{"id":"https://openalex.org/W7225618940","doi":"https://doi.org/10.3390/sci8110360","title":"An Explainable Multimodal Deep Learning Framework for Early Pancreatic Cancer Prediction from Imaging and Clinical Data","display_name":"An Explainable Multimodal Deep Learning Framework for Early Pancreatic Cancer Prediction from Imaging and Clinical Data","publication_year":2026,"publication_date":"2026-11-09","ids":{"openalex":"https://openalex.org/W7225618940","doi":"https://doi.org/10.3390/sci8110360"},"language":"en","primary_location":{"id":"doi:10.3390/sci8110360","doi":"https://doi.org/10.3390/sci8110360","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sci8110360","pdf_url":null,"source":{"id":"https://openalex.org/S4210202795","display_name":"Sci","issn_l":"2413-4155","issn":["2413-4155"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj"],"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":"Sci","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.3390/sci8110360","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102850705","display_name":"Nabil M. AbdelAziz","orcid":"https://orcid.org/0000-0001-6181-097X","observed_orcids":["https://orcid.org/0000-0001-6181-097X"]},"institutions":[{"id":"https://openalex.org/I192398990","display_name":"Zagazig University","ror":"https://ror.org/053g6we49","country_code":"EG","type":"education","lineage":["https://openalex.org/I192398990"]}],"countries":["EG"],"is_corresponding":false,"raw_author_name":"Nabil M. AbdelAziz","raw_affiliation_strings":["Information Systems Department, Faculty of Computers and Informatics, Zagazig University, Zagazig 44511, Egypt"],"raw_orcid":"https://orcid.org/0000-0001-6181-097X","affiliations":[{"raw_affiliation_string":"Information Systems Department, Faculty of Computers and Informatics, Zagazig University, Zagazig 44511, Egypt","institution_ids":["https://openalex.org/I192398990"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5116184516","display_name":"Amira M. Fawzy","orcid":"https://orcid.org/0000-0002-8958-9928","observed_orcids":["https://orcid.org/0000-0002-8958-9928"]},"institutions":[{"id":"https://openalex.org/I192398990","display_name":"Zagazig University","ror":"https://ror.org/053g6we49","country_code":"EG","type":"education","lineage":["https://openalex.org/I192398990"]}],"countries":["EG"],"is_corresponding":false,"raw_author_name":"Amira M. Fawzy","raw_affiliation_strings":["Information Systems Department, Faculty of Computers and Informatics, Zagazig University, Zagazig 44511, Egypt"],"raw_orcid":"https://orcid.org/0000-0002-8958-9928","affiliations":[{"raw_affiliation_string":"Information Systems Department, Faculty of Computers and Informatics, Zagazig University, Zagazig 44511, Egypt","institution_ids":["https://openalex.org/I192398990"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5161331571","display_name":"Ebtehal Karm Shehata","orcid":"https://orcid.org/0009-0004-4213-3749","observed_orcids":["https://orcid.org/0009-0004-4213-3749"]},"institutions":[{"id":"https://openalex.org/I192398990","display_name":"Zagazig University","ror":"https://ror.org/053g6we49","country_code":"EG","type":"education","lineage":["https://openalex.org/I192398990"]}],"countries":["EG"],"is_corresponding":false,"raw_author_name":"Ebtehal Karm Shehata","raw_affiliation_strings":["Information Systems Department, Faculty of Computers and Informatics, Zagazig University, Zagazig 44511, Egypt"],"raw_orcid":"https://orcid.org/0009-0004-4213-3749","affiliations":[{"raw_affiliation_string":"Information Systems Department, Faculty of Computers and Informatics, Zagazig University, Zagazig 44511, Egypt","institution_ids":["https://openalex.org/I192398990"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5113361837","display_name":"Mohamed M. AbdelHafeez","orcid":"https://orcid.org/0009-0000-1032-5499","observed_orcids":["https://orcid.org/0009-0000-1032-5499"]},"institutions":[{"id":"https://openalex.org/I192398990","display_name":"Zagazig University","ror":"https://ror.org/053g6we49","country_code":"EG","type":"education","lineage":["https://openalex.org/I192398990"]}],"countries":["EG"],"is_corresponding":true,"raw_author_name":"Mohamed M. AbdelHafeez","raw_affiliation_strings":["Information Systems Department, Faculty of Computers and Informatics, Zagazig University, Zagazig 44511, Egypt"],"raw_orcid":"https://orcid.org/0009-0000-1032-5499","affiliations":[{"raw_affiliation_string":"Information Systems Department, Faculty of Computers and Informatics, Zagazig University, Zagazig 44511, Egypt","institution_ids":["https://openalex.org/I192398990"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5113361837"],"corresponding_institution_ids":["https://openalex.org/I192398990"],"apc_list":{"value":1200,"currency":"CHF","value_usd":1448},"apc_paid":{"value":1200,"currency":"CHF","value_usd":1448},"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":"8","issue":"11","first_page":"360","last_page":"360"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.7002000212669373,"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":0.7002000212669373,"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":0.2791000008583069,"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/T10231","display_name":"Pancreatic and Hepatic Oncology Research","score":0.002400000113993883,"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/pancreatic-cancer","display_name":"pancreatic cancer","score":0.8320000171661377},{"id":"https://openalex.org/keywords/early-cancer-prediction","display_name":"early cancer prediction","score":0.9319999814033508},{"id":"https://openalex.org/keywords/multimodal-deep-learning","display_name":"multimodal deep learning","score":0.8270000219345093},{"id":"https://openalex.org/keywords/explainable-ai","display_name":"explainable AI","score":0.8690000176429749},{"id":"https://openalex.org/keywords/tumor-staging","display_name":"tumor staging","score":0.4869999885559082},{"id":"https://openalex.org/keywords/tnm-staging","display_name":"TNM staging","score":0.6980000138282776},{"id":"https://openalex.org/keywords/diagnostic-imaging","display_name":"diagnostic imaging","score":0.7940000295639038},{"id":"https://openalex.org/keywords/clinical-data","display_name":"clinical data","score":0.8270000219345093},{"id":"https://openalex.org/keywords/tabnet","display_name":"TabNet","score":0.746999979019165},{"id":"https://openalex.org/keywords/squeeze-and-excitation","display_name":"squeeze-and-excitation","score":0.824999988079071},{"id":"https://openalex.org/keywords/vgg","display_name":"VGG","score":0.7139999866485596},{"id":"https://openalex.org/keywords/efficientnet","display_name":"EfficientNet","score":1.0},{"id":"https://openalex.org/keywords/grad-cam","display_name":"Grad-CAM","score":0.9620000123977661},{"id":"https://openalex.org/keywords/attention-mechanism","display_name":"attention mechanism","score":0.8159999847412109},{"id":"https://openalex.org/keywords/class-imbalance","display_name":"class imbalance","score":0.8119999766349792},{"id":"https://openalex.org/keywords/tumor-segmentation","display_name":"tumor segmentation","score":0.3109999895095825}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7372999787330627},{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.6948999762535095},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6875},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5551000237464905},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5339000225067139},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5081999897956848},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.43389999866485596},{"id":"https://openalex.org/C2780210213","wikidata":"https://www.wikidata.org/wiki/Q212961","display_name":"Pancreatic cancer","level":3,"score":0.41990000009536743},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.3540000021457672},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.3440999984741211},{"id":"https://openalex.org/C146357865","wikidata":"https://www.wikidata.org/wiki/Q1123245","display_name":"Stage (stratigraphy)","level":2,"score":0.3199999928474426},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.3172999918460846},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.303600013256073},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2971999943256378},{"id":"https://openalex.org/C2779974597","wikidata":"https://www.wikidata.org/wiki/Q28448986","display_name":"Clinical Practice","level":2,"score":0.29440000653266907},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2858000099658966},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.28540000319480896},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.2815999984741211},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.27709999680519104},{"id":"https://openalex.org/C201645570","wikidata":"https://www.wikidata.org/wiki/Q830637","display_name":"Radiation treatment planning","level":3,"score":0.2621999979019165},{"id":"https://openalex.org/C2780440489","wikidata":"https://www.wikidata.org/wiki/Q5227278","display_name":"Data-driven","level":2,"score":0.25760000944137573},{"id":"https://openalex.org/C121608353","wikidata":"https://www.wikidata.org/wiki/Q12078","display_name":"Cancer","level":2,"score":0.25029999017715454}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3390/sci8110360","doi":"https://doi.org/10.3390/sci8110360","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sci8110360","pdf_url":null,"source":{"id":"https://openalex.org/S4210202795","display_name":"Sci","issn_l":"2413-4155","issn":["2413-4155"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj"],"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":"Sci","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.3390/sci8110360","doi":"https://doi.org/10.3390/sci8110360","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sci8110360","pdf_url":null,"source":{"id":"https://openalex.org/S4210202795","display_name":"Sci","issn_l":"2413-4155","issn":["2413-4155"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj"],"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":"Sci","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3","score":0.9391000270843506}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":61,"referenced_works":["https://openalex.org/W1998964655","https://openalex.org/W2000585255","https://openalex.org/W2018636585","https://openalex.org/W2079885272","https://openalex.org/W2145872640","https://openalex.org/W2154053567","https://openalex.org/W2514141278","https://openalex.org/W2592929672","https://openalex.org/W2610299685","https://openalex.org/W2617669016","https://openalex.org/W2767236661","https://openalex.org/W2798997960","https://openalex.org/W2806048958","https://openalex.org/W2883780447","https://openalex.org/W2889222893","https://openalex.org/W2889985719","https://openalex.org/W2897490102","https://openalex.org/W2900603051","https://openalex.org/W2907688358","https://openalex.org/W2919115771","https://openalex.org/W2938284053","https://openalex.org/W2945284524","https://openalex.org/W2966327452","https://openalex.org/W2966618486","https://openalex.org/W2984353870","https://openalex.org/W3020417958","https://openalex.org/W3025011581","https://openalex.org/W3030432638","https://openalex.org/W3048617165","https://openalex.org/W3101733737","https://openalex.org/W3112046093","https://openalex.org/W3131500599","https://openalex.org/W3147514193","https://openalex.org/W3153942604","https://openalex.org/W3164789060","https://openalex.org/W3208227361","https://openalex.org/W3217415712","https://openalex.org/W4206530180","https://openalex.org/W4221019801","https://openalex.org/W4224326564","https://openalex.org/W4283780227","https://openalex.org/W4285796925","https://openalex.org/W4296776307","https://openalex.org/W4297312136","https://openalex.org/W4308724733","https://openalex.org/W4321089148","https://openalex.org/W4321460148","https://openalex.org/W4323924189","https://openalex.org/W4360830336","https://openalex.org/W4380853319","https://openalex.org/W4382725557","https://openalex.org/W4383722085","https://openalex.org/W4385767742","https://openalex.org/W4392013871","https://openalex.org/W4394610575","https://openalex.org/W4407883735","https://openalex.org/W4412957379","https://openalex.org/W4416190955","https://openalex.org/W7118361767","https://openalex.org/W7131728563","https://openalex.org/W7157619650"],"related_works":["https://openalex.org/W4416677122","https://openalex.org/W7117582736","https://openalex.org/W7147299289","https://openalex.org/W7157619650","https://openalex.org/W7213562582","https://openalex.org/W7204519740","https://openalex.org/W4417169140","https://openalex.org/W7131132161","https://openalex.org/W4413879804","https://openalex.org/W7140143283"],"abstract_inverted_index":{"Accurate":[0],"early":[1,50,208],"prediction":[2,51],"and":[3,52,61,71,96,106,131,157,182,189,200,214,222,231],"multi-stage":[4,53,212],"diagnostic":[5,56,154],"classification":[6,57],"compliant":[7],"with":[8,75,149],"AJCC":[9],"TNM":[10],"staging":[11],"criteria":[12],"are":[13],"essential":[14],"for":[15,49,79,86,235],"effective":[16],"treatment":[17],"planning":[18],"in":[19],"pancreatic":[20,54,209],"cancer.":[21],"Although":[22],"deep":[23],"learning":[24],"(DL)":[25],"approaches":[26],"show":[27],"promising":[28,205],"results,":[29],"interpreting":[30],"their":[31],"predictions":[32],"remains":[33,226],"challenging.":[34],"This":[35],"study":[36],"proposes":[37],"Squeeze-and-Excitation":[38,76],"Visual-Efficient":[39],"Tabular":[40,83],"Network":[41,84],"(SE-VETNet),":[42],"an":[43],"interpretable":[44],"multimodal":[45,196],"DL":[46],"framework":[47,65],"designed":[48],"cancer":[55,210],"using":[58,220],"medical":[59],"imaging":[60],"clinical":[62,87,97,107,190,216,224],"data.":[63],"The":[64],"integrates":[66],"Visual":[67],"Geometry":[68],"Group":[69],"(VGG)":[70],"EfficientNet":[72],"architectures":[73],"enhanced":[74],"(SE)":[77],"blocks":[78],"feature":[80,88,191],"recalibration,":[81],"alongside":[82],"(TabNet)":[85],"extraction.":[89],"Attention":[90],"mechanisms":[91],"identify":[92],"relevant":[93],"tumor":[94],"characteristics":[95],"attributes.":[98],"An":[99],"optimized":[100],"processing":[101],"pipeline":[102],"preserves":[103],"image":[104],"details":[105],"data":[108],"integrity":[109],"while":[110,125],"improving":[111],"computational":[112],"efficiency.":[113],"To":[114],"address":[115],"class":[116],"imbalance,":[117],"balancing":[118],"is":[119],"applied":[120],"exclusively":[121],"to":[122,207,228],"training":[123],"data,":[124],"five-fold":[126],"cross-validation":[127],"supports":[128],"robust":[129,198],"evaluation":[130],"minimizes":[132],"potential":[133],"bias.":[134],"Experiments":[135],"demonstrate":[136],"consistent":[137],"performance":[138],"across":[139,152],"all":[140],"folds,":[141],"achieving":[142],"a":[143,204],"mean":[144],"test":[145],"accuracy":[146],"of":[147],"99.21%":[148],"minimal":[150],"variability":[151],"five":[153],"cohorts":[155],"(Healthy":[156],"T1\u2013T4).":[158],"Furthermore,":[159],"Explainable":[160],"Artificial":[161],"Intelligence":[162],"(XAI)":[163],"techniques,":[164],"including":[165],"Gradient-weighted":[166],"Class":[167],"Activation":[168],"Mapping":[169],"(Grad-CAM),":[170],"quantitatively":[171],"validated":[172],"against":[173],"expert":[174],"ground-truth":[175],"masks":[176],"via":[177],"Intersection":[178],"over":[179],"Union":[180],"(IoU),":[181],"TabNet":[183],"attention,":[184],"provide":[185],"insights":[186],"into":[187],"image-based":[188],"contributions,":[192],"respectively.":[193],"By":[194],"integrating":[195],"learning,":[197],"evaluation,":[199],"explainability,":[201],"SE-VETNet":[202],"offers":[203],"approach":[206],"prediction,":[211],"staging,":[213],"transparent":[215],"decision-making.":[217],"However,":[218],"validation":[219],"independent":[221],"diverse":[223],"datasets":[225],"necessary":[227],"establish":[229],"generalizability":[230],"assess":[232],"its":[233],"suitability":[234],"practical":[236],"deployment.":[237]},"counts_by_year":[],"updated_date":"2026-11-11T12:16:26.678958","created_date":"2026-11-10T00:00:00"}