A molecular computational model improves the preoperative diagnosis of thyroid nodules

BMC Cancer

Volumn 12, Issue , 2012, Pages

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

^a UNIVERSITY OF PISA   (Italy)

Author keywords

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

Indexed keywords

MESSENGER RNA;

AREA UNDER THE CURVE; ARTICLE; BRAF GENE; C21ORF4 GENE; CANCER CLASSIFICATION; CANCER RISK; CDH1 GENE; CYTOLOGY; DD12 GENE; DIAGNOSTIC ACCURACY; DIAGNOSTIC TEST ACCURACY STUDY; EXON; GENE; GENE EXPRESSION; GENETIC ANALYSIS; HISTOPATHOLOGY; HS.296031 GENE; HUMAN; HUMAN TISSUE; KIT GENE; LSM7 GENE; MAJOR CLINICAL STUDY; MATHEMATICAL MODEL; MOLECULAR DIAGNOSIS; MUTATIONAL ANALYSIS; NATH GENE; NEEDLE BIOPSY; POLYMERASE CHAIN REACTION; PREDICTION; PREOPERATIVE EVALUATION; PYROSEQUENCING; QUANTITATIVE ANALYSIS; RECEIVER OPERATING CHARACTERISTIC; SENSITIVITY AND SPECIFICITY; SYNGR2 GENE; TC1 GENE; THYROID CANCER; THYROID NODULE; THYROID TUMOR;

AREA UNDER CURVE; BAYES THEOREM; BIOPSY, FINE-NEEDLE; COMPUTER SIMULATION; GENE EXPRESSION PROFILING; HUMANS; MODELS, BIOLOGICAL; MOLECULAR DIAGNOSTIC TECHNIQUES; NEURAL NETWORKS (COMPUTER); PREOPERATIVE PERIOD; ROC CURVE; THYROID NODULE; TUMOR MARKERS, BIOLOGICAL;

EID: 84865816246     PISSN: None     EISSN: 14712407     Source Type: Journal    
DOI: 10.1186/1471-2407-12-396     Document Type: Article

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