  Thyroid cancer is a common endocrine carcinoma that occurs in the thyroid
gland. Much effort has been invested in improving its diagnosis, and
thyroidectomy remains the primary treatment method. A successful operation
without unnecessary side injuries relies on an accurate preoperative diagnosis.
Current human assessment of thyroid nodule malignancy is prone to errors and
may not guarantee an accurate preoperative diagnosis. This study proposed a
machine framework to predict thyroid nodule malignancy based on a novel
clinical dataset we collected. The 10-fold cross-validation, bootstrap
analysis, and permutation predictor importance were applied to estimate and
interpret the model performance under uncertainty. The comparison between model
prediction and expert assessment shows the advantage of our framework over
human judgment in predicting thyroid nodule malignancy. Our method is accurate,
interpretable, and thus useable as additional evidence in the preoperative
diagnosis for thyroid cancer.
