基于形态计量分析磁共振图像鉴别转移瘤和多形性胶质母细胞瘤。
Discrimination between metastasis and glioblastoma multiforme based on morphometric analysis of MR images.
机构信息
Institute for Molecules and Materials, Analytical Chemistry Radboud University Nijmegen, Nijmegen, the Netherlands.
出版信息
AJNR Am J Neuroradiol. 2011 Jan;32(1):67-73. doi: 10.3174/ajnr.A2269. Epub 2010 Nov 4.
BACKGROUND AND PURPOSE
Solitary MET and GBM are difficult to distinguish by using MR imaging. Differentiation is useful before any metastatic work-up or biopsy. Our hypothesis was that MET and GBM tumors differ in morphology. Shape analysis was proposed as an indicator for discriminating these 2 types of brain pathologies. The purpose of this study was to evaluate the accuracy of this approach in the discrimination of GBMs and brain METs.
MATERIALS AND METHODS
The dataset consisted of 33 brain MR imaging sets of untreated patients, of which 18 patients were diagnosed as having a GBM and 15 patients, as having solitary metastatic brain tumor. The MR imaging was segmented by using the K-means algorithm. The resulting set of classes (also called "clusters") represented the variety of tissues observed. A morphology-based approach allowed discrimination of the 2 types of tumors. This approach was validated by a leave-1-patient-out procedure.
RESULTS
A method was developed for the discrimination of GBMs and solitary METs. Two masses out of 33 were wrongly classified; the overall results were accurate in 93.9% of the observed cases.
CONCLUSIONS
A semiautomated method based on a morphologic analysis was developed. Its application was found to be useful in the discrimination of GBM from solitary MET.
背景与目的
单独的脑膜瘤和胶质母细胞瘤（GBM）很难通过磁共振成像（MRI）来区分。在进行任何转移性检查或活检之前，鉴别诊断很有用。我们的假设是脑膜瘤和 GBM 肿瘤在形态上存在差异。形态分析被提出作为区分这两种脑病变的指标。本研究旨在评估这种方法在鉴别 GBM 和脑转移瘤中的准确性。
材料与方法
数据集由 33 套未经治疗的患者脑部 MRI 组成，其中 18 名患者被诊断为患有 GBM，15 名患者被诊断为患有孤立性脑转移瘤。MR 成像采用 K-均值算法进行分割。得到的类集（也称为“聚类”）代表了观察到的各种组织。基于形态的方法允许区分这两种类型的肿瘤。该方法通过留一患者法进行验证。
结果
开发了一种用于鉴别 GBM 和孤立性 MET 的方法。在 33 个肿块中，有 2 个被错误分类；在观察到的病例中，总体准确率为 93.9%。
结论
开发了一种基于形态分析的半自动方法。其应用在鉴别 GBM 和孤立性 MET 方面被证明是有用的。
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