Open Access Highly Accessed Research article

A molecular computational model improves the preoperative diagnosis of thyroid nodules

Sara Tomei1*, Ivo Marchetti2, Katia Zavaglia1, Francesca Lessi1, Alessandro Apollo1, Paolo Aretini1, Giancarlo Di Coscio2, Generoso Bevilacqua12 and Chiara Mazzanti1

Author Affiliations

1 Division of Surgical, Molecular, and Ultrastructural Pathology, Section of Molecular Pathology, University of Pisa and Pisa University Hospital, Via Roma 57, Pisa, 56100, Italy

2 Section of Cytopathology, University of Pisa and Pisa University Hospital, Via Roma 57, Pisa, 56100, Italy

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BMC Cancer 2012, 12:396  doi:10.1186/1471-2407-12-396

Published: 7 September 2012



Thyroid nodules with indeterminate cytological features on fine needle aspiration (FNA) cytology have a 20% risk of thyroid cancer. The aim of the current study was to determine the diagnostic utility of an 8-gene assay to distinguish benign from malignant thyroid neoplasm.


The mRNA expression level of 9 genes (KIT, SYNGR2, C21orf4, Hs.296031, DDI2, CDH1, LSM7, TC1, NATH) was analysed by quantitative PCR (q-PCR) in 93 FNA cytological samples. To evaluate the diagnostic utility of all the genes analysed, we assessed the area under the curve (AUC) for each gene individually and in combination. BRAF exon 15 status was determined by pyrosequencing. An 8-gene computational model (Neural Network Bayesian Classifier) was built and a multiple-variable analysis was then performed to assess the correlation between the markers.


The AUC for each significant marker ranged between 0.625 and 0.900, thus all the significant markers, alone and in combination, can be used to distinguish between malignant and benign FNA samples. The classifier made up of KIT, CDH1, LSM7, C21orf4, DDI2, TC1, Hs.296031 and BRAF had a predictive power of 88.8%. It proved to be useful for risk stratification of the most critical cytological group of the indeterminate lesions for which there is the greatest need of accurate diagnostic markers.


The genetic classification obtained with this model is highly accurate at differentiating malignant from benign thyroid lesions and might be a useful adjunct in the preoperative management of patients with thyroid nodules.

Thyroid; Fine-needle aspiration (FNA); Area under the curve (AUC); Computational model; Preoperative diagnosis