Improving Pancreatic Cancer Care by Using Computational Science and Technology to Analyze Clinical and Imaging Data

What this Trial Is About

Researchers are investigating how computational science and technology can improve care for patients with pancreatic cancer and related pancreatic conditions. This multicenter, retrospective observational study collects and shares anonymized clinical and imaging data from patients who have undergone diagnostic procedures or treatment for both benign and malignant pancreatic lesions, as well as from healthy individuals who had abdominal CT scans. The study supports various subprojects aiming to develop artificial intelligence tools for early detection, treatment response assessment, tumor characterization, and outcome prediction in pancreatic diseases. The study does not involve any interventions, as it uses existing clinical and imaging data collected through institutional databases and cohorts. Data include patient demographics, treatment details, tumor characteristics, recurrence information, and various pre- and post-treatment scans such as CT, MRI, and PET-CT. A central database and data sharing infrastructure are maintained, managed by a steering committee representing participating centers to facilitate research projects and data access securely. Participants' data are collected retrospectively and pseudonymized to protect privacy. The study gathers extensive clinical and technical information, including patient health status, treatment types, imaging scans, and follow-up outcomes. Researchers measure outcomes specific to each subproject, with follow-up averaging around five years. The study aims to provide a comprehensive resource to support artificial intelligence research that could enhance diagnosis, treatment planning, and monitoring of pancreatic diseases over time.

Improving Pancreatic Cancer Care by the Use of Computational Science and Technology