An observational study in Pancreatic Neuroendocrine Tumor and Solid Pancreatic Neoplasms, sponsored by Fudan University. Recruiting at 2 sites in China. Open to participants aged 18 Years and older, including healthy volunteers. Per ClinicalTrials.gov, last updated 2026-03-25.
Sponsored by Fudan University · Observational
This prospective study aims to evaluate the sensitivity and specificity of an integrated model using fragmentomic profiles of plasma cell-free DNA for early detection of pancreatic neuroendocrine tumors and differential diagnosis of solid pancreatic tumors.
Pancreatic neuroendocrine tumors (pNETs) are insidious and difficult to diagnose early. Approximately 36.8% of pNET patients have lymph node metastasis[1], and 20% -64% of patients have liver metastasis at the time of diagnosis[2]. The prognosis of pNETs is closely related to tumor grade and the American Joint Committee on Cancer (AJCC) staging. Among patients with known pathological grades in the United States, well-differentiated NETs had the highest median overall survival (OS, 16.2 years), moderately differentiated NETs had the worse OS (8.3 years), and poorly differentiated or undifferentiated NETs had the worst OS (10 months)[3]. The 5-year overall survival rates of localized, locally advanced, and metastatic pNETs were 93%, 77%, and 27%, respectively[4]. Given that the prognosis of early-stage pNETs is significantly better than that of advanced pNETs, early detection of pNETs can provide a cure opportunity and significantly improve survival.
In the past few decades, the application of 68Ga-DOTANOC PET/CT, magnetic resonance imaging (MRI), computed tomography (CT), and endoscopic ultrasound (EUS) has improved the detection rate of pNETs. But their application is limited by high costs, lack of sufficient sensitivity or specificity, and radiation exposure. Therefore, there is an urgent need for accurate and less invasive approaches to use in clinical practice for the early detection of pNETs.
Recently, the study of cell-free DNA (cfDNA) has provided a noninvasive approach for the diagnosis of solid malignancies. cfDNAs represent extracellular DNA fragments released from cell apoptosis and necrosis into human body fluids like plasma, thus carrying the genetic and epigenetic information from the cell and tissue of origin[5]. Among them, circulating tumor DNA (ctDNA), as a part of the total cfDNA, is released into the blood by tumor cells[6]. cfDNA fragmentomics depends on whole genome sequencing, and its characteristics mainly include copy number variation (CNV), nucleosome footprint, fragment length and motif[5, 7, 8], with targets covering the entire genome level. cfDNA fragmentomics has shown excellent predictive performance in multiple studies[5, 9-11]. Therefore, this prospective study aims to evaluate the sensitivity and specificity of an integrated model using fragmentomic profiles of plasma cell-free DNA (cfDNA) for early detection of pancreatic neuroendocrine tumors.
Additionally, once a pancreatic lesion is detected, accurate discrimination between pancreatic ductal adenocarcinoma (PDAC), pNETs and solid pseudopapillary tumor (SPT) is essential. This study therefore has two co-primary objectives: (1) to develop a fragmentomic assay that flags asymptomatic individuals likely to harbor a pNET; (2) to build a differential model that distinguishes PDAC vs pNETs vs SPT in patients with confirmed solid pancreatic neoplasms."
102 studies on the registry are indexed under Adenoma, Islet Cell; 26 are open to participants now.
This study's planned enrollment of 1,000 is above the median of 85 across 22 observational studies indexed under Adenoma, Islet Cell.
Browse Adenoma, Islet Cell studies →Fudan University is the lead sponsor of 1,271 studies on the registry; 624 are open to participants now.
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The study population enrolled by the main center and the sub-center is uniformly divided into four groups with the same enrollment criteria for all.
Exclusion Criteria:
Patients with pancreatic neuroendocrine tumors (pNETs). The prospective cases enrolled by the sub-center shall strictly comply with the study's inclusion/exclusion criteria, consistent with the main center, and no independent adjustment of the enrollment criteria is allowed.
Diagnostic Test: Blood collection
Healthy volunteers. The prospective cases enrolled by the sub-center shall strictly comply with the study's inclusion/exclusion criteria, consistent with the main center, and no independent adjustment of the enrollment criteria is allowed.
Diagnostic Test: Blood collection
Patients with pancreatic ductal adenocarcinoma (PDAC) . The prospective cases enrolled by the sub-center shall strictly comply with the study's inclusion/exclusion criteria, consistent with the main center, and no independent adjustment of the enrollment criteria is allowed.
Diagnostic Test: Blood collection
Patients with solid pseudopapillary tumor (SPT) of pancreas. The prospective cases enrolled by the sub-center shall strictly comply with the study's inclusion/exclusion criteria, consistent with the main center, and no independent adjustment of the enrollment criteria is allowed.
Diagnostic Test: Blood collection
Blood collection for fragmentomic profiles of plasma cell-free DNA. The sub-center shall use the same blood collection consumables (EDTA anticoagulant vacutainer tubes) and blood collection volume (10ml) as the main center; plasma separation shall be completed within 2 hours after blood collection, and all operations shall comply with the study's unified SOP.
Sensitivity and specificity of the integrated fragmentomic model for detecting pNETs
Sensitivity and specificity of the integrated model using fragmentomic profiles of plasma cfDNA for early detection of pNETs
Time frame: From date of first blood draw until first documented pNETs diagnosis, assessed up to 3 years.
Sensitivity and specificity of the model for differential diagnosis among solid pancreatic tumors
Sensitivity and specificity of the model for differential diagnosis among PDAC, pNET and SPT.
Time frame: From first blood draw until histopathological diagnosis, up to 3 years
Positive predictive value and negative predictive value
Positive predictive value (PPV) and negative predictive value (NPV) of the integrated model using fragmentomic profiles of plasma cfDNA for early detection of pNETs
Time frame: From date of first blood draw until first documented pNETs diagnosis, assessed up to 3 years
Accuracy of the model in predicting AJCC stage (where applicable) and tumor grade
Sensitivity and specificity of the integrated model using fragmentomic profiles of plasma cfDNA in predicting AJCC stage (where applicable) and tumor grade
Time frame: From date of first blood draw until first documented histopathological diagnosis, assessed up to 3 years
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