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J Clin Pathol ; 70(6): 500-507, 2017 Jun.
Artículo en Inglés | MEDLINE | ID: mdl-27798083

RESUMEN

AIMS: The distinction between benign and malignant thyroid nodules has important therapeutic implications. Our objective was to develop an assay that could classify indeterminate thyroid nodules as benign or suspicious, using routinely prepared fine needle aspirate (FNA) cytology smears. METHODS: A training set of 375 FNA smears was used to develop the microRNA-based assay, which was validated using a blinded, multicentre, retrospective cohort of 201 smears. Final diagnosis of the validation samples was determined based on corresponding surgical specimens, reviewed by the contributing institute pathologist and two independent pathologists. Validation samples were from adult patients (≥18 years) with nodule size >0.5 cm, and a final diagnosis confirmed by at least one of the two blinded, independent pathologists. The developed assay, RosettaGX Reveal, differentiates benign from malignant thyroid nodules, using quantitative RT-PCR. RESULTS: Test performance on the 189 samples that passed quality control: negative predictive value: 91% (95% CI 84% to 96%); sensitivity: 85% (CI 74% to 93%); specificity: 72% (CI 63% to 79%). Performance for cases in which all three reviewing pathologists were in agreement regarding the final diagnosis (n=150): negative predictive value: 99% (CI 94% to 100%); sensitivity: 98% (CI 87% to 100%); specificity: 78% (CI 69% to 85%). CONCLUSIONS: A novel assay utilising microRNA expression in cytology smears was developed. The assay distinguishes benign from malignant thyroid nodules using a single FNA stained smear, and does not require fresh tissue or special collection and shipment conditions. This assay offers a valuable tool for the preoperative classification of thyroid samples with indeterminate cytology.


Asunto(s)
MicroARNs/metabolismo , Neoplasias de la Tiroides/diagnóstico , Nódulo Tiroideo/diagnóstico , Biopsia con Aguja Fina , Femenino , Humanos , Masculino , Persona de Mediana Edad , Variaciones Dependientes del Observador , Valor Predictivo de las Pruebas
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