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软组织肿瘤的分级:原则与问题

Grading in soft tissue tumors: principles and problems.

作者信息

Oliveira A M, Nascimento A G

机构信息

Division of Anatomic Pathology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA.

出版信息

Skeletal Radiol. 2001 Oct;30(10):543-59. doi: 10.1007/s002560100408.

Abstract

Histologic grading has been considered the most important prognostic factor for soft tissue sarcomas. Several grading systems have been proposed based on the assessment of morphologic features in heterogeneous groups of sarcomas. Currently, the French Federation of Cancer Centers (FNCLCC) and the National Cancer Institute (NCI) grading systems are the most commonly used. These systems are based on a few morphologic predictors of biologic behavior, which is justifiable because of the rarity of soft tissue sarcomas. Nonetheless, over- or underestimation of prognosis may occur because of an uneven representation of specific sarcomas with rather distinct biologic behaviors among studies of grading systems. In addition, lack of standardization of morphologic criteria and frequent omission of the influence of clinical factors on the final survival analyses preclude universal acceptance of a particular grading system. New advances in diagnostic imaging, quantitative morphometric technologies, cytogenetics, and molecular genetics, allied with alternative analytic data systems, may provide better validation, reproducibility, and prognostic capabilities for current and future grading systems. This article summarizes and critically analyzes the various important grading systems that have thus far been proposed and suggests alternatives for the elaboration of more reproducible systems with higher predictive capabilities.

摘要

组织学分级一直被认为是软组织肉瘤最重要的预后因素。基于对肉瘤异质性群体形态学特征的评估,已提出了几种分级系统。目前,法国癌症中心联合会(FNCLCC)和美国国立癌症研究所(NCI)分级系统是最常用的。这些系统基于一些生物学行为的形态学预测指标,鉴于软组织肉瘤的罕见性,这是合理的。然而,由于在分级系统研究中具有相当不同生物学行为的特定肉瘤代表性不均衡,可能会出现预后高估或低估的情况。此外,形态学标准缺乏标准化以及在最终生存分析中经常忽略临床因素的影响,使得特定分级系统难以被普遍接受。诊断成像、定量形态计量技术、细胞遗传学和分子遗传学的新进展,以及替代分析数据系统,可能为当前和未来的分级系统提供更好的验证、可重复性和预后能力。本文总结并批判性地分析了迄今为止提出的各种重要分级系统,并提出了替代方案,以制定更具可重复性、预测能力更高的系统。

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