Lipid-poor adenomas on unenhanced CT: does histogram analysis increase sensitivity compared with a mean attenuation threshold?

Published

Journal Article

OBJECTIVE: The purpose of our study was to evaluate the efficacy of CT histogram analysis for further characterization of lipid-poor adenomas on unenhanced CT. MATERIALS AND METHODS: One hundred thirty-two adrenal nodules were identified in 104 patients with lung cancer who underwent PET/CT. Sixty-five nodules were classified as lipid-rich adenomas if they had an unenhanced CT attenuation of less than or equal to 10 H. Thirty-one masses were classified as lipid-poor adenomas if they had an unenhanced CT attenuation greater than 10 H and stability for more than 1 year. Thirty-six masses were classified as lung cancer metastases if they showed rapid growth in 1 year (n = 27) or were biopsy-proven (n = 9). Histogram analysis was performed for all lesions to provide the mean attenuation value and percentage of negative pixels. RESULTS: All lipid-rich adenomas had more than 10% negative pixels; 51.6% of lipid-poor adenomas had more than 10% negative pixels and would have been classified as indeterminate nodules on the basis of mean attenuation alone. None of the metastases had more than 10% negative pixels. Using an unenhanced CT mean attenuation threshold of less than 10 H yielded a sensitivity of 68% and specificity of 100% for the diagnosis of an adenoma. Using an unenhanced CT threshold of more than 10% negative pixels yielded a sensitivity of 84% and specificity of 100% for the diagnosis of an adenoma. CONCLUSION: CT histogram analysis is superior to mean CT attenuation analysis for the evaluation of adrenal nodules and may help decrease referrals for additional imaging or biopsy.

Full Text

Duke Authors

Cited Authors

  • Ho, LM; Paulson, EK; Brady, MJ; Wong, TZ; Schindera, ST

Published Date

  • July 2008

Published In

Volume / Issue

  • 191 / 1

Start / End Page

  • 234 - 238

PubMed ID

  • 18562751

Pubmed Central ID

  • 18562751

Electronic International Standard Serial Number (EISSN)

  • 1546-3141

Digital Object Identifier (DOI)

  • 10.2214/AJR.07.3150

Language

  • eng

Conference Location

  • United States