Related Experiment Video
Updated: Feb 1, 2026
06:09
Quantification of Tumor Cell Adhesion in Lymph Node Cryosections
Published on: February 9, 2020
13.4K
Pancreatic neuroendocrine tumors: MR imaging features preoperatively predict lymph node metastasis
Haitao Sun1,2, Jianjun Zhou3, Kai Liu3
1Shanghai Institute of Medical Imaging, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China.
Abdominal Radiology (New York)
|December 13, 2018
Summary
Magnetic resonance imaging features, specifically non-enhancement patterns and main pancreatic duct dilation, can predict lymph node metastasis (LNM) in pancreatic neuroendocrine tumors (pNETs). These findings aid in preoperative risk assessment for pNETs with LNM.
Area of Science:
- Oncology
- Radiology
- Gastroenterology
Background:
- Lymph node metastasis (LNM) is a critical prognostic factor in pancreatic neuroendocrine tumors (pNETs).
- Predictive factors for LNM in pNETs are not well-established, necessitating improved preoperative assessment methods.
Purpose of the Study:
- To evaluate the utility of MR imaging features in the preoperative prediction of LNM in pNETs.
- To identify specific MR imaging characteristics associated with LNM in pNETs.
Main Methods:
- Retrospective analysis of 108 patients with pNETs.
- Evaluation of MR imaging morphologic features and quantitative data.
- Univariate and multivariate logistic regression models to identify predictors of LNM.
Main Results:
- Multivariate analysis identified non-enhancement pattern (OR 6.652) and main pancreatic duct dilation (OR 6.745) as independent predictors of LNM.
- Tumor size >2 cm, Ki-67 >5%, pancreatic atrophy, extrapancreatic tumor spread, and certain contrast-to-noise ratios were significant at univariate analysis.
Conclusions:
- Non-enhancement pattern and main pancreatic duct dilation on MR imaging are significant predictors of LNM in pNETs.
- These easily obtainable preoperative radiological predictors can help identify pNETs at high risk for LNM, potentially guiding treatment decisions.
Related Concept Videos
01:23Detailed Structure and Function of Lymph Nodes
4.8K
Lymph nodes are bean-shaped structures that cluster along the lymphatic vessels in the inguinal, axillary, and cervical regions. Each node is divided into compartments by a capsule that extends trabeculae inward.
From a histological perspective, lymph nodes can be split into two main areas: the superficial cortex and the deep medulla. The outer cortex is populated by dendritic cells, macrophages, and B lymphocytes, which are densely packed into follicles. When these B-lymphocytes are presented...
From a histological perspective, lymph nodes can be split into two main areas: the superficial cortex and the deep medulla. The outer cortex is populated by dendritic cells, macrophages, and B lymphocytes, which are densely packed into follicles. When these B-lymphocytes are presented...
4.8K
02:30Metastasis
6.6K
Metastasis is the spread of cancer cells from the original site to distant locations in the body. Cancer cells can spread via blood vessels (hematogenous) as well as lymph vessels in the body.
Epithelial-to-Mesenchymal Transition
The epithelial-to-mesenchymal transition or EMT is a developmental process commonly observed in wound healing, embryogenesis, and cancer metastasis. EMT is induced by transforming growth factor-beta (TGF-β) or receptor tyrosine kinase (RTK) ligands, which further...
Epithelial-to-Mesenchymal Transition
The epithelial-to-mesenchymal transition or EMT is a developmental process commonly observed in wound healing, embryogenesis, and cancer metastasis. EMT is induced by transforming growth factor-beta (TGF-β) or receptor tyrosine kinase (RTK) ligands, which further...
6.6K
02:27Predicting Molecular Geometry
45.8K
VSEPR Theory for Determination of Electron Pair Geometries
45.8K
01:03Prediction Intervals
3.4K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
3.4K
01:07End Point Prediction: Gran Plot
1.2K
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
For potentiometric titration, the Gran plot is created by plotting...
1.2K
01:13Sensitivity, Specificity, and Predicted Value
1.3K
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
Sensitivity is the...
1.3K