Advances for Managing Pancreatic Cystic Lesions: Integrating Imaging and AI Innovations

Area of Science:

• Gastroenterology
• Oncology
• Medical Imaging
• Artificial Intelligence

Background:

• Pancreatic cystic lesions (PCLs) present diagnostic challenges due to their varied nature, ranging from benign to malignant precursors.
• Current diagnostic methods, including imaging and endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA), have limitations in accuracy and reproducibility.
• Accurate differentiation of PCLs is crucial for appropriate patient management and pancreatic cancer prevention.

Purpose of the Study:

• To review current diagnostic and surveillance practices for PCLs.
• To evaluate the limitations of conventional PCL diagnostic methods.
• To explore the potential of artificial intelligence (AI) in improving PCL diagnosis and management.

Main Methods:

• Critical review of existing literature on PCL diagnosis and management.
• Exploration of AI-driven strategies, including deep learning for segmentation and radiomics for heterogeneity analysis.
• Analysis of AI's potential impact on diagnostic accuracy, risk stratification, and clinical decision-making.

Main Results:

• Conventional methods for PCL diagnosis can suffer from observer-dependent interpretation and diagnostic uncertainty.
• AI-driven approaches, such as deep learning and radiomics, show promise in enhancing diagnostic accuracy and objectivity.
• AI can aid in risk stratification, potentially leading to earlier detection of high-risk lesions and reducing unnecessary interventions.

Conclusions:

• AI has the potential to significantly transform PCL management by providing more objective and reproducible assessments.
• AI-driven strategies can improve patient outcomes through earlier detection and more precise management decisions.
• The integration of AI in PCL diagnostics may lead to improved pancreatic cancer prevention strategies.