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小儿低级别神经上皮肿瘤的放射组学基因组学。

Radiohistogenomics of pediatric low-grade neuroepithelial tumors.

机构信息

Department of Diagnostic Imaging, St. Jude Children's Research Hospital, 262 Danny Thomas Place, Mail Stop 220, Memphis, TN, 38105, USA.

Department of Pathology, St. Jude Children's Research Hospital, Memphis, TN, USA.

出版信息

Neuroradiology. 2021 Aug;63(8):1185-1213. doi: 10.1007/s00234-021-02691-1. Epub 2021 Mar 29.

DOI:10.1007/s00234-021-02691-1
PMID:33779771
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8295117/
Abstract

PURPOSE

In addition to histology, genetic alteration is now required to classify many central nervous system (CNS) tumors according to the most recent World Health Organization CNS tumor classification scheme. Although that is still not the case for classifying pediatric low-grade neuroepithelial tumors (PLGNTs), genetic and molecular features are increasingly being used for making treatment decisions. This approach has become a standard clinical practice in many specialized pediatric cancer centers and will likely be more widely practiced in the near future. This paradigm shift in the management of PLGNTs necessitates better understanding of how genetic alterations influence histology and imaging characteristics of individual PLGNT phenotypes.

METHODS

The complex association of genetic alterations with histology, clinical, and imaging of each phenotype of the extremely heterogeneous PLGNT family has been addressed in a holistic approach in this up-to-date review article. A new imaging stratification scheme has been proposed based on tumor morphology, location, histology, and genetics. Imaging characteristics of each PLGNT entity are also depicted in light of histology and genetics.

CONCLUSION

This article reviews the association of specific genetic alteration with location, histology, imaging, and prognosis of a specific tumor of the PLGNT family and how that information can be used for better imaging of these tumors.

摘要

目的

除了组织学，遗传改变现在也被要求用于根据最新的世界卫生组织中枢神经系统肿瘤分类方案对许多中枢神经系统（CNS）肿瘤进行分类。虽然在对儿科低级别神经上皮肿瘤（PLGNT）进行分类时还没有这种情况，但遗传和分子特征越来越多地被用于做出治疗决策。这种方法已成为许多专门的儿科癌症中心的标准临床实践，在不久的将来可能会更广泛地应用。这种 PLGNT 管理模式的转变需要更好地了解遗传改变如何影响各个 PLGNT 表型的组织学和影像学特征。

方法

在这篇最新的综述文章中，采用整体方法解决了遗传改变与每种表现型的组织学、临床和影像学之间的复杂关联。根据肿瘤形态、位置、组织学和遗传学提出了一种新的影像学分层方案。还根据组织学和遗传学描述了每个 PLGNT 实体的影像学特征。

结论

本文综述了特定遗传改变与 PLGNT 家族特定肿瘤的位置、组织学、影像学和预后的关联，以及如何利用这些信息更好地对这些肿瘤进行成像。

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3
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4
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7
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