Related Experiment Video
Updated: Feb 25, 2026
Author Spotlight: Genetically Engineered Mouse Models and Pathological Characterization of Neurofibromatosis Type 1 Associated Tumors
Published on: May 17, 2024
Pancreatic neuroendocrine neoplasms: Correlation between MR features and pathological tumor grades
Feng Jin1, Kai Wang2, Ting-Ting Qin3
1Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
Abstract:
This study investigated the accuracy of MRI features in differentiating the pathological grades of pancreatic neuroendocrine neoplasms (PNENs). A total of 31 PNENs patients were retrospectively evaluated, including 19 cases in grade 1, 5 in grade 2, and 7 in grade 3. Plain and contrastenhanced MRI was performed on all patients. MRI features including tumor size, margin, signal intensity, enhancement patterns, degenerative changes, duct dilatation and metastasis were analyzed. Chi square tests, Fisher's exact tests, one-way ANOVA and ROC analysis were conducted to assess the associations between MRI features and different tumor grades. It was found that patients with older age, tumors with higher TNM stage and without hormonal syndrome had higher grade of PNETs (all P<0.05). Tumor size, shape, margin and growth pattern, tumor pattern, pancreatic and bile duct dilatation and presence of lymphatic and distant metastasis as well as MR enhancement pattern and tumor-topancreas contrast during arterial phase were the key features differentiating tumors of all grades (all P<0.05). ROC analysis revealed that the tumor size with threshold of 2.8 cm, irregular shape, pancreatic duct dilatation and lymphadenopathy showed satisfactory sensitivity and specificity in distinguishing grade 3 from grade 1 and grade 2 tumors. Features of peripancreatic tissue or vascular invasion, and distant metastasis showed high specificity but relatively low sensitivity. In conclusion, larger size, poorlydefined margin, heterogeneous enhanced pattern during arterial phase, duct dilatation and the presence of metastases are common features of higher grade PNENs. Plain and contrast-enhanced MRI provides the ability to differentiate tumors with different pathological grades.
Insights
Magnetic resonance imaging (MRI) accurately differentiates pancreatic neuroendocrine neoplasms (PNENs) grades. Key MRI features like tumor size and enhancement patterns help distinguish higher-grade PNENs, aiding in diagnosis and treatment planning.
Area of Science:
- Radiology and Medical Imaging
- Oncology
- Gastroenterology
Background:
- Pancreatic neuroendocrine neoplasms (PNENs) are rare tumors with varying grades and prognoses.
- Accurate pathological grading is crucial for effective treatment strategies.
- Non-invasive imaging methods to assess PNEN grade are highly desirable.
Purpose of the Study:
- To evaluate the diagnostic accuracy of MRI features in differentiating pathological grades of PNENs.
- To identify specific MRI characteristics associated with different PNEN grades (1, 2, and 3).
Main Methods:
- Retrospective analysis of 31 PNENs patients (19 grade 1, 5 grade 2, 7 grade 3).
- Evaluation of plain and contrast-enhanced MRI scans for tumor size, margin, enhancement patterns, duct dilatation, and metastasis.
- Statistical analysis including Chi-square tests, ANOVA, and ROC analysis to correlate MRI features with tumor grade.
Main Results:
- Older age, higher TNM stage, and absence of hormonal syndrome were associated with higher PNEN grades.
- Tumor size, shape, margin, enhancement patterns, duct dilatation, and metastasis were significant differentiators across all grades.
- Tumor size (>2.8 cm), irregular shape, pancreatic duct dilatation, and lymphadenopathy effectively distinguished grade 3 from lower grades.
Conclusions:
- Larger size, ill-defined margins, heterogeneous arterial phase enhancement, duct dilatation, and metastases indicate higher-grade PNENs.
- Plain and contrast-enhanced MRI are valuable tools for differentiating PNEN pathological grades.
- MRI findings can guide clinical decisions and prognostic assessments for PNEN patients.
