Calogero Casà, Daniel Portik, Ahmed Nadeem Abbasi, Francesco Miccichè
Radiomics is transforming the field of early detection of bilio-pancreatic lesions, offering significant advancements in diagnostic accuracy and personalized treatment planning. By extracting high-dimensional data from medical images such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET), radiomics reveals complex patterns that remain undetectable through traditional imaging evaluation. This review synthesizes recent developments in radiomics, particularly its application to early detection of pancreatic cancer (PC) and biliary duct cancer (BDC). It highlights the role of machine learning algorithms and multi-parametric models in improving diagnostic performance and discusses challenges such as standardization, reproducibility, and the need for larger, multicenter datasets. The integration of radiomics with genomic data and liquid biopsies also presents future opportunities for more individualized patient care.