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Identifying, Diagnosing, and Grading Malignant Peripheral Nerve Sheath Tumors in Genetically Engineered Mouse Models
Published on: May 17, 2024
[CT and MRI imaging features of peripheral primitive neuroectodermal tumors]
Jian-jun Zhou1, Jian-hua Wang, Meng-su Zeng
1Department of Diagnostic Radiology, Zhongshan Hospital, Fudan University, Shanghai, China. zhou.jianjun@zs-hospital.sh.cn
Zhonghua Zhong Liu Za Zhi [Chinese Journal of Oncology]
|December 22, 2009
Summary
Peripheral primitive neuroectodermal tumors (pPNETs) are malignant, often large and ill-defined masses. CT and MRI reveal heterogeneous enhancement and necrosis, aiding in diagnosis of these rare tumors.
Area of Science:
- Radiology
- Oncology
- Pathology
Background:
- Peripheral primitive neuroectodermal tumors (pPNETs) are rare, malignant neoplasms.
- These tumors typically affect children and adolescents.
- Understanding their imaging characteristics is crucial for diagnosis.
Purpose of the Study:
- To investigate the CT and MRI features of peripheral primitive neuroectodermal tumors (pPNETs).
- To evaluate the diagnostic value of imaging findings for pPNETs.
Main Methods:
- Retrospective analysis of clinicopathological data from 9 surgically treated patients with pPNETs.
- Review of CT (4/9) and MRI (6/9) scans, including plain and dynamic contrast-enhanced imaging.
- Correlation of imaging findings with histopathological results.
Main Results:
- Tumors ranged from 7.4-18.3 cm, often presenting as large, ill-defined masses in skeletal muscle, pelvic, or thoracic cavities.
- Imaging showed homogeneous or inhomogeneous density on CT, with iso-density to adjacent muscles.
- MRI revealed variable signal intensities and heterogeneous, delayed enhancement, frequently with necrosis and cystic formation, especially in chest/pelvic tumors.
Conclusions:
- pPNETs are malignant tumors characterized by small, undifferentiated neuroectodermal cells.
- Typical imaging findings include large, ill-defined masses with heterogeneous enhancement and necrosis.
- Imaging features, particularly enhancement patterns and necrosis, are valuable for diagnosing pPNETs.
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