Risk Stratifying Indeterminate Thyroid Nodules With Machine Learning

Up to 30% of thyroid nodules are classified as indeterminate after fine needle aspiration biopsy. These indeterminate thyroid nodules (ITNs) require surgical pathology for definitive diagnosis. Molecular testing provides additional pre-operative cancer risk stratification but adds expense and invasi...

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Bibliographic Details
Published in:The Journal of surgical research 2022-02, Vol.270, p.214-220
Main Authors: Luong, George, Idarraga, Alexander J., Hsiao, Vivian, Schneider, David F.
Format: Article
Language:English
Subjects:
Bioinformatics
Fine needle aspiration biopsy
Indeterminate thyroid nodule
Machine learning
Risk assessment
Thyroid cancer
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Summary:Up to 30% of thyroid nodules are classified as indeterminate after fine needle aspiration biopsy. These indeterminate thyroid nodules (ITNs) require surgical pathology for definitive diagnosis. Molecular testing provides additional pre-operative cancer risk stratification but adds expense and invasive testing. The purpose of this study is to utilize a machine learning (ML) algorithm to predict malignancy of ITNs using data available from less invasive tests. We conducted a retrospective study using medical records from one academic and one community center. Thyroid nodules with an indeterminate diagnosis on fine needle aspiration biopsy and completed diagnostic pathology were included. Linear, non–linear, and non–linear-ensemble ML methods were tested for accuracy when predicting malignancy using 10-fold cross-validation. Classifiers were evaluated using area under the receiver operating characteristics curve (AUROC). A total of 355 nodules met inclusion criteria. Of these, 171 (48.2%) were diagnosed with cancer. A Random Forest classifier performed the best, producing an accuracy of 79.1%, a sensitivity of 75.5%, specificity of 82.4%, positive predicative value of 80.3%, negative predictive value of 79.0%, and an AUROC of 0.859. ML methods accurately risk stratify ITNs using data gathered from existing, non–invasive, and inexpensive diagnostic tests. Applying an ML model with existing data can become a cost-effective alternative to molecular testing. Future studies will prospectively evaluate the performance of this ML approach when combined with expert judgment.
ISSN:0022-4804
1095-8673
DOI:10.1016/j.jss.2021.09.015