{"id":"https://openalex.org/W7167041754","doi":"https://doi.org/10.48550/arxiv.2607.00798","title":"ClinRAG-GRAPH: Clinical-prior Retrieval-Augmented Graph Model with Domain Adversarial Learning for Breast pCR Prediction","display_name":"ClinRAG-GRAPH: Clinical-prior Retrieval-Augmented Graph Model with Domain Adversarial Learning for Breast pCR Prediction","publication_year":2026,"publication_date":"2026-07-01","ids":{"openalex":"https://openalex.org/W7167041754","doi":"https://doi.org/10.48550/arxiv.2607.00798"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.00798","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.00798","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2607.00798","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5044017450","display_name":"Yaofei Duan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Duan, Yaofei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126420301","display_name":"Y Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Yuhao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139917569","display_name":"Tianyu Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Tianyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139854947","display_name":"Yuan Gao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gao, Yuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139914346","display_name":"Luyi Han","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Han, Luyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139868927","display_name":"Xin Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Xin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110371447","display_name":"Xinyu Xie","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xie, Xinyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102352821","display_name":"Xinglong Liang","orcid":"https://orcid.org/0009-0001-3813-6726"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liang, Xinglong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011110524","display_name":"Chunyao Lu","orcid":"https://orcid.org/0000-0002-6911-4036"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lu, Chunyao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024898603","display_name":"Muzhen He","orcid":"https://orcid.org/0000-0001-5588-725X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"He, Muzhen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139866877","display_name":"Patrick Pang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pang, Patrick","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139864290","display_name":"Yue Sun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Yue","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139904539","display_name":"Ning Mao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mao, Ning","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139877119","display_name":"Tao Tan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tan, Tao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139919421","display_name":"Ritse Mann","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mann, Ritse","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"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":0.2535000145435333,"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":0.2535000145435333,"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/T10862","display_name":"AI in cancer detection","score":0.23119999468326569,"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/T11885","display_name":"MRI in cancer diagnosis","score":0.11010000109672546,"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/graph","display_name":"Graph","score":0.4837000072002411},{"id":"https://openalex.org/keywords/breast-cancer","display_name":"Breast cancer","score":0.44020000100135803},{"id":"https://openalex.org/keywords/source-code","display_name":"Source code","score":0.3862000107765198},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3361000120639801},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.3305000066757202},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.3172999918460846},{"id":"https://openalex.org/keywords/breast-imaging","display_name":"Breast imaging","score":0.31679999828338623},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.31679999828338623}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6227999925613403},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5651999711990356},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4837000072002411},{"id":"https://openalex.org/C530470458","wikidata":"https://www.wikidata.org/wiki/Q128581","display_name":"Breast cancer","level":3,"score":0.44020000100135803},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3887999951839447},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.3862000107765198},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3361000120639801},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.3305000066757202},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3172999918460846},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.31679999828338623},{"id":"https://openalex.org/C2777432617","wikidata":"https://www.wikidata.org/wiki/Q22905905","display_name":"Breast imaging","level":5,"score":0.31679999828338623},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.3093000054359436},{"id":"https://openalex.org/C16311509","wikidata":"https://www.wikidata.org/wiki/Q4148050","display_name":"Dependency graph","level":3,"score":0.30149999260902405},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.28929999470710754},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.28769999742507935},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.28279998898506165},{"id":"https://openalex.org/C2779974597","wikidata":"https://www.wikidata.org/wiki/Q28448986","display_name":"Clinical Practice","level":2,"score":0.2766999900341034},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.260699987411499},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.2587999999523163},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.2540999948978424}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.00798","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.00798","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2607.00798","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.00798","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Neoadjuvant":[0],"chemotherapy":[1],"(NAC)":[2],"response":[3,19],"prediction":[4,21,49,168],"is":[5,173],"clinically":[6,113],"important":[7],"for":[8,46,73,121,133],"treatment":[9],"stratification":[10],"in":[11],"breast":[12,130],"cancer.":[13],"However,":[14],"robust":[15,165],"pre-treatment":[16,47,166],"pathological":[17,57],"complete":[18],"(pCR)":[20],"remains":[22],"challenging":[23],"due":[24],"to":[25,89],"insufficient":[26],"cross-modal":[27],"modeling,":[28],"multicenter":[29,128],"imaging":[30],"heterogeneity,":[31],"and":[32,55,65,117,141,157],"weak":[33],"evidence-grounded":[34],"interpretability.":[35],"We":[36,124],"propose":[37],"ClinRAG-GRAPH,":[38],"a":[39,67,84,126],"Clinically":[40],"informed":[41],"Retrieval-Augmented":[42],"Generation":[43],"Graph":[44],"framework,":[45],"pCR":[48,122,167],"from":[50,137],"DCE-MRI,":[51],"structured":[52,74],"clinical":[53],"variables,":[54],"biopsy-derived":[56],"biomarkers.":[58],"ClinRAG-GRAPH":[59,147],"constructs":[60],"an":[61],"intra-patient":[62],"clinical-prior":[63],"graph":[64,70],"applies":[66],"prior-guided":[68],"relation-aware":[69],"convolutional":[71],"network":[72],"multimodal":[75],"representation":[76],"learning.":[77],"To":[78,98],"improve":[79],"cross-center":[80],"robustness,":[81],"we":[82,101],"introduce":[83],"dual-branch":[85],"domain-adversarial":[86],"learning":[87],"strategy":[88],"suppress":[90],"protocol-related":[91],"MRI":[92],"bias":[93],"while":[94],"preserving":[95],"pCR-relevant":[96],"features.":[97],"enhance":[99],"interpretability,":[100],"further":[102],"incorporate":[103],"large":[104],"language":[105],"model":[106],"(LLM)-driven":[107],"subgraph":[108],"RAG":[109],"module":[110],"that":[111,146],"retrieves":[112],"analogous":[114],"historical":[115],"cases":[116],"integrates":[118],"retrieved":[119],"evidence":[120],"inference.":[123],"assemble":[125],"large-scale":[127],"NAC":[129],"cancer":[131],"cohort":[132],"extensive":[134],"validation,":[135],"drawing":[136],"two":[138,160],"public":[139],"sources":[140],"three":[142],"in-house":[143],"centers.Results":[144],"show":[145],"achieves":[148],"AUCs":[149],"of":[150],"0.815":[151],"on":[152,159],"the":[153,176],"internal":[154],"test":[155,162],"set":[156],"0.774/0.712":[158],"external":[161],"sets,":[163],"demonstrating":[164],"across":[169],"centers.":[170],"The":[171],"code":[172],"available":[174],"at":[175],"anonymized":[177],"https://github.com/miccai26-1181/ClinRAG-GRAPH.":[178]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-03T00:00:00"}
