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小儿脑肿瘤:神经病理学家的综合诊断方法

Pediatric brain tumors: A neuropathologist's approach to the integrated diagnosis.

作者信息

Viaene Angela N

机构信息

Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, United States.

Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States.

出版信息

Front Pediatr. 2023 Mar 8;11:1143363. doi: 10.3389/fped.2023.1143363. eCollection 2023.

DOI:10.3389/fped.2023.1143363
PMID:36969278
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10030595/
Abstract

The classification of tumors of the central nervous system (CNS) is a rapidly evolving field. While tumors were historically classified on the basis of morphology, the recent integration of molecular information has greatly refined this process. In some instances, molecular alterations provide significant prognostic implications beyond what can be ascertained by morphologic examination alone. Additionally, tumors may harbor molecular alterations that provide a therapeutic target. Pediatric CNS tumors, in particular, rely heavily on the integration of molecular data with histologic, clinical, and radiographic features to reach the most accurate diagnosis. This review aims to provide insight into a neuropathologist's approach to the clinical workup of pediatric brain tumors with an ultimate goal of reaching an integrated diagnosis that provides the most accurate classification and informs prognosis and therapy selection. The primary focus will center on how histology and molecular findings are used in combination with clinical and radiographic information to reach a final, integrated diagnosis.

摘要

中枢神经系统(CNS)肿瘤的分类是一个快速发展的领域。虽然肿瘤在历史上是根据形态学进行分类的,但最近分子信息的整合极大地完善了这一过程。在某些情况下,分子改变所提供的显著预后意义超出了仅通过形态学检查所能确定的范围。此外,肿瘤可能存在提供治疗靶点的分子改变。特别是儿童中枢神经系统肿瘤,严重依赖于分子数据与组织学、临床和影像学特征的整合,以实现最准确的诊断。本综述旨在深入了解神经病理学家对儿童脑肿瘤临床检查的方法,最终目标是达成综合诊断,以提供最准确的分类,并为预后和治疗选择提供依据。主要重点将集中在组织学和分子发现如何与临床和影像学信息结合起来,以达成最终的综合诊断。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6044/10030595/214cf4d63ab4/fped-11-1143363-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6044/10030595/06937d04a007/fped-11-1143363-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6044/10030595/dc17adcf53c3/fped-11-1143363-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6044/10030595/214cf4d63ab4/fped-11-1143363-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6044/10030595/06937d04a007/fped-11-1143363-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6044/10030595/dc17adcf53c3/fped-11-1143363-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6044/10030595/214cf4d63ab4/fped-11-1143363-g003.jpg

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Lab Invest. 2022 Feb;102(2):160-171. doi: 10.1038/s41374-021-00694-3. Epub 2021 Nov 30.
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