title: A Bayesian classifier for differentiating benign versus malignant thyroid nodules using sonographic features.
abstract: Thyroid nodules are a common, yet challenging clinical problem. The vast majority of these nodules are benign; however, deciding which nodule should undergo biopsy is difficult because the imaging appearance of benign and malignant thyroid nodules overlap. High resolution ultrasound is the primary imaging modality for evaluating thyroid nodules. Many sonographic features have been studied individually as predictors for thyroid malignancy. There has been little work to create predictive models that combine multiple predictors, both imaging features and demographic factors. We have created a Bayesian classifier to predict whether a thyroid nodule is benign or malignant using sonographic and demographic findings. Our classifier performed similar to or slightly better than experienced radiologists when evaluated using 41 thyroid nodules with known pathologic diagnosis. This classifier could be helpful in providing practitioners an objective basis for deciding whether to biopsy suspicious thyroid nodules.
authors.affiliation: Department of Radiology, Stanford University, Stanford, CA, USA.
authors.forename: Yueyi I
authors.initials: YI
authors.lastname: Liu
authors.forename: Aya
authors.initials: A
authors.lastname: Kamaya
authors.forename: Terry S
authors.initials: TS
authors.lastname: Desser
authors.forename: Daniel L
authors.initials: DL
authors.lastname: Rubin
mesh_terms: D000328:Adult; D000368:Aged; D001185:Artificial Intelligence; D001499:Bayes Theorem; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013964:Thyroid Neoplasms / Q000145:classification / Q000000981:diagnostic imaging*; D014463:Ultrasonography / Q000379:methods*