{"id":"https://openalex.org/W7225659798","doi":"https://doi.org/10.1088/2057-1976/aebfa5","title":"Prior-guided whole-slide survival prediction with soft histopathologic priors from vision-language foundation models","display_name":"Prior-guided whole-slide survival prediction with soft histopathologic priors from vision-language foundation models","publication_year":2026,"publication_date":"2026-11-09","ids":{"openalex":"https://openalex.org/W7225659798","doi":"https://doi.org/10.1088/2057-1976/aebfa5","pmid":"https://pubmed.ncbi.nlm.nih.gov/43002790"},"language":"en","primary_location":{"id":"doi:10.1088/2057-1976/aebfa5","doi":"https://doi.org/10.1088/2057-1976/aebfa5","is_oa":false,"landing_page_url":"https://doi.org/10.1088/2057-1976/aebfa5","pdf_url":null,"source":{"id":"https://openalex.org/S2764445381","display_name":"Biomedical Physics & Engineering Express","issn_l":"2057-1976","issn":["2057-1976"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","jufo-1","ki-jl-1","medline","norway-1","poland-40","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310320083","host_organization_name":"IOP Publishing","host_organization_lineage":["https://openalex.org/P4310320083","https://openalex.org/P4310311669"],"host_organization_lineage_names":["IOP Publishing","Institute of Physics"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Biomedical Physics & Engineering Express","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref","pubmed"],"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/A5031491290","display_name":"Songyu Wang","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4750791","display_name":"Henan Agricultural University","ror":"https://ror.org/04eq83d71","country_code":"CN","type":"education","lineage":["https://openalex.org/I4750791"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Songyu Wang","raw_affiliation_strings":["Henan Agricultural University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Henan Agricultural University","institution_ids":["https://openalex.org/I4750791"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035410270","display_name":"Youjun Chen","orcid":"https://orcid.org/0000-0001-6084-9653","observed_orcids":["https://orcid.org/0000-0001-6084-9653"]},"institutions":[{"id":"https://openalex.org/I198645480","display_name":"North China University of Water Resources and Electric Power","ror":"https://ror.org/03acrzv41","country_code":"CN","type":"education","lineage":["https://openalex.org/I198645480"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Youjun Chen","raw_affiliation_strings":["North China University of Water Resources and Electric Power"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"North China University of Water Resources and Electric Power","institution_ids":["https://openalex.org/I198645480"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5161355690","display_name":"Liangqi Zhang","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liangqi Zhang","raw_affiliation_strings":["DeTong Intelligent Technology Co., Ltd"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"DeTong Intelligent Technology Co., Ltd","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5161344390","display_name":"Xiaojun Li","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiaojun Li","raw_affiliation_strings":["DeTong Intelligent Technology Co., Ltd","Tianjin University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"DeTong Intelligent Technology Co., Ltd","institution_ids":[]},{"raw_affiliation_string":"Tianjin University","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5161342648","display_name":"Yuhui Huang","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4750791","display_name":"Henan Agricultural University","ror":"https://ror.org/04eq83d71","country_code":"CN","type":"education","lineage":["https://openalex.org/I4750791"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuhui Huang","raw_affiliation_strings":["Henan Agricultural University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Henan Agricultural University","institution_ids":["https://openalex.org/I4750791"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124262951","display_name":"Xiangyu Li","orcid":"https://orcid.org/0009-0001-5791-1337","observed_orcids":["https://orcid.org/0009-0001-5791-1337"]},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiangyu Li","raw_affiliation_strings":["DeTong Intelligent Technology Co., Ltd","Tianjin University"],"raw_orcid":"https://orcid.org/0009-0001-5791-1337","affiliations":[{"raw_affiliation_string":"DeTong Intelligent Technology Co., Ltd","institution_ids":[]},{"raw_affiliation_string":"Tianjin University","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5161307633","display_name":"Zhihui Feng","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4750791","display_name":"Henan Agricultural University","ror":"https://ror.org/04eq83d71","country_code":"CN","type":"education","lineage":["https://openalex.org/I4750791"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhihui Feng","raw_affiliation_strings":["Henan Agricultural University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Henan Agricultural University","institution_ids":["https://openalex.org/I4750791"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5124262951","https://openalex.org/A5161344390"],"corresponding_institution_ids":["https://openalex.org/I162868743"],"apc_list":{"value":2400,"currency":"USD","value_usd":2400},"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"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/T10862","display_name":"AI in cancer detection","score":0.9984999895095825,"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"}},"topics":[{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9984999895095825,"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":0.0012000000569969416,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.0,"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/vision-language-foundation-models","display_name":"vision-language foundation models","score":0.8830000162124634},{"id":"https://openalex.org/keywords/conch","display_name":"conch","score":0.8429999947547913},{"id":"https://openalex.org/keywords/tumor-microenvironment","display_name":"tumor microenvironment","score":0.7739999890327454},{"id":"https://openalex.org/keywords/prognostic-stratification","display_name":"prognostic stratification","score":0.671999990940094},{"id":"https://openalex.org/keywords/cancer-survival","display_name":"cancer survival","score":0.3440000116825104}],"concepts":[{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.7745000123977661},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.5985000133514404},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5296000242233276},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.524399995803833},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4456999897956848},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.3971000015735626},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3935000002384186},{"id":"https://openalex.org/C10515644","wikidata":"https://www.wikidata.org/wiki/Q543310","display_name":"Survival analysis","level":2,"score":0.37299999594688416},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36010000109672546},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.3578000068664551},{"id":"https://openalex.org/C3019894029","wikidata":"https://www.wikidata.org/wiki/Q332823","display_name":"Overall survival","level":2,"score":0.35760000348091125},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.35179999470710754},{"id":"https://openalex.org/C136948725","wikidata":"https://www.wikidata.org/wiki/Q1458306","display_name":"Soft tissue","level":2,"score":0.33709999918937683},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3327000141143799},{"id":"https://openalex.org/C2778902805","wikidata":"https://www.wikidata.org/wiki/Q322177","display_name":"Ablation","level":2,"score":0.3310000002384186},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.32910001277923584},{"id":"https://openalex.org/C167981619","wikidata":"https://www.wikidata.org/wiki/Q1685498","display_name":"Cross entropy","level":3,"score":0.30399999022483826},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.2863999903202057}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1088/2057-1976/aebfa5","doi":"https://doi.org/10.1088/2057-1976/aebfa5","is_oa":false,"landing_page_url":"https://doi.org/10.1088/2057-1976/aebfa5","pdf_url":null,"source":{"id":"https://openalex.org/S2764445381","display_name":"Biomedical Physics & Engineering Express","issn_l":"2057-1976","issn":["2057-1976"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","jufo-1","ki-jl-1","medline","norway-1","poland-40","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310320083","host_organization_name":"IOP Publishing","host_organization_lineage":["https://openalex.org/P4310320083","https://openalex.org/P4310311669"],"host_organization_lineage_names":["IOP Publishing","Institute of Physics"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Biomedical Physics & Engineering Express","raw_type":"journal-article"},{"id":"pmid:43002790","doi":null,"is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/43002790","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":"Biomedical physics & engineering express","raw_type":"Journal Article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3","score":0.5683000087738037}],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320336593","display_name":"Henan Provincial Science and Technology Research Project","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4416358399","https://openalex.org/W4312504504","https://openalex.org/W7204751663","https://openalex.org/W4414421842","https://openalex.org/W2745940724","https://openalex.org/W3204788326","https://openalex.org/W3203263549","https://openalex.org/W4410815470","https://openalex.org/W3091402370","https://openalex.org/W7084393264"],"abstract_inverted_index":{"Whole-slide":[0],"image":[1],"(WSI)":[2],"survival":[3,60,194],"prediction":[4],"is":[5,14,105],"complicated":[6],"by":[7],"heterogeneous":[8],"tumor":[9],"morphology,":[10],"where":[11],"prognostic":[12,113],"evidence":[13],"distributed":[15],"across":[16,158],"tumor,":[17],"stroma,":[18],"immune":[19],"infiltration,":[20],"necrosis,":[21],"and":[22,26,82,92,126,139,161,175,189],"residual":[23,102],"tissue":[24,32,43,80],"regions":[25],"often":[27],"appears":[28],"as":[29],"mixed":[30],"patch-level":[31,79,183],"evidence.":[33],"Existing":[34],"models":[35],"commonly":[36],"aggregate":[37],"patch":[38],"embeddings":[39],"directly":[40],"or":[41,143],"convert":[42],"cues":[44],"into":[45,78],"hard":[46],"pseudo-labels,":[47],"which":[48],"can":[49],"discard":[50],"uncertainty":[51,185],"at":[52],"tumor-microenvironment":[53],"interfaces.":[54],"We":[55],"propose":[56],"a":[57,97,187],"prior-guided":[58],"WSI":[59],"framework":[61],"that":[62,181],"uses":[63],"soft":[64,168],"histopathologic":[65,184],"priors":[66],"derived":[67],"from":[68,112],"the":[69,129,145,165,172],"CONCH":[70],"vision-language":[71],"foundation":[72],"model.":[73],"Prompt-similarity":[74],"scores":[75],"are":[76],"normalized":[77],"distributions":[81],"used":[83],"to":[84],"guide":[85],"probability-weighted":[86],"tissue-prototype":[87],"initialization,":[88],"entropy-weighted":[89],"prior-consistency":[90],"learning,":[91],"prototype-conditioned":[93],"graph-context":[94,176],"aggregation":[95],"over":[96],"spatial":[98],"k-nearest-neighbor":[99],"graph.":[100],"The":[101],"Others":[103,173],"class":[104],"retained":[106],"for":[107,192],"representation":[108],"learning":[109],"but":[110],"excluded":[111],"prior":[114],"alignment.":[115],"In":[116],"patient-level":[117],"5-fold":[118],"cross-validation":[119],"on":[120],"TCGA":[121],"KIRC,":[122],"KIRP,":[123],"LUAD,":[124],"STAD,":[125],"UCEC":[127],"cohorts,":[128,160],"model":[130],"achieved":[131],"C-index":[132],"values":[133],"of":[134,167],"0.7307,":[135],"0.8153,":[136],"0.6375,":[137],"0.6744,":[138],"0.7029,":[140],"respectively,":[141],"matching":[142],"exceeding":[144],"strongest":[146],"baseline":[147],"in":[148],"every":[149],"cohort.":[150],"Kaplan-Meier":[151],"analysis":[152],"showed":[153],"significant":[154],"high-versus":[155],"low-risk":[156],"separation":[157],"all":[159],"ablation":[162],"studies":[163],"confirmed":[164],"contributions":[166],"priors,":[169],"entropy":[170],"weighting,":[171],"channel,":[174],"aggregation.":[177],"These":[178],"results":[179],"show":[180],"retaining":[182],"provides":[186],"consistent":[188],"interpretable":[190],"signal":[191],"WSI-based":[193],"risk":[195],"stratification.":[196]},"counts_by_year":[],"updated_date":"2026-11-11T12:16:26.678958","created_date":"2026-11-10T00:00:00"}