William C McDonald, Nilanjana Banerji, Kelsey N McDonald, Bridget Ho, Virgilia Macias, Andre Kajdacsy-Balla1. 1. From the Department of Pathology and Laboratory Medicine, Allina Health Laboratories, Minneapolis, Minnesota (Dr W. C. McDonald); the Research Division, John Nasseff Neuroscience Institute, Minneapolis, Minnesota (Dr Banerji and Ms Ho); the Centre for Urban Epidemiology, Institute for Medical Informatics, Biometry and Epidemiology, University Hospital Essen, Essen, Germany (Dr K. N. McDonald); and the Department of Pathology, University of Illinois at Chicago (Drs Macias and Kajdacsy-Balla).
Abstract
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 α 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 one-third fewer IHC stains and detects gonadotroph adenomas with greater sensitivity.
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 α 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 one-third fewer IHC stains and detects gonadotroph adenomas with greater sensitivity.
Authors: C Villa; A Vasiljevic; M L Jaffrain-Rea; O Ansorge; S Asioli; V Barresi; L Chinezu; M P Gardiman; A Lania; A M Lapshina; L Poliani; L Reiniger; A Righi; W Saeger; J Soukup; M Theodoropoulou; S Uccella; J Trouillas; F Roncaroli Journal: Virchows Arch Date: 2019-10-02 Impact factor: 4.064
Authors: Joao Paulo Almeida; Corbin C Stephens; Jennifer M Eschbacher; Michelle M Felicella; Kevin C J Yuen; William L White; Michael A Mooney; Anne Laure Bernat; Ozgur Mete; Gelareh Zadeh; Fred Gentili; Andrew S Little Journal: Pituitary Date: 2019-10 Impact factor: 4.107
Authors: Richard A Hickman; Jeffrey N Bruce; Marc Otten; Alexander G Khandji; Xena E Flowers; Markus Siegelin; Beatriz Lopes; Phyllis L Faust; Pamela U Freda Journal: Neuropathol Appl Neurobiol Date: 2020-12-20 Impact factor: 8.090