An Observational Study on the Application of Artificial Intelligence Model to Predict Diagnosis, Prognosis, and Molecular Alterations in Pancreatic Cancer
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 入组人数
- 700
- 试验地点
- 1
- 主要终点
- Development of an AI algorithm predicting chemotherapy response in patients with pancreatic cancer
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Other
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •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
排除标准
- •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
研究组 & 干预措施
All patients who underwent EUS at the Pancreato-Biliary Endoscopy and Endoscopic Ultrasound Unit of
干预措施: Endoscopic ultrasound (EUS), with or without tissue acquisition (EUS-FNA/FNB), performed according to standard clinical practice. No study-specific intervention is introduced. (Other)
结局指标
主要结局
Development of an AI algorithm predicting chemotherapy response in patients with pancreatic cancer
时间窗: 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.
次要结局
- 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)
- 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)
- 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)
研究者
Paolo Giorgio Arcidiacono, MD
Director, Pancreatico-Biliary Endoscopy and Endosonography Division
IRCCS San Raffaele