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Background  Although the majority of thyroid nodules are benign the process of excluding malignancy is challenging and sometimes involves unnecessary surgical procedures. We explored the development of a predictive model for malignancy in thyroid nodules by integrating a combination of simple demographic, biochemical, and ultrasound characteristics.    Methods  Retrospective case-record review.  We reviewed records of patients with thyroid nodules referred to our institution from 2004 to 2011 ( n  = 536; female 84 %, mean age 51 years). All malignancy was proven histologically while benign disease was either confirmed histologically, or on cytology with minimum 36-month observation period. We focused on the following predictors: age, sex, smoking status, thyroid hormones (FT4 and TSH) and nodule characteristics on ultrasound. Variables were included in a multivariate logistic regression and bootstrap analyses were used to confirm results.    Results  Independent predictors of malignancy in the fully adjusted model were TSH (OR 1.53, 95 % CI 1.10, 2.12,  p  = 0.01), male gender (OR 3.45, 95 % CI 1.33, 8.92,  p  = 0.01), microcalcifications (OR 6.32, 95 % CI 2.82, 14.1,  p  < 0.001), and irregular nodule margins (OR 5.45, 95 % CI 1.61, 18.6,  p  = 0.006) Bootstrap analyses strengthened these associations and a parsimonious analysis consisting of these variables and age-group demonstrated an area under the curve of 0.77. A predictive score was sensitive (86.9 %) at low scores and highly specific (94.87 %) at higher scores for distinguishing benign from malignant disease.    Conclusions  A predictive model for malignancy using a combination of clinical, biochemical, and radiological characteristics may support clinicians in reducing unnecessary invasive procedures in patients with thyroid nodules.    Electronic supplementary material  The online version of this article (doi:10.1186/s13044-016-0033-y) contains supplementary material, which is available to authorized users.

Predicting malignancy in thyroid nodules: feasibility of a predictive model integrating clinical, biochemical, and ultrasound characteristics