title: A bayesian network for differentiating benign from malignant thyroid nodules using sonographic and demographic features.
abstract: OBJECTIVE
  The objective of our study was to create a Bayesian network (BN) that incorporates a multitude of imaging features and patient demographic characteristics to guide radiologists in assessing the likelihood of malignancy in suspicious-appearing thyroid nodules.
  
  
  METHODS
  We built a BN to combine multiple indicators of the malignant potential of thyroid nodules including both imaging and demographic factors. The imaging features and conditional probabilities relating those features to diagnoses were compiled from an extensive literature review. To evaluate our network, we randomly selected 54 benign and 45 malignant nodules from 93 adult patients who underwent ultrasound-guided biopsy. The final diagnosis in each case was pathologically established. We compared the performance of our network with that of two radiologists who independently evaluated each case on a 5-point scale of suspicion for malignancy. Probability estimates of malignancy from the BN and radiologists were compared using receiver operating characteristic (ROC) analysis.
  
  
  RESULTS
  The network performed comparably to the two expert radiologists. Using each radiologist's assessment of the imaging features as input to the network, the differences between the area under the ROC curve (A(z)) for the BN and for the radiologists were -0.03 (BN vs radiologist 1, 0.85 vs 0.88) and -0.01 (BN vs radiologist 2, 0.76 vs 0.77).
  
  
  CONCLUSIONS
  We created a BN that incorporates a range of sonographic and demographic features and provides a probability about whether a thyroid nodule is benign or malignant. The BN distinguished between benign and malignant thyroid nodules as well as the expert radiologists did.
authors.affiliation: Department of Radiology, Stanford University School of Medicine, Richard M. Lucas Center, CA 94305, 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: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D001499:Bayes Theorem; D002277:Carcinoma / Q000000981:diagnostic imaging*; D015331:Cohort Studies; D003936:Diagnosis, Computer-Assisted; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D012372:ROC Curve; D011859:Radiography; D012189:Retrospective Studies; D012737:Sex Factors; D016606:Thyroid Nodule / Q000000981:diagnostic imaging*; D014463:Ultrasonography; D055815:Young Adult