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An Orthotopic Mouse Model of Anaplastic Thyroid Carcinoma
Published on: April 17, 2013
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Imaging of Anaplastic Thyroid Carcinoma
S Ahmed1, M P Ghazarian2, M E Cabanillas3
1From the Departments of Diagnostic Radiology (S.A., T.V., D.F.S., J.M.D.) salmaan.ahmed@mdanderson.org.
AJNR. American Journal of Neuroradiology
|December 16, 2017
Summary
Anaplastic thyroid carcinoma often presents as a large neck mass with frequent extrathyroidal extension and invasion of adjacent structures. Understanding these CT imaging features is crucial for surgical planning and prognosis.
Area of Science:
- Radiology
- Oncology
- Head and Neck Surgery
Background:
- Anaplastic thyroid carcinoma (ATC) is an aggressive malignancy with a poor prognosis.
- Surgical resection and multimodality therapy can improve survival in resectable cases.
Purpose of the Study:
- To characterize the contrast-enhanced high-resolution CT findings of anaplastic thyroid carcinoma.
- To correlate imaging features with surgical resectability and prognosis.
Main Methods:
- Retrospective review of contrast-enhanced high-resolution CT scans from 57 patients with histologically confirmed anaplastic thyroid carcinoma.
- Detailed analysis of tumor size, necrosis, calcification, extrathyroidal extension, visceral space invasion, vascular encasement, and lymphadenopathy.
Main Results:
- Anaplastic thyroid carcinoma commonly appeared as a large neck mass (82% with necrosis) with frequent extrathyroidal extension (91%).
- Tumors frequently invaded the esophagus (62%), trachea (57%), and larynx (29%), with carotid artery (42%) and internal jugular vein (43%) encasement.
- Lymphadenopathy was common in lateral (63%) and central (56%) compartments, often with necrosis (58% of lateral nodes).
Conclusions:
- Contrast-enhanced high-resolution CT reveals characteristic features of anaplastic thyroid carcinoma, including extensive local invasion and lymph node involvement.
- These imaging findings are vital for guiding initial diagnosis, assessing surgical resectability, and determining patient prognosis.
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