cancer","score":1.0},{"id":"https://openalex.org/keywords/model-interpretability","display_name":"model interpretability","score":0.8240000009536743},{"id":"https://openalex.org/keywords/unsupervised-dimensionality-reduction","display_name":"unsupervised dimensionality reduction","score":0.746999979019165},{"id":"https://openalex.org/keywords/cross-validation","display_name":"cross-validation","score":0.621999979019165},{"id":"https://openalex.org/keywords/precision-medicine","display_name":"precision medicine","score":0.7559999823570251}],"concepts":[{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.45304161310195923},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.40263915061950684},{"id":"https://openalex.org/C19527891","wikidata":"https://www.wikidata.org/wiki/Q1120908","display_name":"Medical physics","level":1,"score":0.38435664772987366},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3276328146457672}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:ses.library.usyd.edu.au:2123/21517","doi":null,"is_oa":false,"landing_page_url":"https://hdl.handle.net/2123/21517","pdf_url":null,"source":{"id":"https://openalex.org/S4306402414","display_name":"The Sydney eScholarship Repository (The University of Sydney)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I129604602","host_organization_name":"The University of Sydney","host_organization_lineage":["https://openalex.org/I129604602"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Thesis"},{"id":"mag:2995333296","doi":null,"is_oa":false,"landing_page_url":"https://ses.library.usyd.edu.au/handle/2123/21517","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","score":0.5683000087738037,"display_name":"Good health and well-being"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":82,"referenced_works":["https://openalex.org/W27609994","https://openalex.org/W213900397","https://openalex.org/W402335930","https://openalex.org/W1548779692","https://openalex.org/W1587362683","https://openalex.org/W1680392829","https://openalex.org/W1892105223","https://openalex.org/W1958673254","https://openalex.org/W1963705766","https://openalex.org/W1964957583","https://openalex.org/W1965797467","https://openalex.org/W1972283434","https://openalex.org/W1987012261","https://openalex.org/W1987054640","https://openalex.org/W1987888496","https://openalex.org/W1996223772","https://openalex.org/W1997872096","https://openalex.org/W2003528089","https://openalex.org/W2019607817","https://openalex.org/W2026616100","https://openalex.org/W2038689569","https://openalex.org/W2046241486","https://openalex.org/W2085177488","https://openalex.org/W2091302506","https://openalex.org/W2103004421","https://openalex.org/W2114320967","https://openalex.org/W2117004913","https://openalex.org/W2124351162","https://openalex.org/W2125794840","https://openalex.org/W2126246455","https://openalex.org/W2126415088","https://openalex.org/W2128739912","https://openalex.org/W2135046866","https://openalex.org/W2135248331","https://openalex.org/W2136883754","https://openalex.org/W2140679462","https://openalex.org/W2142839276","https://openalex.org/W2143426320","https://openalex.org/W2148174696","https://openalex.org/W2150148763","https://openalex.org/W2161237731","https://openalex.org/W2169407833","https://openalex.org/W2169551590","https://openalex.org/W2183053883","https://openalex.org/W2199896469","https://openalex.org/W2214821036","https://openalex.org/W2230512165","https://openalex.org/W2234482261","https://openalex.org/W2238218910","https://openalex.org/W2346265746","https://openalex.org/W2372419247","https://openalex.org/W2401944043","https://openalex.org/W2405591800","https://openalex.org/W2433693374","https://openalex.org/W2468543693","https://openalex.org/W2480418585","https://openalex.org/W2507227771","https://openalex.org/W2515401069","https://openalex.org/W2516938563","https://openalex.org/W2530141817","https://openalex.org/W2577794774","https://openalex.org/W2580434384","https://openalex.org/W2588434214","https://openalex.org/W2612201027","https://openalex.org/W2615261123","https://openalex.org/W2619681408","https://openalex.org/W2742147437","https://openalex.org/W2757566931","https://openalex.org/W2763355946","https://openalex.org/W2763910757","https://openalex.org/W2789428140","https://openalex.org/W2801045744","https://openalex.org/W2887717038","https://openalex.org/W2889646458","https://openalex.org/W2893728536","https://openalex.org/W2896933173","https://openalex.org/W2898461260","https://openalex.org/W2899315113","https://openalex.org/W2899847761","https://openalex.org/W2924275361","https://openalex.org/W2963284126","https://openalex.org/W3153627770"],"related_works":["https://openalex.org/W4311718401","https://openalex.org/W1408981388","https://openalex.org/W6941411229","https://openalex.org/W7213544183","https://openalex.org/W7203509347","https://openalex.org/W4387106679","https://openalex.org/W2928897908","https://openalex.org/W7071008952","https://openalex.org/W4393900373","https://openalex.org/W2923055612"],"abstract_inverted_index":{"Radiomics,":[0],"a":[1,102,264],"non-invasive":[2],"and":[3,15,23,50,63,172,192,208,244],"quantitative":[4],"mining":[5],"medical":[6,103],"imaging":[7,94],"information":[8,95,99],"method,":[9,267],"could":[10],"extract":[11],"molecular":[12,97],"biological":[13,61,200],"features":[14,49,188,201],"enormous":[16],"feature":[17,42,115,157,216,235,281],"combinations":[18],"to":[19,66,72,76,100,111,149,197,202,240,276],"customise":[20],"individualised":[21],"treatment":[22],"solve":[24],"the":[25,30,41,51,114,124,154,185,193,215,222,225,232,242,247,258,269,273,278],"problem":[26],"of":[27,32,84,118,120,127,156,187,195,214,224,234,246,272],"heterogeneity,":[28],"satisfying":[29],"standards":[31],"precision":[33],"medicine.":[34],"However,":[35],"it":[36,69,79],"faces":[37],"many":[38],"challenges":[39],"in":[40,113,133,153,206,257],"selection":[43,116,236,266,274],"process,":[44],"including":[45],"redundant":[46],"features,":[47],"irrelevant":[48],"overfitting":[52],"risk.":[53],"More":[54],"important,":[55],"people":[56],"know":[57],"little":[58],"about":[59],"radiomics":[60,196],"background":[62],"its":[64,198],"connection":[65,194],"radiology,":[67],"so":[68],"is":[70,87,123,255],"difficult":[71],"apply":[73],"radiology":[74],"directly":[75],"medicine":[77],"as":[78,139],"lacks":[80],"interpretability.":[81],"The":[82,212],"core":[83],"this":[85,128,134],"thesis":[86,135],"radiomic":[88,93,173],"biology":[89,98],"analysis":[90],"that":[91,237],"connects":[92],"with":[96],"achieve":[101],"\u201cgold":[104],"standard\u201d":[105],"for":[106,175,189],"cancer":[107,210],"management.":[108],"Developing":[109],"methods":[110],"succeed":[112],"process":[117,275],"data":[119,166,171,174,248],"varying":[121],"dimensions":[122,158],"main":[125],"goal":[126],"paper.":[129],"Our":[130],"major":[131],"contributions":[132],"can":[136,217],"be":[137,218],"summarised":[138],"below:":[140],"1.":[141],"We":[142,261],"firstly":[143],"proposed":[144],"an":[145],"unsupervised":[146],"learning":[147,152,182],"framework":[148,233],"guide":[150],"supervised":[151],"reduction":[155],"from":[159],"large":[160],"cohorts":[161],"Non-Small":[162],"Cell":[163],"Lung":[164],"Cancer":[165],"(NSCLC)":[167],"on":[168,231],"both":[169],"clinical":[170,204],"survival":[176],"prediction.":[177],"2.":[178],"An":[179],"interpretable":[180],"machine":[181],"approach":[183],"measures":[184],"contribution":[186],"each":[190],"case":[191],"underlying":[199],"make":[203],"decisions":[205],"leukemia":[207],"breast":[209],"cases.":[211],"weight":[213],"estimated":[219],"by":[220],"measuring":[221],"distance":[223],"approximate":[226],"perturbation":[227],"centre.":[228],"3.":[229],"Based":[230],"we":[238],"proposed,":[239],"ensure":[241],"fairness":[243],"stability":[245],"split":[249],"when":[250],"processing":[251],"classification":[252],"results,":[253],"cross-validation":[254],"embedded":[256],"training":[259],"process.":[260],"further":[262],"propose":[263],"traversal":[265],"optimising":[268],"computational":[270],"complexity":[271],"obtain":[277],"most":[279],"robust":[280],"set.":[282]},"counts_by_year":[],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2019-12-26T00:00:00"}
