Advanced Diagnostic & Interventional Radiology Research Center | Differentiation between mucinous cystic neoplasms

Advanced Diagnostic & Interventional Radiology Research Center | Differentiation between mucinous cystic neoplasms
| Feb 13 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 : Jun 18 2025 - 08:43
  • : 72
  • Study time : 1 minute(s)

Differentiation between mucinous cystic neoplasms and simple cysts of the liver: a systematic review and meta analysis

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Purpose

Radiologic examinations frequently identify cystic liver lesions, which encompass various entities from simple benign cysts to malignant neoplasms. This work analyses the available data to compare diagnostic features of biliary cystic neoplasms and hepatic simple cysts.

Methods

A systematic search of PubMed, Scopus, Embase, and Web of Science up to October 2024 was conducted. The characteristics were categorized into hepatic simple cysts (HSC) and mucinous cystic neoplasms (MCN), including biliary cystadenoma (BCA) and cystadenocarcinoma (BCAC) detected by imaging modalities including ultrasound, CT scans with IV contrast, or MRI. We analyzed biliary cystic neoplasms and hepatic simple cysts across multiple studies using Review Manager Ver. 5, calculating summary measures for each feature.

Results

The study analyzed 577 lesions in 577 patients and 49 studies. Hepatic simple cysts were the most common finding, with 349 identified, mainly in the right hepatic lobe, presented with abdominal pain or incidentally. Intracystic septation was found in 50.1% of HSC lesions, with thick septation in 10.52% of lesions. 228 (49.9%) patients were diagnosed with MCN, with abdominal swelling and pain as the most common presentation. Septation was the most common radiological feature of MCNs, with thick septa in 50.61%. MCNs had internal septa, solid mural nodule, upstream bile duct dilation, presence in the left hepatic lobe, septal thickening, cystic wall enhancement, calcifications, and internal debris. The presence of a cyst in the left lobe was more related to MCNs.

Conclusion

Characterizing cystic liver lesions necessitates a comprehensive evaluation of the lesions’ location, size, and complexity. Imaging and clinical findings are essential for a final diagnosis.

  • Article_DOI :
  • Author(s) : faeze salahshour,gita manzari tavakoli,mahshad afsharzadeh
  • News Group : research,research article
  • News Code : 299813
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