Advanced Diagnostic & Interventional Radiology Research Center | Application of Sonoelastography in Differential Thyroid Nodules

Advanced Diagnostic & Interventional Radiology Research Center | Application of Sonoelastography in Differential Thyroid Nodules
| Jan 3 2026
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Advanced Diagnostic & Interventional Radiology Research Center

COVID-19 pandemic 

During the COVID-19 pandemic, the Radiology Research Center at Tehran University of Medical Sciences continued its research activities despite the challenges posed by the increased demand for CT scans of COVID-19 patients and the necessity of adhering to strict health protocols. This center played a crucial role in improving medical imaging techniques, optimizing diagnostic protocols, and advancing technologies related to CT scan image analysis.

Faculty members, researchers, and staff remained committed to ensuring the safety and well-being of healthcare professionals and patients while actively engaging in imaging data analysis, developing artificial intelligence algorithms for faster disease detection, publishing scientific articles, and presenting their findings at international conferences. These efforts aimed to enhance diagnostic accuracy, improve treatment processes, and alleviate pressure on healthcare systems.

 

Key achievements of the Radiology Research Center during the COVID-19 pandemic include:


✔️ Development and optimization of lung imaging protocols for faster and more accurate COVID-19 diagnosis
✔️ Implementation of artificial intelligence technologies for automated CT scan analysis and reduced diagnosis time
✔️ Publication of high-impact research articles on innovative imaging methods for COVID-19 patients
✔️ Participation in national and international projects focused on COVID-19 diagnosis and patient management

The center remains dedicated to advancing research in medical imaging and continues to contribute as a leading scientific institution in improving the quality of diagnostic and therapeutic services.

 

Some of the center's significant achievements during the pandemic include:

 

  • Release Date : Mar 16 2024 - 10:01
  • : 36
  • Study time : 1 minute(s)

Application of Sonoelastography in Differential Diagnosis of Benign and Malignant Thyroid Nodules

Sonoelastography could help to differentiate benign and malignant thyroid nodules. As our sample size was limited, larger studies are recommended

Application of Sonoelastography in Differential Diagnosis Thyroid Nodules {faces}

Background:

Sonoelastography is a new ultrasound method which could be helpful to determine which thyroid nodule is malignant. We designed this study to evaluate the accuracy of sonoelastography in differentiating of benign and malignant thyroid nodules in Iranian patients.

Methods:

Forty thyroid nodules in forty consecutive patients who had been referred for sonography-guided fine-needle aspiration biopsy were evaluated. Gray scale ultrasound and elastosonography by real-time, freehand technique applied for all patients. Elastography findings were classified into four groups. Nodules which were classified as patterns 1 or 2 in elastogram evaluation were classified as benign and probably malignant if elastogram scans were patterns 3 and 4 of elastogram scan.

Results:

Mean age ± standard deviation (SD) was 42.2 ± 12.6 years, and mean ± SD thyroid-stimulating hormone level was 1.4 ± 1.9 IU/ml. Thirty-five cases (87.5%) were female and 5 (12.5%) were male. Histological examination indicated 27 (67.5%) benign and 13 (32.5%) malignant nodules. The most elastogram score was 2 (50%) followed by score 3. The cut-off point of 2 considered as the best value to differentiate benign and malignant thyroid nodules with sensitivity and specificity of 61% and 78% (area under the curve = 0.76, 95% confidence interval: 0.6–0.92, P = 0.007).

Conclusions:

Sonoelastography could help to differentiate benign and malignant thyroid nodules. As our sample size was limited, larger studies are recommended.

Application of Sonoelastography in Differential Thyroid Nodules

  • Article_DOI : 10.4103/2008-7802.178355
  • Author(s) : arvin aryan,fatemeh esfahanian
  • News Group : research,research article,AI
  • News Code : 278437
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