Diagnostic guidelines for the histological particle algorithm in the periprosthetic neo-synovial tissue.

Q2 Medicine
G Perino, S Sunitsch, M Huber, D Ramirez, J Gallo, J Vaculova, S Natu, J P Kretzer, S Müller, P Thomas, M Thomsen, M G Krukemeyer, H Resch, T Hügle, W Waldstein, F Böettner, T Gehrke, S Sesselmann, W Rüther, Z Xia, E Purdue, V Krenn
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引用次数: 24

Abstract

Background: The identification of implant wear particles and non-implant related particles and the characterization of the inflammatory responses in the periprosthetic neo-synovial membrane, bone, and the synovial-like interface membrane (SLIM) play an important role for the evaluation of clinical outcome, correlation with radiological and implant retrieval studies, and understanding of the biological pathways contributing to implant failures in joint arthroplasty. The purpose of this study is to present a comprehensive histological particle algorithm (HPA) as a practical guide to particle identification at routine light microscopy examination.

Methods: The cases used for particle analysis were selected retrospectively from the archives of two institutions and were representative of the implant wear and non-implant related particle spectrum. All particle categories were described according to their size, shape, colour and properties observed at light microscopy, under polarized light, and after histochemical stains when necessary. A unified range of particle size, defined as a measure of length only, is proposed for the wear particles with five classes for polyethylene (PE) particles and four classes for conventional and corrosion metallic particles and ceramic particles.

Results: All implant wear and non-implant related particles were described and illustrated in detail by category. A particle scoring system for the periprosthetic tissue/SLIM is proposed as follows: 1) Wear particle identification at light microscopy with a two-step analysis at low (× 25, × 40, and × 100) and high magnification (× 200 and × 400); 2) Identification of the predominant wear particle type with size determination; 3) The presence of non-implant related endogenous and/or foreign particles. A guide for a comprehensive pathology report is also provided with sections for macroscopic and microscopic description, and diagnosis.

Conclusions: The HPA should be considered a standard for the histological analysis of periprosthetic neo-synovial membrane, bone, and SLIM. It provides a basic, standardized tool for the identification of implant wear and non-implant related particles at routine light microscopy examination and aims at reducing intra-observer and inter-observer variability to provide a common platform for multicentric implant retrieval/radiological/histological studies and valuable data for the risk assessment of implant performance for regional and national implant registries and government agencies.

组织学粒子算法在假体周围新滑膜组织中的诊断指南。
背景:植入物磨损颗粒和非植入物相关颗粒的识别以及假体周围新滑膜、骨和滑膜样界面膜(SLIM)炎症反应的表征在评估临床结果、与放射学和植入物回收研究的相关性、,以及了解导致关节置换术中植入物失败的生物学途径。本研究的目的是提出一种全面的组织学颗粒算法(HPA),作为常规光学显微镜检查中颗粒识别的实用指南。方法:从两个机构的档案中回顾性选择用于颗粒分析的病例,这些病例具有植入物磨损和非植入物相关颗粒谱的代表性。根据在光学显微镜下、偏振光下以及必要时在组织化学染色后观察到的颗粒大小、形状、颜色和特性,对所有颗粒类别进行了描述。提出了磨损颗粒的统一粒径范围,仅定义为长度测量,聚乙烯(PE)颗粒有五类,传统和腐蚀金属颗粒和陶瓷颗粒有四类。结果:按类别详细描述和说明了所有植入物磨损和非植入物相关颗粒。提出了一种假体周围组织/SIM的颗粒评分系统:1)在光学显微镜下进行磨损颗粒识别,在低(×25、×40和×100)和高放大率(×200和×400)下进行两步分析;2) 通过尺寸测定确定主要磨损颗粒类型;3) 存在与植入物无关的内源性和/或外来颗粒。综合病理学报告的指南也提供了宏观和微观描述以及诊断的章节。结论:HPA应被视为假体周围新滑膜、骨和SLIM组织学分析的标准。它提供了一种基本的,用于在常规光学显微镜检查中识别植入物磨损和非植入物相关颗粒的标准化工具,旨在减少观察者内部和观察者之间的差异,为多中心植入物检索/放射学/组织学研究提供一个通用平台,并为区域和国家植入物植入物性能的风险评估提供有价值的数据登记处和政府机构。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
BMC Clinical Pathology
BMC Clinical Pathology Medicine-Pathology and Forensic Medicine
CiteScore
3.30
自引率
0.00%
发文量
0
期刊介绍: BMC Clinical Pathology is an open access journal publishing original peer-reviewed research articles in all aspects of histopathology, haematology, clinical biochemistry, and medical microbiology (including virology, parasitology, and infection control). BMC Clinical Pathology (ISSN 1472-6890) is indexed/tracked/covered by PubMed, CAS, EMBASE, Scopus and Google Scholar.
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