A Review of Deep Learning Methods for Early Prediction of Pancreatic Cancer in High-Risk Populations
K. Naresh, D. Ganesh · 2024
As the world's second-most frequent cancer, pancreatic adenocarcinoma maintains its status as one of the deadliest malignancies globally. Pancreatic cancer poses a dire prediction, with a mere five-year persistence rate despite extensive research efforts. This challenging malignancy, originating from the pancreas, is elusive in its early phases, often manifesting through jaundice and sudden weight loss. Early detection and improved survival hinge on the potential benefits of treatment for a larger population. Machine and deep learning algorithms, proven effective in general healthcare, emerge as viable tools for classifying or detecting the risk of pancreatic cancer. Focusing on the use of deep learning techniques in the ever-changing domain of digital pathology, this study offers a thorough overview of deep learning methods for early pancreatic cancer prediction. The review starts by explaining the basics of pancreatic cancer and how deep learning models have changed over time. Then it moves on to examine several models that are used for early pancreatic cancer prediction. Detailed discussions on the pros and cons of these models are provided. The paper critically evaluates methodologies for early prediction of pancreatic cancer using deep learning, addressing challenges such as data sparsity, scalability, and interpretability in real-world environments. Additionally, the benefits and drawbacks of various strategies are examined, with a particular emphasis on hybrid models that combine deep learning and traditional methods to enhance robustness. The review contributes to a comprehensive understanding of the evolving landscape of deep learning in the forecast of pancreatic cancer, offering insights into its effectiveness in identifying high-risk individuals and facilitating early intervention strategies within high-risk populations.