McDonald William C, Banerji Nilanjana, McDonald Kelsey N, Ho Bridget, Macias Virgilia, Kajdacsy-Balla Andre
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).
Arch Pathol Lab Med. 2017 Jan;141(1):104-112. doi: 10.5858/arpa.2016-0082-OA. Epub 2016 May 26.
-Pituitary adenoma classification is complex, and diagnostic strategies vary greatly from laboratory to laboratory. No optimal diagnostic algorithm has been defined.
-To develop a panel of immunohistochemical (IHC) stains that provides the optimal combination of cost, accuracy, and ease of use.
-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.
-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.
-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.
垂体腺瘤分类复杂,不同实验室的诊断策略差异很大。尚未确定最佳诊断算法。
开发一组免疫组化(IHC)染色方法,提供成本、准确性和易用性的最佳组合。
我们用类固醇生成因子1(SF-1)、垂体特异性转录因子1(Pit-1)、垂体前叶激素、细胞角蛋白CAM5.2和人绒毛膜促性腺激素α亚基的染色剂检查了136例垂体腺瘤。免疫组化染色采用奥尔雷德系统评分。根据免疫组化结果以及可用的临床和血清学信息,将腺瘤归为金标准类别。采用相关性和聚类分析来开发一种简化腺瘤分类的算法。
该算法包括一个1步或2步过程:(1)筛选步骤,包括对SF-1、Pit-1和促肾上腺皮质激素进行免疫组化染色;(2)当筛选免疫组化模式和临床病史不明确为促性腺激素型(仅SF-1阳性)、促肾上腺皮质激素型(仅促肾上腺皮质激素阳性)或免疫组化无功能细胞型(阴性筛选免疫组化)时,我们随后对催乳素、生长激素、促甲状腺激素和细胞角蛋白CAM5.2进行免疫组化检测。
我们的算法生成的诊断结果与金标准诊断结果之间的比较显示出极好的一致性。与一种常用的方法相比,该方法在某些肿瘤中使用6种垂体前叶激素免疫组化加上一种低分子量细胞角蛋白免疫组化,我们的算法使用的免疫组化染色剂减少了约三分之一,并且对促性腺激素腺瘤的检测灵敏度更高。