A Bayesian network for differentiating benign from malignant thyroid nodules using sonographic and demographic features

American Journal of Roentgenology

Volumn 196, Issue 5, 2011, Pages

  Liu, Yueyi I ^a   Kamaya, Aya ^a   Desser, Terry S ^a   Rubin, Daniel L ^a  

^a STANFORD UNIVERSITY   (United States)

Author keywords

Artificial intelligence; Bayesian network; Thyroid nodules

Indexed keywords

ADOLESCENT; ADULT; AGED; AREA UNDER THE CURVE; ARTICLE; BAYES THEOREM; BIOPSY; COMPARATIVE STUDY; CONTROLLED STUDY; DEMOGRAPHY; DIFFERENTIAL DIAGNOSIS; ECHOGRAPHY; FEMALE; HISTOPATHOLOGY; HUMAN; HUMAN TISSUE; MAJOR CLINICAL STUDY; MALE; PRIORITY JOURNAL; PROBABILITY; RADIOLOGIST; RATING SCALE; RECEIVER OPERATING CHARACTERISTIC; SENSITIVITY AND SPECIFICITY; THYROID CANCER; THYROID NODULE; AGE; CARCINOMA; COHORT ANALYSIS; COMPUTER ASSISTED DIAGNOSIS; MIDDLE AGED; PREDICTIVE VALUE; RADIOGRAPHY; RETROSPECTIVE STUDY; SEX DIFFERENCE;

ADOLESCENT; ADULT; AGE FACTORS; AGED; AGED, 80 AND OVER; BAYES THEOREM; CARCINOMA; COHORT STUDIES; DIAGNOSIS, COMPUTER-ASSISTED; DIAGNOSIS, DIFFERENTIAL; FEMALE; HUMANS; MALE; MIDDLE AGED; PREDICTIVE VALUE OF TESTS; RETROSPECTIVE STUDIES; ROC CURVE; SEX FACTORS; THYROID NODULE; YOUNG ADULT;

EID: 79959582397     PISSN: 0361803X     EISSN: 15463141     Source Type: Journal    
DOI: 10.2214/AJR.09.4037     Document Type: Article

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