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NCT Number: NCT07803770

EUS-Based Artificial Intelligence to Predict Outcomes 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.

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Key information

Conditions

Pancreatic Neoplasms Artificial Intelligence (AI) Digestive System Diseases Digestive System Neoplasms Endocrine Gland Neoplasms Endocrine System Diseases Endoscopic Ultrasound (EUS) Neoplasms Neoplasms by Site Pancreatic Diseases Pancreatic Ductal Adenocarcinoma (PDAC) Radiomics

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

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.

Primary outcomes

  1. 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:

    • 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.

Secondary outcomes

  1. 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.

  2. 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.

  3. 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.

Other outcomes

  1. 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.

  2. 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.

  3. 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.

Study contacts

Contact information is provided by the study sponsor or research team.

Gaetano Lauri, MD, PhDs

CONTACT

[email protected]

0226436303

Matteo Tacelli, MD, PhD

CONTACT

[email protected]

0226435607

Sponsors and collaborators

Lead sponsor

IRCCS San Raffaele

Other

Registry information

Official study title

An Observational Study on the Application of Artificial Intelligence Model to Predict Diagnosis, Prognosis, and Molecular Alterations in Pancreatic Cancer

Acronym: EUS-AI-R

Important dates

Study start
2026
Primary completion
2028
Study completion
2028
First posted
Sep 4, 2026
Registry last updated
Sep 4, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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