Intraoperative multiple-staged resection and tumor tissue identification using frozen sections provide the best result for the accurate localization and complete resection of tumors in Cushing's disease

Jung Soo Lim, Seung Ku Lee, Se Hoon Kim, Eun Jig Lee, Sun Ho Kim

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12 Citations (Scopus)

Abstract

The treatment of choice in Cushing's disease (CD) is surgical removal; however, most tumors are too small to be detected. The objective was to establish a method to achieve the complete removal of tumors on the basis of the results of high-resolution magnetic resonance imaging (MRI), inferior petrosal sinus sampling (IPSS), and a surgical resection technique using frozen biopsy. Eighteen patients who underwent transsphenoidal surgery from 2004 to 2010 were included. High-resolution MRI and IPSS, multiple-staged resection, and tumor tissue identification in frozen sections (surgical and histological identification, SHI) were performed. All patients achieved surgical remission, as confirmed by 24 h urinary free cortisol excretion tests. Visible microlesions were identified on the initial MRI in 11 patients (61%). The SHI findings agreed with the MRI findings in 10 of the 11 patients (90.9%) and with IPSS lateralization in 6 of the 11 patients (54.5%). In the 7 patients whose lesions were not visible on the initial MRI, only 1 (14.3%) showed an agreement between IPSS and SHI. In 3 of the 7 patients, the microlesions were identified by additional MRI. The rate of concordance with SHI was 77.8% for the overall MRI and 38.9% for IPSS. High-resolution MRI is better than IPSS for localizing corticotroph adenomas. In patients with lesions not visible on the initial MRI, additional MRI should be performed using a different protocol. Although high-resolution MRI is better for localizing tumors, SHI remains an important approach for removing the tumors completely.

Original languageEnglish
Pages (from-to)452-461
Number of pages10
JournalEndocrine
Volume40
Issue number3
DOIs
Publication statusPublished - 2011 Dec

All Science Journal Classification (ASJC) codes

  • Endocrinology, Diabetes and Metabolism
  • Endocrinology

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