Predicting malignancy in thyroid nodules: feasibility of a predictive model integrating clinical, biochemical, and ultrasound characteristics.
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.
Centre for Endocrine and Diabetes Science, Institute of Molecular and Experimental Medicine, Cardiff University School of Medicine, University Hospital Wales, Cardiff, CF14 4XN UK.|Endocrinology and Diabetes Department, Prince Charles Hospital, Cwm Taf Health Board, Gurnos Estate Merthyr Tydfil, CF47 9DT UK.
Justyna
J
Witczak
Centre for Endocrine and Diabetes Science, Institute of Molecular and Experimental Medicine, Cardiff University School of Medicine, University Hospital Wales, Cardiff, CF14 4XN UK.|Endocrinology and Diabetes Department, Prince Charles Hospital, Cwm Taf Health Board, Gurnos Estate Merthyr Tydfil, CF47 9DT UK.
Peter
P
Taylor
Endocrinology and Diabetes Department, Prince Charles Hospital, Cwm Taf Health Board, Gurnos Estate Merthyr Tydfil, CF47 9DT UK.
Jason
J
Chai
Endocrinology and Diabetes Department, Prince Charles Hospital, Cwm Taf Health Board, Gurnos Estate Merthyr Tydfil, CF47 9DT UK.
Bethan
B
Amphlett
Radiology Department, Prince Charles Hospital, Cwm Taf Health Board, Gurnos Estate Merthyr Tydfil, CF47 9DT UK.
Jean-Marc
JM
Soukias
Endocrinology and Diabetes Department, Prince Charles Hospital, Cwm Taf Health Board, Gurnos Estate Merthyr Tydfil, CF47 9DT UK.
Gautam
G
Das
Clinical Biochemistry Department, Prince Charles Hospital, Cwm Taf Health Board, Gurnos Estate Merthyr Tydfil, CF47 9DT UK.
Brian P
BP
Tennant
Clinical Biochemistry Department, Prince Charles Hospital, Cwm Taf Health Board, Gurnos Estate Merthyr Tydfil, CF47 9DT UK.|Faculty of Life Sciences and Education, University of South Wales, Glyntaff Campus, Pontypridd, CF37 1DL UK.
John
J
Geen
Centre for Endocrine and Diabetes Science, Institute of Molecular and Experimental Medicine, Cardiff University School of Medicine, University Hospital Wales, Cardiff, CF14 4XN UK.|Endocrinology and Diabetes Department, Prince Charles Hospital, Cwm Taf Health Board, Gurnos Estate Merthyr Tydfil, CF47 9DT UK.
Onyebuchi E
OE
Okosieme
Diagnosis; Predictive model; TSH; Thyroid cancer; Thyroid nodule; Ultrasound