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神经肿瘤学:2023年更新版

Neurooncology: 2023 update.

作者信息

Mittelbronn Michel

机构信息

National Center of Pathology (NCP), Laboratoire National de Santé (LNS), Dudelange, Luxembourg.

Luxembourg Center of Neuropathology (LCNP), Luxembourg.

出版信息

Free Neuropathol. 2023 Mar 20;4:4. doi: 10.17879/freeneuropathology-2023-4692. eCollection 2023 Jan.

DOI:10.17879/freeneuropathology-2023-4692
PMID:37283935
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10227754/
Abstract

This article presents some of the author's neuropathological highlights in the field on neuro-oncology research encountered in 2022. Major advances were made in the development of more precise, faster, easier, less invasive and unbiased diagnostic tools ranging from immunohistochemical prediction of 1p/19q loss in diffuse glioma, methylation analyses in CSF samples, molecular profiling for CNS lymphoma, proteomic analyses of recurrent glioblastoma, integrated molecular diagnostics for better stratification in meningioma, intraoperative profiling making use of Raman effect or methylation analysis, to finally, the assessment of histological slides by means of machine learning for the prediction of molecular tumor features. In addition, as the discovery of a new tumor entity may also be a highlight for the neuropathology community, the newly described high-grade glioma with pleomorphic and pseudopapillary features (HPAP) has been selected for this article. Regarding new innovative treatment approaches, a drug screening platform for brain metastasis is presented. Although diagnostic speed and precision is steadily increasing, clinical prognosis for patients with malignant tumors affecting the nervous system remains largely unchanged over the last decade, therefore future neuro-oncological research focus should be put on how the amazing developments presented in this article can be more sustainably applied to positively impact patient prognosis.

摘要

本文介绍了作者在2022年神经肿瘤学研究领域遇到的一些神经病理学亮点。在开发更精确、更快、更简便、侵入性更小且无偏差的诊断工具方面取得了重大进展,这些工具包括弥漫性胶质瘤中1p/19q缺失的免疫组化预测、脑脊液样本中的甲基化分析、中枢神经系统淋巴瘤的分子谱分析、复发性胶质母细胞瘤的蛋白质组分析、脑膜瘤更好分层的综合分子诊断、利用拉曼效应或甲基化分析的术中分析,最后是通过机器学习评估组织学切片以预测分子肿瘤特征。此外,由于发现新的肿瘤实体也可能是神经病理学领域的一个亮点,本文选择了新描述的具有多形性和假乳头特征的高级别胶质瘤(HPAP)。关于新的创新治疗方法,介绍了一种用于脑转移的药物筛选平台。尽管诊断速度和精度在稳步提高,但在过去十年中,影响神经系统的恶性肿瘤患者的临床预后基本没有变化,因此未来神经肿瘤学研究的重点应放在如何更可持续地应用本文中介绍的惊人进展,以积极影响患者预后。

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J Neuropathol Exp Neurol. 2024 Jan 19;83(2):139-141. doi: 10.1093/jnen/nlad114.

本文引用的文献

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Free Neuropathol. 2022 Feb 24;3:4. doi: 10.17879/freeneuropathology-2022-3804. eCollection 2022 Jan.
2
From Research to Diagnostic Application of Raman Spectroscopy in Neurosciences: Past and Perspectives.从拉曼光谱在神经科学中的研究到诊断应用:过去与展望
Free Neuropathol. 2022 Aug 5;3:19. doi: 10.17879/freeneuropathology-2022-4210. eCollection 2022 Jan.
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MHC class II-restricted antigen presentation is required to prevent dysfunction of cytotoxic T cells by blood-borne myeloids in brain tumors.MHC Ⅱ类限制的抗原呈递对于防止血源性髓样细胞在脑肿瘤中引起细胞毒性 T 细胞功能障碍是必需的。
Cancer Cell. 2023 Feb 13;41(2):235-251.e9. doi: 10.1016/j.ccell.2022.12.007. Epub 2023 Jan 12.
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Method of the Year 2022: long-read sequencing.2022年度方法:长读长测序。
Nat Methods. 2023 Jan;20(1):1. doi: 10.1038/s41592-022-01759-x.
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Machine learning can aid in prediction of IDH mutation from H&E-stained histology slides in infiltrating gliomas.机器学习有助于从浸润性神经胶质瘤的 H&E 染色组织学切片中预测 IDH 突变。
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An Audit of Diagnostic Disparity between Intraoperative Frozen Section Diagnosis and Final Histopathological Diagnosis of Central Nervous System Lesions at a Tertiary Care Center.某三级医疗中心中枢神经系统病变术中冰冻切片诊断与最终组织病理学诊断之间诊断差异的审计
J Lab Physicians. 2022 Aug 17;14(4):384-393. doi: 10.1055/s-0042-1750064. eCollection 2022 Dec.
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Neuro Oncol. 2023 Aug 3;25(8):1452-1460. doi: 10.1093/neuonc/noac264.
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