• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

扩散张量侵袭性表型可预测胶质母细胞瘤的无进展生存期。

Diffusion tensor invasive phenotypes can predict progression-free survival in glioblastomas.

作者信息

Mohsen L A, Shi V, Jena R, Gillard J H, Price S J

机构信息

Department of Radiology, University of Cambridge , Cambridge , UK.

出版信息

Br J Neurosurg. 2013 Aug;27(4):436-41. doi: 10.3109/02688697.2013.771136. Epub 2013 Feb 27.

DOI:10.3109/02688697.2013.771136
PMID:23445331
Abstract

INTRODUCTION

Glioblastomas multiformes (GBM) remain incurable in most cases. Their invasion into normal brain makes current therapies ineffective. Post-mortem studies suggest about a 25% of GBMs invade less than 1 cm from the tumour bulk and 20% invade more than 3 cm.

AIM OF STUDY

The study aims to use DTI to assess tumour extension and determine how previously reported patterns relate to the progression-free survival (PFS).

MATERIALS AND METHODS

Twenty-five patients with GBM treated according to the EORTC/NCIC protocol were retrospectively analysed. Patients were imaged post-operatively at 1.5 T. The sequences were composed of standard anatomical and a standard DTI sequence. As described earlier p and q maps were constructed. For each of the p and q maps, regions of interest were drawn around the visible abnormality. Patients were assigned a diffuse, localised or minimally invasive pattern. Progression was defined according to the RANO criteria (4) and PFS determined in days. Kaplan-Meier plots of survival for the three groups were plotted as were the proportion of patients who had not progressed at 24 months.

RESULTS

The median PFS for the diffuse group was 278 days, for the localised group 605 days and 820 days for the minimally invasive group. Three-fourth of the minimally invasive group were progression-free at 24 months (LOG RANK 9.25; p = 0.010).

CONCLUSION

It is possible to identify three invasive phenotypes in GBMs using Diffusion tensor imaging , and these three phenotypes have different progression free survival. A minimal phenotype (20% of patients) predicts a greater delay to progression.

摘要

引言

多形性胶质母细胞瘤(GBM)在大多数情况下仍无法治愈。它们侵入正常脑组织使得当前治疗方法无效。尸检研究表明,约25%的GBM从肿瘤主体侵入距离小于1厘米,20%侵入距离大于3厘米。

研究目的

本研究旨在使用扩散张量成像(DTI)评估肿瘤扩展情况,并确定先前报道的模式与无进展生存期(PFS)之间的关系。

材料与方法

对按照欧洲癌症研究与治疗组织/加拿大国立癌症研究所(EORTC/NCIC)方案治疗的25例GBM患者进行回顾性分析。患者术后在1.5T磁场下进行成像。序列包括标准解剖序列和标准DTI序列。如前所述构建p图和q图。对于每个p图和q图,在可见异常周围绘制感兴趣区域。将患者分为弥漫型、局限型或微侵袭型模式。根据RANO标准(4)定义进展情况,并以天数确定PFS。绘制三组患者生存的Kaplan-Meier曲线以及24个月时未进展患者的比例。

结果

弥漫型组的中位PFS为278天,局限型组为605天,微侵袭型组为820天。四分之三的微侵袭型组患者在24个月时无进展(对数秩检验9.25;p = 0.010)。

结论

使用扩散张量成像可以在GBM中识别出三种侵袭性表型,并且这三种表型具有不同的无进展生存期。一种微侵袭表型(20%的患者)预示进展延迟时间更长。

相似文献

1
Diffusion tensor invasive phenotypes can predict progression-free survival in glioblastomas.扩散张量侵袭性表型可预测胶质母细胞瘤的无进展生存期。
Br J Neurosurg. 2013 Aug;27(4):436-41. doi: 10.3109/02688697.2013.771136. Epub 2013 Feb 27.
2
Predicting survival in glioblastomas using diffusion tensor imaging metrics.利用弥散张量成像指标预测胶质母细胞瘤患者的生存情况。
J Magn Reson Imaging. 2010 Oct;32(4):788-95. doi: 10.1002/jmri.22304.
3
Extent of resection of peritumoral diffusion tensor imaging-detected abnormality as a predictor of survival in adult glioblastoma patients.肿瘤周围弥散张量成像检测到的异常切除范围可预测成人胶质母细胞瘤患者的生存。
J Neurosurg. 2017 Jan;126(1):234-241. doi: 10.3171/2016.1.JNS152153. Epub 2016 Apr 8.
4
Glioblastomas with oligodendroglial component have the same clinical phenotype as classical glioblastomas.具有少突胶质细胞成分的胶质母细胞瘤与经典胶质母细胞瘤具有相同的临床表型。
Br J Neurosurg. 2013 Aug;27(4):419-24. doi: 10.3109/02688697.2013.767315. Epub 2013 Feb 18.
5
Prognosis prediction of measurable enhancing lesion after completion of standard concomitant chemoradiotherapy and adjuvant temozolomide in glioblastoma patients: application of dynamic susceptibility contrast perfusion and diffusion-weighted imaging.胶质母细胞瘤患者在完成标准同步放化疗及辅助替莫唑胺治疗后可测量强化病灶的预后预测:动态磁敏感对比灌注成像和扩散加权成像的应用
PLoS One. 2014 Nov 24;9(11):e113587. doi: 10.1371/journal.pone.0113587. eCollection 2014.
6
MR Imaging Analysis of Non-Measurable Enhancing Lesions Newly Appearing after Concomitant Chemoradiotherapy in Glioblastoma Patients for Prognosis Prediction.胶质母细胞瘤患者同步放化疗后新出现的不可测量强化病变的磁共振成像分析用于预后预测
PLoS One. 2016 Nov 11;11(11):e0166096. doi: 10.1371/journal.pone.0166096. eCollection 2016.
7
Detection of occult neoplastic infiltration in the corpus callosum and prediction of overall survival in patients with glioblastoma using diffusion tensor imaging.使用弥散张量成像检测胼胝体隐匿性肿瘤浸润并预测胶质母细胞瘤患者的总生存期。
Eur J Radiol. 2019 Mar;112:106-111. doi: 10.1016/j.ejrad.2019.01.015. Epub 2019 Jan 15.
8
Chemoirradiation for glioblastoma multiforme: the national cancer institute experience.多形性胶质母细胞瘤的化放疗:美国国家癌症研究所的经验。
PLoS One. 2013 Aug 5;8(8):e70745. doi: 10.1371/journal.pone.0070745. Print 2013.
9
The clinical characteristics and prognostic factors of multiple lesions in glioblastomas.胶质母细胞瘤多发病灶的临床特征和预后因素。
Clin Neurol Neurosurg. 2020 Aug;195:105891. doi: 10.1016/j.clineuro.2020.105891. Epub 2020 May 7.
10
Predicting glioblastoma progression using MR diffusion tensor imaging: A systematic review.使用磁共振扩散张量成像预测胶质母细胞瘤进展:一项系统综述。
J Neuroimaging. 2025 Jan-Feb;35(1):e13251. doi: 10.1111/jon.13251.

引用本文的文献

1
Predicting brain tumour growth patterns using a novel MRI-based tumour spread map: application to radiotherapy planning.使用基于磁共振成像(MRI)的新型肿瘤扩散图谱预测脑肿瘤生长模式:在放射治疗计划中的应用
Med Phys. 2025 May;52(5):2909-2921. doi: 10.1002/mp.17640. Epub 2025 Jan 25.
2
Diffusion tensor imaging in detecting gliomas sub-regions of infiltration, local and remote recurrences: a systematic review.弥散张量成像检测胶质瘤浸润亚区、局部和远处复发:系统评价。
Neurosurg Rev. 2024 Jul 2;47(1):301. doi: 10.1007/s10143-024-02529-3.
3
Association of MRI Volume Parameters in Predicting Patient Outcome at Time of Initial Diagnosis of Glioblastoma.
胶质母细胞瘤初诊时MRI体积参数与预测患者预后的相关性
Brain Sci. 2023 Nov 10;13(11):1579. doi: 10.3390/brainsci13111579.
4
Integrated molecular and multiparametric MRI mapping of high-grade glioma identifies regional biologic signatures.高级别胶质瘤的整合分子和多参数 MRI 图谱可识别区域性生物学特征。
Nat Commun. 2023 Sep 28;14(1):6066. doi: 10.1038/s41467-023-41559-1.
5
Preoperative Diagnosis and Molecular Characterization of Gliomas With Liquid Biopsy and Radiogenomics.基于液体活检和放射基因组学的胶质瘤术前诊断及分子特征分析
Front Neurol. 2022 May 26;13:865171. doi: 10.3389/fneur.2022.865171. eCollection 2022.
6
Predicting Survival in Glioblastoma Patients Using Diffusion MR Imaging Metrics-A Systematic Review.使用扩散磁共振成像指标预测胶质母细胞瘤患者的生存率——一项系统综述
Cancers (Basel). 2020 Oct 4;12(10):2858. doi: 10.3390/cancers12102858.
7
Motor Functional Reorganization Is Triggered by Tumor Infiltration Into the Primary Motor Area and Repeated Surgery.肿瘤浸润至初级运动区和重复手术引发运动功能重组。
Front Hum Neurosci. 2020 Aug 14;14:327. doi: 10.3389/fnhum.2020.00327. eCollection 2020.
8
Multi-scale segmentation in GBM treatment using diffusion tensor imaging.使用弥散张量成像进行 GBM 治疗中的多尺度分割。
Comput Biol Med. 2020 Aug;123:103815. doi: 10.1016/j.compbiomed.2020.103815. Epub 2020 May 22.
9
Applications of radiomics and machine learning for radiotherapy of malignant brain tumors.放射组学和机器学习在恶性脑肿瘤放疗中的应用。
Strahlenther Onkol. 2020 Oct;196(10):856-867. doi: 10.1007/s00066-020-01626-8. Epub 2020 May 11.
10
Semi-automated construction of patient individualised clinical target volumes for radiotherapy treatment of glioblastoma utilising diffusion tensor decomposition maps.利用弥散张量分解图半自动构建胶质母细胞瘤放射治疗的个体化临床靶区。
Br J Radiol. 2020 Apr;93(1108):20190441. doi: 10.1259/bjr.20190441. Epub 2020 Jan 22.