Management of Pancreatic Cystic Lesions Using Artificial Intelligence Based on EUS and Multimodal Data

Huazhong University of Science and Technology500 enrolled

Overview

The primary objective is to construct a multimodal AI model (Cyst-AI) based on EUS images and clinical data such as imaging features(CT or MRI) and laboratory tests to assist endoscopists in the diagnosis of pancreatic cystic lesions(PCLs), mainly differentiating mucinous from non-mucinous lesions. The secondary objective is to evaluate the model's effectiveness in risk stratification and clinical management for patients with PCLs.

With the development of medical imaging technology, the detection rate of pancreatic cystic lesions (PCLs) has been increasing notably. Although most cysts are benign, a considerable subset has the potential for malignant transformation. Clinical management is based on diagnosis and risk stratification. For PCLs，different diagnosis and risk stratification lead to entirely different clinical strategies and outcomes, which are closely related to the quality of life, economic burden, and psychological stress of patients. Endoscopic ultrasound (EUS) has played a crucial role in the further differential diagnosis of PCLs. Artificial intelligence (AI) has also shown great potential in clinical diagnosis and management. Thus, we plan to retrospectively collect patients' EUS imaging data, radiological and laboratory tests, and other clinical information to construct a model named Cyst-AI which integrates the function of diagnosis and clinical management, to assist in clinical decision-making.

Study Type

OBSERVATIONAL

Enrollment

500

Conditions

Pancreatic Cystic Lesion Mucinous Cystadenoma of Pancreas Intraductal Papillary Mucinous Neoplasm of Pancreas

Eligibility

Sex: ALLMin age: 18 Years

Medical Language ↔ Plain English

Inclusion criteria: * Patients whose EUS results indicates pancreatic cystic or cystoid lesions; * Mucinous lesions: including mucinous cystic neoplasm (MCN), intraductal papillary mucinous neoplasm (IPMN); * Non-mucinous lesions: including pancreatic pseudocyst, serous cystic neoplasm (SCN), cystic neuroendocrine tumor (cNET). Exclusion criteria: * Patients whose age is less than 18 years old; * Patients who have undergone pancreatic surgery before the EUS examination; * Patients who have received chemotherapy and radiotherapy for pancreatic tumors before the EUS examination; * Pathological results indicate that pancreatic lesions are metastatic lesions from other sites; * Patients whose EUS images or reports are missing; * EUS image quality does not meet the requirements for review, such as blurry imaging or containing artifacts, biopsy needles, measuring scales, or other additional annotations that are not part of the original EUS image; * Patients whose final diagnosis is unclear.

Locations (2)

Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology

Wuhan, Hubei, China

NOT_YET_RECRUITING

Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology

Wuhan, Hubei, China

RECRUITING

Outcomes

Primary Outcomes

The performance of the diagnostic model in differentiating mucinous from non-mucinous PCLs

The performance of the Cyst-AI diagnostic model will be evaluated using the area under the receiver operating characteristic curve (AUC-ROC), with sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) calculated from the model's predictions on the independent validation dataset. PCLs: pancreatic cystic lesions.

Time frame: Within 3 months upon completion of the diagnostic model training.

The risk stratification performance of the clinical management model for mucinous PCLs

The performance of the Cyst-AI risk stratification model to correctly classify lesions into "low risk", "intermediate risk" and "high risk", will be evaluated using the area under the receiver operating characteristic curve (AUC-ROC), with sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) calculated from the model's predictions on the independent validation dataset. PCLs: pancreatic cystic lesions.

Time frame: Within 3 months upon completion of the risk stratification model training.

Secondary Outcomes

The performance of the diagnostic model in differentiating specific types of PCLs

Time frame: Within 3 months upon completion of the diagnostic model training.

The clinical management performance of the clinical management model for mucinous PCLs

The performance of the Cyst-AI clinical management model to provide accurate clinical recommendations, will be evaluated using the area under the receiver operating characteristic curve (AUC-ROC), with sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) calculated from the model's predictions on the independent validation dataset. PCLs: pancreatic cystic lesions.

Time frame: Within 3 months upon completion of the clinical management model training.

The performance of the model in assisting endoscopists of different levels in diagnosing and managing PCLs

The performance of the Cyst-AI model in assisting endoscopists will be evaluated using the area under the receiver operating characteristic curve (AUC-ROC), with sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) calculated from the model's predictions on the independent validation dataset. PCLs: pancreatic cystic lesions.

Time frame: Within 1 months upon completion of the human-machine confrontational crossover study

The impact of the model on the decision-making process of endoscopists

Questionnaire for endoscopists after assessment will be used to evaluate the degree of impact.

Time frame: Within 1 months upon completion of the human-machine confrontational crossover study.