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Preoperative Molecular Markers in Thyroid Nodules
- Source :
- Frontiers in Endocrinology, Frontiers in Endocrinology, Vol 9 (2018)
- Publication Year :
- 2018
- Publisher :
- Frontiers Media SA, 2018.
-
Abstract
- The need for distinguishing benign from malignant thyroid nodules has led to the pursuit of differentiating molecular markers. The most common molecular tests in clinical use are Afirma® Gene Expression Classifier (GEC) and Thyroseq® V2. Despite the rapidly developing field of molecular markers, several limitations exist. These challenges include the recent introduction of the histopathological diagnosis “Non-Invasive Follicular Thyroid neoplasm with Papillary-like nuclear features”, the correlation of genetic mutations within both benign and malignant pathologic diagnoses, the lack of follow-up of molecular marker negative nodules, and the cost-effectiveness of molecular markers. In this manuscript, we review the current published literature surrounding the diagnostic value of Afirma® GEC and Thyroseq® V2. Among Afirma® GEC studies, sensitivity (Se), specificity (Sp), positive predictive value (PPV), and negative predictive value (NPV) ranged from 75 to 100%, 5 to 53%, 13 to 100%, and 20 to 100%, respectively. Among Thyroseq® V2 studies, Se, Sp, PPV, and NPV ranged from 40 to 100%, 56 to 93%, 13 to 90%, and 48 to 97%, respectively. We also discuss current challenges to Afirma® GEC and Thyroseq® V2 utility and clinical application, and preview the future directions of these rapidly developing technologies.
- Subjects :
- Thyroid nodules
Pathology
medicine.medical_specialty
non-invasive follicular thyroid neoplasm with papillary-like nuclear features
Endocrinology, Diabetes and Metabolism
030209 endocrinology & metabolism
Review
medicine.disease_cause
lcsh:Diseases of the endocrine glands. Clinical endocrinology
03 medical and health sciences
chemistry.chemical_compound
0302 clinical medicine
Endocrinology
Molecular marker
Thyroseq
thyroid cancer
Medicine
Afirma
Thyroid cancer
Thyroid neoplasm
lcsh:RC648-665
business.industry
Gene expression classifier
medicine.disease
molecular test
Predictive value
chemistry
030220 oncology & carcinogenesis
business
Subjects
Details
- Language :
- English
- ISSN :
- 16642392
- Volume :
- 9
- Database :
- OpenAIRE
- Journal :
- Frontiers in Endocrinology
- Accession number :
- edsair.doi.dedup.....3d9359d668a6ae78e1fb4ef8f6bed19d
- Full Text :
- https://doi.org/10.3389/fendo.2018.00179