Pattern recognition of benign nodules at ultrasound of the thyroid: which nodules can be left alone?

AJR Am J Roentgenol. 2009 Jul;193(1):207-13. doi: 10.2214/AJR.08.1820.


Objective: The purpose of this study was to evaluate morphologic features predictive of benign thyroid nodules.

Materials and methods: From a registry of the records of 1,232 fine-needle aspiration biopsies performed jointly by the cytology and radiology departments at a single institution between 2005 and 2007, the cases of 650 patients were identified for whom both a pathology report and ultrasound images were available. From the alphabetized list generated, the first 500 nodules were reviewed. We analyzed the accuracy of individual sonographic features and of 10 discrete recognizable morphologic patterns in the prediction of benign histologic findings.

Results: We found that grouping of thyroid nodules into reproducible patterns of morphology, or pattern recognition, rather than analysis of individual sonographic features, was extremely accurate in the identification of benign nodules. Four specific patterns were identified: spongiform configuration, cyst with colloid clot, giraffe pattern, and diffuse hyperechogenicity, which had a 100% specificity for benignity. In our series, identification of nodules with one of these four patterns could have obviated more than 60% of thyroid biopsies.

Conclusion: Recognition of specific morphologic patterns is an accurate method of identifying benign thyroid nodules that do not require cytologic evaluation. Use of this approach may substantially decrease the number of unnecessary biopsy procedures.

MeSH terms

  • Adolescent
  • Adult
  • Aged
  • Aged, 80 and over
  • Algorithms*
  • Artificial Intelligence*
  • Female
  • Humans
  • Image Enhancement / methods
  • Image Interpretation, Computer-Assisted / methods*
  • Male
  • Middle Aged
  • Pattern Recognition, Automated / methods*
  • Reproducibility of Results
  • Sensitivity and Specificity
  • Thyroid Nodule / diagnostic imaging*
  • Ultrasonography
  • Young Adult