EUS-Based Artificial Intelligence to Predict Outcomes in Pancreatic Cancer (EUS-AI-R)
An Observational Study on the Application of Artificial Intelligence Model to Predict Diagnosis, Prognosis, and Molecular Alterations in Pancreatic Cancer
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).
Patients who underwent endoscopic ultrasound (EUS) with tissue acquisition (EUS-FNA/FNB) 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).
Retrospective multimodal data, including EUS imaging (B-mode, elastography, contrast-enhanced EUS), clinical and laboratory variables, CT/MRI imaging, digital pathology, and molecular data when available, will be used to develop and internally validate multiple AI models.
In 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.
No additional procedures beyond standard clinical practice will be performed.
Study Overview
Status
Conditions
Detailed Description
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.
Recent 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.
The 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.
The study consists of two phases:
- Retrospective phase: development, training, and internal validation of AI models using approximately 500 patients from an institutional database.
- Prospective phase: application of the selected model to an independent cohort (200 patients) to assess feasibility, calibration, and real-world performance.
Multiple 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.
The study is observational and does not modify standard clinical practice.
Study Type
Enrollment (Estimated)
Contacts and Locations
Study Contact
- Name: Gaetano Lauri, MD, PhDs
- Phone Number: 0226436303
- Email: lauri.gaetano@hsr.it
Study Contact Backup
- Name: Matteo Tacelli, MD, PhD
- Phone Number: 0226435607
- Email: tacelli.matteo@hsr.it
Study Locations
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Lombardy
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Milan, Lombardy, Italy, 20132
- IRCCS Ospedale San Raffaele
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Contact:
- Laura Apadula
- Phone Number: 0226433979
- Email: apadula.laura@hsr.it
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Participation Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
Inclusion Criteria:
- Pathologically confirmed diagnosis (final pathology report) of pancreatic cancer obtained through endoscopic ultrasound-guided tissue sampling (EUS-FNA or EUS-FNB)
- Age ≥ 18 years at the time of diagnosis
- Minimum follow-up duration of 6 months after diagnosis
- Absence of other concomitant neoplastic diseases
- Age>= 18 years
- Capacity to understand and make informed decisions
- Written informed consent provided by the patient
Exclusion Criteria:
- 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.
- Age<18 years
- Inability to understand and make informed decisions
- Refusal to participate in the study
Study Plan
How is the study designed?
Design Details
Cohorts and Interventions
Group / Cohort |
Intervention / Treatment |
|---|---|
|
All patients who underwent EUS at the Pancreato-Biliary Endoscopy and Endoscopic Ultrasound Unit of
|
Endoscopic ultrasound (EUS), with or without tissue acquisition (EUS-FNA/FNB), performed according to standard clinical practice.
No study-specific intervention is introduced.
|
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Development of an AI algorithm predicting chemotherapy response in patients with pancreatic cancer
Time Frame: 6 months
|
Development of AI models to predict chemotherapy response in patients with PDAC by evaluating the predictive performance of:
Model performance will be assessed using AUC-ROC, sensitivity, and specificity. |
6 months
|
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Recurrence-Free Survival (RFS) Rate based on AI Models
Time Frame: 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
|
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) / 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
|
|
Progression-Free Survival (PFS) Rate based on AI Models
Time Frame: 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
|
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
|
|
Overall Survival (OS) Rate based on AI Models
Time Frame: From the date of histological diagnosis of pancreatic cancer until the date of death from any cause, assessed up to 6 months
|
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
|
Other Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Diagnostic Accuracy and Discrimination (AUC-ROC)
Time Frame: 1 year
|
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
|
|
Diagnostic Accuracy (Sensitivity and Specificity) of AI Models
Time Frame: 1 year
|
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.
|
1 year
|
|
Model Calibration, Stability, and Prospective Feasibility
Time Frame: 1 year
|
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.
|
1 year
|
Collaborators and Investigators
Sponsor
Study record dates
Study Major Dates
Study Start (Estimated)
Primary Completion (Estimated)
Study Completion (Estimated)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Actual)
Study Record Updates
Last Update Posted (Actual)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
- EUS-AI-R
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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