[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100654837":3},{"organization":4,"outcomesModule":7,"designInfo":37,"detailedDescription":40,"studyPopulation":41,"armGroups":42,"interventions":47,"overallOfficials":38,"centralContacts":51,"locations":61,"responsibleParty":80,"collaborators":38,"id":84,"slug":85,"hasResults":86,"nctId":87,"briefTitle":88,"officialTitle":89,"acronym":90,"eligibilityCriteria":91,"healthyVolunteers":86,"sex":92,"minAge":93,"maxAge":38,"enrollmentInfo":94,"targetDuration":97,"studyType":98,"phases":38,"briefSummary":99,"conditions":100,"keywords":38,"overallStatus":106,"whyStopped":38,"lastUpdateSubmitDate":107,"lastUpdatePostDateStruct":108,"startDateStruct":111,"completionDateStruct":113,"leadSponsor":115,"locationsCount":116},{"fullName":5,"class":6},"IRCCS San Raffaele","OTHER",{"primaryOutcomes":8,"secondaryOutcomes":13,"otherOutcomes":26},[9],{"measure":10,"description":11,"timeFrame":12},"Development of an AI algorithm predicting chemotherapy response in patients with pancreatic cancer","Development of AI models to predict chemotherapy response in patients with PDAC by evaluating the predictive performance of:\n\n1. An EUS-based AI model (pre-treatment EUS images\u002Fvideos)\n2. A clinical\u002Fexposome-based AI model (pre-treatment clinical and laboratory variables)\n3. A radiology-based AI model (pre-treatment CT\u002FMRI radiomics)\n4. A digital pathology-based AI model (histopathology slides)\n5. A multimodal AI model combining all available data sources (EUS + clinical\u002Fexposome + CT\u002FMRI radiomics + digital pathology ± molecular data when available) Chemotherapy response will be defined according to radiological response criteria (RECIST) and biochemical response assessed by CA19-9 levels.\n\nModel performance will be assessed using AUC-ROC, sensitivity, and specificity.","6 months",[14,18,22],{"measure":15,"description":16,"timeFrame":17},"Recurrence-Free Survival (RFS) Rate based on AI Models","To evaluate the predictive performance of the AI models (single-modality and multimodal) using pre-treatment imaging features to determine the rate and time to recurrence-free survival (RFS) \u002F time to recurrence.","From the date of histological diagnosis of pancreatic cancer until the date of first documented recurrence or death from any cause, whichever came first, assessed up to 6 months",{"measure":19,"description":20,"timeFrame":21},"Progression-Free Survival (PFS) Rate based on AI Models","To evaluate the predictive performance of the AI models (single-modality and multimodal) using pre-treatment imaging features to determine the progression-free survival (PFS) rate.","From the date of histological diagnosis of pancreatic cancer until the date of first documented disease progression or death from any cause, whichever came first, assessed up to 6 months",{"measure":23,"description":24,"timeFrame":25},"Overall Survival (OS) Rate based on AI Models","To evaluate the predictive performance of the AI models (single-modality and multimodal) using pre-treatment imaging features to determine the overall survival (OS) rate.","From the date of histological diagnosis of pancreatic cancer until the date of death from any cause, assessed up to 6 months",[27,31,34],{"measure":28,"description":29,"timeFrame":30},"Diagnostic Accuracy and Discrimination (AUC-ROC)","Evaluation of the Area Under the Receiver Operating Characteristic Curve (AUC-ROC) for the artificial intelligence (machine learning and deep learning) models developed on retrospective multimodal and multi-omic data (EUS, clinical, exposome, radiomics, pathology, and molecular data) to predict chemotherapy response and oncological outcomes in pancreatic cancer patients.","1 year",{"measure":32,"description":33,"timeFrame":30},"Diagnostic Accuracy (Sensitivity and Specificity) of AI Models","Assessment of sensitivity and specificity rates across validation folds of the AI models developed and internally validated on retrospective data for predicting chemotherapy response and oncological outcomes.",{"measure":35,"description":36,"timeFrame":30},"Model Calibration, Stability, and Prospective Feasibility","Evaluation of model calibration and stability across validation folds on retrospective data, alongside the assessment of feasibility and preliminary performance when the selected AI approach is applied prospectively in an independent patient population.",{"allocation":38,"interventionModel":38,"interventionModelDescription":38,"primaryPurpose":38,"observationalModel":39,"timePerspective":6,"maskingInfo":38},null,"COHORT","PDAC remains one of the leading causes of cancer-related mortality, largely due to late diagnosis and limited predictive tools for treatment response. EUS represents the most sensitive modality for detecting pancreatic lesions and allows tissue acquisition for histological confirmation.\n\nRecent advances in AI, including machine learning (ML) and deep learning (DL), have demonstrated strong potential in improving diagnostic accuracy, prognostic stratification, and prediction of treatment response in oncology.\n\nThe EUS-AI-R study aims to integrate multimodal data, including EUS imaging, clinical variables, radiological imaging, digital histopathology, and molecular data, into AI-based predictive models capable of estimating chemotherapy response and survival outcomes in PDAC patients.\n\nThe study consists of two phases:\n\n* Retrospective phase: development, training, and internal validation of AI models using approximately 500 patients from an institutional database.\n* Prospective phase: application of the selected model to an independent cohort (200 patients) to assess feasibility, calibration, and real-world performance.\n\nMultiple modality-specific models (EUS-based, clinical-based, radiology-based, pathology-based) and a multimodal integrated model will be developed and compared. Model performance will be evaluated using AUC-ROC, sensitivity, specificity, calibration, and concordance index for survival outcomes.\n\nThe study is observational and does not modify standard clinical practice.","All patients who underwent EUS at the Pancreato-Biliary Endoscopy and Endoscopic Ultrasound Unit of IRCCS San Raffaele Hospital with an histological diagnosis of PDAC.",[43],{"label":44,"type":38,"description":38,"interventionNames":45},"All patients who underwent EUS at the Pancreato-Biliary Endoscopy and Endoscopic Ultrasound Unit of",[46],"Other: Endoscopic ultrasound (EUS), with or without tissue acquisition (EUS-FNA\u002FFNB), performed according to standard clinical practice. No study-specific intervention is introduced.",[48],{"type":6,"name":49,"description":49,"armGroupLabels":50,"otherNames":38},"Endoscopic ultrasound (EUS), with or without tissue acquisition (EUS-FNA\u002FFNB), performed according to standard clinical practice. No study-specific intervention is introduced.",[44],[52,57],{"name":53,"role":54,"phone":55,"phoneExt":38,"email":56},"Gaetano Lauri, MD, PhDs","CONTACT","0226436303","lauri.gaetano@hsr.it",{"name":58,"role":54,"phone":59,"phoneExt":38,"email":60},"Matteo Tacelli, MD, PhD","0226435607","tacelli.matteo@hsr.it",[62],{"facility":63,"status":38,"city":64,"state":65,"zip":66,"country":67,"countryCode":68,"cosmosGeoPoint":69,"geoPoint":74,"contacts":75},"IRCCS Ospedale San Raffaele","Milan","Lombardy","20132","Italy","IT",{"type":70,"coordinates":71},"Point",[72,73],9.18951,45.46427,{"lat":73,"lon":72},[76],{"name":77,"role":54,"phone":78,"phoneExt":38,"email":79},"Laura Apadula","0226433979","apadula.laura@hsr.it",{"type":81,"investigatorFullName":82,"investigatorTitle":83,"investigatorAffiliation":5,"oldNameTitle":38,"oldOrganization":38},"PRINCIPAL_INVESTIGATOR","Paolo Giorgio Arcidiacono, MD","Director, Pancreatico-Biliary Endoscopy and Endosonography Division","100654837","eus-based-artificial-intelligence-to-predict-outcomes-in-pancreatic-cancer-100654837",false,"NCT07803770","EUS-Based Artificial Intelligence to Predict Outcomes in Pancreatic Cancer","An Observational Study on the Application of Artificial Intelligence Model to Predict Diagnosis, Prognosis, and Molecular Alterations in Pancreatic Cancer","EUS-AI-R","Inclusion Criteria:\n\n* Pathologically confirmed diagnosis (final pathology report) of pancreatic cancer obtained through endoscopic ultrasound-guided tissue sampling (EUS-FNA or EUS-FNB)\n* Age ≥ 18 years at the time of diagnosis\n* Minimum follow-up duration of 6 months after diagnosis\n* Absence of other concomitant neoplastic diseases\n* Age\\>= 18 years\n* Capacity to understand and make informed decisions\n* Written informed consent provided by the patient\n\nExclusion Criteria:\n\n* All patients who underwent endoscopic ultrasound with tissue sampling at the Pancreato-Biliary Endoscopy and Endoscopic Ultrasound Unit of IRCCS San Raffaele Hospital but do not meet the inclusion criteria will be excluded from the final analysis cohorts.\n* Age\\\u003C18 years\n* Inability to understand and make informed decisions\n* Refusal to participate in the study","ALL","18 Years",{"count":95,"type":96},700,"ESTIMATED","6 Months","OBSERVATIONAL","The EUS-AI-R study is an observational, single-center, two-phase (retrospective-prospective) study designed to develop and validate artificial intelligence (AI) models for predicting chemotherapy response and oncological outcomes in patients with pancreatic ductal adenocarcinoma (PDAC).\n\nPatients who underwent endoscopic ultrasound (EUS) with tissue acquisition (EUS-FNA\u002FFNB) for suspected pancreatic lesions at IRCCS San Raffaele Hospital between January 1st, 2019 and January 2026 will be retrospectively included. All patients have a histologically confirmed diagnosis of PDAC and a minimum follow-up of six months. These data derive from an IRB-approved institutional study (BIOPANCREAS; NCT06552078).\n\nRetrospective multimodal data, including EUS imaging (B-mode, elastography, contrast-enhanced EUS), clinical and laboratory variables, CT\u002FMRI imaging, digital pathology, and molecular data when available, will be used to develop and internally validate multiple AI models.\n\nIn the prospective phase, the best-performing AI model will be applied to an independent cohort of patients undergoing EUS at the same institution to evaluate feasibility, calibration, and real-world performance.\n\nNo additional procedures beyond standard clinical practice will be performed.",[101,102,103,104,105],"Pancreatic Neoplasms","Pancreatic Ductal Adenocarcinoma (PDAC)","Artificial Intelligence (AI)","Endoscopic Ultrasound (EUS)","Radiomics","NOT_YET_RECRUITING","2026-09-02",{"date":109,"type":110},"2026-09-04","ACTUAL",{"date":112,"type":96},"2026-10-31",{"date":114,"type":96},"2028-12-31",{"name":5,"class":6},1]