Thyroid carcinoma is a common endocrine cancer with a favorable prognosis if subjected to timely treatment. However, the clinical identification of follicular thyroid carcinoma (FTC) among patients with benign thyroid nodules is still a challenge. Preoperative fine needle aspiration-based cytology cannot always differentiate follicular carcinomas from benign follicular neoplasias. Because current methods fail to improve preoperative diagnosis of thyroid nodules, new molecular-based diagnoses should be explored. We conducted a microarray-based study to reveal the genetic profiles unique to FTC and follicular adenomas (FAs), to identify the most parsimonious number of genes that could accurately differentiate between benign and malignant follicular thyroid neoplasia. We confirmed our data by quantitative RT-PCR and immunohistochemistry in two independent validation sets with a total of 114 samples. We were able to identify three genes, cyclin D2 (CCND2), protein convertase 2 (PCSK2), and prostate differentiation factor (PLAB), that allow the accurate molecular classification of FTC and FA. Two independent validation sets revealed that the combination of these three genes could differentiate FTC from FA with a sensitivity of 100%, specificity of 94.7%, and accuracy of 96.7%. In addition, our model allowed the identification of follicular variants of papillary thyroid carcinoma with an accuracy of 85.7%. Three-gene profiling of thyroid nodules can accurately predict the diagnosis of FTC and FA with high sensitivity and specificity, thus identifying promising targets for further investigation to ultimately improve preoperative diagnosis.