Clinical trials

Pancreatic ductal adenocarcinoma (PDAC), representing 85-95% of pancreatic cancers, is a highly lethal malignancy with a dismal 5-year survival rate below 8%. Emerging evidence highlights the critical need for non-invasive imaging biomarkers to stratify prognosis and guide therapeutic strategies. Notably, the biomechanical properties of PDAC-associated extracellular matrix (ECM), characterized by extensive interstitial fibrosis, are intrinsically linked to tumorigenesis, progression, and metastatic dissemination. Three-dimensional magnetic resonance elastography (3D-MRE), as an advanced imaging modality, enables precise quantification of tissue shear stiffness in both normal pancreatic parenchyma and neoplastic lesions. Significantly, the biomechanical heterogeneity captured by MRE holds untapped potential to serve as a prognostic biomarker for PDAC. Despite its technical merits, no studies to date have systematically explored MRE-derived imaging signatures in predicting PDAC survival outcomes or therapeutic responses, underscoring a pivotal gap in translational oncology research.

Pancreatic ductal adenocarcinoma (PDAC) accounts for 85-95% of pancreatic cancer and is one of the deadliest tumors in the world, with a survival rate of less than 8%, and identifying key prognostic or predictive factors facilitates risk stratification and prospective assessment in clinical trials. The extracellular matrix (ECM) surrounding PDAC often exhibits a large number of interstitial fibrosis, which is closely related to the formation, development and metastasis of PDAC. High order three-dimensional MR elastography (3D-MRE) allows non-invasive measurements of sheer stiffness in normal pancreas and pancreatic lesions. However, there are no reports about the application of MRE imaging biomarkers to predict the prognosis of PDAC at home and abroad.

Patients who visited our hospital for various reasons from January 2024, underwent CT scans involving the pancreas, and were eligible for spectral post-processing reconstruction were included in the study. This research collected spectral CT data related to the pancreas at different phases, as well as physiological and pathological states for these patients. Quantitative analysis was conducted on post-processed data under different physiological and pathological states, including parameters such as pancreatic iodine uptake features, attenuation interval slopes, and extracellular volume size. In conjunction with general patient status, biochemical tests, and postoperative pathological results, the study aimed to identify correlations between parameters, develop models, and conduct research by comparing traditional CT data, which could be matched with spectral CT from the PACS database since its establishment.