Value of conventional MRI texture analysis in differential diagnosis of glioblastomas and primary central nervous system lymphoma

王敏红; 周理想

doi:10.19401/j.cnki.1007-3639.2019.04.007

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Value of conventional MRI texture analysis in differential diagnosis of glioblastomas and primary central nervous system lymphoma

更新时间：2026-01-27

- Value of conventional MRI texture analysis in differential diagnosis of glioblastomas and primary central nervous system lymphoma
- China Oncology Vol. 29, Issue 4, Pages: 284-288(2019)
- 作者机构：
  
  1. 皖南医学院第一附属医院放射科,安徽,芜湖,241001
  2. 皖南医学院第一附属医院药学部,安徽,芜湖,241001
- 作者简介：
- 基金信息：
- DOI：10.19401/j.cnki.1007-3639.2019.04.007
  CLC： R739.41
- Published Online：17 May 2019，
  
  Published：17 May 2019
- 稿件说明：
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王敏红，周理想，冯湛. 常规MRI纹理分析鉴别脑胶质母细胞瘤和原发性中枢神经系统淋巴瘤的价值[J]. 中国癌症杂志, 2019, 29(4): 284-288.

王敏红, 周理想. Value of conventional MRI texture analysis in differential diagnosis of glioblastomas and primary central nervous system lymphoma[J]. China Oncology, 2019, 29(4): 284-288.
王敏红，周理想，冯湛. 常规MRI纹理分析鉴别脑胶质母细胞瘤和原发性中枢神经系统淋巴瘤的价值[J]. 中国癌症杂志, 2019, 29(4): 284-288. DOI： 10.19401/j.cnki.1007-3639.2019.04.007.

王敏红, 周理想. Value of conventional MRI texture analysis in differential diagnosis of glioblastomas and primary central nervous system lymphoma[J]. China Oncology, 2019, 29(4): 284-288. DOI： 10.19401/j.cnki.1007-3639.2019.04.007.

摘要

背景与目的：影像组学是近年来研究的热点，可量化肿瘤异质性，广泛应用于病灶定性、临床分期、疗效评价及危险因素分层分析等。该研究旨在探讨常规磁共振成像（magnetic resonance imaging，MRI）影像学纹理分析对脑胶质母细胞瘤和原发性中枢神经系统淋巴瘤的鉴别诊断价值。方法：回顾性分析2012年6月—2017年7月在皖南医学院第一附属医院经术后病理学检查证实的35例脑胶质母细胞瘤及15例原发性中枢神经系统淋巴瘤患者临床及影像学资料。所有患者术前均接受常规MRI平扫，包括轴位T1WI、T

WI和T2WI液体衰减反转恢复序列（T

-weighted fluid-attenuated inversion recovery，T2-FLAIR）。利用MaZda软件于3个平扫序列上显示肿瘤病灶最大层面手动勾画感兴趣区（region of interest， ROI），提取并分析其纹理特征。结果：通过对大量的纹理特征进行统计筛选，灰度共生矩阵类参数中T1WI自相关、T1WI熵、T2WI均值、T2-FLAIR均值及T2-FLAIR熵在二者之间的差异有统计学意义。基于这些纹理参数构建多变量logistic回归分析，显示该模型受试者工作特征曲线（receiver operating characteristic curve，ROC）下面积为0.94。结论：常规MRI纹理分析可提供可靠、量化的客观依据，无需增强检查，有助于鉴别脑胶质母细胞瘤和原发性中枢神经系统淋巴瘤。

Abstract

Background and purpose: Radiomics is a hot topic in recent years

which can quantify tumor heterogeneity

and is widely used in lesion characterization

clinical staging

efficacy evaluation and risk factor stratification. This study was designed to investigate the differential diagnosis of glioblastoma and primary central nervous system lymphoma by conventional magnetic resonance imagine (MRI) texture analysis. Methods: The clinical and imaging data of 35 cases of glioblastoma and 15 cases of primary central nervous system lymphoma confirmed by postoperative pathology from Jun. 2012 to Jul. 2017 in the First Affiliated Hospital of Wannan Medical College were retrospectively analyzed. All patients underwent conventional MRI scan

including axial T1WI

T2WI and T2WI-weighted fluid-attenuated inversion recovery (T2-FLAIR). The texture features of the lesion was extracted by MaZda software to manually draw the region of interest (ROI) on the maximum level of tumor delineation on three plain scan sequences. Results: Through a statistical screening of a large number of texture features

the differences in T1WI autocorrelation

T1WI entropy

T2WI mean

T2-FLAIR mean and T2-FLAIR entropy among the galactic co-occurrence matrix parameters were statistically significant between glioblastoma and primary central nervous system lymphoma. Logistic regression analysis showed the area under the receiver operating characteristic curve (ROC) of the model was 0.94. Conclusion: Conventional MRI texture analysis provides reliable and quantified objective basis without enhanced examination

for the differential diagnosis of glioblastoma and primary central nervous system lymphoma.

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