Advancements in Radiomics-Based AI for Pancreatic Ductal Adenocarcinoma

Area of Science:

• Oncology
• Medical Imaging
• Artificial Intelligence

Background:

• Artificial intelligence (AI), deep learning, and radiomics are revolutionizing pancreatic ductal adenocarcinoma (PDAC) research.
• These technologies offer novel methods for disease detection, classification, prognosis, and treatment assessment.

Purpose of the Study:

• To provide a comprehensive literature review of AI applications in PDAC.
• To analyze AI's role in disease detection, classification, survival prediction, treatment response, and radiogenomics.
• To identify research gaps and future directions for clinical translation.

Main Methods:

• Systematic literature review of studies on AI in PDAC.
• Analysis of methodologies, findings, and limitations of existing research.
• Focus on AI applications in early detection, diagnosis, prognosis, and treatment evaluation.

Main Results:

• AI demonstrates significant potential in enhancing early detection and diagnostic precision for PDAC.
• Current AI approaches show promise in prognosis and treatment response assessment.
• Challenges remain in clinical applicability, generalizability, interpretability, and routine integration.

Conclusions:

• AI-driven approaches offer strengths in analyzing PDAC but face hurdles in clinical translation.
• Future research should focus on multi-institutional collaborations and explainable AI (XAI).
• Integrating multi-modal data with AI is crucial for advancing personalized medicine in PDAC.