{"id":"https://openalex.org/W2591845194","doi":"https://doi.org/10.1117/12.2253486","title":"Risk prediction of small pulmonary nodules based on novel CT image texture markers","display_name":"Risk prediction of small pulmonary nodules based on novel CT image texture markers","publication_year":2017,"publication_date":"2017-03-03","ids":{"openalex":"https://openalex.org/W2591845194","doi":"https://doi.org/10.1117/12.2253486","mag":"2591845194"},"language":"en","primary_location":{"id":"doi:10.1117/12.2253486","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2253486","pdf_url":null,"source":{"id":"https://openalex.org/S183492911","display_name":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","issn_l":"0277-786X","issn":["0277-786X","1996-756X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315543","host_organization_name":"SPIE","host_organization_lineage":["https://openalex.org/P4310315543"],"host_organization_lineage_names":["SPIE"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SPIE Proceedings","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/A5101774760","display_name":"Fangfang Han","orcid":"https://orcid.org/0000-0002-9299-0915"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Fangfang Han","raw_affiliation_strings":["Northeastern Univ. (China)"],"affiliations":[{"raw_affiliation_string":"Northeastern Univ. (China)","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018527188","display_name":"Bowen Song","orcid":"https://orcid.org/0000-0002-8586-0573"},"institutions":[{"id":"https://openalex.org/I59028903","display_name":"Ocean University of China","ror":"https://ror.org/04rdtx186","country_code":"CN","type":"education","lineage":["https://openalex.org/I59028903"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bowen Song","raw_affiliation_strings":["Ocean Univ. of China (China)"],"affiliations":[{"raw_affiliation_string":"Ocean Univ. of China (China)","institution_ids":["https://openalex.org/I59028903"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101874046","display_name":"He Ma","orcid":"https://orcid.org/0009-0006-7343-5015"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"He Ma","raw_affiliation_strings":["Northeastern Univ. (China)"],"affiliations":[{"raw_affiliation_string":"Northeastern Univ. (China)","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100374757","display_name":"Wei Qian","orcid":"https://orcid.org/0000-0002-9563-721X"},"institutions":[{"id":"https://openalex.org/I164936912","display_name":"The University of Texas at El Paso","ror":"https://ror.org/04d5vba33","country_code":"US","type":"education","lineage":["https://openalex.org/I164936912"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wei Qian","raw_affiliation_strings":["Univ. of Texas at El Paso (United States)","Northeastern Univ. (China)"],"affiliations":[{"raw_affiliation_string":"Univ. of Texas at El Paso (United States)","institution_ids":["https://openalex.org/I164936912"]},{"raw_affiliation_string":"Northeastern Univ. (China)","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5110322946","display_name":"Zhengrong Liang","orcid":null},"institutions":[{"id":"https://openalex.org/I59553526","display_name":"Stony Brook University","ror":"https://ror.org/05qghxh33","country_code":"US","type":"education","lineage":["https://openalex.org/I59553526"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhengrong Liang","raw_affiliation_strings":["Stony Brook Univ. (United States)"],"affiliations":[{"raw_affiliation_string":"Stony Brook Univ. (United States)","institution_ids":["https://openalex.org/I59553526"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5101774760"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1996,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.54912466,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"10134","issue":null,"first_page":"101343Q","last_page":"101343Q"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10202","display_name":"Lung Cancer Diagnosis and Treatment","score":0.9998000264167786,"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"}},"topics":[{"id":"https://openalex.org/T10202","display_name":"Lung Cancer Diagnosis and Treatment","score":0.9998000264167786,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9988999962806702,"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/T10552","display_name":"Colorectal Cancer Screening and Detection","score":0.9783999919891357,"subfield":{"id":"https://openalex.org/subfields/2730","display_name":"Oncology"},"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/malignancy","display_name":"Malignancy","score":0.7016775608062744},{"id":"https://openalex.org/keywords/nodule","display_name":"Nodule (geology)","score":0.5706626772880554},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5669534802436829},{"id":"https://openalex.org/keywords/texture","display_name":"Texture (cosmology)","score":0.5515862107276917},{"id":"https://openalex.org/keywords/biopsy","display_name":"Biopsy","score":0.5199279189109802},{"id":"https://openalex.org/keywords/image-texture","display_name":"Image texture","score":0.509425699710846},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4848652780056},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4804251194000244},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4789743423461914},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.45216619968414307},{"id":"https://openalex.org/keywords/receiver-operating-characteristic","display_name":"Receiver operating characteristic","score":0.44683176279067993},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4327693283557892},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.4244392514228821},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.3928639888763428},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3275696039199829},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.2678590416908264},{"id":"https://openalex.org/keywords/pathology","display_name":"Pathology","score":0.2545594573020935},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.14811712503433228},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.14627090096473694}],"concepts":[{"id":"https://openalex.org/C2779399171","wikidata":"https://www.wikidata.org/wiki/Q1483951","display_name":"Malignancy","level":2,"score":0.7016775608062744},{"id":"https://openalex.org/C2776731575","wikidata":"https://www.wikidata.org/wiki/Q2916245","display_name":"Nodule (geology)","level":2,"score":0.5706626772880554},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5669534802436829},{"id":"https://openalex.org/C2781195486","wikidata":"https://www.wikidata.org/wiki/Q289436","display_name":"Texture (cosmology)","level":3,"score":0.5515862107276917},{"id":"https://openalex.org/C2775934546","wikidata":"https://www.wikidata.org/wiki/Q179991","display_name":"Biopsy","level":2,"score":0.5199279189109802},{"id":"https://openalex.org/C63099799","wikidata":"https://www.wikidata.org/wiki/Q17147001","display_name":"Image texture","level":4,"score":0.509425699710846},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4848652780056},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4804251194000244},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4789743423461914},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.45216619968414307},{"id":"https://openalex.org/C58471807","wikidata":"https://www.wikidata.org/wiki/Q327120","display_name":"Receiver operating characteristic","level":2,"score":0.44683176279067993},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4327693283557892},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.4244392514228821},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.3928639888763428},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3275696039199829},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.2678590416908264},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.2545594573020935},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.14811712503433228},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.14627090096473694},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2253486","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2253486","pdf_url":null,"source":{"id":"https://openalex.org/S183492911","display_name":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","issn_l":"0277-786X","issn":["0277-786X","1996-756X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315543","host_organization_name":"SPIE","host_organization_lineage":["https://openalex.org/P4310315543"],"host_organization_lineage_names":["SPIE"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SPIE Proceedings","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.699999988079071}],"awards":[],"funders":[{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1986649315","https://openalex.org/W1999216165","https://openalex.org/W2044465660","https://openalex.org/W2050448346","https://openalex.org/W2078014989","https://openalex.org/W2080883421","https://openalex.org/W2109364352","https://openalex.org/W2127007253","https://openalex.org/W2140783866","https://openalex.org/W6663773524","https://openalex.org/W6670551380","https://openalex.org/W6676003510","https://openalex.org/W6680984004"],"related_works":["https://openalex.org/W3006513224","https://openalex.org/W2044270176","https://openalex.org/W2374828682","https://openalex.org/W2153116791","https://openalex.org/W2388733570","https://openalex.org/W1980033651","https://openalex.org/W4230530180","https://openalex.org/W2134401318","https://openalex.org/W1555506570","https://openalex.org/W1966354130"],"abstract_inverted_index":{"Among":[0],"the":[1,37,40,49,75,90,98,115,120,126,130,139,148,151,156,162,168,176,184,192,201,217,222,229,246],"detected":[2],"small":[3,91],"nodules":[4,41,92,182],"sized":[5],"from":[6,56,138,174,235],"3":[7],"to":[8,63,88,129,146],"30mm":[9],"in":[10,18,30,73,119,161,214],"CT":[11,238],"images,":[12],"a":[13,43],"significant":[14],"portion":[15],"is":[16],"undetermined":[17],"terms":[19,215],"of":[20,39,52,117,150,216],"malignancy":[21,38],"which":[22],"needs":[23],"biopsy":[24],"or":[25,69],"other":[26],"follow-up":[27],"means,":[28],"resulting":[29],"excessive":[31],"risk":[32,99,247],"and":[33,84,165,179,187,199,211],"cost.":[34],"Therefore,":[35],"predicting":[36],"becomes":[42],"clinically":[44],"desirable":[45],"task.":[46],"Based":[47],"on":[48,225],"previous":[50],"study":[51,61],"texture":[53,67,71,81,111,233],"features":[54,68],"extracted":[55],"gray-tone":[57],"spatial-dependence":[58],"matrices,":[59],"this":[60],"aims":[62],"find":[64],"more":[65],"efficient":[66],"image":[70,80,105,110,232,239],"markers":[72,82,112,234],"discriminating":[74],"nodule":[76,135,236],"malignancy.":[77],"Two":[78],"new":[79,109,153,231],"(median":[83],"variance)":[85],"are":[86,159,208],"proposed":[87,152],"classify":[89],"into":[93],"different":[94,226],"malignant":[95,189,206,227],"levels,":[96,228],"thus":[97,123],"prediction":[100],"could":[101],"be":[102],"performed":[103],"through":[104],"analysis.":[106],"These":[107],"two":[108,230],"can":[113,124],"minimize":[114],"effect":[116],"outliers":[118],"feature":[121,131],"series,":[122],"reduce":[125],"noise":[127],"influence":[128],"classification.":[132],"Total":[133],"1,353":[134],"samples":[136],"selected":[137],"Lung":[140],"Image":[141],"Database":[142],"Consortium":[143],"were":[144],"used":[145],"evaluate":[147],"efficiency":[149],"features.":[154],"All":[155],"classification":[157,172],"results":[158],"shown":[160,242],"ROC":[163],"curves":[164],"tabulated":[166],"by":[167],"AUC":[169,219],"values.":[170,220],"The":[171],"outcomes":[173,224],"(1)":[175],"most":[177,185,193,202],"likely":[178,180,186,188,194,196,203,205],"benign":[181,197],"vs.":[183,195,204],"nodules,":[190,198,207],"(2)":[191],"(3)":[200],"0.9125&plusmn;0.0096,":[209],"0.9239&plusmn;0.0147,":[210],"0.8888&plusmn;0.0197,":[212],"respectively,":[213],"largest":[218],"From":[221],"experimental":[223],"volumetric":[237],"data":[240],"have":[241],"encouraging":[243],"performance":[244],"for":[245],"prediction.":[248]},"counts_by_year":[{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
