ObjectiveTo summarize the current application status and research progress of extracellular volume (ECV) fraction based on imaging examinations in pancreatic diseases. MethodThe literature relevant to research was summarized, including the clinical studies of the ECV fraction that based on computed tomography and magnetic resonance imaging in pancreatic inflammation, neoplastic lesions, fibrosis, and other diseases. ResultsBiopsy of pancreas was technically challenging due to its unique anatomical location. The ECV fraction was the quantitative index of extracellular matrix that played a regulatory role in the process of tumor proliferation and invasion. And the production of collagen fibers and the deposition of extracellular matrix could increase the extracellular space in the progression of tissue fibrosis. Therefore, the ECV fraction obtained based on imaging examination could not only avoid invasive examination, but also reflect the status of tumor microenvironment and evaluate the degree of tissue fibrosis. The ECV fraction had the potential to serve as a novel quantitative imaging evaluation index for pancreatic diseases. ConclusionsAccording to the current research status and progress of ECV fraction in pancreatic-related diseases, ECV fraction is increasingly being utilized as a non-invasive biomarker across various pancreatic-related conditions. It holds the potential to predict tumor grading, degree of fibrosis, post-chemotherapy response, cancer patient survival, etc. Consequently, it exhibits promising prospects for clinical application research.
Prenatal diagnosis, as one of the core components in the prevention and control of birth defects, is constrained by both “time sensitivity” and “data availability”. The diagnostic model driven by expert experience and manual interpretation can no longer meet the demands of rapidly evolving detection technologies, which generate massive, high-dimensional data. Additionally, issues such as delayed professional training and regional development imbalances further hinder the overall improvement of prenatal diagnosis efficacy. This article systematically elaborates on typical application scenarios of artificial intelligence-assisted prenatal diagnosis from two core aspects: the intelligent optimization of diagnostic technologies and the standardization of institutional and personnel management. It also explores the potential of emerging intelligent technologies like federated learning and digital twins, aiming to promote the transformation and upgrading of the prenatal diagnosis field from standardization and normalization toward precision and systematic high-quality development.
