Context.-Pituitary adenoma classification is complex, and diagnostic strategies vary greatly from laboratory to laboratory. No optimal diagnostic algorithm has been defined.Objective.-To develop a panel of immunohistochemical (IHC) stains that provides the optimal combination of cost, accuracy, and ease of use.Design.-We examined 136 pituitary adenomas with stains of steroidogenic factor 1 (SF-1), Pit-1, anterior pituitary hormones, cytokeratin CAM5.2, and a subunit of human chorionic gonadotropin. Immunohistochemical staining was scored using the Allred system. Adenomas were assigned to a gold standard class based on IHC results and available clinical and serologic information. Correlation and cluster analyses were used to develop an algorithm for parsimoniously classifying adenomas.Results.-The algorithm entailed a 1-or 2-step process: (1) a screening step consisting of IHC stains for SF-1, Pit-1, and adrenocorticotropic hormone; and (2) when screening IHC pattern and clinical history were not clearly gonadotrophic (SF-1 positive only), corticotrophic (adrenocorticotropic hormone positive only), or IHC null cell (negative-screening IHC), we subsequently used IHC for prolactin, growth hormone, thyroid-stimulating hormone, and cytokeratin CAM5.2.Conclusions.-Comparison between diagnoses generated by our algorithm and the gold standard diagnoses showed excellent agreement. When compared with a commonly used panel using 6 IHC for anterior pituitary hormones plus IHC for a low-molecular-weight cytokeratin in certain tumors, our algorithm uses approximately onethird fewer IHC stains and detects gonadotroph adenomas with greater sensitivity.
Context.—
We previously examined pituitary adenomas with immunohistochemical (IHC) stains for steroidogenic factor 1, Pit-1, anterior pituitary hormones, cytokeratin CAM 5.2, and the α-subunit of human chorionic gonadotropin and found that a screening panel comprising stains for steroidogenic factor 1, Pit-1, and adrenocorticotropic hormone successfully classified most cases and reduced the overall number of stains required.
Objectives.—
To examine the potential role of IHC stain for T-box transcription factor (Tpit) in the classification of our series of pituitary adenomas and to update our screening panel as necessary.
Design.—
We collected 157 pituitary adenomas from 2 institutions and included these in tissue microarrays. Immunostains for Tpit were scored in a blinded fashion using the Allred system. Adenomas were assigned to a gold standard class based on IHC pattern followed by application of available clinical and serologic information. Test characteristics were calculated. Correlation analyses, cluster analyses, and classification tree analyses were used to see whether IHC staining patterns reliably reflected adenoma class.
Results.—
Of the cases collected, 147 (93.6%) had sufficient material for Tpit analysis. IHC stain for Tpit identified 8 null cell adenomas (all nonfunctioning clinically) as silent corticotrophs; Tpit stains showed better sensitivity, specificity, positive predictive value, and negative predictive value than IHC for adrenocorticotropic hormone and cytokeratin CAM 5.2. Correlation analyses continued to show the expected relationships among IHC stains. Cluster analyses showed grouping of adenomas into clinically consistent groups. Classification tree analysis underscored the central role of transcription factor IHC stains, including Tpit, in adenoma classification.
Conclusions.—
Substitution of Tpit stain for the adrenocorticotropic hormone stain improves our prior algorithm by reducing the number of false-negatives and false-positives. As a result, fewer adenomas are classified as null cell adenoma, and more adenomas are classified as silent corticotroph adenoma.
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