Pancreatic NeoplasmsEUS-AI-RNCT07803770
EUS-Based Artificial Intelligence to Predict Outcomes in Pancreatic Cancer
An observational study of Pancreatic Neoplasms and Pancreatic Ductal Adenocarcinoma (PDAC), run by IRCCS San Raffaele at 1 site in Italy.
Trial summary
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). Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Pancreatic Neoplasms, Pancreatic Ductal Adenocarcinoma (PDAC), Artificial Intelligence (AI)
- What is being tested?
- Endoscopic ultrasound (EUS), with or without tissue acquisition (EUS-FNA/FNB), performed according to standard clinical practice. No study-specific intervention is introduced.
- How many people
- 700 participants
- Where
- 1 siteItaly
- Study dates
- 31 Oct 2026to 31 Dec 2028
- How long will I take part?
- 6 Months
- Phase
- Observational
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: 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.
Who may be able to join
- 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
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Italy1 site
IRCCS Ospedale San Raffaele
Milan, Lombardy, 20132
Laura Apadula, 0226433979, apadula.laura@hsr.it
About this study
Official title: 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).
Read more
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.
Read the 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.
- Conditions
- Pancreatic Neoplasms, Pancreatic Ductal Adenocarcinoma (PDAC), Artificial Intelligence (AI), Endoscopic Ultrasound (EUS), Radiomics
- Being tested
- Endoscopic ultrasound (EUS), with or without tissue acquisition (EUS-FNA/FNB), performed according to standard clinical practice. No study-specific intervention is introduced.
- Medical subject headings
- Pancreatic Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Endoscopic ultrasound (EUS), with or without tissue acquisition (EUS-FNA/FNB), performed according to standard clinical practice. No study-specific intervention is introduced.Other
Endoscopic ultrasound (EUS), with or without tissue acquisition (EUS-FNA/FNB), performed according to standard clinical practice. No study-specific intervention is introduced.
| Study group | Role | What they receive |
|---|---|---|
| All patients who underwent EUS at the Pancreato-Biliary Endoscopy and Endoscopic Ultrasound Unit of | Not described Receives: Other: 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 being measured
The primary outcome is the main question the study is designed to answer.
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:
- An EUS-based AI model (pre-treatment EUS images/videos)
- A clinical/exposome-based AI model (pre-treatment clinical and laboratory variables)
- A radiology-based AI model (pre-treatment CT/MRI radiomics)
- A digital pathology-based AI model (histopathology slides)
- A multimodal AI model combining all available data sources (EUS + clinical/exposome + CT/MRI 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.
Model performance will be assessed using AUC-ROC, sensitivity, and specificity.
Measured: 6 months
Secondary outcomes (3)
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) / time to recurrence.
Measured: 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
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.
Measured: 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
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.
Measured: From the date of histological diagnosis of pancreatic cancer until the date of death from any cause, assessed up to 6 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted4 Sep 2026
- Study start31 Oct 2026 (estimated)
- Primary completion31 Dec 2028 (estimated)
- Study completion31 Dec 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Pancreatic NeoplasmsStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Other
- Enrolment
- 700 participants (estimated)
- Target duration
- 6 Months
Sponsor and investigators
- Lead sponsor
- IRCCS San Raffaele
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Paolo Giorgio Arcidiacono, MD, IRCCS San Raffaele
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07803770
- Sponsor's protocol ID
- EUS-AI-R
- Acronym
- EUS-AI-R
- Status last verified
- Sep 2026
- First submitted
- 22 Apr 2026
- Last updated
- 4 Sep 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No