A molecular computational model improves the preoperative diagnosis of thyroid nodules

Sara Tomei, Ivo Marchetti, Katia Zavaglia, Francesca Lessi, Alessandro Apollo, Paolo Aretini, Giancarlo Di Coscio, Generoso Bevilacqua, Chiara Mazzanti

Research output: Contribution to journalArticle

10 Citations (Scopus)


Background: 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.Methods: 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.Results: 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.Conclusion: 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.

Original languageEnglish
Article number396
JournalBMC Cancer
Publication statusPublished - 7 Sep 2012



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

ASJC Scopus subject areas

  • Genetics
  • Oncology
  • Cancer Research

Cite this

Tomei, S., Marchetti, I., Zavaglia, K., Lessi, F., Apollo, A., Aretini, P., Di Coscio, G., Bevilacqua, G., & Mazzanti, C. (2012). A molecular computational model improves the preoperative diagnosis of thyroid nodules. BMC Cancer, 12, [396]. https://doi.org/10.1186/1471-2407-12-396