{"id":"https://openalex.org/W7225197461","doi":"https://doi.org/10.48550/arxiv.2611.03109","title":"Multimodal Prompt Learning with Dual-Granularity Contrastive Alignment for Preoperative Prediction of Lung Adenocarcinoma Invasiveness Using 3D CT","display_name":"Multimodal Prompt Learning with Dual-Granularity Contrastive Alignment for Preoperative Prediction of Lung Adenocarcinoma Invasiveness Using 3D CT","publication_year":2026,"publication_date":"2026-11-02","ids":{"openalex":"https://openalex.org/W7225197461","doi":"https://doi.org/10.48550/arxiv.2611.03109"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2611.03109","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2611.03109","pdf_url":"https://arxiv.org/pdf/2611.03109","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,"listed_in":[],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2611.03109","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5160539669","display_name":"Qing Huang","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Qing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140786983","display_name":"Xiaotong Tan","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tan, Xiaotong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102519526","display_name":"Lintong Yao","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yao, Lintong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027394528","display_name":"Wufeng Xue","orcid":"https://orcid.org/0000-0002-9776-0975","observed_orcids":["https://orcid.org/0000-0002-9776-0975"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xue, Wufeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102973033","display_name":"Ruobing Huang","orcid":"https://orcid.org/0000-0001-5672-6896","observed_orcids":["https://orcid.org/0000-0001-5672-6896","https://orcid.org/0009-0000-0831-7705"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Ruobing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5160489345","display_name":"Dong Ni","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ni, Dong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5160522274","display_name":"Hao Wu","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Hao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5160525510","display_name":"Tian Xiao","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiao, Tian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5160455455","display_name":"Jun Cheng","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cheng, Jun","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":0.6035000085830688,"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":0.6035000085830688,"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":0.290800005197525,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.06350000202655792,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/lung-adenocarcinoma","display_name":"lung adenocarcinoma","score":0.9879999756813049},{"id":"https://openalex.org/keywords/preoperative-prediction","display_name":"preoperative prediction","score":0.7009999752044678},{"id":"https://openalex.org/keywords/tumor-invasion","display_name":"tumor invasion","score":0.9340000152587891},{"id":"https://openalex.org/keywords/three-dimensional-computed-tomography","display_name":"three-dimensional computed tomography","score":0.8399999737739563},{"id":"https://openalex.org/keywords/image-text-alignment","display_name":"image-text alignment","score":0.8619999885559082},{"id":"https://openalex.org/keywords/contrastive-alignment","display_name":"contrastive alignment","score":0.8659999966621399},{"id":"https://openalex.org/keywords/multiple-instance-learning","display_name":"multiple instance learning","score":0.8980000019073486},{"id":"https://openalex.org/keywords/vascular-features","display_name":"vascular features","score":0.5960000157356262},{"id":"https://openalex.org/keywords/pulmonary-nodule-classification","display_name":"lung nodule classification","score":0.5040000081062317}],"concepts":[{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.5720000267028809},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5493999719619751},{"id":"https://openalex.org/C2781182431","wikidata":"https://www.wikidata.org/wiki/Q356033","display_name":"Adenocarcinoma","level":3,"score":0.5435000061988831},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.517300009727478},{"id":"https://openalex.org/C190892606","wikidata":"https://www.wikidata.org/wiki/Q3512464","display_name":"Radiological weapon","level":2,"score":0.5116000175476074},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.48240000009536743},{"id":"https://openalex.org/C2777714996","wikidata":"https://www.wikidata.org/wiki/Q7886","display_name":"Lung","level":2,"score":0.46149998903274536},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4603999853134155},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.4528000056743622},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3601999878883362},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.3353999853134155},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.31779998540878296},{"id":"https://openalex.org/C544519230","wikidata":"https://www.wikidata.org/wiki/Q32566","display_name":"Computed tomography","level":2,"score":0.2919999957084656},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.27790001034736633},{"id":"https://openalex.org/C3020132585","wikidata":"https://www.wikidata.org/wiki/Q2671652","display_name":"Diagnostic accuracy","level":2,"score":0.263700008392334},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.26249998807907104},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.26190000772476196}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2611.03109","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2611.03109","pdf_url":"https://arxiv.org/pdf/2611.03109","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,"listed_in":[],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2611.03109","doi":"https://doi.org/10.48550/arxiv.2611.03109","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2611.03109","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,"listed_in":[],"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2611.03109","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2611.03109","pdf_url":"https://arxiv.org/pdf/2611.03109","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,"listed_in":[],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"display_name":"Good health and well-being","score":0.9391000270843506,"id":"https://metadata.un.org/sdg/3"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2894585861","https://openalex.org/W7172300631","https://openalex.org/W7172329785","https://openalex.org/W4394605020","https://openalex.org/W7170881108","https://openalex.org/W4417113772","https://openalex.org/W4417529029","https://openalex.org/W4306973591","https://openalex.org/W7213355406","https://openalex.org/W7160925883"],"abstract_inverted_index":{"Accurate":[0],"preoperative":[1],"prediction":[2,226],"of":[3,157,160,164,171,193,201],"lung":[4],"adenocarcinoma":[5,25],"invasiveness":[6,225],"is":[7,59],"important":[8],"for":[9,40,65],"surgical":[10],"planning":[11],"and":[12,51,67,132,162,195,198,211],"individualized":[13],"treatment.":[14],"However,":[15],"overlapping":[16],"CT":[17,88,111,139],"appearances":[18],"make":[19],"it":[20],"challenging":[21],"to":[22,97,117],"distinguish":[23],"invasive":[24],"(IAD)":[26],"from":[27,79,93],"Pre-IAD":[28],"lesions.":[29],"We":[30],"propose":[31],"a":[32,229],"multimodal":[33],"prompt-learning":[34],"framework":[35,128],"with":[36,61,90,113,124,176],"dual-granularity":[37,219],"image-text":[38],"alignment":[39,85,107,133,197],"3D":[41,46],"CT-based":[42,224],"IAD":[43],"classification.":[44],"A":[45,55,102],"ResNet-CBAM":[47],"encoder":[48],"extracts":[49],"global":[50,194],"spatial":[52,110],"visual":[53,179],"features.":[54],"frozen":[56],"RadBERT":[57],"backbone":[58,180],"adapted":[60],"separate":[62],"learnable":[63],"prompts":[64,72],"report-level":[66],"keyword-level":[68],"text":[69],"encoding.":[70],"These":[71,215],"are":[73],"initialized":[74],"using":[75],"semantic":[76,100],"representations":[77,89],"derived":[78],"real-world":[80],"clinical":[81],"reports.":[82],"Global":[83],"contrastive":[84],"connects":[86],"whole-volume":[87],"reports":[91],"generated":[92],"clinician-annotated":[94],"radiological":[95,115,220],"attributes":[96],"provide":[98],"overall":[99],"supervision.":[101],"multiple":[103],"instance":[104],"learning-based":[105],"local":[106,119,196],"module":[108],"associates":[109],"features":[112],"vascular-focused":[114],"keywords,":[116],"capture":[118],"imaging":[120],"characteristics":[121],"potentially":[122],"associated":[123],"tumor":[125],"invasion.":[126],"The":[127],"jointly":[129],"optimizes":[130],"classification":[131],"objectives,":[134],"while":[135,227],"inference":[136,231],"requires":[137],"only":[138],"images.":[140],"Experiments":[141],"included":[142],"1,593":[143],"pathologically":[144],"confirmed":[145],"nodules.":[146],"On":[147],"the":[148,151,172,177,190,199],"test":[149],"set,":[150],"proposed":[152],"method":[153],"achieved":[154],"an":[155],"accuracy":[156],"87.92%,":[158],"F1-score":[159],"87.78%,":[161],"AUC":[163,167],"0.9499.":[165],"Its":[166],"significantly":[168],"exceeded":[169],"that":[170,218],"image-only":[173],"Res-CBAM":[174],"baseline":[175],"same":[178],"(0.9297;":[181],"DeLong":[182],"test,":[183],"p":[184],"=":[185],"0.0078).":[186],"Ablation":[187],"analyses":[188],"supported":[189],"complementary":[191],"contributions":[192],"benefit":[200],"report-based":[202],"prompt":[203],"initialization.":[204],"Qualitative":[205],"visualizations":[206],"showed":[207],"activation":[208],"within":[209],"nodules":[210],"adjacent":[212],"vascular":[213],"structures.":[214],"findings":[216],"suggest":[217],"supervision":[221],"can":[222],"enhance":[223],"preserving":[228],"CT-only":[230],"workflow.":[232]},"counts_by_year":[],"updated_date":"2026-11-05T12:25:35.769452","created_date":"2026-11-04T00:00:00"}