Radiomics is a mathematical approach to medical images to extract quantitative features generating a “radiomics signature.” The radiomics workflow involves image acquisition and pre-processing, region of interest segmentation, feature extraction, and then model training and validation. It has generated promising results, however, clinical implementation for early detection remains a challenge. Pancreatic ductal adenocarcinoma (PDAC), the most common pancreatic cancer, has a highly aggressive nature with an aggregated 5-year survival rate of only 13%. Early detection of PDAC provides timely surgical intervention, hoping for improved survival rates. Radiomics has been applied to the detection of PDAC; however, its sensitivity to variations in image acquisition parameters has posed significant challenges, limiting the development of robust and generalizable models. This review explores the current landscape of radiomics for the early detection of PDAC, highlighting key challenges within the radiomics workflow and barriers to its progression from a proof-of-concept into clinical practice.
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