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ID06843629

Using PET Imaging Targeted at MSLN for Diagnosis and Staging of Pancreatic, Ovarian, and Lung Adenocarcinoma Tumors

Led by Union Hospital, Tongji Medical College, Huazhong University of Science and Technology · Updated on 2026-05-28

100

Participants Needed

1

Research Sites

26 weeks

Total Duration

AI-Summary

What this Trial Is About

Researchers are evaluating the use of integrated PETMR or PETCT imaging with targeted MSLN-specific agents to study patients who have or are suspected to have pancreatic cancer, ovarian cancer, lung adenocarcinoma, and other malignant tumors that show high expression of mesothelin MSLN. The goal is to improve diagnosis, staging, and assessment of tumor burden by comparing this method with current standards like pathological diagnosis and 18FFDG PET scans. The study also involves healthy volunteers to understand the drugs behavior and safety in the body. The study uses specific positron imaging agents targeting MSLN, such as the 68GaGa-NOTA-MSLN antibody fragment, to visualize tumors and their characteristics. Patients undergo PETMR or PETCT scans for diagnosis and staging, while healthy volunteers receive the imaging agent for pharmacokinetic analysis to examine distribution, metabolism, and safety. This approach aims to clarify lesion presence, location, and nature, and to help guide therapeutic decisions. Participants will be involved in imaging procedures and monitored through visual and quantitative assessments of lesions and drug biodistribution over a one-year period. Blood and urine samples will be collected to measure radioactivity, and tumor tissue pathology will be reviewed. The study includes safety monitoring and pharmacokinetic evaluations to determine drug metabolism. The total duration of participant involvement is one year, with assessments designed to evaluate diagnostic performance and drug behavior in both patients and healthy individuals.

CONDITIONS

Brief Title

Clinical Application of PET Imaging Targeting MSLN in Malignant Tumors

Research Team

X

Xiaoli Lan, PhD

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