Annals of Surgical Oncology

, Volume 20, Issue 11, pp 3484–3490 | Cite as

Does Bethesda Category Predict Aggressive Features in Malignant Thyroid Nodules?

  • David A. Kleiman
  • Toni Beninato
  • Ashwin Soni
  • Yiyan Shou
  • Rasa Zarnegar
  • Thomas J. FaheyIII
Endocrine Tumors



It has been speculated that the Bethesda Classification System for thyroid fine-needle aspirate (FNA) may be used to predict aggressive features among histologically proven malignancies. We sought to evaluate whether malignancies that were characterized as Bethesda category V or VI have more aggressive features than malignancies that were category III or IV.


A prospectively maintained database was reviewed to identify thyroid malignancies treated at a single center from 2004 to 2009. Only cancers that could be definitively matched to a preoperative FNA were included. Associations between Bethesda category, patient demographics, histopathologic findings, and outcomes were examined.


A total of 360 cancers were analyzed: 73 (20 %) were Bethesda category III or IV and 287 (80 %) were category V or VI. The majority of Bethesda III and IV cancers were follicular variants of papillary thyroid carcinoma (fvPTC), whereas the majority of Bethesda V and VI cancers were classic PTC (52 and 67 %, respectively, p < 0.01). Extrathyroidal extension (30 vs. 16 %, p = 0.02), lymph node metastases (50 vs. 31 %, p = 0.05), and multifocality (51 vs. 37 %, p = 0.03) were more common among Bethesda V and VI nodules. However, when Bethesda III or IV classic PTC and fvPTC were compared to Bethesda V or VI cancers of the same histologic subtype, there were no differences in any features. Recurrence and overall survival were the same in all groups.


Bethesda category may help to predict the most likely histologic subtype of thyroid cancer, but it does not have any prognostic significance once the histologic diagnosis is known.


Papillary Thyroid Carcinoma Thyroid Malignancy Extrathyroidal Extension Central Neck Dissection Common Histologic Subtype 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This study was supported in part by Grant TL1RR000459 of the Clinical and Translational Science Center at Weill Cornell Medical College, and by a donation from the Dancers Care Foundation.

Financial Disclosures



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Copyright information

© Society of Surgical Oncology 2013

Authors and Affiliations

  • David A. Kleiman
    • 1
  • Toni Beninato
    • 1
  • Ashwin Soni
    • 1
  • Yiyan Shou
    • 1
  • Rasa Zarnegar
    • 1
  • Thomas J. FaheyIII
    • 1
  1. 1.Division of Endocrine and Minimally Invasive Surgery, Department of SurgeryNew York Presbyterian Hospital/Weill Cornell Medical CollegeNew YorkUSA

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