title: A molecular computational model improves the preoperative diagnosis of thyroid nodules.
abstract: 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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Division of Surgical, Molecular, and Ultrastructural Pathology, Section of Molecular Pathology, University of Pisa, Italy. sara.tomei@for.unipi.it
authors.forename: Sara
authors.initials: S
authors.lastname: Tomei
authors.forename: Ivo
authors.initials: I
authors.lastname: Marchetti
authors.forename: Katia
authors.initials: K
authors.lastname: Zavaglia
authors.forename: Francesca
authors.initials: F
authors.lastname: Lessi
authors.forename: Alessandro
authors.initials: A
authors.lastname: Apollo
authors.forename: Paolo
authors.initials: P
authors.lastname: Aretini
authors.forename: Giancarlo
authors.initials: G
authors.lastname: Di Coscio
authors.forename: Generoso
authors.initials: G
authors.lastname: Bevilacqua
authors.forename: Chiara
authors.initials: C
authors.lastname: Mazzanti
mesh_terms: D019540:Area Under Curve; D001499:Bayes Theorem; D014408:Biomarkers, Tumor / Q000235:genetics / Q000378:metabolism; D044963:Biopsy, Fine-Needle; D003198:Computer Simulation; D020869:Gene Expression Profiling; D006801:Humans; D008954:Models, Biological; D025202:Molecular Diagnostic Techniques / Q000379:methods*; D016571:Neural Networks, Computer; D057234:Preoperative Period; D012372:ROC Curve; D016606:Thyroid Nodule / Q000175:diagnosis* / Q000235:genetics / Q000378:metabolism / Q000473:pathology
chemical_list: D014408:Biomarkers, Tumor