Radiomics and artificial intelligence in the differential diagnosis of tumor and non-tumor diseases of the pancreas. Review

Abstract

This work provides a comprehensive overview of the recent advancements in the field of radiomic diagnostics and artificial intelligence (AI) in the diagnosis of pancreatic diseases. The integration of radiochemical analysis and AI has allowed for more accurate and precise diagnoses of pancreatic diseases, including pancreatic cancer. The review highlights the different stages of radiomic analysis, such as data collection, preprocessing, tumour segmentation, data detection and extraction, modeling, statistical processing, and data validation, which are essential for the accurate diagnosis of pancreatic diseases. Furthermore, the review evaluates the possibilities of using AI and artificial neural networks in surgical and oncological pancreatology. The features and advantages of using radiochemical analysis and AI in the diagnosis and prognosis of pancreatic cancer are also described. These advancements have the potential to improve patient outcomes, as early and accurate diagnosis can lead to earlier treatment and better chances of recovery. However, the limitations associated with the use of radiometry and AI in pancreatology are also noted, such as the lack of standardization and the potential for false positives or false negatives. Nevertheless, this work highlights the potential benefits of incorporating radiochemical analysis and AI in the diagnosis and treatment of pancreatic diseases, which can ultimately lead to better patient care and outcomes.