Related Experiment Videos
An algorithmic approach to sellar region masses.
B K Kleinschmidt-DeMasters1, M B S Lopes, Richard A Prayson
1From the Departments of Pathology, Neurology, and Neurosurgery, Anschutz Medical Campus, University of Colorado School of Medicine, Aurora (Dr Kleinschmidt-DeMasters);
Archives of Pathology & Laboratory Medicine
|February 28, 2015
Summary
This study offers a practical diagnostic algorithm for sellar region masses, simplifying the identification of pituitary adenomas and other lesions. A cost-effective panel of stains aids pathologists in daily practice.
Area of Science:
- Neuropathology
- Sellar Region Pathology
- Diagnostic Histopathology
Background:
- Sellar region masses are predominantly pituitary adenomas (85%-90%).
- Accurate diagnosis requires a practical approach beyond electron microscopy for daily practice.
- Nonadenomatous lesions also occur in the sellar region.
Purpose of the Study:
- To present an algorithmic approach for diagnosing sellar region masses.
- To define a cost-effective, limited panel of stains for routine pathology practice.
- To aid pathologists in small to medium-sized centers.
Main Methods:
- Pooling expertise from three neuropathologists specializing in sellar region masses.
- Developing a single-page algorithmic diagram for lesion classification.
- Illustrating a wide range of sellar region lesions.
Main Results:
- A differential diagnosis followed by reticulin and synaptophysin stains aids adenoma diagnosis.
- Five immunohistochemical stains (CAM5.2, FSH, GH, PRL, ACTH) facilitate adenoma subtyping.
- CAM5.2 and clinical data help identify aggressive variants; MIB-1, TTF-1, and S-100 aid in specific cases.
Conclusions:
- Pituitary adenomas, normal pituitary gland, and nonadenomatous masses are readily diagnosed.
- The proposed method is suitable for non-tertiary pathology laboratory settings.
- Simplifies the diagnostic workflow for sellar region lesions.