Physics-informed deep learning sharpens nano diagnostics for elusive pancreatic cancer

Abstract

Pancreatic disease affects over 10% of the world population, and the most dangerous is pancreatic cancer (PC). The disease is mostly of late age of onset, especially in developed countries, and is associated with poor prognosis due to late presentation. Present screening tests like imaging and biomarkers are insensitive for the high-risk group. Invasive and noninvasive imaging modalities are other diagnostic tests with variable accuracy and accompanying risks. Chemotherapy and surgery are the first lines of treatment, but only 15%–20% of patients are eligible for surgery and the rate of recurrence is very high. Emerging technologies, including physics-informed deep learning (PIDL) and artificial intelligence (AI), are improving early detection techniques by evaluating images and synthesizing data more efficiently. Nanomedicine and AI-driven radiomics are individualizing diagnoses, enhancing drug delivery, and tackling tumor microenvironment issues. Hybrid model methodologies are improving prediction precision in oncology research, while computational drug development and liquid biopsy technologies enable early diagnosis and personalized treatment. The amalgamation of AI, imaging, nanomedicine, and physics-informed models has the potential to transform PC diagnostics, enhancing early detection and patient prognoses.