Research article

Development of a clinical decision model for thyroid nodules

Alexander Stojadinovic^1,3 , George E Peoples^2,3 , Steven K Libutti⁴ , Leonard R Henry^3,5 , John Eberhardt⁶ , Robin S Howard⁷ , David Gur^8,9 , Eric A Elster⁵ and Aviram Nissan^3,10

¹Department of Surgery, Division of Surgical Oncology, Walter Reed Army Medical Center,Washington, D.C., USA

²Department of Surgery, Brooke Army Medical Center, Fort Sam Houston, TX, USA

³The United States Military Cancer Institute, Washington, D.C., USA

⁴Angiogenesis Section, Surgery Branch, National Cancer Institute, Bethesda, MD, USA

⁵Department of Surgery, National Naval Medical Center, Bethesda, MD, USA

⁶BioInformatics Division, DecisionQ, Washington, D.C., USA

⁷Department of Clinical Investigation, Division of Biostatistics, Walter Reed Army Medical Center, Washington, D.C., USA

⁸Department of Radiology, University of Pittsburgh, PA, USA

⁹Magee-Women's Hospital, Pittsburgh, PA, USA

¹⁰Department of Surgery, Hadassah-Hebrew University Medical Center, Mount Scopus, Jerusalem, Israel

author email corresponding author email

BMC Surgery 2009, 9:12doi:10.1186/1471-2482-9-12

Published:	10 August 2009

Abstract

Background

Thyroid nodules represent a common problem brought to medical attention. Four to seven percent of the United States adult population (10–18 million people) has a palpable thyroid nodule, however the majority (>95%) of thyroid nodules are benign. While, fine needle aspiration remains the most cost effective and accurate diagnostic tool for thyroid nodules in current practice, over 20% of patients undergoing FNA of a thyroid nodule have indeterminate cytology (follicular neoplasm) with associated malignancy risk prevalence of 20–30%. These patients require thyroid lobectomy/isthmusectomy purely for the purpose of attaining a definitive diagnosis. Given that the majority (70–80%) of these patients have benign surgical pathology, thyroidectomy in these patients is conducted principally with diagnostic intent. Clinical models predictive of malignancy risk are needed to support treatment decisions in patients with thyroid nodules in order to reduce morbidity associated with unnecessary diagnostic surgery.

Methods

Data were analyzed from a completed prospective cohort trial conducted over a 4-year period involving 216 patients with thyroid nodules undergoing ultrasound (US), electrical impedance scanning (EIS) and fine needle aspiration cytology (FNA) prior to thyroidectomy. A Bayesian model was designed to predict malignancy in thyroid nodules based on multivariate dependence relationships between independent covariates. Ten-fold cross-validation was performed to estimate classifier error wherein the data set was randomized into ten separate and unique train and test sets consisting of a training set (90% of records) and a test set (10% of records). A receiver-operating-characteristics (ROC) curve of these predictions and area under the curve (AUC) were calculated to determine model robustness for predicting malignancy in thyroid nodules.

Results

Thyroid nodule size, FNA cytology, US and EIS characteristics were highly predictive of malignancy. Cross validation of the model created with Bayesian Network Analysis effectively predicted malignancy [AUC = 0.88 (95%CI: 0.82–0.94)] in thyroid nodules. The positive and negative predictive values of the model are 83% (95%CI: 76%–91%) and 79% (95%CI: 72%–86%), respectively.

Conclusion

An integrated predictive decision model using Bayesian inference incorporating readily obtainable thyroid nodule measures is clinically relevant, as it effectively predicts malignancy in thyroid nodules. This model warrants further validation testing in prospective clinical trials.