论著摘要 |【MR】基于磁共振成像的影像组学资料预测治疗前新诊断的胶质母细胞瘤患者的预后(双语版)

2017-12-21 17:37:13 admin 0

Magnetic Resonance Imaging-Based Radiomic Profiles Predict Patient Prognosis in Newly Diagnosed Glioblastoma Before Therapy.

发表日期: 2017.05.12   来源:Tomography. 2016 Sep;2(3):223-228.

作者:

McGarry SD1, Hurrell SL1, Kaczmarowski AL1 , Cochran EJ2, Connelly J3, Rand SD1, Schmainda KM4, LaViolette PS4.

作者介绍:

1. Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin.

2. Department of Pathology, Medical College of Wisconsin, Milwaukee, Wisconsin.

3. Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin.

4. Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin; Department of Biophysics, Medical College of Wisconsin, Milwaukee, Wisconsin.

摘要

磁共振成像(MRI)用于诊断和监测脑肿瘤。从医学成像中提取附加信息并将其与感兴趣的临床变量相关联,广义上被定义为影像组学。这里发现原发性胶质母细胞瘤(GBM)的多参数MRI影像组学资料(RP)与患者预后相关。对81例GBM患者的手术前临床影像进行分析。四个MRI对比度对齐,由钆对比度增强和T2 /流体衰减反转恢复高信号定义的边界掩蔽,并基于图像强度进行轮廓。通过为每个体素分配4位数字代码来指示分割RP,将这些分割组合以进行可视化和量化。然后将每个RP体积与总体存活率进行比较。然后基于显著的RP和优化的体积阈值生成组合分类器。五个RPs预测治疗前的总生存期。将RP分类器与单个预后评分相结合,预测患者生存率优于单独使用(P <.005)。用与不良预后相关的1个编码的体素RP经病理证实含有细胞密集肿瘤。本研究采用影像组学分析方法确定了原发性GBM患者与肿瘤诊断预后不良相关的影像学特征。该工具可用于计划手术切除或放射治疗边缘。

Abstact

Magnetic resonance imaging (MRI) is used to diagnose and monitor brain tumors. Extracting additional information from medical imaging and relating it to a clinical variable of interest is broadly defined as radiomics. Here, multiparametric MRI radiomic profiles (RPs) of de novo glioblastoma (GBM) brain tumors is related with patient prognosis. Clinical imaging from 81 patients with GBM before surgery was analyzed. Four MRI contrasts were aligned, masked by margins defined by gadolinium contrast enhancement and T2/fluid attenuated inversion recovery hyperintensity, and contoured based on image intensity. These segmentations were combined for visualization and quantification by assigning a 4-digit numerical code to each voxel to indicate the segmented RP. Each RP volume was then compared with overall survival. A combined classifier was then generated on the basis of significant RPs and optimized volume thresholds. Five RPs were predictive of overall survival before therapy. Combining the RP classifiers into a single prognostic score predicted patient survival better than each alone (P < .005). Voxels coded with 1 RP associated with poor prognosis were pathologically confirmed to contain hypercellular tumor. This study applies radiomic profiling to de novo patients with GBM to determine imaging signatures associated with poor prognosis at tumor diagnosis. This tool may be useful for planning surgical resection or radiation treatment margins.

关键词:

解剖,脑胶质瘤,预后,影像组学

Keywords:

autopsy; glioblastoma; prognosis; radiomics

阅读原文:PMID: 27774518   PMCID: PMC5074084  DOI: 10.18383/j.tom.2016.00250


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