Multimodality imaging and genomics of granulosa cell tumors
Autor: | Erik Soule, Priya Bhosale, Sherif B. Elsherif, Matthew Bourne, Chandana Lall |
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Rok vydání: | 2019 |
Předmět: |
medicine.medical_specialty
Urology Sex Cord-Gonadal Stromal Tumors Genomics Disease medicine.disease_cause Bioinformatics Multimodal Imaging 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Internal medicine Genotype medicine Humans Radiology Nuclear Medicine and imaging Granulosa Cell Tumor Ovarian Neoplasms Mutation Radiological and Ultrasound Technology business.industry Gastroenterology Hepatology Phenotype 030220 oncology & carcinogenesis Female Carcinogenesis business |
Zdroj: | Abdominal Radiology. 45:812-827 |
ISSN: | 2366-0058 2366-004X |
DOI: | 10.1007/s00261-019-02172-3 |
Popis: | The purpose of this article is to review the imaging findings and genomics of granulosa cell tumors (GCTs) in order to aid in diagnosis and management of GCTs. GCTs are the most common type of sex cord-stromal tumors of the ovary. They are usually diagnosed initially with ultrasound and are subsequently further characterized with CT and MRI. PET/CT is often ordered as well to measure the extent of disease and for follow-up, but its usefulness is in question as some GCTs lack FDG avidity. There is significant variability in imaging phenotypes of GCTs, ranging from mostly cystic to almost solid. More resources have recently been dedicated to understanding the genetics and molecular mechanisms of GCT development. Current research shows that the main cause of GCT carcinogenesis is the FOXL2 mutation, but there are several other noteworthy mutations that contribute to the pathogenesis of this disease. Certain mutations, like GATA4, are known to be associated with more aggressive disease and higher rates of recurrence. Using this information, imaging protocols can be altered depending on the genotype of the tumor. Further understanding of the genetic alterations that underpin the development of GCTs is indicated as genotypic knowledge could be used to guide optimal imaging and management strategies. |
Databáze: | OpenAIRE |
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