Artificial Intelligence (AI), Endoscopic Ultrasound (EUS), Pancreatic Ductal Adenocarcinoma (PDAC), Pancreatic Neoplasms, Radiomics
Conditions
Brief 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). 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.
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.
Interventions
OTHEREndoscopic ultrasound (EUS), with or without tissue acquisition (EUS-FNA/FNB), performed according to standard clinical practice. No study-specific intervention is introduced.
Endoscopic ultrasound (EUS), with or without tissue acquisition (EUS-FNA/FNB), performed according to standard clinical practice. No study-specific intervention is introduced.
Sponsors
IRCCS San Raffaele
Study design
Observational model
COHORT
Time perspective
OTHER
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Development of an AI algorithm predicting chemotherapy response in patients with pancreatic cancer | 6 months | Development of AI models to predict chemotherapy response in patients with PDAC by evaluating the predictive performance of: 1. An EUS-based AI model (pre-treatment EUS images/videos) 2. A clinical/exposome-based AI model (pre-treatment clinical and laboratory variables) 3. A radiology-based AI model (pre-treatment CT/MRI radiomics) 4. A digital pathology-based AI model (histopathology slides) 5. 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
| Measure | Time frame | Description |
|---|---|---|
| Recurrence-Free Survival (RFS) Rate based on AI Models | 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. |
| Progression-Free Survival (PFS) Rate based on AI Models | 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. |
| Overall Survival (OS) Rate based on AI Models | 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. |
Countries
Italy
Contacts
CONTACTGaetano Lauri, MD, PhDs
CONTACTMatteo Tacelli, MD, PhD
Outcome results
None listed