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Tundra Space

Clinical Research Directory

Browse clinical research sites, groups, and studies.

3 clinical studies listed.

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Neuroendocrine Tumors, NET

Tundra lists 3 Neuroendocrine Tumors, NET clinical trials. Each listing includes eligibility criteria, study locations, and direct links to research sites in the Tundra directory.

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ACTIVE NOT RECRUITING

NCT01763554

Review of Medical Records of Patients Who Have Been Treated With Lutetium at the Cross Cancer Institute Between January 2010 and April 30, 2014

Retrospective review of the medical files of 115 patients with neuroendocrine tumours who were treated with Lutetium-177 DOTA-TATE under Health Canada's Special Access Programme (SAP) at the Cross Cancer Institute between January 2010 and April 30, 2014. Efficacy, safety, and other relevant data will be collected to support a separate clinical trial application.

Gender: All

Ages: 14 Years - 90 Years

Updated: 2026-11-03

1 state

Neuroendocrine Tumors, NET
NOT YET RECRUITING

NCT07621146

A Comparison of [68Ga]DOTATATE and [18F]AmBF3TATE for the Staging and Assessment of Neuroendocrine Malignancies

Neuroendocrine tumours (NETs) are generally slow growing, but some can be aggressive and resistant to treatment. Compared to healthy cells, the surface of these tumor cells has a greater number of molecules called somatostatin receptors (SSTR) which requires specific PET scan tracers to sufficiently capture on images. The current standard of care tracer at BC Cancer for SSTRs on NETs is 68Ga-DOTATATE. This project seeks to identify if 18F-AmBF3-TATE (a tracer that has established safety from phase 1 trial results), is comparable in disease detection, no. of lesions identified, image quality, safety and overall accuracy, to 68Ga-DOTATATE.

Gender: All

Ages: 18 Years - Any

Updated: 2026-10-06

1 state

Neuroendocrine Tumors, NET
Pheochromocytoma/Paraganglioma
SSTR-expressing Tumors
+1
RECRUITING

NCT07463872

Management of Pancreatic Cystic Lesions Using Artificial Intelligence Based on EUS and Multimodal Data

The primary objective is to construct a multimodal AI model (Cyst-AI) based on EUS images and clinical data such as imaging features(CT or MRI) and laboratory tests to assist endoscopists in the diagnosis of pancreatic cystic lesions(PCLs), mainly differentiating mucinous from non-mucinous lesions. The secondary objective is to evaluate the model's effectiveness in risk stratification and clinical management for patients with PCLs.

Gender: All

Ages: 18 Years - Any

Updated: 2026-03-11

1 state

Pancreatic Cystic Lesion
Mucinous Cystadenoma of Pancreas
Intraductal Papillary Mucinous Neoplasm of Pancreas
+3