{"id":"https://openalex.org/W7222971306","doi":"https://doi.org/10.3390/cancers18203362","title":"Convolutional, State-Space and Transformer Liver-Tumor Segmentation Models Trained on Hepatocellular Carcinoma Fail on the Same External Cases: Lesion Size Outweighs Architecture in Cross-Dataset Generalization","display_name":"Convolutional, State-Space and Transformer Liver-Tumor Segmentation Models Trained on Hepatocellular Carcinoma Fail on the Same External Cases: Lesion Size Outweighs Architecture in Cross-Dataset Generalization","publication_year":2026,"publication_date":"2026-10-17","ids":{"openalex":"https://openalex.org/W7222971306","doi":"https://doi.org/10.3390/cancers18203362","pmid":"https://pubmed.ncbi.nlm.nih.gov/42938894","pmcid":"https://www.ncbi.nlm.nih.gov/pmc/articles/13702596"},"language":"en","primary_location":{"id":"doi:10.3390/cancers18203362","doi":"https://doi.org/10.3390/cancers18203362","is_oa":true,"landing_page_url":"https://doi.org/10.3390/cancers18203362","pdf_url":null,"source":{"id":"https://openalex.org/S168415115","display_name":"Cancers","issn_l":"2072-6694","issn":["2072-6694"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-1","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Cancers","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.3390/cancers18203362","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5002423094","display_name":"H\u00fcseyin Kutlu","orcid":"https://orcid.org/0000-0003-0091-9984","observed_orcids":["https://orcid.org/0000-0003-0091-9984"]},"institutions":[{"id":"https://openalex.org/I198587356","display_name":"Ad\u0131yaman University","ror":"https://ror.org/02s4gkg68","country_code":"TR","type":"education","lineage":["https://openalex.org/I198587356"]},{"id":"https://openalex.org/I20126385","display_name":"Inonu University","ror":"https://ror.org/04asck240","country_code":"TR","type":"education","lineage":["https://openalex.org/I20126385"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"H\u00fcseyin Kutlu","raw_affiliation_strings":["Department of Biostatistics and Medical Informatics, Faculty of Medicine, Ad\u0131yaman University, 02040 Ad\u0131yaman, T\u00fcrkiye","Department of Biostatistics and Medical Informatics, Faculty of Medicine, \u0130n\u00f6n\u00fc University, 44280 Malatya, T\u00fcrkiye"],"raw_orcid":"https://orcid.org/0000-0003-0091-9984","affiliations":[{"raw_affiliation_string":"Department of Biostatistics and Medical Informatics, Faculty of Medicine, Ad\u0131yaman University, 02040 Ad\u0131yaman, T\u00fcrkiye","institution_ids":["https://openalex.org/I198587356"]},{"raw_affiliation_string":"Department of Biostatistics and Medical Informatics, Faculty of Medicine, \u0130n\u00f6n\u00fc University, 44280 Malatya, T\u00fcrkiye","institution_ids":["https://openalex.org/I20126385"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042276658","display_name":"Cemil \u00c7olak","orcid":"https://orcid.org/0000-0001-5406-098X","observed_orcids":["https://orcid.org/0000-0001-5406-098X"]},"institutions":[{"id":"https://openalex.org/I20126385","display_name":"Inonu University","ror":"https://ror.org/04asck240","country_code":"TR","type":"education","lineage":["https://openalex.org/I20126385"]}],"countries":["TR"],"is_corresponding":true,"raw_author_name":"Cemil \u00c7olak","raw_affiliation_strings":["Department of Biostatistics and Medical Informatics, Faculty of Medicine, \u0130n\u00f6n\u00fc University, 44280 Malatya, T\u00fcrkiye"],"raw_orcid":"https://orcid.org/0000-0001-5406-098X","affiliations":[{"raw_affiliation_string":"Department of Biostatistics and Medical Informatics, Faculty of Medicine, \u0130n\u00f6n\u00fc University, 44280 Malatya, T\u00fcrkiye","institution_ids":["https://openalex.org/I20126385"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025938906","display_name":"Sami Akbulut","orcid":"https://orcid.org/0000-0002-6864-7711","observed_orcids":["https://orcid.org/0000-0002-6864-7711"]},"institutions":[{"id":"https://openalex.org/I20126385","display_name":"Inonu University","ror":"https://ror.org/04asck240","country_code":"TR","type":"education","lineage":["https://openalex.org/I20126385"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Sami Akbulut","raw_affiliation_strings":["Department of Biostatistics and Medical Informatics, Faculty of Medicine, \u0130n\u00f6n\u00fc University, 44280 Malatya, T\u00fcrkiye","Department of Surgery and Liver Transplantation, Faculty of Medicine, \u0130n\u00f6n\u00fc University, 44280 Malatya, T\u00fcrkiye"],"raw_orcid":"https://orcid.org/0000-0002-6864-7711","affiliations":[{"raw_affiliation_string":"Department of Biostatistics and Medical Informatics, Faculty of Medicine, \u0130n\u00f6n\u00fc University, 44280 Malatya, T\u00fcrkiye","institution_ids":["https://openalex.org/I20126385"]},{"raw_affiliation_string":"Department of Surgery and Liver Transplantation, Faculty of Medicine, \u0130n\u00f6n\u00fc University, 44280 Malatya, T\u00fcrkiye","institution_ids":["https://openalex.org/I20126385"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5042276658"],"corresponding_institution_ids":["https://openalex.org/I20126385"],"apc_list":{"value":2900,"currency":"CHF","value_usd":3499},"apc_paid":{"value":2900,"currency":"CHF","value_usd":3499},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.72491097,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"18","issue":"20","first_page":"3362","last_page":"3362"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9814000129699707,"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"}},"topics":[{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9814000129699707,"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/T10862","display_name":"AI in cancer detection","score":0.005100000184029341,"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"}},{"id":"https://openalex.org/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.0034000000450760126,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/hepatocellular-carcinoma","display_name":"hepatocellular carcinoma","score":0.9629999995231628},{"id":"https://openalex.org/keywords/liver-tumor-segmentation","display_name":"liver tumor segmentation","score":0.9929999709129333},{"id":"https://openalex.org/keywords/cross-dataset-generalization","display_name":"cross-dataset generalization","score":0.9670000076293945},{"id":"https://openalex.org/keywords/model-architecture-comparison","display_name":"model architecture comparison","score":0.6389999985694885},{"id":"https://openalex.org/keywords/convolutional-neural-networks","display_name":"convolutional neural networks","score":0.9259999990463257},{"id":"https://openalex.org/keywords/state-space-models","display_name":"state-space model","score":0.9800000190734863},{"id":"https://openalex.org/keywords/transformer","display_name":"transformer models","score":0.6399999856948853},{"id":"https://openalex.org/keywords/selective-prediction","display_name":"selective prediction","score":0.5460000038146973},{"id":"https://openalex.org/keywords/transarterial-chemoembolization","display_name":"transarterial chemoembolization","score":0.5},{"id":"https://openalex.org/keywords/external-validation","display_name":"external validation","score":0.3190000057220459}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7336000204086304},{"id":"https://openalex.org/C2778019345","wikidata":"https://www.wikidata.org/wiki/Q1148337","display_name":"Hepatocellular carcinoma","level":2,"score":0.6901000142097473},{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.6876000165939331},{"id":"https://openalex.org/C2781156865","wikidata":"https://www.wikidata.org/wiki/Q827023","display_name":"Lesion","level":2,"score":0.6179999709129333},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5954999923706055},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4821000099182129},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.39750000834465027},{"id":"https://openalex.org/C72563966","wikidata":"https://www.wikidata.org/wiki/Q1303415","display_name":"Cohort","level":2,"score":0.3871999979019165},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.3799000084400177},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.3725999891757965},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.34950000047683716},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.3467000126838684},{"id":"https://openalex.org/C118671147","wikidata":"https://www.wikidata.org/wiki/Q578714","display_name":"Quantile","level":2,"score":0.3398999869823456},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3391000032424927},{"id":"https://openalex.org/C2778559731","wikidata":"https://www.wikidata.org/wiki/Q23808793","display_name":"Radiomics","level":2,"score":0.33390000462532043},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.3312000036239624},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.27880001068115234},{"id":"https://openalex.org/C2777546739","wikidata":"https://www.wikidata.org/wiki/Q33525","display_name":"Carcinoma","level":2,"score":0.2759999930858612},{"id":"https://openalex.org/C22029948","wikidata":"https://www.wikidata.org/wiki/Q45089","display_name":"Dice","level":2,"score":0.26829999685287476},{"id":"https://openalex.org/C207103383","wikidata":"https://www.wikidata.org/wiki/Q3930246","display_name":"Hazard ratio","level":3,"score":0.25690001249313354}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/cancers18203362","doi":"https://doi.org/10.3390/cancers18203362","is_oa":true,"landing_page_url":"https://doi.org/10.3390/cancers18203362","pdf_url":null,"source":{"id":"https://openalex.org/S168415115","display_name":"Cancers","issn_l":"2072-6694","issn":["2072-6694"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-1","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Cancers","raw_type":"journal-article"},{"id":"pmh:oai:pubmedcentral.nih.gov:13702596","doi":null,"is_oa":true,"landing_page_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13702596/","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13702596/pdf/cancers-18-03362.pdf","source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Cancers (Basel)","raw_type":"Text"},{"id":"pmid:42938894","doi":null,"is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/42938894","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Cancers","raw_type":"Journal Article"}],"best_oa_location":{"id":"doi:10.3390/cancers18203362","doi":"https://doi.org/10.3390/cancers18203362","is_oa":true,"landing_page_url":"https://doi.org/10.3390/cancers18203362","pdf_url":null,"source":{"id":"https://openalex.org/S168415115","display_name":"Cancers","issn_l":"2072-6694","issn":["2072-6694"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-1","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Cancers","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3","score":0.766700029373169}],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F385521110","display_name":"\u0130n\u00f6n\u00fc \u00dcniversitesi","ror":null},{"id":"https://openalex.org/F4320327128","display_name":"In\u00f6n\u00fc \u00dcniversitesi","ror":"https://ror.org/04asck240"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7222971306.pdf","grobid_xml":"https://content.openalex.org/works/W7222971306.grobid-xml"},"referenced_works_count":34,"referenced_works":["https://openalex.org/W2083927153","https://openalex.org/W2910094941","https://openalex.org/W3014974815","https://openalex.org/W3115587706","https://openalex.org/W3168283161","https://openalex.org/W3172681723","https://openalex.org/W3217738361","https://openalex.org/W4211169066","https://openalex.org/W4220681474","https://openalex.org/W4224282293","https://openalex.org/W4312428231","https://openalex.org/W4317209201","https://openalex.org/W4318978952","https://openalex.org/W4327620096","https://openalex.org/W4361000000","https://openalex.org/W4362633622","https://openalex.org/W4366831358","https://openalex.org/W4382725557","https://openalex.org/W4383868277","https://openalex.org/W4387831664","https://openalex.org/W4388651713","https://openalex.org/W4389244888","https://openalex.org/W4392293823","https://openalex.org/W4393175789","https://openalex.org/W4394840979","https://openalex.org/W4394857110","https://openalex.org/W4399125735","https://openalex.org/W4400152047","https://openalex.org/W4400812331","https://openalex.org/W4400839968","https://openalex.org/W4405489375","https://openalex.org/W4408267899","https://openalex.org/W4408646762","https://openalex.org/W4410015099"],"related_works":["https://openalex.org/W7154448605","https://openalex.org/W7172448843","https://openalex.org/W7133201246","https://openalex.org/W4412082231","https://openalex.org/W7163082362","https://openalex.org/W4406462839","https://openalex.org/W7125546483","https://openalex.org/W7140226552","https://openalex.org/W7161417276","https://openalex.org/W4410811835"],"abstract_inverted_index":{"Background/Objectives:":[0],"Architectural":[1],"innovation":[2],"is":[3,17],"widely":[4],"assumed":[5],"to":[6,62,208],"improve":[7],"the":[8,104,121,155,186,201],"cross-institutional":[9],"generalization":[10],"of":[11,180,185,191,227],"tumor":[12],"segmentation":[13,229],"models,":[14],"yet":[15],"this":[16],"rarely":[18],"tested.":[19],"We":[20,72],"compared":[21],"three":[22],"architectural":[23,239],"families":[24],"under":[25],"an":[26],"identical,":[27],"leak-safe":[28],"protocol":[29],"in":[30,165,178],"hepatocellular":[31],"carcinoma":[32],"(HCC)":[33],"segmentation.":[34],"Methods:":[35],"Convolutional":[36],"(nnU-Net":[37],"ResEnc-M),":[38],"state-space":[39,107],"(U-Mamba":[40],"Bot)":[41],"and":[42,67,81,101,106,131,182],"transformer":[43,122],"(SwinUNETR)":[44],"models":[45,108],"were":[46,109,215],"trained":[47],"on":[48,112,125,133,148],"HCC-TACE-Seg":[49],"(n":[50,64,69],"=":[51,65,70,94,145],"103)":[52],"with":[53],"identical":[54],"patient-disjoint":[55],"five-fold":[56],"splits,":[57],"then":[58],"applied":[59],"without":[60],"adaptation":[61],"3D-IRCADb":[63],"15)":[66],"LiTS":[68,126,149,204],"118).":[71],"assessed":[73],"discrimination,":[74],"case-level":[75],"failure":[76,226],"concordance,":[77],"lesion-volume":[78],"dependence,":[79],"calibration":[80],"selective":[82,242],"prediction.":[83],"Results:":[84],"Internal":[85],"performance":[86,98,164],"differed":[87],"(Dice":[88],"0.685,":[89],"0.662,":[90],"0.479;":[91],"Friedman":[92],"p":[93,128],"0.022)":[95],"but":[96],"external":[97,114],"was":[99,123,137,230],"low":[100],"largely":[102],"convergent:":[103],"convolutional":[105],"formally":[110],"equivalent":[111],"both":[113],"cohorts":[115],"(pre-specified":[116],"margin":[117],"\u00b10.05":[118],"Dice),":[119],"whereas":[120],"inferior":[124],"(0.222;":[127],"<":[129,173],"0.0001)":[130],"inconclusive":[132],"3D-IRCADb.":[134],"Case-level":[135],"Dice":[136,207],"highly":[138],"concordant":[139],"across":[140,159],"all":[141,166],"pairings":[142],"(Spearman":[143],"\u03c1":[144],"0.84\u20130.96;":[146],"0.67\u20130.81":[147],"after":[150],"adjustment":[151],"for":[152],"lesion":[153,234],"volume),":[154],"same":[156],"cases":[157,181,205],"failing":[158],"architectures.":[160],"Lesion":[161],"volume":[162],"predicted":[163],"six":[167],"architecture":[168],"\u00d7":[169],"cohort":[170,188],"combinations;":[171],"lesions":[172],"5":[174],"mL":[175],"failed":[176],"completely":[177],"61\u201376%":[179],"formed":[183],"1.9%":[184],"development":[187],"against":[189],"31.4%":[190],"LiTS.":[192],"Neither":[193],"liver-mask":[194],"constraint":[195],"nor":[196],"ensembling":[197],"recovered":[198],"performance.":[199],"Deferring":[200],"30%":[202],"least-confident":[203],"raised":[206],"0.516":[209],"(0.450":[210],"when":[211],"only":[212],"empty":[213],"predictions":[214],"removed).":[216],"Conclusions:":[217],"Under":[218],"a":[219,245],"single-source,":[220],"TACE-treated":[221],"HCC":[222],"training":[223],"regime,":[224],"cross-dataset":[225],"liver-tumor":[228],"governed":[231],"primarily":[232],"by":[233,238],"size":[235],"rather":[236],"than":[237],"family;":[240],"confidence-based":[241],"prediction":[243],"offers":[244],"deployable":[246],"mitigation.":[247]},"counts_by_year":[],"updated_date":"2026-11-06T12:05:46.655746","created_date":"2026-10-20T00:00:00"}
